Reliable Simulation Research Articles

Three-point bending response and energy absorption of novel sandwich beams with combined re-entrant double-arrow auxetic honeycomb cores

The response and energy absorption of novel sandwich beams with combined re-entrant double-arrow auxetic honeycomb (RDAH) cores subjected to three-point bending were studied experimentally and numerically. Two typical sandwich beams loaded at different loading positions were considered. Quasi-static three-point bending experiments were conducted to obtain the failure modes and force–displacement curves. The reliable numerical simulation models were further established based on experimental validations. The results indicate that when the loading roller is located directly above the re-entrant cell, the RDAH core sandwich beam has better load-carrying and energy absorption capacity. Subsequently, the influence of face sheet distribution, cell-wall thickness, impact velocity and cell configuration on the structural response were explored. For the sandwich beams with same total mass, the arrangement where the thickness of the front face sheet is larger than that of the back face sheet is beneficial for improving the load-carrying and energy absorption capacity. In addition, the cell-wall thickness has an influence on the local deformation mode of the sandwich beam, and increasing its value can produce more stable deformation and improve the load-carrying capacity. Increasing impact velocity has a significant influence on the initial deformation but little influence on the final deformation of the sandwich beams. As the impact velocity increases, the total energy absorption of the sandwich beam gradually increases, and the negative Poisson's ratio characteristic of the core still exists. Compared to the traditional re-entrant honeycomb (RH) core sandwich beams, RDAH core sandwich beams have better energy absorption capacity and bending resistance.

Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

In cellular automata (CA) modeling, spatial heterogeneity can be delineated by geographical area partitioning. The dual constrained space clustering method is a prevalent approach for providing an objective and effective representation of differences within urban regions. However, previous studies faced issues by ignoring spatial heterogeneity, which could lead to an over- or under-estimation of the simulation results. Accordingly, this study attempts to incorporate spatially heterogeneous area partitioning into vector-based cellular automata (VCA), producing more accurate and reliable simulations of urban land-use change. First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which can effectively capture the spatial heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new VCA-based framework, was utilized for simulating land-use changes in distinct urban partitions. Finally, the constructed partitioned VCA model was applied to simulate rapid urban development in Jiangyin city from 2012 to 2017. The results indicated that the combination of DSC clustering and UrbanVCA model could obtain satisfying results as the average FoM values for the partitions and the entire study area exceeded 0.22. Furthermore, a comparative analysis of results from traditional area-partitioned CA models revealed that the proposed area partitioning approach had the potential to yield more accurate simulation outcomes as the FoM values were higher and SHDI and LSI metrics were closer to real-world observations, indicating its good performance in simulating fragmented urban landscapes.

Experimental and numerical analyses of seismic behaviors of L-shaped unreinforced masonry walls

This study investigates the seismic behavior of L-shaped unreinforced concrete-block masonry fence walls. Using a large-scale shaking table, the walls are subjected to uniaxially artificial ground motions that gradually increase in intensity, and the out-of-plane response and failure mode are analyzed. The seismic behavior is then simulated by LS-DYNA using a finite element model under identical conditions as the shaking table tests. The validity of the numerical model is evaluated by comparing the linear response and failure mode of the tests with those of the simulations. Practical modeling techniques and calibrated material model parameters that facilitate reliable simulation results are also presented. The numerical model and techniques are useful in determining the geometrical features required for earthquake-resistant fence walls, such as the maximum permissible width and height as well as the appropriate height- and width-to-thickness ratios, depending on wall support conditions.

Hybrid Assimilation of Snow Cover Improves Land Surface Simulations over Northern China

Abstract Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, there is a small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here, we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021/22. Compared to the open-loop experiment (without SCF assimilation), the root-mean-square error (RMSE) of SCF is reduced by 6% through the original EnSRF and is even lower (by 14%) in the combined DI and EnSRF (EnSRFDI) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling–Gupta efficiency (KGE) increasing at 60% and 56%–70% stations, respectively, particularly under conditions with near-freezing temperature, in which reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products. Significance Statement Due to the small spread between the seasonal snowpack of ensemble simulations, ensemble snow cover fraction (SCF) data assimilation (DA) proves to be ineffective. Therefore, we apply a hybrid method that combines the direct insertion (DI) and ensemble square root filter (EnSRF) to assimilate the spaceborne SCF into a land surface model (LSM) driven by high-resolution climate forcings. Our results reveal the applicability of the EnSRFDI to further improve snow cover simulations over regions with high SCF. Furthermore, the DA experiments were validated through a large number of in situ observations from the China Meteorological Administration. The uncertainties of snow depth and soil temperature simulations are also slightly reduced by the SCF DAs, particularly over regions with a poor LSM performance.

