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Zero Poisson's ratio (ZPR) honeycombs are widely used in aerospace applications due to their high load carrying capacity, tunable performance and lightweight. However, its structural design is difficult and often requires designers to have extensive experience. With the gradual development of artificial intelligence, it becomes possible to obtain structural configurations using meta -models and desired mechanical properties. In this paper, a petal-shaped honeycomb (PSH) with bi-directional tunable mechanical properties possessing ZPR effect is designed. Parametric modelling and Latin hypercube sampling (LHS) are applied to reveal the effect of structural parameters on the bi-directional mechanical properties. Combined with Python scripts to automate the running of finite element analyses and complete the collection of results. An artificial neural network (ANN) is improved to achieve the performance prediction of the PSH with a minimum error of only 0.032%. The inverse design of the PSH is completed based on the mechanical properties required for the conceptual application with a minimum error of 2.375%. An automatic design system for PSH is proposed by integrating parametric models, Python scripts and modified ANN. The overall process reduces human control time through the automation of scripts, improves the honeycomb design efficiency, and provides a new systematic approach for the design of ZPR honeycombs. [ABSTRACT FROM AUTHOR]

• A novel Ring Hoop Plane Strain Test (RHPST) enhancing the characterization of tubes' plastic behavior has been engineered. • A meta -modeling approach integrating RSM and FEM was developed for the optimization-based design of the RHPST. • 3D DIC technology was used to precisely probe the edge effects and homogeneity of strain fields in RHPST sample. • A tailored correction was developed, improving the plane strain zone extent covering about 78% of the RHPST specimen's width. • Innovative grooved ring specimen was produced, improving plane strain zone extent up to 90% of the gauge width. The accurate characterization of the plastic behavior of tubular materials is challenging due to the difficulties in acquiring proper specimens for mechanical testing. In this study, we propose a novel testing technique, namely the ring hoop plane strain Test (RHPST), as a supplementary test for characterizing materials experiencing distinct strain paths compared to standard uniaxial tensile tests. To design a suitable ring specimen's geometry that satisfies the necessary plane strain conditions, we developed a meta -modelling approach merging response surface method (RSM) and the finite element method (FEM). Experimental tests are conducted on RHPST specimens, and 3D digital image correlation (DIC) technique is used to monitor the strain fields within the specimen's gauge section. However, observations indicate the presence of edge effects on the gauge specimen. These edge effects significantly influence and limit the homogeneity of the plane strain field. To tackle this limitation, a correction procedure is developed for separating the authentic plane strain area, constituting approximately 78% of the RHPST specimen's width. Furthermore, we successfully fabricated a novel grooved ring specimen (GRHPST), which exhibits a wider region of the plane strain state encompassing up to 90% of the gauge width, while minimizing the impact of edge effects. The true stress-true strain curve obtained from the GRHPST specimen exhibits exceptional agreement with the curve predicted using the Hill48 yield criterion. Notably, no additional corrections are required for the measured true stress-true strain curve, thereby affirming the efficacy of the GRHPST specimen as an adept test for characterizing the anisotropic plastic behavior of AA6063 tubes. [ABSTRACT FROM AUTHOR]

The effect of seventeen parameters (material, design and operating) on the efficiency of the long-term operation of borehole thermal energy storage (BTES) has been investigated in this paper. Global sensitivity analysis was used to determine this effect and the use of the fast meta-model to predict the thermal efficiency of BTES has also been proposed. Carrying out research on the real object using this many input parameters is impossible, therefore, the use of 3D transient simulation studies of heat and mass transport in the BTES was chosen. An efficient numerical technique, coupled with the design and analysis of the computational experiment method was used to significantly reduce the required calculation time. The results showed that the temperature of the inlet fluid during charging and discharging, as well as the thermal conductivity of the rock mass and the arrangement of the borehole heat exchangers, had a crucial impact on the efficiency of the BTES. In order to obtain high efficiency values for the BTES, it is necessary to produce the highest possible inlet fluid temperature during charging and the lowest possible temperature of the inlet fluid during discharging. The highest values of BTES efficiency can be achieved using a compact arrangement of the borehole heat exchangers (BHE spacing 1.5–3.5 m) and for low values of the thermal conductivity of the rock mass (less than 2.45 W/(m ⋅ K). Image 1 • The influence of 17 parameters on BTES efficiency was analysed. • The meta-model to predict BTES efficiency was proposed. • Four from seventeen parameters have a crucial impact on BTES efficiency. • 41% of the analysed BTES configurations achieved efficiency above 0.5. [ABSTRACT FROM AUTHOR]

