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The position and strength of the Hadley cell circulation determine the habitable zones in the tropics, yet our understanding of and ability to predict changes in the circulation is limited. One potentially important source of uncertainty is the dependence of the Hadley cell on turbulent drag. Here, the sensitivity of the Hadley cell and associated features such as the intertropical convergence zone to variations in the magnitude of the turbulent drag is explored with an atmospheric general circulation model in aquaplanet configuration. The tropical circulation and precipitation, and extratropical features such as the polar jet stream, displayed a strong sensitivity to the strength of the parameterized turbulent drag, with distinct low- or high-drag regimes. However, the response of the meridional heat transport produced a surprising departure from previous expectations: with greater drag, simulations exhibited less heat transport than low-drag simulations, which is in the opposite sense to that from Held and Hou. This may be due to the energetic constraints in the present model framework. When exposed to a uniform global warming, the response of the ITCZ precipitation depends strongly on the choice of drag, whereas most simulations exhibit a poleward expansion of the subtropics. Data availability statement: The ICON output subsets used in this work and example bash scripts and Python codes detailing how these subsets were created are available at https://doi.org/10.17043/flynn-2022-icon-drag-1.

A new one-dimensional 1.5-order planetary boundary layer (PBL) scheme, based on the K–ε turbulence closure applied to the Reynolds-averaged Navier–Stokes (RANS) equations, is developed and implemented within the Weather Research and Forecasting (WRF) Model. The new scheme includes an analytic solution of the coupled equations for turbulent kinetic energy and dissipation rate. Different versions of the PBL scheme are proposed, with increasing levels of complexity, including a model for the calculation of the Prandtl number, a correction to the dissipation rate equation, and a prognostic equation for the temperature variance. Five different idealized cases are tested: four of them explore convective conditions, and they differ in initial thermal stratification and terrain complexity, while one simulates the very stable boundary layer case known as GABLS. For each case study, an ensemble of different large-eddy simulations (LES) is taken as reference for the comparison with the novel PBL schemes and other state-of-the-art 1- and 1.5-order turbulence closures. Results show that the new PBL K–ε scheme brings improvements in all the cases tested in this study. Specifically, the more significant are obtained with the turbulence closure including a prognostic equation for the temperature variance. Moreover, the largest benefits are obtained for the idealized cases simulating a typical thermal circulation within a two-dimensional valley. This suggests that the use of prognostic equations for dissipation rate and temperature variance, which take into account their transport and history, is particularly important with the increasing complexity of PBL dynamics.

Directed energy deposition (DED) offers an unprecedentedly convenient and efficient additive manufacturing approach to novel alloy designs such as high entropy alloys. As a critical component of the novel DED system, a powder mixer to stably and uniformly mix different powders plays an important role in the DED process. In this paper, the computational fluid dynamics-discrete phase model (CFD-DPM) method was used to simulate the characteristics of the gas-solid coupled fluid inside the powder mixer. The influence of the structural details of the powder mixer on the motion characteristics of the gas-solid coupled fluid was investigated by numerical simulation. Based on the numerical simulation results, the range of parameters of critical structure such as the inlet angle, the diameter and height mixing chamber, and the height of the bottom was determined. The difference in powder mixing uniformity among different powder mixers was qualitatively analyzed through powder mixing experiments. The BSE-EDS results of mixed powder samples show that the mixer with reasonable structural parameters has better mixing uniformity. In summary, this work provides a powerful reference for the rapid optimization design of the powder mixer structure.

International audience; We introduce a new mechanism for self-actuating deployable structures, based on printing a dense pattern of closely-spaced plastic ribbons on sheets of pre-stretched elastic fabric. We leverage two shape-changing effects that occur when such an assembly is printed and allowed to relax: first, the incompressible plastic ribbons frustrate the contraction of the fabric back to its rest state, forcing residual strain in the fabric and creating intrinsic curvature. Second, the differential compression at the interface between the plastic and fabric layers yields a bilayer effect in the direction of the ribbons, making each ribbon buckle into an arc at equilibrium state and creating extrinsic curvature. We describe an inverse design tool to fabricate low-cost, lightweight prototypes of freeform surfaces using the controllable directional distortion and curvature offered by this mechanism. The core of our method is a parameterization algorithm that bounds surface distortions along and across principal curvature directions, along with a pattern synthesis algorithm that covers a surface with ribbons to match the target distortions and curvature given by the aforementioned parameterization. We demonstrate the flexibility and accuracy of our method by fabricating and measuring a variety of surfaces, including nearly-developable surfaces as well as surfaces with positive and negative mean curvature, which we achieve thanks to a simple hardware setup that allows printing on both sides of the fabric.

In the present work, a novel algorithm for the continuous projection of point triangles belonging to a triangle mesh is presented. The algorithm uses the normals defined at the vertices of the triangle to perform the projection. The direction of projection is not chosen a priori but depends on the point to be projected: the proposed algorithm, named Fast Continuous Projection method (FCP) lets the projection direction vary continuously on the mesh. Moreover, the direction is coherent with the original surface that is approximated with the triangle mesh. An optimized version of the algorithm is also presented: this uses pre-evaluated matrices to reduce the calculation time. This algorithm can be effectively used when a large set of points has to be projected on a coarse mesh as, for example, to generate the scanning vectors for laser engraving/milling.

Introduction. Truss structures are widespread in construction due to a number of advantages, such as economy, versatility, and scalability. Accordingly, their modeling and calculation are urgent tasks in the design of building structures. Automatic solution to these problems causes an increase in design efficiency, calculation accuracy, and lower costs. The objective of the study is to examine the functionality and operation algorithm of the software module developed by the authors that generates the geometry of two-dimensional truss structures for subsequent modeling.Materials and Methods. Following the research of the widespread truss configurations, the classification of chords available in the software under consideration is given. The method of parameterizing a truss structure is provided. This method includes base geometric parameters of the structure such as dimensions, model construction rules, and additional features, as well as a comprehensive algorithm. The software is developed in JavaScript.Results. The software module has been integrated into a web application for calculating two-dimensional rod structures. To illustrate the functionality of the software, the examples of user interface are given as well as an example problem. The example includes configuration and calculation of an inclined truss structure. The results, such as support reactions and internal forces with axial force diagram, are provided.Discussion and Conclusions. Using this software module within the framework of the tool for calculating rod structures allows for the simplified process of modeling and calculating complex truss structures, design time, and resource reduction. The software module provides tools for specifying various types of structures, applying loads and assigning properties of a rod system, which makes it a useful instrument for design engineers.