Deep-learning-based reduced-order modeling to optimize recuperative burner operating conditions

This study analyzed a recuperative burner system that is critical for energy efficiency and pollutant reduction in the firing processes required in the manufacturing industries. We aimed to optimize the operating conditions of a recuperative burner using computational fluid dynamics (CFD) combined with a novel reduced-order deep-learning technique. The Reynolds-averaged Navier–Stokes model and finite-rate/eddy-dissipation models were used to generate reliable CFD simulation results considering four operating conditions (temperature and mass flow rate of air and fuel). We first validated the CFD model with two-dimensional axis-symmetric experimental burner results and created a proper orthogonal decomposition transformer model using large-scale snapshots of the CFD results and various operating conditions. Subsequently, a genetic algorithm was employed to find the optimal conditions for five different objective functions: fuel economy, decrease in carbon monoxide emissions, reduction in nitrogen oxide emissions, decrease in carbon dioxide production, and an all-encompassing view of the four objectives. Finally, by comparing our proposed approach with previous methods, we confirmed that the obtained optimal operating conditions improve the performance of the recuperative burner. This study provides an optimized framework for recuperative burners to reduce environmental pollution, with potential applications in many industries, such as ceramics, steel, and batteries.

Deep learning–assisted phase equilibrium analysis for producing natural hydrogen

The development of natural hydrogen is an emerging topic in the current energy transition trend. The production process involves compositional multiphase flow via subsurface porous media. This makes studying the compositional phase equilibrium behavior essential for reliable reservoir simulation and prediction. Herein, we develop an iterative flash calculation scheme and a deep learning algorithm using a thermodynamics-informed neural network (TINN) to perform accurate, robust, and fast phase equilibrium calculations for realistic fluid mixtures of natural hydrogen. The application of TINN architecture can accelerate the calculations for nearly 20 times. The effect of capillarity on phase equilibrium states is demonstrated. Based on simulation results, suggestions for the natural hydrogen industry chain are provided to control the possible phase transitions under certain environmental conditions that may be observed in the natural hydrogen reservoirs, storage and transportation facilities. The extremely low critical temperature of hydrogen challenges the robustness of flash calculations but facilitates the separation of impurities by liquefying certain undesired species. Moreover, phase transitions under control can be an effective approach for carbon dioxide capture and sequestration with optimized operating conditions over the phase equilibrium analysis.

Modeling Single-Atom Catalysis.

Electronic structure calculations represent an essential complement of experiments to characterize single-atom catalysts (SACs), consisting of isolated metal atoms stabilized on a support, but also to predict new catalysts. However, simulating SACs with quantum chemistry approaches is not as simple as often assumed. In this work,the essential factors that characterize a reliable simulation of SACs activity are examined. The Perspective focuses on the importance of precise atomistic characterization of the active site, since even small changes in the metal atom's surroundings can result in large changes in reactivity. The dynamical behavior and stability of SACs under working conditions, as well as the importance of adopting appropriate methods to solve the Schrödinger equation for a quantitative evaluation of reaction energies are addressed. The Perspective also focuses on the relevance of the model adopted. For electrocatalysis this must include the effects of the solvent, the presence of electrolytes, the pH, and the external potential. Finally, it is discussed how the similarities between SACs and coordination compounds may result in reaction intermediates that usually are not observed on metal electrodes. When these aspects are not adequately considered, the predictive power of electronic structure calculations is quite limited.

