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Spectroscopic ellipsometry and some other optical metrology techniques, such as reflectometry and scatterometry, are model-based optical measurements and, therefore, require appropriate modeling to determine the geometric and material properties of substrates, thin films, and multilayer structures. Parametric sensitivity analysis (SA) provides essential assistance in the model-building process to quantify the relative importance of model parameters for model output and to identify those with high/little influence. SA can be performed in a variety of ways, and this article discusses an application of the Morris or elementary effect (EE) method, a screening type SA procedure, to spectroscopic ellipsometry modeling. The method is a global SA technique and uses a stepping of m parameters along certain so-called "trajectories" or sequences of points in parameter space, randomly constructed in order to maximally fill the volume of the m -dimensional parameter space. However, it was thought that the EE method relies greatly on a sampling strategy or a way of selecting "optimized trajectories" in the parameter space, i.e., a necessary number of trajectories chosen to be well spread over the space to properly cover the entire realistic ranges of all input factors. Here, we use two sampling methods for selecting trajectories with possibly different distributions and investigate their effects on the estimation of various sensitivity measures in spectroscopic ellipsometry data modeling. The SA results indicate that the performance of the sampling strategy should not be judged only by maximizing the trajectory spread but also include some additional convergence criteria for the sensitivity measures. [ABSTRACT FROM AUTHOR]

Typically, the parameters entering a physical simulation model carry some kind of uncertainty, e.g., due to the intrinsic approximations in a higher fidelity theory from which they have been obtained. Global sensitivity analysis (GSA) targets quantifying which parameter uncertainties impact the accuracy of the simulation results, e.g., to identify which parameters need to be determined more accurately. We present a GSA approach based on the Cramérs–von Mises distance. Unlike prevalent approaches, it combines the following properties: (i) it is equally suited for deterministic as well as stochastic model outputs, (ii) it does not require gradients, and (iii) it can be estimated from numerical quadrature without further numerical approximations. Using quasi-Monte Carlo for numerical integration and a first-principles kinetic Monte Carlo model for the CO oxidation on RuO2(110), we examine the performance of the approach. We find that the results agree very well with what is known in the literature about the sensitivity of this model and that the approach converges in a modest number of quadrature points. Furthermore, it appears to be robust against even extreme relative noise. All these properties make the method particularly suited for expensive (kinetic) Monte Carlo models because we can reduce the number of simulations as well as the target variance of each of these. [ABSTRACT FROM AUTHOR]

Nanofilms (NFs) have been widely used in many emerging applications, such as microelectronic devices, spintronics, and optical instruments. Characterizing the thermal conductivity (k) of NFs, kNFs, is nontrivial for both fundamental science and industrial applications. Time-domain thermoreflectance (TDTR) is a powerful technique for thermal characterizations under nano-to-micro-scales. However, both the kNFs and the interface thermal resistance between layers are generally unknown parameters when analyzing TDTR signals. So that an effective thermal resistance model (ETRM) is often utilized, where the impact of heat capacity of NFs, cNFs, has been always ignored. Previous studies have proposed viewpoints on how to validate this assumption, however, which still needs to be verified further by considering the parameters' sensitivities for TDTR signals. In this work, we have highlighted the significance of sensitivity analysis for the investigation of the impact of cNFs with the example experiments on Ni NFs. The error of ETRM has been quantitatively and systematically studied. We found that the application requirements of ETRM in TDTR data analysis rely on the sensitivity relations between kNFs and interface thermal resistance. Finally, suggestions for future characterization of the kNFs have been discussed for reference. Our results and conclusions deepen the understanding of both the transient thermal transport process of multilayer NFs and the ability of TDTR on characterizing kNFs. [ABSTRACT FROM AUTHOR]

In recent years, composite structures have been used in a large number of applications in aerospace, machinery, marine, and civil engineering. However, there are inevitably many uncertainties in the whole life cycle of composite structures, which can easily lead to structural damage and failure. Therefore, it is important to analyze the reliability and sensitivity of composite structures. At present, most of the contributions use the first-order reliability method (FORM) and the second-order reliability method (SORM) to study the reliability of composite structures and compare them with the results of the Monte Carlo simulation. However, both methods have their limitations. FORM cannot guarantee the calculation accuracy for the highly nonlinear limit state equation, and the calculation efficiency of SORM is too low. Therefore, this paper proposes to use importance sampling (IS) and backpropagation neural network-based Monte Carlo (MC-BPNN) to study the reliability, sensitivity, and dispersion of delamination growth of composite laminates. The results show that compared with FORM and SORM, IS and MC-BPNN have higher calculation accuracy and efficiency and can more accurately evaluate the failure degree of composite structures and ensure their safe operation in the field of aerospace equipment. The universality of this method can make it being widely used in the reliability and sensitivity analysis of different composite materials as well as dispersion analysis. [ABSTRACT FROM AUTHOR]

In the exploration of humid air discharges at atmospheric pressure using chemical kinetics, the mapping of chemical reaction pathways emerges as a crucial instrument for deciphering the underlying mechanisms of the reaction system. In this study, we employed a force-directed layout method to create diagrams of chemical reaction pathways based on simulation results from a global model of humid air discharges at atmospheric pressure. This innovative approach aids in addressing the challenges traditionally associated with mapping these pathways, notably the difficulty in balancing intuitiveness with the precise representation of physical data. In our method, the mass of each node in the diagram is determined by the species density, and the natural length between nodes is defined according to reaction rates, with the forces acting upon the nodes dictated by the variance between the actual distance and this natural length. The final arrangement of the nodes is established upon reaching a stable equilibrium after undergoing damped motion in response to these forces. This methodology not only provides a tangible and intuitive visualization of the complex interactions within discharge plasmas but also enables a detailed sensitivity analysis to assess the significance of various reactions. [ABSTRACT FROM AUTHOR]

As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs. [ABSTRACT FROM AUTHOR]

Ordinary differential equations are often employed in chemical kinetics mathematical modeling. The theoretical results for a model of a multi-step chemical kinetic process are presented in this article. This model seeks to describe both the complicated kinetics of complex chemical processes and the steady state behavior of chemical species. Reduction techniques are used to divide fast and slow processes according to their time scales, which simplifies the model. As a result, the quick processes are removed, making the slow processes the main emphasis of a reduced-dimensional model. The paper concentrates on a two-step reversible reaction mechanism for model reduction, which reduces the complexity of the entire reaction process. The phase flow of solution trajectories close to equilibrium points is also given special consideration in the analysis as it offers a clear and pertinent depiction of the behavior of the system. The physical properties of the observed data are further shown via MATLAB simulations. Sensitivity analysis computes parameters, revealing their impact on species behavior, visually presenting the parameter impact. [ABSTRACT FROM AUTHOR]