The Arctic amplification of climate change is strongly connected to the sea ice in the Arctic because of the sea ice albedo and insulation effects. This thesis presents numerical modeling studies on landfast ice and the kinetic energy dissipation within sea ice in the Arctic. Landfast ice is sea ice that is nearly immobile and attached to the coast. As an extension to the land, landfast ice provides a platform for tourism, transportation, and hunting for the local communities, as well as for oil and gas drilling and scientific observation. Although landfast ice plays an important role in the Arctic coastal regions, it is challenging to represent landfast ice in numerical sea ice models with a standard viscous-plastic (VP) rheology. In this thesis, I implement a lateral drag parameterization to improve the representation of the landfast ice in the sea ice model and study the kinetic energy dissipation by sea ice internal stress. This thesis has three scientific questions: (1) How can we improve the representation of landfast ice in the sea ice numerical model? (2) What is the influence on the Arctic Ocean hydrography with the presence of landfast ice cover in the sea ice model? (3) Does the kinetic energy dissipation depend on the details of the VP yield curves? With higher resolution, the heterogeneity of the sea ice cover increases and deformation localizes at distinct linear kinematic features. Does this change have an effect on the mechanical energy cycle? Firstly, coastal drag parameterization is implemented in the coarse sea ice model, as a function of coastline length, sea ice thickness, and sea ice drift velocity. The results show that the new parameterization improves the simulation of landfast ice in deep marginal seas of the Arctic. Furthermore, the combination of lateral and basal drag parameterization simulates realistic landfast ice distributions in the Arctic. Secondly, the landfast ice cover is switched on and off in the sea ice model to study the influence of the landfast ice on the Arctic hydrography. The results show that the presence of the landfast ice decreases the salinity in the upper ocean due to the reduced new ice formation releasing less salt in the water. Furthermore, the fresher upper ocean signal formed in the deep marginal sea is transported from the coast to the central Arctic, leading to a fresher upper ocean in the Makarov Basin. The salt budget analysis demonstrates that the salt is advected of the Arctic through the boundaries. Lastly, the kinetic energy dissipation with different yield curves and three model resolutions is evaluated. The standard ellipse, Mohr--Coulomb yield curve with elliptical plastic potential, Truncated Ellipse Method and Teardrop yield curves are employed in the 4.5 km sea ice model to compare the sea ice thickness, drift velocity, and kinetic energy dissipation differences. The results indicate that the simulations with yield curves with more shear strength lead to smaller sea ice drift and hence to smaller wind energy input and energy loss due to ocean drag. Moreover, the impact of the different yield curves on the net energy dissipation is small, but simulations with similar yield curves have similar kinetic energy dissipation within the ice. Finally, experiments with different model resolutions suggest that the higher the resolution of the simulation, the more deformation and dissipation are localized along shear lines. More localization leads to smaller mean drift, less kinetic energy input, and loss by ocean drag.

This thesis is divided into two parts. The first part regards to data fitting by B-spline curves which is frequently used in 3D reconstruction. We establish that the progressive and iterative approximation for least squares B-spline curve and surface fitting (LSPIA) is equivalent to the standard gradient descent method. Moreover, we propose an extension that combines this method with parameter correction (LSPIA-PC) to improve the accuracy of curve representation. Based on the equivalence of LSPIA and gradient descent, we propose to use the neural network framework for the implementation of both LSPIA and LSPIA-PC. In the second part, we focus on point cloud parameterization, which is an essential task in many industrial applications. The applications include 3D reconstruction, rendering and the analysis and extraction of geometric information. We propose a novel idea to parameterize point cloud data via local parameterizations consisting of 12 points. These local parameterizations are obtained by evaluating a trained residual neural network plus the use of the Levenberg-Marquardt algorithm. We evaluate the performance of our method on zero-residual and non-zero-residual examples and compare it to existing methods. The proposed approaches provide valuable insights for researchers who are interested in employing neural networks in the field of CAGD. Moreover, this work can be a starting point for building new strategies to approach the problem of fitting and point cloud parameterization. Diese Arbeit ist in zwei Teile unterteilt. Der erste Teil befasst sich mit der Approximation von Daten durch B-Spline-Kurven, die häufig bei der 3D-Rekonstruktion eingesetzt wird. Wir zeigen, dass die "progressive and iterative approximation for least squares B-spline curve and surface fitting" (LSPIA) äquivalent zur klassischen Gradientenabstiegsmethode ist. Darüber hinaus schlagen wir eine Erweiterung vor, die diese Methode mit Parameterkorrektur (LSPIA-PC) kombiniert, um die Genauigkeit der Kurvenrepräsentation zu verbessern. Basierend auf der Äquivalenz von LSPIA und der Gradientenabstiegsmethode schlagen wir vor, das Framework der neuronalen Netze für die Implementierung von LSPIA und LSPIA-PC zu verwenden. Im zweiten Teil konzentrieren wir uns auf die Parameterisierung von Punktdaten, die eine wesentliche Aufgabe in vielen industriellen Anwendungen darstellt. Zu den Anwendungen gehören 3D-Rekonstruktion, Rendering und die Analyse und Extraktion geometrischer Informationen. Wir schlagen eine neuartige Idee vor, um eine Punktwolke durch lokale Parametrisierungen, die aus 12 Punkten bestehen, zu parameterisieren. Diese lokalen Parametrisierungen werden durch die Auswertung eines trainierten residualen neuronalen Netzwerks und der Verwendung des Levenberg-Marquardt-Algorithmus gewonnen. Wir evaluieren die Leistung unserer Methode anhand von Null-Residuum und Nicht-Null-Residuum Beispielen und vergleichen sie mit bestehenden Methoden. Die vorgeschlagenen Ansätze liefern wertvolle Erkenntnisse für Forscher, die daran interessiert sind, neuronale Netze im Bereich der CAGD einzusetzen. Darüber hinaus kann diese Arbeit ein Ausgangspunkt sein, um neue Strategien zur Approximation und Parameterisierung von Punktdaten zu entwickeln. submitted by Dany Esteban Rios Rodas Dissertation Universität Linz 2023

The effects of aircraft icing comprise of (i) simple inconveniences, like flight delays, (ii) increased fuel consumption and (iii) severe performance degradations, potentially resulting in the loss of control over the aircraft. The accretion of ice onto the aircraft is considered the reason for several documented fatal crashes. Because of this safety hazard for aviation, weather services around the world use aircraft icing prediction schemes to forecast the location and intensity of icing environments, based on the output of numerical weather prediction models. Although aircraft icing occurs by the freezing of supercooled liquid droplets impinging onto the aircraft’s surface, current icing prediction schemes do not consider microphysical quantities, like the droplets’ size or their spectrum, in their design. Analytical icing related algorithms tend to respect the droplets’ size, but in a simplified, heuristic way. The present work develops a new physically based aircraft icing parameterization employing the two main physical processes involved in aircraft icing, namely the droplet impingement and the freezing of the impinged droplets. The former is represented by the droplets’ impingement efficiency, which is computed by evaluating droplet trajectories in the vicinity of the object. The freezing model used in this new parameterization allows for the differentiation of different icing phases, a dry icing phase solely accreting rime ice and a wet icing phase accreting glaze ice and a liquid water film due to the release of latent heat whilst freezing, allowing for a more detailed forecast and analysis. This new aircraft icing parameterization is implemented in a numerical weather forecast model to simulate icing conditions for two former case studies and an additional icing incident. The simulations reproduce the results of the case studies properly and enhance them with additional icing related quantities. Contrary to currently used heuristic approaches to the impingement efficiency, this new physically based approach gives a greater differentiation of high impingement efficiency values. Using this new parameterization, the present work further investigates the role of microphysical quantities by conducting sensitivity studies focused on the explicit consideration of the droplet spectrum and the aerosol load, which is represented by the cloud droplet number density for one experiment and by the number density of aerosols acting as cloud condensation nuclei for the droplet nucleation parameterization for the other. Since the new aircraft icing parameterization introduces further influencing quantities, a further sensitivity investigation focusing on the relative airspeed is conducted. The present work shows that the explicit consideration of the cloud droplet spectrum affects the impingement and icing related quantities only minorly. The alteration of the cloud droplet number density, as a proxy for the effect of the aerosol load, has large implications for the impingement, accretion rates and amounts as well as composition of the accreted material. Altering the number density of cloud condensation nuclei instead affects the impingement mainly in low impinging environments and the composition of the accreted material due to impacts on the accretion of rime ice. Glaze ice and liquid water accretion are largely unaffected. The relative airspeed also affects the droplet impingement, but its largest effect is on the accretion rates caused by the impinging water and the composition of the accreted material. The newly developed physically based parameterization of aircraft icing also gives the possibility to design a new online icing prediction scheme, as it is implemented into an operatively used numerical weather forecast model. This requires additional efforts in designing an appropriate icing intensity scale as well as in testing, validating and tuning.