Characterization of the Mechanical Properties of Low Stiffness Marine Power Cables through Tension, Bending, Torsion, and Fatigue Testing

The exploitation and harnessing of offshore marine renewable energy have led to an increased demand for reliable marine power cables with long service lives. These cables constitute a considerable share of the total installation cost of offshore renewable energy facilities and have high maintenance and repair costs. The critical characteristics of these power cables must be determined to reduce the risk of exceeding their ultimate strength or fatigue life, which can result in unwanted and unexpected failures. This study investigates dynamic marine power cables that are suitable for application in devices that harness energy from ocean currents, waves, and tides. Tension, bending, torsion, and fatigue tests were conducted on three dynamic power cables (1 kV, 3.6 kV, and 24 kV) that have high flexibility, i.e., low mechanical stiffness. The specimen lengths and axial pretension force were varied during the tests. The results are discussed in terms of the mechanical fatigue degradation and ultimate design load, and the key observations and lessons learned from the tests are clarified. The study’s main contribution is the results from physical component testing of the dynamic marine power cables without metallic armors, which can be used to calibrate numerical models of this type of dynamic marine power cable in the initial design of, e.g., inter-array cables between floating wave energy converters. The benefits offered by this type of cable and the importance of the results for creating reliable numerical simulation models in the future are highlighted.

Reliability and availability simulation for vessel cooling system of the high temperature engineering test reactor

The article deals with the problem of risk analysis for new and unique nuclear facilities where safety-related systems are continuously operated during normal exploitation. A non-standard approach to system modeling was applied which goes beyond traditional method suitable for on-demand safety systems with a fixed mission time. As an example, the Vessel Cooling System (VCS) of the Japanese High Temperature Engineering Test Reactor (HTTR) has been analyzed in terms of life-cycle reliability and availability. Normal operation and emergency conditions have been considered, differing in the requirements for the system performance. In the former, the VCS reliability for cooling the biological shield around the Reactor Pressure Vessel (RPV) has been examined. In the latter, the VCS availability to remove the residual heat from the reactor core and RPV has been simulated. The results are of high importance for the safety and profitability of the HTTR-based electricity-hydrogen cogeneration plant (HTTR-GT/H2). The high reliability of the VCS under emergency conditions has been confirmed, thus contributing to the probabilistic safety assessment (PSA). Moreover, the fraction of the Forced Outage Rate (FOR) caused by VCS failures has been determined, thus contributing to the plant profitability studies.

Challenges in dynamic heat source modeling in high-power laser beam welding

The amount of absorbed energy in the keyhole as well as its spatial and temporal distribution is essential to model the laser beam welding process. The recoil pressure, which develops because of the evaporation process induced by the absorbed laser energy at the keyhole wall, is a key determining factor for the macroscopic flow of the molten metal in the weld pool during high-power laser beam welding. Consequently, a realistic implementation of the effect of laser radiation on the weld metal is crucial to obtain reliable and accurate simulation results. In this paper, we discuss manyfold different improvements on the laser-material interaction, namely, the ray tracing method, in the numerical simulation of the laser beam welding process. The first improvement relates to locating the exact reflection points in the ray tracing method using a so-called cosine condition in the determination algorithm for the intersection of reflected rays and the keyhole surface. A second correction refers to the numerical treatment of the Gaussian distribution of the laser beam, whose beam width is defined by a decay of the laser intensity by a factor of 1/e2, thus ignoring around 14% of the total laser beam energy. In the third step, the changes in the laser radiation distribution in the vertical direction were adapted by using different approximations for the converging and the diverging regions of the laser beam, thus mimicking the beam caustic. Finally, a virtual mesh refinement was adopted in the ray tracing routine. The obtained numerical results were validated with experimental measurements.

Nonlinear Model Predictive Control using Neural ODE Replicas of Dynamic Simulators

We propose simulation-based nonlinear model predictive control as a first step towards autonomous decision-making for stable operation of large complex dynamical systems such as chemical plants. The effect of abrupt external disturbances should be quickly eliminated, taking into account such complex dynamic responses, to maintain stable production. In this paper, we propose a control system to eliminate these effects. The system uses engineering models, including dynamic simulators, based on chemical engineering knowledge. Dynamic simulators are generally not differentiable with respect to actions; however, differentiable models are advantageous for fast nonlinear optimization. To take advantage of both reliable dynamic simulators and differentiable models, we introduce neural ordinary differentiable equation models and clone the behaviour of simulators on them. The cloned differentiable neural replica model is then incorporated into a gradient-based nonlinear model predictive control. Evaluation of this method in a real methanol distillation plant confirms that it can significantly remove abrupt heavy rain disturbances compared to existing methods.