Sheet-bulk metal forming (SBMF) is an emerging manufacturing technology that offers vast potential for lightweight design and functional integration. However, product designers often lack information about the required manufacturing parameters for their specific design. This problem is currently tackled by a knowledge acquisition process from manufacturing experts in combination with meta-modelling techniques. These meta-models predict resulting manufacturing parameters for given part design properties. When employing this design-for-manufacturing approach, it is key for product designers to evaluate many design alternatives that are all manufacturable with given forming machines. This is currently done by a genetic algorithm-based optimization of the meta-models with regard to manufacturing features. This papers proposes a novel approach for generating many design alternatives based on the meta-models and gathering them in a solution space. In doing so it is possible to review all manufacturable sheet-bulk metal forming part variants and let the product designers decide which part design fits best. The approach is applied to a locking teeth part, which highlights the usability of the new method. [ABSTRACT FROM AUTHOR]

Current rates of retrofitting residential buildings are inadequate for meeting goals set by governments to reduce emissions. Plans, laws, policies, programs, and regulations must be tailored, yet adaptive to changes in technology, and have a way to communicate the evidence behind them. Surrogate models allow for accurate predictions much quicker than traditional brute-force methods with only small amounts of error. This research presents a methodology for developing a surrogate model that takes advantage of ANN's ability to make accurate predictions. Modelling is supported through analysing the alignment of input and output interactions with building science knowledge through SHAP values. Surrogate models developed using this methodology were able to predict the total annual site energy of an archetype representing 21% of Toronto's single-family housing stock with an average of 6% error, equivalent to 2.7 GJ per year. This research is another example of how the increased interpretability of black-box models support further development of surrogate models to solve optimisation problems such as developing effective retrofitting plans and policy strategies to accelerate rates of retrofitting existing housing. [ABSTRACT FROM AUTHOR]

In the automotive industry, high efforts are made to design high-efficiency, low-cost, requirement-optimized electric drives. Traditionally, time-consuming and expensive 3D finite element analyses are used to assess the effects of different design parameters from various engineering disciplines such as the electromagnetics, heat transfer, and mechanics. Often, to decrease the calculation efforts, scalable, physics-based reduced order models are used. For setting up and interpreting such reduced order models, simulation experts employ implicit knowledge by applying heuristic correction factors. This paper proposes a novel method to formalizing such implicit simulation knowledge using Gaussian Processes. Furthermore, the successful application of the method to a case study of electric motors is reported by validating the aforementioned correction factors for different motor designs. [ABSTRACT FROM AUTHOR]

Abstract The meta-modelling approach based on an adaptive sparse grid interpolator is proposed for tackling the identification problem of parametric hysteresis models for steels with different microstructures. Parametric models of Jiles-Atherton and Mel'gui, respectively, have been considered in this work. The main advantage of the present approach is the separation of the calculation procedure in a computationally demanding off-line phase, which has to be carried out only once, and a very fast on-line evaluation. This decomposition is particularly interesting when a large amount of successive evaluations has to be carried out. Especially in the case that we are interested in a particular family of ferromagnetic materials (e.g. steels subjected to different treatments), where the sought parameters are lying in a specific interval, a single meta-model may be sufficient to be used for the study of a wide range of specimens. The steel samples considered in this study have been obtained from industrially produced low carbon steel, 84% cold rolled, and isothermally annealed in laboratory. [ABSTRACT FROM AUTHOR]