Ein Leistungstransformator ist eine der wichtigsten Betriebsmittel im Stromnetz. Viele Leistungstransformatoren werden durch natürlichen Ölkreislauf gekühlt. Ihre Lebensdauer ist auf den Verlust von Eigenschaften der Isolationsmaterialen wie Isolierfestigkeit, Zugfestigkeit zurückzuführen. Der Verlauf der Wicklung-Heißpunkttemperatur nach „Leitfaden für die Belastung von mineralölgefüllten Leistungstransformatoren (IEC 60076-7:2018)“ ist ein Schlüsselfaktor für die Bestimmung der Isolationsfähigkeit der Isoliermaterialen von Leistungstransformatoren. Die Wicklung-Heißpunkttemperatur während des Betriebs begrenzt außerdem die Belastbarkeit eines Transformators. Es ist daher von großer Bedeutung, die Wicklungs-Heißpunkttemperatur des Transformators zu kennen.Die Wicklung-Heißpunkttemperatur ist die Summe aus der Temperatur der obersten Ölschicht und dem Heißpunkttemperaturanstieg über die oberste Ölschicht. Normalerweise ist die Messung der Temperatur der obersten Ölschicht einfacher und wirtschaftlicher im Vergleich zur direkten Messung der Heißpunkttemperatur. In unserem Fall ist die oberste Öltemperatur als bekannt vorausgesetzt. So wird der Heißpunkttemperaturanstieg bestimmt und die Wicklungs-Heißpunkttemperatur ermittelt.Mit Hilfe der empfohlenen empirischen stationären oder dynamischen thermischen Modelle (IEEE C 57.91-2011 und IEC 60076-7:2018) kann der Heißpunkttemperaturanstieg abgeschätzt, bestimmt und analysiert werden. Gleichzeitig brauchen die Modelle eine Erwärmungsprüfung vor der Inbetriebnahme des Transformators, um die Parameter der Modelle zu erfassen. Den ermittelten Parameter kann man in thermischen Modellen einsetzen. Die im Betrieb befindende Transformatoren besitzen in der großen Ordnung keine Datensätze der Erwärmungsprüfung. Es ist herausfordernd die Datensätze für die Transformatoren abzuschätzen. Das führt zu den möglichen Abweichungen zwischen dem realen und modellierten thermischen Zustand. Die Untersuchung der Methoden für die Parametrierung bzw. Bestimmung der Definitionsbereiche der Parameter für den Heißpunkttemperaturanstieg über die oberste Ölschicht bei unterschiedlichen Leistungstransformatoren sind daher sinnvoll.Mittels Sensitivitätsanalyse der Eingangsparameter der thermischen Modelle und Zeitreihensimulationen werden für unterschiedliche Belastungsszenarien geeignete Lösungen gegeben, um die praxisgerechte Parametrierung der thermischen Modelle zu ermöglichen., The power transformer is one of the most important operating equipment in the power grid. Many power transformers are cooled by natural oil circulation. Their service life is constrained due to the loss of properties of the insulation materials such as insulation strength and tensile strength. According to "Guideline for loading of mineral oil-filled power transformers (IEC 60076.7:2018) " the winding hottest-spot temperature profile is as a key factor for determination of residual insulation properties of the transformer’s materials. Moreover, the winding hottest-spot temperature limits the loading capability of the transformer. It is therefore of great importance to know the winding hottest-spot temperature of the transformer.The winding hottest-spot temperature is the sum of the top-oil temperature and the hot-spot temperature rise above the top-oil temperature. Measuring the top-oil temperature is easier and more economical compared to measuring the hottest-spot temperature directly. In our case, the top-oil temperature is assumed to be known. Thus, to obtain the hottest-spot temperature the hot-spot temperature rise should be determined.Using the recommended empirical steady-state or dynamic thermal models (IEEE C 57.91-2011 and IEC 60076-7:2018), the hot-spot temperature rise can be estimated, determined, and analyzed. At the same time, the models need a heat-run test before the transformer is putted into operation, to capture the parameters of the models. The determined parameters can be used in the thermal models. In large order the transformers in operation do not have heat-test data sets. It`s challenging to estimate the parameters for thermal models for the transformers in operation. This leads to the possible deviations between the real and modelled thermal behavior. Therefore, the investigation of the methods for parameterization or determination of the range of values of the model parameters for the hot-spot temperature rise over the top-oil temperature for different power transformers is useful.Using a sensitivity analysis of the input parameters of the thermal models and time-series simulations, suitable solutions are given for different load scenarios in order to enable the practical parameterization of the thermal models.

Soil liquefaction during earthquakes is a complex geotechnical engineering problem. Although various analytical approaches exist for predicting liquefaction risk, their limitations have led researchers to explore using artificial intelligence and machine learning methods. Machine learning has the potential to significantly improve the ability to predict soil liquefaction and mitigate the associated risks. This study proposes a hyper parameterized artificial neural network architecture using random search, grid search, and Bayesian optimization algorithms to predict the factor of safety against liquefaction. The performances of hyper parameterized machine learning algorithms, including artificial neural networks (ANN), decision trees (DT), random forest (RF), and support vector regression (SVR), were compared. Statistical tests show that the proposed ANN outperformed the others with a determination coefficient of 0.99 at a 95% significance level. Hyperparameter optimization significantly improved learning performance with up to a 48% reduction in RMSE scores. The proposed method was compared with previous studies, and performance results confirmed its effectiveness and generalization ability. In conclusion, this study highlights the potential of machine learning algorithms for predicting soil liquefaction and emphasizes the importance of hyperparameter optimization for improving model performance. The findings of this study have practical implications for improving liquefaction risk assessment and mitigating the associated hazards. © 2023 Elsevier B.V.

Offshore wind energy has seen a steady increase in the latest years as it serves as an ideal alternative energy source to meet our renewable energy goals. Due to this increase in interest and in installations of offshore wind farms a better understanding of the impact of waves on the power production of wind farms in necessary. This wave-wind farm power production interaction has scarcely been examined, however, some exceptions of small scale large-eddy-simulations exist. Unfortunately, these studies are not able to take multiple wind farms under real weather conditions into account. Here, we show the power production and wake lengths of 1250 offshore wind turbines located in the German Bight simulated by a stand-alone atmospheric (WRF) model and a coupled atmosphere-wave (WRF-SWAN) model. The coupled atmosphere-wave model estimates larger (smaller) power production in case of waves and wind traveling in the same (opposed) direction compared to the stand-alone atmospheric model. The relative difference between the two models can be as much as 20% for the two-week averaged grid power production, while the difference in total power over this two-week period is equal to 9%. Moreover, the wind farm wake lengths of waves and wind traveling in the same (opposed) direction are longer (shorter) for the coupled atmosphere-wave model compared to the stand-alone atmospheric model. Here, the relative difference of the mean wake length between the two models can be up to 25%. This shows the importance of waves as a part of the offshore wind environment and can be an important factor in future wind assessment or power production estimation studies.