A rectangular cylinder, with an aspect ratio of 5, is a widely used bluff body in engineering practice. It undergoes intricate dynamical behavior in response to minute alterations in the flow angle of attack (α). These modifications invariably precipitate the failure of wake control for classical flow control methods with various α values. In this study, global linear instability, adjoint method, and sensitivity analysis are employed to identify the optimal position for flow control. It is found that the sensitive region gradually transitions from the leeward side to the downwind side of the model as α and Reynolds number (Re) increase. So, we set up airflow orifices for flow control in both positions. Jet flow control on the leeward side effectively inhibits vortex shedding (α ≤ 2°). High-order dynamic mode decomposition is employed to reveal the inherent mechanism of control. Suction control on the downside effectively mitigates the shear layer separation phenomenon induced by the altered spatial structure associated with higher α. A novel zero-net-mass-flux wake control, bionics-based breathe-valve control (BVC), is proposed to optimize the control effect. BVC is applicable for various α and Re, with optimal effectiveness achievable through jet velocity adjustments. The prediction-control approach in this investigation provides a targeted method to mitigate flow-induced vibration. [ABSTRACT FROM AUTHOR]

Viral infections spread by mosquitoes are a growing threat to human health and welfare. Zika virus (ZIKV) is one of them and has become a global worry, particularly for women who are pregnant. To study ZIKV dynamics in the presence of demographic stochasticity, we consider an established ZIKV transmission model that takes into consideration the disease transmission from human to mosquito, mosquito to human, and human to human. In this study, we look at the local stability of the disease-free and endemic equilibriums. By conducting the sensitivity analysis both locally and globally, we assess the effect of the model parameters on the model outcomes. In this work, we use the continuous-time Markov chain (CTMC) process to develop and analyze a stochastic model. The main distinction between deterministic and stochastic models is that, in the absence of any preventive measures such as avoiding travel to infected areas, being careful from mosquito bites, taking precautions to reduce the risk of sexual transmission, and seeking medical care for any acute illness with a rash or fever, the stochastic model shows the possibility of disease extinction in a finite amount of time, unlike the deterministic model shows disease persistence. We found that the numerically estimated disease extinction probability agrees well with the analytical probability obtained from the Galton–Watson branching process approximation. We have discovered that the disease extinction probability is high if the disease emerges from infected mosquitoes rather than infected humans. In the context of the stochastic model, we derive the implicit equation of the mean first passage time, which computes the average amount of time needed for a system to undergo its first state transition. [ABSTRACT FROM AUTHOR]

This paper presents two sensitivity analyses, one of which is for the time-domain transmission line (TL) model using the latest weight-based algorithm that is a combination of the coherence-optimal sampling (COS) and the l1-minimum problem (l1-M) and the other is for the high-frequency field line coupling model based on the algorithm that combines the Latin hypercube sampling (LHS) with the l1-M for the first time, where the COS and the LHS are employed to draw samples and the polynomial chaos expansion (PCE) coefficients are obtained by solving the l1-M. Both algorithms are within the PCE framework. The results show that for the time-domain TL model, the capacitance has the greatest impact on the voltage response, followed by the resistance. The height and the radius have the least impact, while there is little difference between them. For the high-frequency field line coupling model, the azimuth angle has the greatest impact on the current in the TL, followed by the elevation angle. The terminal load, the conductivity, the field amplitude, the relative permittivity, and the TL radius have the least effect, and there is little difference among them. Additionally, both algorithms only require no more than 0.60% of the total Monte Carlo (MC) computation time when yielding solutions with almost identical accuracy. Compared to performing a sensitivity analysis using the MC method, the use of two algorithms significantly improves efficiency. [ABSTRACT FROM AUTHOR]

We present a sensitivity analysis of the multipactor susceptibility zones to variations in the secondary electron yield (SEY) of materials, specifically focusing on the first and second unity crossover points of SEYs. In conducting this research, we leveraged our semi-analytic approach, which allows for the rapid prediction of the full multipactor zones with enhanced accuracy. Using this approach, we unveil several unique features of multipactor susceptibility zones, including the infinite extension of different-order multipactor zones and the overlap between them. Building upon this prediction capability, our results complement previous findings on the same topic and reveal that the multipactor zones depend not only on the first crossover point but also on the second crossover point of SEY, which this latter predominantly impacts multipactor susceptibility zones for low SEY materials. To validate our predictions, we present two distinct sets of multipactor experiments, providing empirical support for our results. [ABSTRACT FROM AUTHOR]

This paper presents a novel multiclass, multiparameter real-valued weightless neural network-based classifier for fault detection and identification. In contrast to primitive variants of weightless neural nets, the proposed method is capable of multiclass identification with real-valued input features and has improved recognition and generalization capabilities. The major accomplishments of the proposed method include an improved input-to-address mapping strategy that is suitable for address assignment within discriminator units and an effective memory expansion scheme based on similarity metric-based membership value. The developed classifier is utilized for fault detection and identification in single-phase and three-phase induction motors. The sensitivity analysis of the method is investigated for design parameter variation and the fault diagnosis is performed for multiple faults of the motor. The proposed method achieves the highest accuracy of 99.6% and 89.25% for single-phase and three-phase induction motor fault identification, respectively. [ABSTRACT FROM AUTHOR]

The seismic performance of isolated girder bridges is accurately investigated and addressed by considering the theoretical foundation of seismic isolation, taking into account the operational period's service state and changes in relevant performance indicators of vulnerable components. The analysis encompasses a comprehensive parameter analysis of seismic performance during the operational period, utilizing finite element analysis software, which involves comparing and analyzing structural deflections, deformation performance, and damage performance of seismically isolated girder bridges under varying parameters. The research findings indicate that, in terms of evaluating the damage state of the member, the displacement of the girder end of the main girder is primarily influenced by the interface friction coefficient of the isolation device, followed by the post-yield stiffness of the bearing. Conversely, abutment lateral stiffness has a relatively minor influence. Moreover, it is observed that the mechanical properties of the bearing (friction coefficient and post-yield stiffness) exert a significant impact on the structural seismic response. Therefore, more attention should be paid to the functional condition and the variation of mechanical performance parameters of the seismic isolation bearing during the operational phase. [ABSTRACT FROM AUTHOR]