Operating high-speed trains imposes excessive vibrations to bridges raising concerns about their safety. In this context, it was shown that some conventional design methods such as those related to the running safety suffer from a vague scientific background questioning their reliability or optimality. Therefore, the current article is devoted to updating the conventional design methodology, using Reliability-Based Design Optimization (RBDO) to propose the minimum allowable mass and stiffness which assures satisfying the target reliability. These proposed minimum design values can conceptually replace the conventional partial safety factor-based design method for running safety without the need for dynamic analysis. If the mass and stiffness resulting from the control of other limit states meet the proposed minimum values, the desired target reliability for running safety will be assured. This is achieved by adaptively training Kriging meta-models as a surrogate for the computational models decoupling the RBDO problem. In this regard, a new stopping criteria is proposed using mis-classification ratio of the cross-validated model; which reduces the generalization error of the trained meta-model and consequently the estimated failure probability. Moreover, due to the dependence of the design variables, the Copula concept is used to refine the augmented space and reformulate the RBDO problem. • A novel and straightforward dynamic design method is proposed for railway bridges. • The Copula concept is adopted to reformulate RBDO problems with dependent variables. • A new stopping criterion is proposed for adaptive training of surrogate models. • An extensive data is collected for variables in dynamic behavior of railway bridges. • Proper PDFs are assigned for contributing random variables of railway bridges. [ABSTRACT FROM AUTHOR]

Sequential bifurcation (or SB) is an efficient and effective factor-screening method; i.e., SB quickly identifies the important factors (inputs) in experiments with simulation models that have very many factors—provided the SB assumptions are valid. The specific SB assumptions are: (i) a second-order polynomial is an adequate approximation (a valid metamodel) of the input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation is monotonic); (iii) so-called “heredity” applies; i.e., if a specific input has a “small” first-order effect, then this input has “small” second order effects. Moreover, SB assumes Gaussian simulation outputs if the simulation model is stochastic (random). A generalization of SB called “multiresponse SB” (or MSB) uses the same assumptions, but allows multiple types of simulation responses (outputs). In this article, we develop heuristic practical methods for testing whether these assumptions hold, and we evaluate these methods through Monte Carlo experiments and a case study (namely, a Chinese logistics network). [ABSTRACT FROM AUTHOR]

This paper addresses the needs of generic and shared PSS conceptual modelling approaches. The development of conceptual models for PSS is considered in this research as the basis to develop an integrated approach for PSS design. The objective of the paper is to present a generic PSS-dedicated meta-model, resulting from a procedure of integration of various conceptual models developed in the scientific literature of PSS design methods. The procedure of knowledge extraction and integration is explained and the generic PSS meta-model is presented, together with an associated modelling procedure. These results constitute a path forward towards more generic PSS design methods. [ABSTRACT FROM AUTHOR]

Meta-models play a cornerstone role in Model-Driven Engineering as they are used to define the abstract syntax of modelling languages, and so models and all sorts of model transformations depend on them. However, there are scarce tools and methods supporting their Validation and Verification (V&V), which are essential activities for the proper engineering of meta-models. In order to fill this gap, we propose two complementary meta-model V&V languages. The first one has similar philosophy to the xUnit framework, as it enables the definition of meta-model unit test suites comprising model fragments and assertions on their (in-)correctness. The second one is directed to express and verify expected properties of a meta-model, including domain and design properties, quality criteria and platform-specific requirements. As a proof of concept, we have developed tooling for both languages in the Eclipse platform, and illustrate its use within an example-driven approach for meta-model construction. The expressiveness of our languages is demonstrated by their application to build a library of meta-model quality issues, which has been evaluated over the ATL zoo of meta-models and some OMG specifications. The results show that integrated support for meta-model V&V (as the one we propose here) is urgently needed in meta-modelling environments. [ABSTRACT FROM AUTHOR]