Bioenergy has been identified as a key component of climate change mitigation. Therefore, quantifying the net carbon balance of bioenergy feedstocks is crucial for accurate projections of climate mitigation benefits. Switchgrass (Panicum virgatum) has many characteristics of an ideal bioenergy crop with high yields, low maintenance, and deep roots with potential for belowground carbon sequestration. However, the assessments of net annual carbon exchange between switchgrass fields and the atmosphere are rare. Here we present observations of net carbon fluxes in a minimally managed switchgrass field in Virginia (Ameriflux site US-SB2) over 5 years (3–7 years since establishment). Average annual net ecosystem exchange (NEE) of carbon was near zero (60 g C m−2 year−1) but the net ecosystem carbon balance that includes harvested carbon (HC) was a net source of carbon to the atmosphere (313 g C m−2 year−1). The field alternated between a large and small source of carbon annually, with the interannual variability most strongly correlated with the day of the last frost and the interaction of temperature and precipitation. Overall, the consistent source of carbon to the atmosphere at US-SB2 differs substantially from other eddy covariance studies that report switchgrass fields to be either neutral or a sink of carbon when accounting for both NEE and HC. This study illustrates that predictions of net carbon climate benefits from bioenergy crops cannot assume that the ecosystem will be a net sink of carbon from the atmosphere. Background climate, management, and land-use history may determine whether widespread deployment of switchgrass as a bioenergy feedstock results in realized climate change mitigation. Published version

The small rail roadway stacker is the core equipment of the automated three-dimensional warehouse. Its design directly affects the development trend of the logistics industry, enterprise production efficiency and economic benefits. In this paper, a parametric model of the small rail roadway stacker is developed using the parametric design method, according to the working principle and load-bearing. The safety of the small-sized tracked roadway stacker in three working states is analyzed using the finite element method, according to the working conditions and stress conditions of the small rail roadway stacker. Consequently, it is deduced that all the safety parameters meet the design requirements in the limit state. Moreover, the SolidWorks and ANSYS Workbench software are used for secondary development, while C# is used to compile the parametric design software of the small rail roadway stacker, which integrates the functions of three-dimensional model design, engineering drawing generation and finite element security analysis into a program software. Furthermore, by the visual parameter setting and command operation, the program background automatically calls the design software for design and safety analysis. Finally, the example verifies the efficiency of the parametric design software for the small rail roadway stacker.

Optimal mapping is one of the longest standing problems in computational mathematics.It is natural to measure relative length error to assess map quality.Maximum of such error is called quasi-isometry constant, and its minimization is a nontrivial max-norm optimization problem.We present physics-based quasi-isometric stiffening (QIS) algorithm for max-norm minimization of hyperelastic distortion.QIS perfectly equidistributes distortion over all the domain for the ground truth test (unit hemisphere flattenning) and,when it is not possible, tends to create zones where all cells have the same distortion.Each such zone corresponds to a fragment of elastic material that became rigid under stiffening, reaching the deformation limit.As such, maps built by QIS are related to de Boor equidistribution principle that asks an integral of certain error indicator function to be the same over each mesh cell.We prove that our method can build in a finite number of stepsa deformation whose maximum distortion is arbitrarily close to the (unknown) minimum.We performed a massive testing: more than 75'000 maps were computed, and QIS won 100\% of the tests.To sum up, we reliably build 2D and 3D mesh deformations with smallest known distortion estimates for very stiff problems.

International audience; Many different models have been published with the aim of describing electrolyte thermodynamics. In this study two successful Equations of State (EoS) are compared. The models investigated in this work are the electrolyte Cubic Plus Association (e-CPA) EoS and the electrolyte Polar Perturbed Chain Statistical Associating Fluid Theory (e-PPC-SAFT). The model terms are not changed from previous works, but they are reparameterized with various models for the relative static permittivity. The models are parameterized as similarly and fairly as possible by identifying key parameters in the models that are intended to describe the same physics. Several different sets of comparable parameters are investigated to test the models with the inclusion of various physics, and to avoid favoring one model due to the choice of adjustable parameters. The parameters of the models are optimized with an objective function containing the properties mean ionic activity coefficients (MIAC), osmotic coefficients and density in a wide temperature range of 273.15-473.15 K and they are tested for their ability to quantitatively describe these properties. The difference between e-CPA and e-PPC-SAFT is generally found to be relatively minor and the permittivity model and considered adjustable parameters are generally more important for the overall model performance. It is found that the permittivity models that only predict part of experimental decrease tend to perform the best for the properties MIAC, osmotic coefficients and density. It is also found that including both association parameters and dispersion/attractive parameters for ions is better than neglecting one type of parameters, even when the same number of adjustable parameters is included. To be able to capture the temperature trend of MIAC it is found to be important to include an additional temperature dependency, which in this work is a temperature dependent interaction parameter for the physical term. It is also found that it is important to include sufficiently strong ion association to be able to obtain the correct qualitative trend for individual ion activity coefficients.

Physically-based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, issues still arise, especially in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point, over one snow season. Their sensitivity relative i) to physical calibration for albedo and fresh snow density and ii) to forcing perturbation is evaluated based on ensemble approaches. Results indicate larger uncertainty depending on the model calibration than between the two models (even though the significant differences in their physical complexity). We also confirm the importance of albedo parameterization, even though ablation is driven by sublimation. SnowModel is particularly sensitive to this choice as it strongly affects both the sublimation and the melt rates. However, the day of snow-free snow surface is not sensitive to the parameterization as it only varies every eight days. The albedo parameterization of SNOWPACK has stronger consequences on melt at the end of the season leading to a date difference of the end of the season of 41 days. However, despite these differences, the sublimation ratio ranges are in agreement for the two models: 42.7–63.5 % for SnowModel and 51.3 and 64.6 % for SNOWPACK, and are related to the albedo calibration choice for the two models. Finally, the sensitivity of both models to the forcing data was in the same order of magnitude and highly influenced by the precipitation uncertainties.

Необхідний рівень безпеки машин і механізмів досягається за рахунок використання відповідних систем управління безпекою промислового обладнання, в тому числі програмованих електронних. Такі системи зазвичай включають в себе різноманітні охоронні пристрої для управління налаштуваннями промислового обладнання. Оскільки електронні системи управління в даний час вважаються найбільш перспективними системами управління в цій області, вивчення параметрів безпеки їх прикладної підтримки визначає актуальність даного дослідження. У цьому дослідженні проаналізовано основні вимоги стандартів IEC 61508 та IEC 62061 на відповідність сучасним вимогам безпеки вбудованого та прикладного програмного забезпечення для електронних систем управління машинами та механізмами. Дане дослідження пропонує алгоритм покрокового впровадження програмного забезпечення для електронних систем управління машинами відповідно до базових стандартів безпеки як для вбудованого, так і прикладного програмного забезпечення. Тестування визначено як основний метод перевірки прикладного програмного забезпечення. За результатами аналізу було встановлено, що специфікація вимог безпеки, як вбудованого, так і прикладного програмного забезпечення, повинна висвітлювати необхідні характеристики кожної підсистеми, надаючи інформацію, що дозволяє вибрати обладнання, що відповідає існуючим вимогам безпеки. Наводяться відповідні рекомендації щодо специфіки практичного застосування цих стандартів. The required level of safety of machines and mechanisms is achieved through the use of appropriate safety management systems for industrial equipment, including programmable electronic ones. Such systems usually include a variety of security devices for managing industrial equipment settings. Since electronic control systems are currently considered the most promising control systems in this area, the study of the security parameters of their application support determines the relevance of this study. This study analyses the main requirements of IEC 61508 and IEC 62061 standards for compliance with modern safety requirements of embedded and applied software for electronic control systems of machines and mechanisms. This study proposes an algorithm for step-by-step implementation of software for electronic machine control systems in accordance with basic security standards for both built-in and application software. Testing has been determined as the main method of verification of application software. Based on the results of the analysis, it was found that the specification of security requirements, both built-in and application software, should highlight the necessary characteristics of each subsystem, providing information that allows choosing the equipment that meets existing security requirements. Relevant recommendations are given on the specifics of practical application of these standards.