The current investigation is based on the impact of the nanoparticle shape on the micropolar hybrid nanofluid flow in a vertical plate. Furthermore, the aim of this investigation is to optimize the skin friction as well as the Nusselt number using a statistical approach known as "Response Surface Methodology" (RSM). The micropolar hybrid nanofluid is considered due to its enhanced thermal properties likely the Hamilton–Crosser thermal conductivity, Gharesim model viscosity, etc. A suitable similarity rule is adopted for the transformation of the designed model into ordinary and then solved numerically utilizing the shooting-based Runge–Kutta fourth-order technique. The simulated results of diversified parameters are presented through graphs. Furthermore, RSM is employed to design and develop a mathematical model to get an optimized hear transfer rate along with the rate of shear stress. The required components are carefully selected, and the corresponding responses are recorded. The collected data is subsequently employed in constructing a response surface through regression analysis. This process allows for the determination of optimal conditions to enhance heat transfer, which is then confirmed through analysis of variance testing. However, the major outcomes of the study are; for the case of suction with increasing particle concentrations, the shear rate, hear transfer rate, and couple stress coefficients are enhanced significantly. Furthermore, the non-Newtonian parameter and the magnetic parameter also favor in enhancing the rate coefficients. [ABSTRACT FROM AUTHOR]

Reactive species (RS) play a critical role in postoperative complications in the medical application of lasers. From the mechanistic point, the RS arising from laser irradiation are produced from two channels: ionization and dissociation of water in the breakdown plasma, and the violent collapse of the laser-induced cavitation bubble. The latter channel is especially important for nanosecond pulses but poorly understood. In this paper, we conducted a simulation of the chemical reactions coupled with bubble dynamic calculation to quantitatively identify the RS produced in the collapsing bubble. The generation mechanism is explored by the analysis of the reaction pathway. Our calculation shows that while the absolute quantity of the produced RS is small, very high concentrations can be achieved inside the strongly compressed bubble. The initial composition of the bubble recovered from plasma recombination and expansion is found to influence the chemical reactions significantly. Unlike the direct splitting of water molecules in radiolysis and photolysis, the RS productions mainly involve the decomposition of hydrogen peroxide and the reactions between hydrogen, oxygen, and various free radicals. Furthermore, the produced RS is observed to increase with pulse energy as a result of the larger-sized bubbles and more violent collapses. These findings complement our current knowledge of RS in laser surgery and can be used to develop strategies to mitigate the adverse effects or exploit the associated benefits. [ABSTRACT FROM AUTHOR]

To evaluate the extraction capabilities of ion optics and promote the generation of highly collimated ion beams for propulsion, the properties of the upstream sheath of the ion optics and how those properties relate to the beam divergence are investigated numerically and theoretically. The characteristics of the beam divergence at different grid parameters are studied from the behaviors of the impingement current and divergence angle obtained by simulations. Additionally, the simulations indicate the existence of an optimal structure for the upstream sheath of the ion optics, one that corresponds to a moderate focusing effect and a relatively small divergence angle. The plasma densities at the dividing points of different sheath structures are then derived with the matching model of the ion optics and the Child–Langmuir law, coupled with semi-empirical approaches based on the simulation results. According to the theoretical analyses, the range of existence of the most-desirable sheath structure depends on the strength of the penetration of the extraction field, the voltage between the grid apertures, and the distance between the upstream surfaces of the grids. Also, sensitivity analyses are performed with the numerical partial derivatives of the models to investigate how the grid parameters affect the sheath structures. The plasma densities at the dividing points generally vary synchronously with the changes of grid parameters, but the ranges of variations are different. Consequently, the desirable sheath structure and operating conditions of the ion optics can be achieved by correctly adjusting the grid parameters. [ABSTRACT FROM AUTHOR]

Accurate minute solar forecasts play an increasingly crucial role in achieving optimal intra-day power grid dispatch. However, continuous changes in cloud distribution and coverage pose a challenge to solar forecasting. This study presents a convolutional neural network-long short-term memory (CNN-LSTM) model to predict the future 10-min global horizontal irradiance (GHI) integrating all-sky image (ASI) and GHI sequences as input. The CNN is used to extract the sky features from ASI and a fully connected layer is used to extract historical GHI information. The resulting temporary information outputs are then merged and forwarded to the LSTM for forecasting the GHI values for the next 10 min. Compared to CNN solar radiation forecasting models, incorporating GHI into the forecasting process leads to an improvement of 18% in the accuracy of forecasting GHI values for the next 10 min. This improvement can be attributed to the inclusion of historical GHI sequences and regression via LSTM. The historical GHI contains valuable meteorological information such as aerosol optical thickness. In addition, the sensitivity analysis shows that the 1-lagged input length of the GHI and ASI sequence yields the most accurate forecasts. The advantages of CNN-LSTM facilitate power system stability and economic operation. Codes of the CNN-LSTM model in the public domain are available online on the GitHub repository https://github.com/zoey0919/CNN-LSTM-for-GHI-forecasting. [ABSTRACT FROM AUTHOR]

Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search. [ABSTRACT FROM AUTHOR]

In the last decades, finite-element models of the middle ear have been widely used to predict the middle-ear vibration outputs. Even with the simplest linear assumption for material properties of the structures in the middle ear, these models need tens of parameters. Due to the complexities of measurements of material properties of these structures, accurate estimations of the values of most of these parameters are not possible. In this study, we benefited from the stochastic finite-element model of the middle ear we had developed in the past, to perform global sensitivity analysis. For this aim, we implemented Sobol′ sensitivity analysis which ranks the importance of all uncertain parameters and interactions among them at different frequencies. To decrease the computational costs, we found Sobol′ indices from surrogate models that we created using stochastic finite-element results and the polynomial chaos expansion method. Based on the results, the Young's modulus and thickness of the tympanic membrane, Young's modulus and damping of the stapedial annular ligaments, and the Young's modulus of ossicles are among the parameters with the greatest impacts on vibrations of the umbo and stapes footplate. Furthermore, the most significant interactions happen between the Young's modulus and thickness of the tympanic membrane. [ABSTRACT FROM AUTHOR]