Context: Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities.Objective: The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines. Instead of defining "yet another CIG formalism and its manager", we propose META-GLARE (META Guideline Acquisition, Representation, and Execution), a "meta"-system to define new CIG systems.Method and Materials: In this paper, META-GLARE, a meta-system to define new CIG systems, is presented. We try to capture the commonalities among current CIG approaches, by providing (i) a general manager for the acquisition, consultation and execution of hierarchical graphs (representing the control flow of actions in CIGs), parameterized over the types of nodes and of arcs constituting it, and (ii) a library of different elementary components of guidelines nodes (actions) and arcs, in which each type definition involves the specification of how objects of this type can be acquired, consulted and executed. We provide generality and flexibility, by allowing free aggregations of such elementary components to define new primitive node and arc types.Results: We have drawn several experiments, in which we have used META-GLARE to build a CIG system (Experiment 1 in Section 8), or to extend it (Experiments 2 and 3). Such experiments show that META-GLARE provides a useful and easy-to-use support to such tasks. For instance, re-building the Guideline Acquisition, Representation, and Execution (GLARE) system using META-GLARE required less than one day (Experiment 1).Conclusions: META-GLARE is a meta-system for CIGs supporting fast prototyping. Since META-GLARE provides acquisition and execution engines that are parametric over the specific CIG formalism, it supports easy update and construction of CIG systems. [ABSTRACT FROM AUTHOR]

The transition from a product–oriented business towards a PSS-oriented business, known in the scientific literature as ‘servitization’ involves complex changes for decision-makers. Over the past years, the scientific literature has provided consistent advances in PSS decision-support systems including PSS modelling. However, concerning PSS modelling languages or formalisms, most initiatives remain context dependent; to date only a small a piece of literature addresses the need for reproducibility of PSS modelling methods. The objective of this paper is to make a first step forward in this direction, by proposing an iterative procedure dedicated to build generic meta-models and by applying it to generate a first proposal of PSS meta-model, expected to be re-usable in several distinct modelling and decision-making contexts. [ABSTRACT FROM AUTHOR]

This work is motivated by a real-world case study where it is necessary to integrate and relate existing ontologies through meta-modelling . For this, we introduce the Description Logic SHIQM which is obtained from SHIQ by adding statements that equate individuals to concepts in a knowledge base. In this new extension, concepts can be individuals of another concept (called meta-concept ) which itself can be an individual of yet another concept (called meta–meta-concept ) and so on. We define an algorithm that checks consistency of SHIQM by modifying the Tableau algorithm for SHIQ . From the practical point of view, this has the advantage that we can reuse the code of existing OWL reasoners. From the theoretical point of view, it has a similar advantage of reuse. We make use of the existing results and proofs that lead to correctness of the algorithm for SHIQ in order to prove correctness of the algorithm for SHIQM . [ABSTRACT FROM AUTHOR]

Complex environmental model outputs used to inform decisions often have systematic errors and are of inappropriate resolution, requiring downscaling and bias correction for local applications. Here we provide a new interpretation of dasymetric modelling (DM) as a spatial bias correction framework useful in environmental modelling. DM is based on areal interpolation where estimates of some variable at target zones are obtained from overlapping source zones using ancillary information. We explore DM by downscaling runoff output from a distributed hydrological model using two meta-models and describe the properties of the methodology in detail. Consistent with properties of linear scaling bias correction, results show that the methodology 1) reduces errors compared to the source data and meta-models, 2) improve the spatial structure of the estimates, and 3) improve the performance of the downscaled estimates, particularly where meta-models perform poorly. The framework is simple and useful in ensuring spatial coherence of downscaled products. • We introduce dasymetric modelling (DM) as a spatial bias correction method. • Relationship between DM and linear scaling bias correction is shown. • DM consists of meta-modelling, bias correction, and areal interpolation components. • Performance of meta-models are significantly improved by DM. • DM corrects the wider spatial autocorrelation patterns in meta-model outputs. [ABSTRACT FROM AUTHOR]

Abstract: Moment-independent sensitivity methods are attracting increasing attention among practitioners, since they provide a thorough way of investigating the sensitivity of model output under uncertainty. However, their estimation is challenging, especially in the presence of computationally intensive models. We argue that replacement of the original model by a metamodel can contribute in lowering the computation burden. A numerical estimation procedure is set forth. The procedure is first tested on analytical cases with increased structural complexity. We utilize the emulator proposed in . Results show that the emulator allows an accurate estimation of density-based sensitivity measures, when the main structural features of the original model are captured. However, performance deteriorates for a model with interactions of order higher than 2. For this test case, also a kriging emulator is investigated, but no gain in performance is registered. However, an accurate estimation is obtained by applying a logarithmic transformation of the model output for both the kriging and emulators. These findings are then applied to the investigation of a benchmark environmental case study, the LevelE model. Results show that use of the metamodel allows an efficient estimation of moment-independent sensitivity measures while leading to a notable reduction in computational burden. [Copyright &y& Elsevier]