We investigate the Hölder geometry of curves generated by iterated function systems (IFS) in a complete metric space. A theorem of Hata from 1985 asserts that every connected attractor of an IFS is locally connected and path-connected. We give a quantitative strengthening of Hata’s theorem. First we prove that every connected attractor of an IFS is (1/s)-Hölder path-connected, where s is the similarity dimension of the IFS. Then we show that every connected attractor of an IFS is parameterized by a (1/ α)-Hölder curve for all α > s. At the endpoint, α = s, a theorem of Remes from 1998 already established that connected self-similar sets in Euclidean space that satisfy the open set condition are parameterized by (1/s)-Hölder curves. In a secondary result, we show how to promote Remes’ theorem to self-similar sets in complete metric spaces, but in this setting require the attractor to have positive s-dimensional Hausdorff measure in lieu of the open set condition. To close the paper, we determine sharp Hölder exponents of parameterizations in the class of connected self-affine Bedford-McMullen carpets and build parameterizations of self-affine sponges. An interesting phenomenon emerges in the self-affine setting. While the optimal parameter s for a self-similar curve in ℝ n is always at most the ambient dimension n, the optimal parameter s for a self-affine curve in ℝ n may be strictly greater than n.

En el presente documento se establecerá una propuesta en la cual se busca realizar un estudio en el área de gestión de accesos con el fin de parametrizar los usuarios que ingresan a la compañía otorgándoles los accesos a los diferentes aplicativos, sistemas y herramientas de manera más eficiente. Para Realizar este posible análisis se abarcará los siguientes ítems con los cuales se podrá lograr una propuesta de reingeniería, donde se describirá el flujo del proceso de gestión de acceso en la compañía Colombiana de Comercio Corbeta S.A.S con el fin de realizar un análisis detallado de los problemas, las pérdidas de información y la gestión que se realiza en el área frente a la creación, parametrización, modificación de los usuarios que ingresan o pertenecen a la compañía. Se establecerá el gestor de identidades que maneja la compañía actualmente con el cual se realizará un breve análisis de las falencias que se encuentran día a día al momento de realizar una creación, modificación o parametrización de los usuarios, el tiempo de respuesta del gestor, las fallas que presenta al momento de replicar los accesos a los usuarios, el rendimiento y los aplicativos sobre los cuales gobierna. Finalmente se establecerá la pérdida de información que se presenta en la base de datos, los usuarios que pueden acceder a ella, las vulnerabilidades, riesgos y cómo afecta esto en el proceso diario que realiza el área gestión de accesos con el fin de tener una propuesta de mejora para la compañía frente a los problemas que se evidenciaron en la práctica profesional In this document, a proposal will be established in which it is sought carry out a study in the area of ​​access management in order to parameterize the users who enter the company granting them access to the different applications, systems and tools more efficiently. To carry out this possible analysis, the following items will be covered with the which a reengineering proposal can be achieved, where the flow of the access management process in the Colombian company of Commerce Corbeta S.A.S in order to carry out a detailed analysis of the problems, the losses of information and the management that is carried out in the area against the creation, parameterization, modification of the users who enter or belong to the company. The identity manager that currently manages the company will be established with which a brief analysis of the shortcomings that are found day by day will be carried out. day at the time of making a creation, modification or parameterization of the users, the response time of the manager, the failures that it presents at the moment to replicate access to users, performance and applications on the which governs Finally, the loss of information that is presented in the database, users who can access it, vulnerabilities, risks and how this affects the daily process carried out by the access management area with in order to have a proposal for improvement for the company in the face of the problems that evidenced in professional practice Ingeniero de Telecomunicaciones Pregrado

This study develops an advanced physically‐based parameterization of heterogeneous ice nucleation in cirrus clouds that includes an updated parameterization of stochastic homogeneous freezing of supercooled solution droplets. Both components are formulated based on the same methodology and level of approximation, without numerical integration of the underlying ice supersaturation equation. The new scheme includes measured ice nucleation spectra describing deterministic ice activation from an arbitrary number of types of ice‐nucleating particles (INPs), tracks the competition for available water vapor between the different ice nucleation modes, and allows for new ice formation and growth within pre‐existing cirrus clouds. The computationally efficient scheme works with a minimal set of physical input parameters and predicts total nucleated ice crystal number concentrations (ICNCs) along with the maximum ice supersaturation attained during cirrus formation events. Aspects of its implementation into host models are discussed, including the provision of suitably parameterized vertical wind speeds. The parameterization is validated by comparisons to numerical simulations. First off‐line applications to mineral dust and aviation soot particles are presented, including ICNC ensemble statistics resulting from the coupling with statistics of updraft speed variability., Plain Language Summary: Two decades after introduction of the first parameterization of cirrus cloud formation by freezing of ubiquitous liquid solution droplets, an improved version is developed based on the latest experimental findings regarding solid ice‐nucleating particles, a small subset of the atmospheric aerosol. The new scheme allows to predict ice crystal formation in cirrus from competing homogeneous freezing and heterogeneous ice activation more realistically and with greater computational efficiency. It considers new developments regarding the properties of vertical wind speeds (triggering ice formation) and the molecular kinetics of water vapor uptake onto ice crystals (controlling ice growth). This study explains the foundation of cirrus ice formation and growth based on cloud physical theory, derives and explains the parameterization, discusses its use in host models to facilitate applications, checks its performance by comparison to comprehensive numerical simulations, and presents first results involving mineral dust and aircraft‐emitted soot particles as examples for good and poor atmospheric ice‐nucleating particles, respectively., Key Points: Competing ice nucleation processes in cirrus are predicted reliably and efficiently. Partial activation of dust particles may occur frequently in cirrus formation. Nucleation of ice within already‐existing cirrus requires high updraft speeds.

Testing of lithium-ion batteries depends greatly on accurate temperature control in order to generate reliable experimental data. Reliable data is essential to parameterise and validate battery models, which are essential to speed up and reduce the cost of battery pack design for multiple applications. There are many methods to control the temperature of cells during testing, such as forced air convection, liquid cooling or conduction cooling using cooling plates. Depending on the size and number of cells, conduction cooling can be a complex and costly option. Although easier to implement, forced air cooling is not very effective and can introduce significant errors if used for battery model parametrisation. Existing commercially available immersion baths are not cost effective (∼£3320) and are usually too small to hold even one large pouch cell. Here, we describe an affordable but effective cooling method using immersion cooling. This bath is designed to house eight large lithium-ion pouch cells (300mm x 350mm), each immersed in a base oil cooling fluid (150L total volume). The total cost of this setup is only £1670. The rig is constructed using a heater, chilling unit, and a series of pumps. This immersion bath can maintain a temperature within 0.5 °C of the desired set point, it is operational within the temperature range 5 – 55 °C and has been validated at a temperature range of 25 – 45 °C.