To realize extraordinary wave phenomena, metamaterials need to attain unique effective material properties. In this work, we propose an inverse design strategy for metamaterials with specific anisotropic EMD (effective mass density). Although the conventional inverse homogenization technique has been extended to various fields, few works have been published to explore the inverse realization of an EMD tensor, each component of which is supposed to gain a given value at a target frequency. To this end, we propose a calculation scheme, in which the EMD tensor can be calculated in a much similar way to the homogenized static stiffness. Therefore, the scheme is quite convenient for sensitivity analysis. The coating layer interfacing the core and matrix is chosen as the design region because it directly determines the motion of the core. The matrix layout, which not only contributes to the stiffness of the metamaterial but also highly affects the core's local motion, is chosen carefully. The perfect transmodal Fabry–Pérot interference phenomenon is considered in this work, and through several numerical examples, the phenomenon is ideally realized. The proposed design strategy could be critically useful in designing locally resonant metamaterials with general anisotropy. [ABSTRACT FROM AUTHOR]

The alteration of electronic properties in chemically modified graphene can be utilized for chemical and biosensing applications. Thus, it is essential to understand how the alteration of density of states and conductance spectra of functionalized zigzag and armchair graphene nanoribbon (GNR) affects its sensitivity. In this aspect, the current–voltage characteristics of GNR based sensors are modeled using the non-equilibrium Green's function method. Our findings show that the presence of chemical moiety at one edge of the zigzag GNR structure opens the bandgap that reduces the current conduction and enhances the sensitivity for detection. However, double edge functionalization restores the semi-metallic character of the zigzag ribbon that reduces the sensitivity. Both single and double edge atomic substitution in armchair ribbon makes it n-type, which shows the alteration in current conduction for detecting the presence of the chemical species. We further found that increasing the width of the ribbon decreases the device sensitivity while it increases for the double edge substituted zigzag structure. The study thus provides essential information and insights into utilizing and operating different edge structures of graphene based sensors for effective detection of chemical and biomolecular species. [ABSTRACT FROM AUTHOR]

Atomic-level information is essential to explain the specific interactions governing protein–protein recognition in terms of structure and dynamics. Of particular interest is a characterization of the time-dependent kinetic aspects of protein–protein association and dissociation. A powerful framework to characterize the dynamics of complex molecular systems is provided by Markov State Models (MSMs). The central idea is to construct a reduced stochastic model of the full system by defining a set of conformational featured microstates and determining the matrix of transition probabilities between them. While a MSM framework can sometimes be very effective, different combinations of input featurization and simulation methods can significantly affect the robustness and the quality of the information generated from MSMs in the context of protein association. Here, a systematic examination of a variety of MSMs methodologies is undertaken to clarify these issues. To circumvent the uncertainties caused by sampling issues, we use a simplified coarse-grained model of the barnase–barstar protein complex. A sensitivity analysis is proposed to identify the microstates of an MSM that contribute most to the error in conjunction with the transition-based reweighting analysis method for a more efficient and accurate MSM construction. [ABSTRACT FROM AUTHOR]

Slurry transportation via pipelines has garnered growing attention across various industries worldwide, thanks to its efficiency and environmental friendliness. It has emerged as a vital tool for conveying significant volumes of raw phosphate material from extraction points to industrial plants, where it is processed into fertilizers. Yet, optimal and secure pipeline operations necessitate the careful calibration of several physical parameters and their interplay to minimize energy losses. A thorough exploration of the flow pressure drop and the various factors that influence it constitutes a crucial step in attaining this goal. The computational fluid dynamics techniques required to simulate three-dimensional slurry pipe flows pose formidable challenges, primarily due to their high computational costs. Furthermore, numerical solutions for slurry flows are frequently subject to uncertainties arising from the initial and boundary conditions in the mathematical models employed. In this study, we propose the use of polynomial chaos expansions to estimate the uncertainty inherent in the desired slurry flow and perform a sensitivity analysis of flow energy efficiency. In this framework, five parameters are considered as random variables with a given probability distribution over a prescribed range of investigation. The uncertainty is then propagated through the two-phase flow model to statistically quantify their effect on the results. Our findings reveal that variations in slurry velocity and particle size play a pivotal role in determining energy efficiency. Therefore, controlling these factors represents a critical step in ensuring the efficient and safe transportation of slurry through pipelines. [ABSTRACT FROM AUTHOR]

Flat plate solar collectors are technology with the most solar thermal energy field applications, and different studies based on artificial intelligence have been used to model these systems. This research study presents a 9E analysis based on a digital twin model coupled with global sensitivity analysis and multi-objective optimization of a solar system integrated with an array of flat plate solar collectors to satisfy residential hot water demand that represents a case study with different applications. A model based on artificial neural networks was trained, and a global sensitivity analysis using the Sobol method and a multi-objective optimization study using a genetic algorithm were also implemented. The main outcomes revealed that the digital twin model presented a high correlation above 0.99, and the 9E analysis reported a maximum value of 25.18% for thermal efficiency and 0.266% for exergetic efficiency. Also, a value of 1798.5 kgCO2/year was obtained for the amount of CO2 mitigated, $1342.9 USD for net present value, $0.0104 USD/kWh for levelized cost of energy, and 92.62, 0.519 kgCO2/year, $3.43, $1.34, and $0.00752 USD/year for energoenvironmental, exergoenvironmental, enviroeconomic energoenviroeconomic, and exergoenviroeconomic indicators, respectively. The methodology and the 9E analysis results provide a comprehensive approach that determines the optimal choice by analyzing the system's viability with different assessments and goes beyond the conventional analyses currently presented in the literature as it shows an untapped market potential for the best decision-making. [ABSTRACT FROM AUTHOR]