Advances in data‐assisted high‐throughput computations for material design

AbstractExtensive trial and error in the variable space is the main cause of low efficiency and high cost in material development. The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations. Because of their numerous variables in material design, however, the variable space is still too large to be accessed thoroughly even with a computational approach. High‐throughput computations (HTC) make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic, robust, and concurrent streamlines. The efficiency of HTC, which is one of the pillars of materials genome engineering, has been verified in many studies, but its applications are still limited by demanding computational costs. Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem. In the past years, many studies have focused on the development and application of HTC and data combined approaches, which is considered as a new paradigm in computational materials science. This review focuses on the main advances in the field of data‐assisted HTC for material research and development and provides our outlook on its future development.

Operating strategy optimization considering battery aging for a sector coupling system providing frequency containment reserve

Battery storage systems are increasingly used in the frequency containment reserve (FCR) application. Coupling the battery with a power-to-heat module can further increase the system’s profitability. Different studies optimized the hybrid system’s operation by considering country-specific regulations. However, lithium-ion battery aging has not yet been included. This work analyses the impact of system dimension and operation on net present value, considering battery aging. The operation strategy consists of adjusting the target SoE and using the deadband degree of freedom, which allows to suspend provision of FCR for minor frequency deviations. Battery lifetime is simulated using a semi-empirical aging model describing graphite anode aging mechanisms. The highest NPV is obtained with a battery dimension of 0.5 Wh/W, 90% target SoE, and normal deadband utilization. For the considered NMC battery cells, deadband utilization contributes to battery lifetime extension when operating at high SoE. Without this cell chemistry-specific lifetime gain, a lower target SoE of 60% and a larger system size of 0.6 is economically more advantageous. In a sensitivity analysis, battery aging was detected as the most crucial parameter influencing system profitability at high FCR prices. The results highlight the importance of including reliable battery aging simulations in future FCR operation optimizations.

Saturable Voltage-Behind-Reactance Models of Induction Machines Including Air-Gap Flux Harmonics

Accurate and numerically efficient models of induction machines are of critical importance for reliable analysis and simulation of large-scale power systems. Specifically, modeling magnetic saturation that causes air-gap flux harmonics, which can significantly affect machine dynamics, has been the focus of many research efforts. In this paper, two voltage-behind-reactance (VBR) models are proposed for induction machines which formulate the air-gap flux harmonics caused by the main flux saturation. Specifically, the proposed constant-parameter VBR (CPVBR) model can be conveniently interfaced with the external network using constant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RL</i> branches and controlled voltage sources. The numerical performance of the two proposed VBR models is validated and benchmarked against the state-of-the-art models of induction machines with saturation and air-gap flux harmonics. It is shown that the proposed models offer superior numerical performance (i.e., higher accuracy and simulation speed) compared to the existing models, which would be beneficial for offline and/or real-time simulations of power systems.

Modeling Analysis of BTI-Driven Degradation of a Ring Oscillator Designed in a 28-nm CMOS Technology

With tightening reliability margins, product-level aging analysis is gradually gaining impetus and is set to become an integral part of the modern design flow. Increased emphasis is placed on the development of physics-based compact models for the phenomena responsible for transistor degradation and their integration into EDA environments. Commercial Process Design Kits (PDKs) of advanced technologies have also started to include compact models for transistor degradation along with a dedicated reliability simulation framework. In this work, we present a comprehensive study of the compact aging models in one such commercial PDK, with the help of extensive measurement data from individual devices as well as Ring Oscillators (RO). Then, we perform our own extraction of model parameter shifts from full Id-Vgs fitting of the measured BTI-stressed devices to gain insight into the modeling procedure adopted by the foundry. We finally use the parameters extracted thusly to investigate the impact of the time-0 and the time-dependent device-to-device variability on RO degradation.