[ES] En el presente trabajo, se desarrollará el prototipo virtual de un motor de combustión interna alternativo de cuatro tiempos, tipo plano, más concretamente bóxer. Considerando motores reales utilizados en la industria del automóvil (Subaru, Porsche, etc.) y empleando el programa de modelado 3D Inventor, se diseñarán los distintos componentes para posteriormente proceder al ensamblaje y simulación dinámica del mismo. Finalmente, se realizará un análisis de las posibles fuerzas actuantes sobre los elementos más comprometidos mediante el método de elementos finitos., [CAT] Lo primero de todo me gustaría dedicar este trabajo a mis padres por todo el apoyo que me han dado durante estos cuatro largos años, así como a mi hermana que ha tenido que aguantarme durante todos esos momentos de frustración y desesperación. También me gustaría agradecer a esa gran familia que han sido mi grupo de amigos, con los que tantos momentos he vivido, y en especial a mis dos murcianos, por tantas dudas que me han resuelto. Finalmente, no me querría olvidar de todos los profesores que he tenido durante la carrera, que gracias a su vocación de enseñanza y divulgación son capaces de transmitir su pasión por la ingeniería., [EN] In the present project will be developed the virtual protype of a four-strokes reciprocating internal combustion engine, flat type, more specifically boxer. Considering real engines used by the automotive industry (Subaru, Porsche¿) and using the 3D modelling program Autodesk Inventor, the different components will be designed for subsequent assembly and dynamically simulated. Finally, an analysis of the possible forces acting on the most compromised elements will be carried out using the finite element method.

Hydrological modeling for landscape and catchment scale applications requires upscaling of soil hydraulic parameters which are generally only available at point scale. We present a case study where hourly root zone soil water content and drainage observations from nine flat, pastoral sites (Waikato and Canterbury regions in New Zealand) were used to develop an upscaling approach to parameterize the soil water balance module of the TopNet catchment model, based on scaling multi-layer soil profile information from the national soil data base, S-map, to the single-layer soil profile used in TopNet. Using a Bayesian calibration approach, the hydraulic behavioral parameters of TopNet's soil water balance module were identified. Of the eleven calibration parameters considered three were found to be insensitive to data (stress point, unsaturated hydraulic conductivity and infiltration rate); three were correlated and could be determined from specific soil water content observations (wilting point, field capacity and drainable water); and five were correlated and could be determined from combined specific soil water content and drainage observations (drainage rate, saturated hydraulic conductivity profile, effective soil depth, soil water holding capacity and wetting front suction). Based on the eight correlated parameters, upscaling functions were then developed to derive suitable model parameters from S-map-hydro for each site. The validity of the upscaling functions was verified at each site. The approach used in this research can be used to parameterize the TopNet model at other similar locations, and also provides a transferable framework to parameterize other catchment-scale hydrology models where point-scale soil hydraulic data available., New Zealand Ministry of Business, Innovation and Employment under the Endeavor program S-map Next Generation C09X1612

Access point for public codes of WP2.

Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties., Plain Language Summary: Deep neural nets are hard to interpret due to their hundred thousand or million trainable parameters without further postprocessing. We demonstrate in this paper the usefulness of a network type that is designed to drastically reduce this high dimensional information in a lower‐dimensional space to enhance the interpretability of predictions compared to regular deep neural nets. Our approach is, on the one hand, able to reproduce small‐scale cloud related processes in the atmosphere learned from a physical model that simulates these processes skillfully. On the other hand, our network allows us to identify key features of different cloud types in the lower‐dimensional space. Additionally, the lower‐order manifold separates tropical samples from polar ones with a remarkable skill. Overall, our approach has the potential to boost our understanding of various complex processes in Earth System science., Key Points: A Variational Encoder Decoder (VED) can predict sub‐grid‐scale thermodynamics from the coarse‐scale climate state. The VED's latent space can distinguish convective regimes, including shallow/deep/no convection. The VED's latent space reveals the main sources of convective predictability at different latitudes., EC ERC HORIZON EUROPE European Research Council http://dx.doi.org/10.13039/100019180, Columbia sub‐award 1, Advanced Research Projects Agency - Energy http://dx.doi.org/10.13039/100006133, Deutsches Klimarechenzentrum http://dx.doi.org/10.13039/100018730, National Science Foundation Science and Technology Center Learning the Earth with Artificial intelligence and Physics

The aerosol impact on liquid water path (LWP) is a key uncertainty in the overall climate impact of aerosol. However, despite a significant effort in this area, the size of the effect remains poorly constrained, and even the sign is unclear. Recent studies have shown that the relationship between droplet number concentration (Nd) and LWP is an unreliable measure of the impact of Nd variations on LWP due to the difficulty in establishing causality. In this work, we use satellite observations of the short-term development of clouds to examine the role of Nd perturbations in LWP variations. Similar to previous studies, an increase followed by a general decrease in LWP with increasing Nd is observed, suggesting an overall negative LWP response to Nd and a warming LWP adjustment to aerosol. However, the Nd also responds to the local environment, with aerosol production, entrainment from the free troposphere and wet scavenging all acting to modify the Nd. Many of these effects act to further steepen the Nd–LWP relationship and obscure the causal Nd impact on LWP. Using the temporal development of clouds to account for these feedbacks in the Nd–LWP system, a weaker negative Nd–LWP relationship is observed over most of the globe. This relationship is highly sensitive to the initial cloud state, illuminating the roles of different processes in shaping the Nd–LWP relationship. The nature of the current observing system limits this work to a single time period for observations, highlighting the need for more frequent observations of key cloud properties to constrain cloud behaviour at process timescales.

Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non-linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed-forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; b) high optically thin cirrus-like clouds are separated from low optically thick cumulus clouds; and c) shallow convective processes are associated with large-scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub-grid-scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties, main paper: 30 pages, 11 figures; supporting informations: 37 pages, 19 figures, 11 tables; Submitted to 'Journal of Advances in Modeling Earth Systems' (JAMES)

Optimization of dry deposition velocity calculation has been of great interest. Every time, determining the value of the concentration boundary layer (CBL) thickness led to a waste of numerical calculation time, which appears as a huge time in large-scale climate models. The goal of this study is to optimize the numerical calculation time in the three-layer deposition model for smooth surfaces through the development of a MATLAB code that can parameterize the appropriate concentration boundary layer height (y+cbl) and internal integral calculation intervals for each particle diameter Dp (0.01–100 µm) and friction velocity u* (0.01–100 m/s). The particle concentration, as a solution to the particle flux equation, is obtained and modeled numerically by performing the left Riemann sum using MATLAB software. On the other hand, the number of subdivisions N of the Riemann sum was also parameterized for each Dp and u* in order to lessen the numerical calculation time. From a numerical point of view, the new parameterizations were tested by several computers; about 78% on the average of the computation time was saved when compared with the original algorithm. In other words, on average, about 1.2 s/calculation was gained, which is valuable in climate models simulations when millions of dry deposition calculations are needed.