Parametric sensitivity analysis (SA) is an essential tool in optical data analysis aiming to quantify the relative importance of optical model parameters and identify those with a low influence which should remain fixed during analysis in order to simplify a model. Typically, such sensitivity studies are performed using a local sensitivity analysis, also referred to as "one-at-a-time" analysis, which estimates parameter sensitivity by evaluating the changes in model output while perturbing one parameter at a time and holding all other parameters fixed. All the pitfalls of the local sensitivity approach are quite obvious and well known; however, local SA is still prevailing in the area of data analysis for spectroscopic ellipsometry. In this study, we discuss applications of a global qualitative analysis introduced by Morris in 1991 which determines an overall significance for each model parameter and describes its nonlinear effect on model's output and/or interactions with other parameters. The Morris method allows effective separation of the model parameters into different groups such as parameters with negligible effects, parameters with large linear effects without interactions, and parameters with large nonlinear and/or interaction effects. Here, we carried out the Morris sensitivity analysis to reveal the model parameter significance ranking in a test case of the B-spline model for titanium nitride (TiN). Such a screening approach can be considered as a practical tool for the initial global SA strategy prior to true "all-at-a-time" global sensitivity analysis. [ABSTRACT FROM AUTHOR]

Detailed balance models of the performance of upconverter-backed single-junction solar cells show significantly improved solar cell efficiency of over 47% under 1-sun and 63% under concentration. Realizing these predicted gains, however, requires finding or engineering materials that can realize the upconversion performance needed. Semiconductor upconversion heterostructures show the greatest potential in this respect, with over 39% solar cell efficiency predicted using a kinetic rate model to describe the upconverter photophysics. Although the kinetic rate model used was based on realistic material parameters, material design and engineering requires trade-offs that should be informed by a sensitivity analysis of the upconverter kinetic rate model assumptions. Here, we analyze the robustness of the kinetic rate model by considering how the internal upconversion quantum efficiency is affected by variations in the solar spectrum splitting (i.e., photon absorption energy ranges), upconverter absorption cross section, carrier relaxation and recombination rates, and solar concentration. We further analyze the upconverter-backed solar cell performance as a function of these variations using detailed balance methods. The results show that the theoretical performance of this upconversion paradigm under concentrated sunlight agrees with previous models and exceeds 60% solar energy conversion efficiency. More importantly, the results show that the predicted performance is relatively insensitive to the assumptions made in the model, suggesting that practical realization of such a semiconductor upconverter heterostructure paradigm is possible. [ABSTRACT FROM AUTHOR]

A complicated coupling system performs fuel filling operation of a ship on the sea that involves tank sloshing and filling impact. This research elaborated on the sloshing load and flow characteristics of the variable mass tank and revealed its physical process through numerical simulations to provide safety assurance for fuel filling. First, this work clarified the difference between the numerical theory of variable mass tank sloshing and that of ordinary sloshing. On this foundation, a numerical model was developed for a three-dimensional variable mass tank sloshing. In addition, the model was validated by comparing the results of the previous experiments. Second, the sloshing pressure and internal free surface of the tank were monitored by numerical simulations. At the same time, a series of sensitivity analyses were carried out against the sloshing period, filling rate, and sloshing amplitude. Finally, the baffle was added to the sloshing tank to study the influence of internal barrier structure on the sloshing flow and pressure characteristics. The following conclusions were obtained from this work: Fuel filling caused an increase in liquid mass, suppressing sloshing and increasing the tank load. Overall, the sloshing load increased with the filling rate. Moreover, the impact of internal barrier structure on sloshing is discussed in this study, and it was discovered that the sloshing effect could be efficiently suppressed by the barrier structure. [ABSTRACT FROM AUTHOR]

A deterministic excitation (DE) paradigm is formulated, according to which the abrupt late Pleistocene glacial terminations correspond to the excitation, by the orbital forcing, of nonlinear relaxation oscillations (ROs) internal to the climate system in the absence of any stochastic parameterization. Specific rules are derived from the DE paradigm: they parameterize internal climate feedbacks, which, when activated by the crossing of certain tipping points, excite a RO. Such rules are then applied to the output of an energy-balance model simulating the fluctuations induced by realistic orbital forcing on the glacial state. The timing of the glacial terminations, thus, obtained in a reference simulation is found to be in good agreement with proxy records. A sensitivity analysis insures the robustness of the timing. The potential irrelevance of noise allowing DE to hold is discussed, and a possible explanation of the 100-kyr cycle problem based on DE is outlined. In conclusion, the DE paradigm provides the simplest possible dynamical systems characterization of the link between orbital forcing and glacial terminations implied by the Milankovitch hypothesis. [ABSTRACT FROM AUTHOR]

For large public buildings, a siphon drainage system facilitates good on building safety and operation. However, conventional siphon drainage systems are designed based on full pipe flow, which is suitable only under specific criteria and cannot assess the detailed flow characteristics from priming to the entire siphon formation process. In this paper, a full-size experimental platform for a single outlet siphon drainage system is proposed to research the flow characteristics of the siphon drainage. Then, a full-scale numerical model of a siphon drainage system is established and the process of the siphon drainage is simulated by the volume of fluid model. The accuracy of the numerical model is verified by comparing its results with the experimental data. Moreover, a sensitivity analysis of different pipeline structure parameters on siphon priming time is presented, which shows that a larger length and diameter of the suspension pipe and a larger riser pipe diameter prolong the siphon formation time. Finally, theoretical formulas are derived to describe the mathematical relationships between the suspension pipe length and maximum displacement and the suspension pipe length and siphon priming time. [ABSTRACT FROM AUTHOR]

In this article, we contemplate the dynamics of a three-tier system of nutrient, phytoplankton, and zooplankton with a gestation delay of discrete type and a distributed delay in nutrient recycling. Phytoplankton secretion-mediated alteration in the grazing pattern of zooplankton is encapsulated by a Monod–Haldane functional response. We carry out global sensitivity analysis for identifying the crucial model parameters having a significant impact on zooplankton density. The system potentially exhibits bistable configurations under identical ecological conditions by allowing different bifurcation scenarios, including multiple saddle-node and transcritical bifurcations with varying input rates of nutrients and inhibitory effects of phytoplankton against zooplankton. We observe that the gestation delay in zooplankton is responsible for the emergence of noxious bloom events. Interestingly, when the delay parameter crosses a threshold, the system experiences chaotic disorder, which prognosticates the onset of irregular bloom. Furthermore, by adding Gaussian white noise, we have extended the deterministic model to its stochastic counterpart. We found that white noise appears to regulate the survival and extinction of interacting populations. Comprehensive numerical simulations are consistent with mathematical results prognosticated by linear analysis. [ABSTRACT FROM AUTHOR]