Optical ray-tracing simulation method for the investigation of radiance non-uniformity of an integrating sphere

The paper deals with possibilities and limits of optical simulations of extended uniform light sources. Optical simulations of ideal and real light sources are performed using optical ray-tracing software which is capable to simulate non-imaging systems such as Lambertian disc and integrating spheres. The simulation methodology is validated by comparing the developed simulation model with a real experiment previously performed in optical laboratory. The comparison is made on the radiance uniformity measurement and simulation results of a general purpose integrating sphere rather than on less convenient and more frequent irradiance uniformity measurements. The aim is to develop a reliable simulation method for the investigation of radiance non-uniformity of any real extended uniform light source.

Climate, Variability, and Climate Sensitivity of “Middle Atmosphere” Chemistry Configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6))

AbstractSimulating whole atmosphere dynamics, chemistry, and physics is computationally expensive. It can require high vertical resolution throughout the middle and upper atmosphere, as well as a comprehensive chemistry and aerosol scheme coupled to radiation physics. An unintentional outcome of the development of one of the most sophisticated and hence computationally expensive model configurations is that it often excludes a broad community of users with limited computational resources. Here, we analyze two configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6)) with simplified “middle atmosphere” chemistry at nominal 1 and 2° horizontal resolutions. Using observations, a reanalysis, and direct model comparisons, we find that these configurations generally reproduce the climate, variability, and climate sensitivity of the 1° nominal horizontal resolution configuration with comprehensive chemistry. While the background stratospheric aerosol optical depth is elevated in the middle atmosphere configurations as compared to the comprehensive chemistry configuration, it is comparable among all configurations during volcanic eruptions. For any purposes other than those needing an accurate representation of tropospheric organic chemistry and secondary organic aerosols, these simplified chemistry configurations deliver reliable simulations of the whole atmosphere that require 35% and 86% fewer computational resources at nominal 1 and 2° horizontal resolution, respectively.

English

English

Monotonic and Cyclic Modelling of Structural Steel for Finite Element Analysis

Nowadays, as a response to the promoted concept of performance-based seismic design, the finite element method (FEM) has become an omnipresent practice that supports the simulation of structural elements. This approach requires constitutive numerical models, which could reproduce both elastic and plastic behaviour of steel. Looking forward to a reliable FEM simulation, the modelling and calibration of steel undergoing monotonic and cyclic loading tests is investigated in this paper. With respect to monotonic tensile test, a sensitivity analysis regarding mesh refinement and finite element type is developed. Another inspected issue, such as high-strength steel modelling, taking into consideration low ductility and absence of yield plateau, is also presented. And finally, a model for structural steel, using the combined isotropic-kinematic hardening model, is introduced to simulate metal non-linear behaviour under ultra-low-cycle fatigue regime. All these numerical simulations are processed via Abaqus software package, and are validated by a set of experimental tests.

Coupling effect of strain gradient strengthening and thermal softening on the microscale plastic behavior of metallic materials

Size effects and thermal effects together determine the microscale plastic response of metallic structures in high-integrated microelectromechanical systems (MEMS) at different temperatures. To investigate the microscale plastic behavior of metallic materials at different temperatures, a thermo-mechanical coupled microscale plasticity model is developed. In the mechanical part of this model, the framework of conventional plasticity theory is maintained, and the plastic strain gradient is introduced as an internal variable to increase the tangential hardening modulus without the introduction of higher-order stress and higher-order boundary conditions. The influence of temperature on the mechanical parameters (e.g. the yield strength) is calibrated by experiment results. In the thermal part of this model, the heat generation in the plastic deformation stage is calculated by the difference between the plastic work and the hardening stored energy influenced by the plastic strain gradient. Due to the strong nonlinearity of the coupled equations, a finite element solution algorithm that combines the radial return method and the staggered solution scheme is proposed. The effectiveness of this model and its solution algorithm is verified by comparisons with the experiment results and a numerical benchmark example. Finally, taking the tensile behavior of a plate with a hole in its center as an example, the coupling effect of strain gradient strengthening and thermal softening on the microscale plastic behavior of metallic materials is investigated. The results show that the microscale plastic behavior of metallic materials at high temperatures depends on the competition between thermal softening and strain gradient strengthening. Our study provides a theoretical basis and a reliable simulation method for the design of MEMS at different temperatures.