The change in wind direction and speed with height, referred to as vertical wind shear, causes enhanced turbulence in the atmosphere. As a result, there are enhanced interactions between ice particles that break up during collisions in clouds which could cause heavy snowfall. For example, intense dual-polarization Doppler signatures in conjunction with strong vertical wind shear were observed by an X-band weather radar during a wintertime high-intensity precipitation event over the Swiss Alps. An enhancement of differential phase shift (Kdp>1° km-1) around -15 °C suggested that a large population of oblate ice particles was present in the atmosphere. Here, we show that ice-graupel collisions are a likely origin of this population, probably enhanced by turbulence. We perform sensitivity simulations that include ice-graupel collisions of a cold frontal passage to investigate whether these simulations can capture the event better and whether the vertical wind shear had an impact on the secondary ice production (SIP) rate. The simulations are conducted with the Consortium for Small-scale Modeling (COSMO), at a 1 km horizontal grid spacing in the Davos region in Switzerland. The rime-splintering simulations could not reproduce the high ice crystal number concentrations, produced too large ice particles and therefore overestimated the radar reflectivity. The collisional-breakup simulations reproduced both the measured horizontal reflectivity and the ground-based observations of hydrometeor number concentration more accurately (∼20 L-1). During 14:30-15:45 UTC the vertical wind shear strengthened by 60 % within the region favorable for SIP. Calculation of the mutual information between the SIP rate and vertical wind shear and updraft velocity suggests that the SIP rate is best predicted by the vertical wind shear rather than the updraft velocity. The ice-graupel simulations were insensitive to the parameters in the model that control the size threshold for the conversion from ice to graupel and snow to graupel., Atmospheric Chemistry and Physics, 23 (4), ISSN:1680-7375, ISSN:1680-7367

The number of offspring and the population size greatly influence the performance of the Plant Propagation Algorithm, a crossover-less metaheuristic, when applied to the Euclidean traveling salesman problem. However, with large volumes of experimental data, a clearly characterizable relation emerges, facilitating both an exact parameterization as well as opening up perspectives on a precisely predictable outcome. These results directly question its position between exact, approximation, and PTAS algorithms. Although the metaheuristic is thereby officially parameter sensitive when deployed to the Euclidean traveling salesman problem, it does appear to be largely insensitive to different problem instances. This is very different from earlier results on continuous optimization, which were completely opposite. Why is that, and what does it mean for our practices in algorithmic benchmarking?

This paper describes the data used for Stock Synthesis assessment for the Eastern Atlantic and Mediterranean bluefin tuna. The initial model configuration, fleet definitions, selectivity modeling and main parameterization are also outlined. The model runs from 1950 to 2020 and is fit to length composition data and pair age-length data treated as conditional age-at-length.

Background Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations—e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible. Results In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets. Conclusion The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application (https://acc2.ncbr.muni.cz) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets. Graphic Abstract

The study reported here has revealed the issue related to the inefficient scaling of the uniformity of jacket model designs in the processes involving a typical representative as a result of modification parameters uncertainty. A variant has been proposed to synchronize the critical points of silhouetted allowances by grouping the numeric series in the vector of choosing the value for an increase in the allowance according to the characteristics of style varieties. The influence of shape-forming segmentation on the formation of a classifier of the structural and technological solutions for a jacket has been determined. The built model to support modification vectors has made it possible to describe the sequence of procedures execution by the method of typical representation. The presence of one design category, the same structural parts, the uniformity of style simplifies the processes of choosing and selecting the most characteristic models of the jacket. It was found that the morphological combination of attributes of the physical appearance affects the adjustment of style preferences in a manufacturer’s products. The parameters for typical segmentation relative to the junction points of the structural zones of the optimized five-seam prototype design have been defined as the most influential vectors of jacket modification. A method for scaling the allowance for free fitting has been devised on the basis of data from empirical research. An adequate regression model has been derived for normalizing the silhouette allowance parameters. The constructed model makes it possible to scale silhouette structures by changing the increments at the corner points of the contour according to the prototype of gradation under an automated mode. Practical recommendations have been compiled on the parameters of zonal-modular modification of silhouette designs of jacket varieties: a linear character of the state silhouetted transformation relative to ASi1=5 cm. The normalized parameters for constructing functional and decorative parts have been proposed

Introduction Due to limitation of water and soil resources resulted from geological and climatic conditions of Iran as well as necessity of self-reliance in infrastructural issues, efficient usage of water and soil resources available in Iran is inevitable. Climatic changes, reduced biodiversity in the region and concerns about food security are regarded as important issues; hence, evaluation of conditions to attain improved crop production seems essential. To investigate yield improvement methods, yield potential and yield limiting factors (climate, soil, water, and genetic factors) should be determined and evaluated in the first step. Simulation models may be used as scaled-up designs of field experiments to overcome limitations such as time and costs. Crop simulation models are mathematical representations of plant growth processes as influenced by interactions among genotype, environment and crop management. Using crop simulation models can be an efficient complement to experimental research. Models are being used to understand the response of crops to possible changes in crop, cultural management, and environmental variables. Crop models use various plant and environmental parameters to simulate crop growth and should be calibrated and evaluated before usage. Materials and Methods SSM-iCrop model predicts phenological stages as a function of temperature, day length. Calculation of phenological development in the model is based on the biological day concept. A biological day is a day with optimal temperature, photoperiod, and moisture conditions for plant development. Leaf area development and senescence is a function of temperature, provide nitrogen for leaf growth, plant density and nitrogen remobilization. To simulate leaf area expansion, the first step is to determine on each day the increase in leaf number on the main stem using the phyllochron (temperature unit between emergences of successive leaves) concept. In this model biomass is estimated as a function of the received radiation and temperature. Daily increase of crop mass is estimated as the product of incident photosynthetic active radiation (PAR, MJ m-2d-1), the fraction of that radiation intercepted by the crop (FINT) and efficiency with which the intercepted PAR is used to produce crop dry mass, i.e., radiation use efficiency (RUE, g MJ-1). Yield formation in the model is simply simulated as total dry matter production during seed filling period plus a fraction of crop dry mass at BSG (as mobilized dry matter). Modeling seed growth rate and yield formation in the current model is based on a modified linear increase in harvest index concept as described by Soltani and Sinclair (2011). The model needs daily weather data, i.e. maximum and minimum temperatures, rainfall, and solar radiation. The model can be run under multiple scenarios/treatments over many years. Results As a result of the SSM-iCrop model parameterization, three early, medium and late maturing cultivars were determined for canola, which their cumulative degree days (GDD) for growth period completion were estimated as 2000, 2500 and 2700 °C days. After determination of the required parameters, the model was run based on sowing date, management, and meteorological statistics of the region using the data from the papers which were not used for parameterization, so as to validate the model. The average of the simulated data for days to maturity and yield were 222 (days) and 383 (g.m-2), respectively, whereas observed values for this traits were 223 (days) and 359 (g.m-2). Conclusion: Based on the 1:1 line and statistics of r=0.87, CV=18% and RMSE=67.04 (g.m-2) for grain yield and r=0.97, CV=5% and RMSE=10.68 (days) for days to maturity, it may be concluded that simulation canola growth using SSM-iCrop model has been satisfactory and indicates accurate estimation of the model parameters, as well as serving as a verification of the model efficiency in prediction of canola yield under climatic conditions of major canola production regions of Iran.

Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.

Agent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species., This work was supported by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competências em Cloud Computing, co-financed by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica - Programas Integrados de IC\&DT.

ispartof: AGRICULTURAL WATER MANAGEMENT vol:284 status: accepted

In this paper, an assessment of strength properties of the existing blade-disc dovetail joint is presented, taking the parametric optimization of its geometry. A sensitivity of the analysis was carried out to determine the relationship between selected geometric parameters and strength properties of the structure questioned. Results from experiments and computer simulations were compared. In addition, the fatigue life of the structure was analyzed for different materials and using the change of speed ranges and parameterization of geometry.