The usage of direct numerical simulations for research of turbulent combustion for space propulsion applications is explored. With this goal in mind, the combustion near the injection of a fuel-rich methane–oxygen flame at 20 bar is simulated using a massively parallelized solver. The statistical properties of the relevant physical fields are examined to study interactions between turbulence and combustion. This analysis is complemented by an investigation and quantification of the error sources in direct numerical simulations of turbulent diffusion flame. A method to estimate the statistical error is derived based on the classical inference theory. In addition, critical resolution criteria are discussed using a mesh sensitivity analysis. [ABSTRACT FROM AUTHOR]

Perturbations of flow control parameters may yield a significant alteration in the boundary layer stability. Based on the previously established parameter-associated sensitivity, the present work derives the optimal minor parameter perturbation analytically under the constraint of base flow energy variation. Specifically, the steady blowing-suction factor and the generalized Hartree parameter are examined at Mach number 4.5 to stabilize the mode S. Good agreement between the linear stability theory calculation, sensitivity theory, and Lagrangian approach is achieved for the optimal parametric state. The optimal state occurs if the contribution of the base velocity distortion has the greatest advantage over the temperature counterpart. Contributions of various physical sources to the growth rate behave similarly and collapse onto one correlation if normalized by the maximum, particularly for the major four: advection, mean shear, base temperature gradient, and pressure gradient. When the parameter perturbation further becomes finite, the optimal state is found on the constraint border of control parameters. Although the favorable pressure gradient and wall suction stabilize the broadband mode S, an unusual opposite tendency may occur for a single-frequency disturbance. In this unusual parametric range, positive contributions of both the major and minor physical sources to the growth rate are promoted. The contributive increase in major and minor sources are attributed to the enhancement of mean shear and viscous effect, respectively. Whether the parametric influence is stabilization or destabilization is intrinsically determined by the sensitivities, and the intermediate process is analyzed. Finally, given the modification to the critical Reynolds number, the input control parameter perturbation is inversely obtained and verified. [ABSTRACT FROM AUTHOR]

Consider the standard stochastic reaction network model where the dynamics is given by a continuous-time Markov chain over a discrete lattice. For such models, estimation of parameter sensitivities is an important problem, but the existing computational approaches to solve this problem usually require time-consuming Monte Carlo simulations of the reaction dynamics. Therefore, these simulation-based approaches can only be expected to work over finite time-intervals, while it is often of interest in applications to examine the sensitivity values at the steady-state after the Markov chain has relaxed to its stationary distribution. The aim of this paper is to present a computational method for the estimation of steady-state parameter sensitivities, which instead of using simulations relies on the recently developed stationary finite state projection algorithm [Gupta et al., J. Chem. Phys. 147, 154101 (2017)] that provides an accurate estimate of the stationary distribution at a fixed set of parameters. We show that sensitivity values at these parameters can be estimated from the solution of a Poisson equation associated with the infinitesimal generator of the Markov chain. We develop an approach to numerically solve the Poisson equation, and this yields an efficient estimator for steady-state parameter sensitivities. We illustrate this method using several examples. [ABSTRACT FROM AUTHOR]

We present a Monte Carlo model that simulates the effects of non-equilibrium carrier-carrier scattering and the presence of layers of ErAs nanoislands in a GaAs terahertz antenna detector. To minimize computing time, we split the model into two simulations on numerical grids with optimized resolutions. First, we calculate the effects of the ErAs nanoislands on carrier lifetime in a high resolution volume of GaAs. We then incorporate those results into a larger, lower resolution, two-dimensional simulation that models the antenna detector. The computational results match experimental data presented by Kadow et al. [Appl. Phys. Lett. 75, 3548–3550 (1999)] and show that the lifetime of the carriers is closely linked to the periodicity of the nanoisland layers. Our results also highlight how the periodicity of the nanoisland layers affects the sensitivity and bandwidth of the terahertz detector, information that can be used to create custom devices with optimal parameters. [ABSTRACT FROM AUTHOR]

We present and experimentally validate a model describing the sensitivity of the tilt angle, expansion, and the propulsion velocity of a tin micro-droplet irradiated by a 1 μm Nd:YAG laser pulse to its relative alignment. This sensitivity is particularly relevant in industrial plasma sources of extreme ultraviolet light for nanolithographic applications. Our model has but a single parameter: the dimensionless ratio of the laser spot size to the effective size of the droplet, which is related to the position of the plasma critical density surface. Our model enables the development of straightforward scaling arguments, in turn enabling precise control of the alignment sensitivity. [ABSTRACT FROM AUTHOR]

Time delayed pump–probe measurement techniques, such as Time Domain Thermoreflectance (TDTR), have opened up a wealth of opportunities for metrology at ultra-fast timescales and nanometer length scales. For nanoscale thermal transport measurements, typical thermal lifetimes used to measure thermal conductivity and thermal boundary conductance span from sub-picosecond to ∼ 6 nanoseconds. In this work, we demonstrate a simple rearrangement and validation of a configuration that allows access to the entire 12.5 ns time delay available in the standard pulse train. By reconfiguring a traditional TDTR system so that the pump and probe arrive concurrently when the delay stage reaches its midpoint, followed by unwrapping the temporal scan, we obtain a dataset that is bounded only by the oscillator repetition rate. Sensitivity analysis along with conducted measurements shows that great increases in measurement sensitivity are available with this approach, particularly for thin films with low thermal conductivities. [ABSTRACT FROM AUTHOR]

Solar-driven ejection–compression refrigeration is helpful for building space cooling but is based on the precondition of solar heat being infinite and free. This is not valid in many cases, such as multistorey buildings in metropolises with limited space. Even worse, in the carbon-neutral future, space surrounding buildings will be occupied by PV (photovoltaic) panels in priority. Therefore, a potential ejector-partially coupled enhanced compression refrigeration cycle consuming less heat is presented. Models of the new cycle for a multistorey building are established. Then, energy comparison and advanced exergy analysis are conducted. The analysis results first show that the new cycle needs no auxiliary system. Powered by limited low-grade heat, its C O P e increased by 20.66% and total annual cost reduced by 7.8%, compared with the traditional ejector–compressor cycle. Advanced exergy analysis also finds that the order of improvement potential is compressor (4739.45 W), evaporator (4329.85 W), and condenser (2843.51 W), according to the sum of avoidable exergy destruction. This is different from the results of conventional exergy analysis. The sensitivity analysis of the ejector components' efficiencies is investigated, which has seldom been reported for refrigeration system with ejector. The results reveal that once the efficiencies of ejector components reach a certain point (0.94), the performance no longer improves. Generally, this work provides a potential ejection–compression cycle, demonstrates system improvement direction through advanced exergy analysis, and reveals good understanding of the system. [ABSTRACT FROM AUTHOR]