Abstract: Sustainable natural resource management requires inputs from both the natural and the social sciences. Since natural and social systems are inter-related and inter-dependent, it is essential that these data can be integrated within a given analysis, which requires that they are spatially compatible. However, existing environmental and socio-economic monitoring networks tend to observe, collect and report socio-economic and biophysical data separately; with the result that much of these data are spatially incompatible, adding to the complexity of objective and consistent resource management. We present an approach for overcoming spatial incompatibilities between socio-economic and biophysical data; based on a meta-modelling approach using Geographical Information Systems and an application of a water-use simulation model. The method is developed and applied to the irrigation agriculture sector in the Inkomati Water Management Area in South Africa. Agricultural census data, which are measured on a magisterial district scale, are integrated with geo-referenced land-cover data, which are independent of political boundaries. This allows us to increase the resolution at which data on the economic value derived from irrigation water are presented, from coarse magisterial district scale to a finer ‘meso-zone’ scale, enabling more efficient allocations of irrigation water within magisterial districts. [Copyright &y& Elsevier]

Abstract: In the present study, the effect of the opening size and building direction on night hours thermal comfort in a naturally ventilated rural house is investigated. Initially, the airflow in and around the building is simulated using a validated computational fluid dynamics (CFD) model. Local climate night-time data (wind velocity and direction, temperature and relative humidity) are recorded in a weather station and the prevailing conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are then used to evaluate indoor thermal comfort. For this reason, special thermal comfort indices, i.e. the well-known predicted mean vote (PMV) index and its modifications especially for natural ventilation, are calculated with respect to various residential activities. Mean values of these indices (output variables) within the occupied zone are calculated for different combinations of opening sizes and building directions (input variables), to generate a database of input–output pairs. Finally, the database is used to train and validate Radial Basis Function Artificial Neural Network (RBF ANN) input–output “meta-models”. It is demonstrated that the proposed methodology leads to reliable thermal comfort predictions, while the optimum design variables are easily recognized. [Copyright &y& Elsevier]

Abstract: A fully functional meta-model co-simulation environment that supports integration of many different simulation tool specific models into a co-simulation is described in this paper. The continuously increasing performance of modern computer systems has a large influence on simulation technologies. It results in more and more detailed simulation models. Different simulation models typically focus on different parts (sub-systems) of the complete system, e.g., the gearbox of a car, the driveline, or even a single bearing inside the gearbox. To fully understand the complete system it is necessary to investigate several or all parts simultaneously. This is especially true for transient (dynamic) simulation models with several interconnected parts. One solution for a more complete and accurate system analysis is to couple different simulation models into one coherent simulation, also called a co-simulation. This also allows existing simulation models to be reused and preserves the investment in these models. Existing co-simulation applications are often capable of interconnecting two specific simulators where a unique interface between these tools is defined. However, a more general solution is needed to make co-simulation modelling applicable for a wider range of tools. Any such solution must also be numerically stable and easy to use in order to be functional for a larger group of people. The presented approach for mechanical system co-simulations is based upon a general framework for co-simulation and meta-modelling . Several tool specific simulation models can be integrated and connected by means of a meta-model. A platform independent, centralised, meta-model simulator is presented that executes and monitors the co-simulation. All simulation tools that participate in the co-simulation implement a single, well defined, external interface that is based on a numerically stable method for force/moment interaction. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to integrate typed attributed graph transformation with node type inheritance. Borrowing concepts from object oriented systems, the main idea is to enrich the attributed type graph with an inheritance relation and a set of abstract nodes. In this way, a node type inherits the attributes and edges of all its ancestors. Based on these concepts, it is possible to define abstract productions, containing abstract nodes. These productions are equivalent to a number of concrete productions, resulting from the substitution of the abstract node types by the node types in their inheritance clan. Therefore, productions become more compact and suitable for their use in combination with meta-modelling. The main results of this paper show that attributed graph transformation with node type inheritance is fully compatible with the existing concept of typed attributed graph transformation. [Copyright &y& Elsevier]