The interaction between water vapor and atmos-pheric aerosol leads to enhancement in aerosol water content,which facilitates haze development, but its concentrations, sources,and impacts remain largely unknown in polluted urban environ-ments. Here, we show that the Indian capital, Delhi, which tops thelist of polluted capital cities, also experiences the highest aerosolwater yet reported worldwide. This high aerosol water promotessecondary formation of aerosols and worsens air pollution. Wereport that severe pollution events are commonly associated withhigh aerosol water which enhances light scattering and reducesvisibility by 70%. Strong light scattering also suppresses theboundary layer height on winter mornings in Delhi, inhibitingdispersal of pollutants and further exacerbating morning pollutionpeaks. We provide evidence that ammonium chloride is the largestcontributor to aerosol water in Delhi, making up 40% on average, and we highlight that regulation of chlorine-containing precursorsshould be considered in mitigation strategies., Environmental Science & Technology, 56 (11), ISSN:0013-936X, ISSN:1520-5851

Many animals have protective anatomical structures that allow for growth and flexibility; these structures contain thin seams called sutures that help the structure to absorb impacts. In this study, we parameterized the stiffness and toughness of a curved archway structure based on three geometric properties of a suture through finite element, quasi-static, three-point bending simulations. Each archway consisted of two symmetric pieces linked by a dovetail suture tab design. The three parameters included suture tab radii (1–5 mm), tangent lengths (0–20 mm), and contact angles (0–40°). In the simulations, a steel indenter was displaced 6.5 mm to induce progressive tab disengagement. Sutures with large contact angles and large tangent lengths generally led to stiffer and tougher structures. Sutures with a small tab radius exhibited the most sensitivity to the input parameters, and the smallest tab radius led to the stiffest and toughest archways. Results suggested that it was a combination of the largest number of tab repeats with the largest possible contact surface area that improved the mechanical response of the archway. The study revealed several suture geometries that hold significant promise, which can aid in the development of hemispherical 3D structures for dynamic impact applications.

In this article the original method of constructing a numerical-analytical scheme for investigation the solutions of nonlinear systems of ordinary differential equations under nonlinear integral boundary conditions is substantiated. At the heart of the method lies transition from given integral boundary conditions to parameterized conditions of model type, which have a simple appearance of the initial conditions. For a model parameterized problem, a constructive numerically-analytical scheme is constructed, which is built on parameterized approximations with improved convergence characteristics. The connection between the solutions of the model and transitional boundary value problems is established. It is proved that by dividing the segment of integration in half, twice can be improved the sufficient conditions of uniform convergence for parameterized sequential approximations. This technique and its advantages are illustrated by example of one integral boundary value problem, in which to perform sufficient convergence conditions you need to split the integration segment in half., У даній статті обгрунтований оригінальний метод побудови чисельно-аналітичної схеми дослідження розв'язків нелінійних систем звичайних диференціальних рівнянь, підпорядкованих нелінійним інтегральним крайовим умовам. В основі методу лежить перехід від заданих інтегральних крайових умов до параметризованих умов модельного типу, які мають простий вигляд початкових умов. Для модельної параметризованої задачі побудована конструктивна чисельно-аналітична схема, яка базується на параметризованих послідовних наближеннях із покращеними характеристиками збіжності. Встановлено зв'язок між розв'язками модельної та вихідної крайових задач. Доведено, що діленням відрізка інтегрування навпіл у два рази можна покращити достатні умови рівномірної збіжності параметризованих послідовних наближень. Цю техніку та її переваги продемонстровано на прикладі інтегральної крайової задачі, в якій для виконання достатніх умов збіжності потрібно поділити відрізок інтегрування навпіл.

Four state-of-the-science numerical weather prediction (NWP) models were used to perform mountain wave (MW)-resolving hindcasts over the Drake Passage of a 10-day period in 2010 with numerous observed MW cases. The Integrated Forecast System (IFS) and the Icosahedral Nonhydrostatic (ICON) model were run at Δx ≈ 9 and 13 km globally. The Weather Research and Forecasting (WRF) Model and the Met Office Unified Model (UM) were both configured with a Δx = 3-km regional domain. All domains had tops near 1 Pa (z ≈ 80 km). These deep domains allowed quantitative validation against Atmospheric Infrared Sounder (AIRS) observations, accounting for observation time, viewing geometry, and radiative transfer. All models reproduced observed middle-atmosphere MWs with remarkable skill. Increased horizontal resolution improved validations. Still, all models underrepresented observed MW amplitudes, even after accounting for model effective resolution and instrument noise, suggesting even at Δx ≈ 3-km resolution, small-scale MWs are underresolved and/or overdiffused. MW drag parameterizations are still necessary in NWP models at current operational resolutions of Δx ≈ 10 km. Upper GW sponge layers in the operationally configured models significantly, artificially reduced MW amplitudes in the upper stratosphere and mesosphere. In the IFS, parameterized GW drags partly compensated this deficiency, but still, total drags were ≈6 times smaller than that resolved at Δx ≈ 3 km. Meridionally propagating MWs significantly enhance zonal drag over the Drake Passage. Interestingly, drag associated with meridional fluxes of zonal momentum (i.e., ) were important; not accounting for these terms results in a drag in the wrong direction at and below the polar night jet. Significance Statement This study had three purposes: to quantitatively evaluate how well four state-of-the-science weather models could reproduce observed mountain waves (MWs) in the middle atmosphere, to compare the simulated MWs within the models, and to quantitatively evaluate two MW parameterizations in a widely used climate model. These models reproduced observed MWs with remarkable skill. Still, MW parameterizations are necessary in current Δx ≈ 10-km resolution global weather models. Even Δx ≈ 3-km resolution does not appear to be high enough to represent all momentum-fluxing MW scales. Meridionally propagating MWs can significantly influence zonal winds over the Drake Passage. Parameterizations that handle horizontal propagation may need to consider horizontal fluxes of horizontal momentum in order to get the direction of their forcing correct.

ispartof: QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY vol:147 issue:735 pages:825-846 status: published

Statistical relationships between surface ozone (O-3) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and -regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O-3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain similar to 50% of the variability in daily maximum O-3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O-3 variability. In summer, daily O-3 variances were mostly associated with relative humidity, while winter O-3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O-3. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O-3,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O-3 levels modelled with the three statistical models compared to measured O-3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O-3 precursors coupled with meteorological conditions in daily variances of O-3 levels in continental South Africa.

Arctic shrubs reduce surface albedo in winter when branches protrude above the snow. To calculate the albedo of those mixed surfaces, the branch area index (BAI) of Arctic shrubs needs to be known. Moreover, an exposed-vegetation function is required to determine the BAI for protruding branches only. This study used a structural analysis of 30Betula glandulosashrubs, sampled near Umiujaq, northern Quebec, to (i) establish an allometric relationship between shrub height and BAI and (ii) determine a specific exposed-vegetation function for Arctic shrubs. The spectral albedo (400–1080 nm) of mixed surfaces was then simulated with the equations derived from this study and validated with in situ measured spectra. Shrubs were sampled from two sites, one along the coast and the other in a nearby valley. The shrub height–BAI relationship varied between both sites. BAI values of shrubs growing in the wind-sheltered valley were 30%–50% lower. The exposed-vegetation function obtained here differed from the linear functions found in the literature. The linear functions strongly overestimated the BAI of exposed branches. Albedo was well simulated with an accuracy of 3% when using an allometric relationship adapted to the environmental conditions of our study site. However, simulated albedo values were consistently higher than field measurements, probably because radiation absorbed by impurities and buried branches was neglected in the model. We conclude that specific exposed-vegetation and allometric equations need to be implemented in models to accurately simulate the albedo of mixed snow–shrub surfaces.

The role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.