A full scale hydrodynamic simulation intended for the accurate description of shock-induced detonation transition was conducted as a part of an ignition sensitivity analysis of an energetic component system. The system is composed of an exploding foil initiator (EFI), a donor explosive unit, a stainless steel gap, and an acceptor explosive. A series of velocity interferometer system for any reflector measurements were used to validate the hydrodynamic simulations based on the reactive flow model that describes the initiation of energetic materials arranged in a train configuration. A numerical methodology with ignition and growth mechanisms for tracking multi-material boundary interactions as well as severely transient fluid-structure coupling between high explosive charges and metal gap is described. The free surface velocity measurement is used to evaluate the sensitivity of energetic components that are subjected to strong pressure waves. Then, the full scale hydrodynamic simulation is performed on the flyer impacted initiation of an EFI driven pyrotechnical system. [ABSTRACT FROM AUTHOR]

In the last decade, first-principles-based microkinetic modeling has been developed into an important tool for a mechanistic understanding of heterogeneous catalysis. A commonly known, but hitherto barely analyzed issue in this kind of modeling is the presence of sizable errors from the use of approximate Density Functional Theory (DFT). We here address the propagation of these errors to the catalytic turnover frequency (TOF) by global sensitivity and uncertainty analysis. Both analyses require the numerical quadrature of high-dimensional integrals. To achieve this efficiently, we utilize and extend an adaptive sparse grid approach and exploit the confinement of the strongly non-linear behavior of the TOF to local regions of the parameter space. We demonstrate the methodology on a model of the oxygen evolution reaction at the Co3O4 (110)-A surface, using a maximum entropy error model that imposes nothing but reasonable bounds on the errors. For this setting, the DFT errors lead to an absolute uncertainty of several orders of magnitude in the TOF. We nevertheless find that it is still possible to draw conclusions from such uncertain models about the atomistic aspects controlling the reactivity. A comparison with derivative-based local sensitivity analysis instead reveals that this more established approach provides incomplete information. Since the adaptive sparse grids allow for the evaluation of the integrals with only a modest number of function evaluations, this approach opens the way for a global sensitivity analysis of more complex models, for instance, models based on kinetic Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Two-dimensional molybdenum disulfide (MoS2) is a promising material for the next generation of switchable transistors and photodetectors. In order to perform large-scale molecular simulations of the mechanical and thermal behavior of MoS2-based devices, an accurate interatomic potential is required. To this end, we have developed a Stillinger-Weber potential for monolayer MoS2. The potential parameters are optimized to reproduce the geometry (bond lengths and bond angles) of MoS2 in its equilibrium state and to match as closely as possible the forces acting on the atoms along a dynamical trajectory obtained from ab initio molecular dynamics. Verification calculations indicate that the new potential accurately predicts important material properties including the strain dependence of the cohesive energy, the elastic constants, and the linear thermal expansion coefficient. The uncertainty in the potential parameters is determined using a Fisher information theory analysis. It is found that the parameters are fully identified, and none are redundant. In addition, the Fisher information matrix provides uncertainty bounds for predictions of the potential for new properties. As an example, bounds on the average vibrational thickness of a MoS2 monolayer at finite temperature are computed and found to be consistent with the results from a molecular dynamics simulation. [ABSTRACT FROM AUTHOR]

The drift velocity and first Townsend ionization coefficient of electrons in gaseous tetrahydrofuran are measured over the range of reduced electric fields 4-1000 Td using a pulsed-Townsend technique. The measured drift velocities and Townsend ionization coefficients are subsequently used, in conjunction with a multi-term Boltzmann equation analysis, as a further discriminative assessment on the accuracy and completeness of a recently proposed set of electron-THF vapor cross sections. In addition, the sensitivity of the transport coefficients to uncertainties in the existing cross sections is presented. As a result of that analysis, a refinement of the momentum transfer cross section for electron-THF scattering is presented, along with modifications to the neutral dissociation and dissociative electron attachment cross sections. With these changes to the cross section database, we find relatively good self-consistency between the measured and simulated drift velocities and Townsend coefficients. [ABSTRACT FROM AUTHOR]

Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111). [ABSTRACT FROM AUTHOR]

Motivated by the predictions of a theoretical model developed to describe the acoustic flow force exerted on closely spaced nano-fibers in a viscous medium, we have demonstrated a novel concept for a particle velocity-based directional acoustic sensor. The central element of the concept exploits the acoustically induced normal displacement of a fine mesh as a measure of the collinear projection of the particle velocity in the sound wave. The key observations are (i) the acoustically induced flow force on an individual fiber within the mesh is nearly independent of the fiber diameter and (ii) the mesh-flow interaction can be well-described theoretically by a nearest neighbor coupling approximation. Scaling arguments based on these two observations indicate that the refinement of the mesh down to the nanoscale leads to significant improvements in performance. The combination of the two dimensional nature of the mesh together with the nanoscale dimensions provides a dramatic gain in the total length of fiber exposed to the flow, leading to a sensitivity enhancement by orders of magnitude. We describe the fabrication of a prototype mesh sensor equipped with optical readout. Preliminary measurements carried out over a considerable bandwidth together with the results of numerical simulations are in good agreement with the theory, thus providing a proof of concept. [ABSTRACT FROM AUTHOR]

Near a tipping point, small changes in a certain parameter cause an irreversible shift in the behavior of a system, called critical transitions. Critical transitions can be observed in a variety of complex dynamical systems, ranging from ecology to financial markets, climate change, molecular bio-systems, health, and disease. As critical transitions can occur suddenly and are hard to manage, it is important to predict their occurrence. Although it is very tough to predict such critical transitions, various recent works suggest that generic early warning signals can detect the situation when systems approach a critical point. The most important indicator that predicts the risk of an upcoming critical transition is critical slowing down (CSD). CSD indicates a slow recovery rate from external perturbations of the stable state close to a bifurcation point. In this contribution, we study a two dimensional prey–predator model. Without any noise, the prey–predator model shows bistability and tri-stability due to the Allee effect in predators. We explore the critical transitions when external noise is added to the prey–predator system. We investigate early warning indicators, e.g., recovery rate, lag-1 autocorrelation, variance, and skewness to predict the critical transition. We explore the confidence domain method using the stochastic sensitivity function (SSF) technique near a stable equilibrium point to find a threshold value of noise intensity for a transition. The SSF technique in a two stage transition through confidence ellipse is described. We also show that the possibility of a transition to the predator-free state is independent of initial conditions. Our result may serve as a paradigm to understand and predict the critical transition in a two dimensional system. [ABSTRACT FROM AUTHOR]