Abstract: This paper argues for means to rigorously specify data models and communications services of industrial data communications standards. It uses the example of the recently published IEC 61850 standard “Communications networks and systems in substations”. The latter applies mainly to electrical supply systems such as substations and decentralised power resources (based on wind, photovoltaic, fuel cells, hydropower). However, the concepts of the IEC 61850 could be used in other industrial areas, as well. The paper shows how the complex standard can be modelled elegantly and precisely with a meta-modelling approach, in which we utilise the UML for the model representation. The conceptual approach presented makes the inherent complexity of the standard''s data model manageable for both humans and machines. That is, it facilitates human comprehension and machine processability and thereby contributes to a better understanding of the standard as well as to a better utilisation of the standard through functionality provided in today''s CASE tools. One important aspect is that it allows one to establish and maintain consistency across the standard''s data and communications models. [Copyright &y& Elsevier]

Abstract: This paper presents a part of wider research endeavor within the field of Computer Supported Cooperative Work (CSCW) to leverage the use of ethnography for systems design. It investigates the role of ethnography in the development of CSCW systems and its relevance to real world problems, particularly, to uncover the social organisation of work practices. The usage of ethnography to inform CSCW design constitutes many challenges to systems design. The most significant of these design challenges is the inevitable need for well-established methods for applying the ethnography within CSCW to inform systems design. We have developed an integrative approach based on ethnography and meta-modelling for use in the context of CSCW research and practice. We have also applied this approach to the analysis of cooperative work in the academic domain of investigation. [Copyright &y& Elsevier]

One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours. [Copyright &y& Elsevier]

The paper deals with the modelling of Lyee user requirements and guidelines to support their capture. The paper presents a 2-layer meta-model relating the set of concepts to capture user requirements to the set of concepts for the formulation of software requirements that are the input of the LyeeALL generation mechanism. It exemplifies the former with examples and introduces the guidance support for capturing these user centric requirements. [Copyright &y& Elsevier]

Strategic decision-making on long-term drought risk management can be supported by integrated assessment models to explore uncertain future conditions and potential policy actions. Such models have to meet many – sometimes conflicting – requirements posed by policy-makers, model developers, and stakeholders. This paper discusses the case of the National Water Model (NWM) that is applied for national policy-making on drought risk management in the Netherlands. The case demonstrates that the chosen assembled model set-up (in which several existing models are combined) is cost-effective and increases stakeholder acceptance, but also leads to high model complexity and computation time. To be effective for policy-making, integrated assessment models need to produce relevant model outcomes that are accepted by stakeholders, within acceptable time and cost limits. For this, the model set-up must support simulations at different aggregation levels (allowing both detailed analysis and exploratory analysis of many scenario/strategy combinations) while maintaining internal consistency. • Decision-making for drought risk management requires Integrated Assessment Models. • Integrated Assessment Models must meet many – sometimes conflicting – requirements. • There is a clear trade-off between model complexity and timeliness. • Effective models are those that allow simulations at different aggregation levels. [ABSTRACT FROM AUTHOR]

• We propose a new procedure to derive indices for index-based insurances. • First step is using biophysical modelling to simulate impact of weather anomalies. • Second step is deriving meta-models from input/output structure of biophysical model. • A case study is shown where the basis risk is minimized. Agricultural insurance is crucial for transferring a significant portion of the risk due to unfavourable weather conditions outside the farm. Index-based insurances were proposed as an alternative to traditional products based on direct damage assessment because of their potential to reduce insurance costs while being unaffected by subjectivity during damage quantification. However, they may be affected by basis risk when indices are poorly related with the underlying biological processes and thus with actual yield losses. To overcome this limitation, we developed a new framework to derive indices characterized by a low basis risk, based (i) on the use of a complex biophysical crop model extended to account for the extreme weather events of interest and (ii) on crop- and region-specific meta-models derived from the input-output structure of the crop model. The procedure was applied to frost and drought-heat damage to barley, soft and durum wheat in two Italian regions. Meta-models resulted easy to understand for farmers and accurate in reproducing percentage yield losses, with mean normalized relative root mean square error equal to 27.0% and 39.0% for damage caused respectively by frost and by the combined effect of drought and heat. The procedure, successfully adopted in operational contexts for the injuries and crops considered in this study, is going to be extended to other weather events, crops and regions. [ABSTRACT FROM AUTHOR]

In the field of pressure vessel design, much research has been done to predict the structural behaviour of round nozzles connected to spherical and cylindrical structures. This has been motivated by the fact that the majority of pressure vessels in industry have round bodies, as these are better suited to withstanding pressure. A large demand however also exists for flat-sided pressure vessels with round nozzles, particularly in the air-cooled heat exchanger industry, but there is a lack of research and guidelines on accurately predicting how these structures behave at the nozzle interface. This paper presents a new model that predicts the linearised stresses occurring in these structures in order to gain a better understanding of how they behave. [ABSTRACT FROM AUTHOR]

• It is challenging to efficiently predict long-term reservoir responses. • A meta-model is proposed to deep learn the relationship between the material properties and reservoir responses. • The established meta-model can predict the hydrate reservoir responses with significantly reduced computational demand. • This meta-model allows real-time prediction to be made and adjusted according to the observed reservoir response at the production site. The responses of hydrate reservoir during gas production are complex due to the spatially and temporally evolving thermo-hydro-mechanical properties. Accurate modeling of the behavior, therefore, requires a coupled multiphysics simulator with a large number of parameters, leading to substantial computational demands. This makes it challenging to efficiently predict long-term reservoir responses. In this study, by utilizing an artificial neural network (ANN) algorithm, a meta-model is proposed to deep learn the relationship between the material properties and reservoir responses, including borehole displacement and fluid production. As such, a set of 950 coupled thermo-hydro-mechanical simulations of a one-layer sediment axisymmetric model is carried out for six-day gas production via depressurization. Eighteen input parameters are considered in each simulation covering four physical aspects, namely hydrate dissociation, thermal flow, fluid flow, and mechanical response. With this comprehensive dataset of the responses, a meta-model is established based on the trained neural network, resulting in an efficient prediction of the responses with significantly reduced computational demand. The model is then further utilized to predict the future reservoir responses, and it is found that the results are in a good agreement with those from the fully-coupled simulator. [ABSTRACT FROM AUTHOR]

• We characterise the design space for multi-level modelling solutions in the form of a feature model. • We provide a classification and comparison of existing multi-level modelling tools. • We propose a new conceptual approach to multi-level modelling, embedded within two meta-levels. • We realise the ideas in practice on a practical tool called TOTEM. • We report on empirical and analytical evaluations of the tool. Model driven Engineering (MDE) advocates the active use of models throughout the different software development phases. In MDE, models are described using meta-models, one meta-level above. This approach effectively leaves developers with one single meta-level to create their models. However, there are scenarios where the use of multiple meta-levels results in simpler models with less accidental complexity. Hence, to simplify modelling in these cases, several multi-level modelling approaches and tools have recently emerged to increase the flexibility in modelling. While they provide advanced primitives to simplify modelling, there are possibilities to improve interoperability with mainstream two-level modelling approaches based on the Meta-Object Facility (MOF) standard of the Object Management Group (OMG), and achieve wider adoption. For this purpose, we first characterise the design space of multi-level modelling approaches using a feature model. On such a basis, we provide a detailed comparison of existing multi-level modelling tools, identifying gaps and research opportunities. As a result of this gap analysis, we propose a new approach to multi-level modelling that embeds multiple meta-levels within one meta-model (i.e., encoding objects as classes, and instantiation as inheritance), and a tool – called TOTEM – which implements these concepts. The tool capabilities and its benefits in terms of interoperability with mainstream, standard modelling frameworks are illustrated through an example, as well as with empirical and analytical evaluations. [ABSTRACT FROM AUTHOR]