A pyrotechnically actuated pin puller is considered an efficient and important separation device because of its advantages of low pyroshock and no debris. However, its large size, complicated structure, and difficulty in performance testing hinder the large-scale application in weapon systems. Herein, we report a new pin puller structure with the gas inlet channels designed on the pin shaft. The novel design achieves the combination of the initiator and the pin shaft and reduces the size of the pin puller by 2 cm. A mathematical model of the movement process of this pin puller is established and further used to predict the variation rule of performance parameters. In particular, equivalent testing based on a closed bomb test (CBT) is proposed to examine the internal pressure of the pin puller calculated using the model. The experimental results of the equivalent testing are indeed consistent with the model simulation results and readily used to optimize the pin puller further in terms of the sensitivity analysis results of design parameters. The results show that our CBT-based equivalent testing can provide a facile way for the examination of the pressure of pyrotechnically actuated devices. [ABSTRACT FROM AUTHOR]

This study proposes a new physics-constrained data-driven approach for sensitivity analysis and uncertainty quantification of large-scale chaotic Partial Differential Equations (PDEs). Unlike conventional sensitivity analysis, the proposed approach can manipulate the unsteady sensitivity function (i.e., tangent) for PDE-constrained optimizations. In this new approach, high-dimensional governing equations from physical space are transformed into an unphysical space (i.e., Hilbert space) to develop a closure model in the form of a Reduced-Order Model (ROM). This closure model is derived explicitly from the governing equations to set strong constraints on manifolds in Hilbert space. Afterward, a new data sampling method is proposed to build a data-driven approach for this framework. A series of least squares minimizations are set in the form of a novel auto-encoder system to solve this closure model. To compute sensitivities, least-squares shadowing minimization is applied to the ROM. It is shown that the proposed approach can capture sensitivities for large-scale chaotic dynamical systems, where finite difference approximations fail. [ABSTRACT FROM AUTHOR]

Reaction-diffusion models are widely used to study spatially extended chemical reaction systems. In order to understand howthe dynamics of a reaction-diffusionmodel are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on the stochastic models of spatially extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios. [ABSTRACT FROM AUTHOR]

A broadband seismometer has been identified as an important scientific instrument that can be deployed on the surface of Earth's Moon in the near future. To achieve the goals of the International Lunar Network, this next generation of seismometers is required to achieve at least 2 × 10−10 ms−2 Hz−1/2 at 1 mHz, which are several orders of magnitude more sensitive at the same frequency than the ones deployed during the Apollo program; their goal is to capture the lunar seismic noise floor, observe the lunar normal modes, and record distant teleseisms from all over the Moon. The Electrostatic Frequency Reduction (EFR) technique has been employed in a seismometer design in our laboratory and can enhance the Moonquake measurement sensitivity covering a longer period down to 1 mHz. EFR has advantages over the traditional frequency reduction techniques since it can be autonomously tuned and lowers the resonance frequency without modifying the mechanical design of a capacitive readout seismometer, an approach desirable for spaceflight-qualified instrumentation. A drawback is that we have also found that the EFR as well as other frequency reduction techniques amplify the temperature sensitivity of a seismometer beyond the limit of a conventional temperature control system. Here, we describe quantitatively and analytically the temperature dependence of a spring suspended mass model of a seismometer and provide practical solutions to minimize the thermal effects on a lunar seismometer using the EFR technique. By choosing the materials for the seismometer housing and the spring suspension that could balance the shear modulus and thermal expansion coefficients, one could mitigate the thermal sensitivity of the seismometer using the EFR technique. These modifications allow us to approach the stringent requirements for instrument self-noise necessary for the science objective. [ABSTRACT FROM AUTHOR]

The compressible-boundary-layer stability can be considerably influenced by base flow distortion. The distortion may originate from perturbations of flow parameters, such as the Mach number. In this paper, sensitivities of the boundary layer stability to certain flow parameters are derived analytically by utilizing the homotopy analysis (with codes shared), in conjunction with a direct-adjoint stability theory. The sensitivities can be categorized according to the routes the distortion evolves. Route I is that parameters distort the base flow (Sensitivity A), which, in turn, affect the eigenvalue of the linear stability equation (Sensitivity B). Route II gives rise to the effects of flow parameters onto eigenvalues caused by direct perturbation of the linear operators (Sensitivity C). Results indicate that Sensitivity A is characterized by the only peak found on the sensitivity profile that corresponds to the maximum gradient of base flow; for Sensitivity B, production terms, e.g., the mean-shear terms, are found to be significant, while for Sensitivity C, which is rarely discussed in existing literature, the pressure gradient terms in the momentum equations are dominant in affecting the stability via route II. Furthermore, route II can be more significant than route I. Having examined the variation of the mean shear gradient, d (ρ ¯ d u ¯ / d y) / d y , near the critical layer yc, it is proven that the sensitivity of the eigenvalue to the velocity or temperature distortion is negative at yc under certain assumptions, particularly for the temperature-relevant sensitivity that has hardly been discussed before. [ABSTRACT FROM AUTHOR]

In the presence of multiscale dynamics in a reaction network, direct simulation methods become inefficient as they can only advance the system on the smallest scale. This work presents stochastic averaging techniques to accelerate computations for obtaining estimates of expected values and sensitivities with respect to the steady state distribution. A two-time-scale formulation is used to establish bounds on the bias induced by the averaging method. Further, this formulation provides a framework to create an accelerated "averaged" version of most single-scale sensitivity estimation methods. In particular, we propose the use of a centered ergodic likelihood ratio method for steady state estimation and show how one can adapt it to accelerated simulations of multiscale systems. Finally, we develop an adaptive "batch-means" stopping rule for determining when to terminate the micro-equilibration process. [ABSTRACT FROM AUTHOR]