Reliable Simulation Research Articles

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.

Temperature relaxation and generalized Coulomb logarithm in two-temperature dense plasmas relevant to inertial confinement fusion implosions

Detailed knowledge of energy exchange between electrons and ions is of fundamental importance for the description of temperature relaxation and also other nonequilibrium physics in Inertial Confinement Fusion (ICF). We present a theoretical model for the temperature relaxation rate and the related generalized Coulomb logarithm based on the Quantum Lenard–Balescu (QLB) kinetic equation, where no special cutoffs are needed to be introduced. To describe the collective modes characterizing the ionic acoustic waves, a single-pole approximation is introduced for the ionic dielectric response. The proposed model for the generalized Coulomb logarithm is examined over a wide range of plasma conditions for electron temperatures between 102 and 105 eV and electron densities between 1022 and . The values of the generalized Coulomb logarithm are demonstrated to be in excellent agreement with the ones evaluated using the original QLB kinetic approach but with much less computational cost. Comparisons with molecular dynamics simulations and other theoretical approaches are presented. For further applications of our model, we present results for the recently measured experimental Coulomb logarithm. Compared to other widely applied models such as the Landau–Spitzer Coulomb logarithm, our model provides more consistent descriptions for the results of molecular dynamics simulations and also for the experimental outcomes. Our model for the generalized Coulomb logarithm is easy to calculate and can benefit efficient and reliable simulations for the ICF implosions.

An analytical approach based on coupled multi-physics model for photovoltaic arrays performance simulation

Simulating the output performance of a proposed photovoltaic plant is a necessary phase prior to the construction stage and has been intensively studied for decades. The requirement for accurate and reliable simulation needs a comprehensive analysis of the photovoltaic power generator considering environmental factors and device behaviors. Unlike other published work, this paper presents an analytical approach to simulate the output performance of photovoltaic arrays using coupled multi-physics modeling without the support of historical data. The interplay among the electrical and thermal models, device characteristics, and device topologies are deeply investigated and formulated. In the electrical model, a parameter extraction method, as well as an iterative algorithm, are utilized on the equivalent circuit model for a quick solution of the I–V characteristics of the photovoltaic module. To obtain accurate results of the output power and its interaction with the environment, irradiation, temperature, humidity, wind speed, and wind direction are analyzed along with the photovoltaic output power in the thermal model. Meanwhile, this output power is constrained to the maximum power point tracking mechanism of the inverter. Real data from a photovoltaic plant are used for comprehensive analysis in both long-term and short-term scenarios. The experiment shows that the proposed approach is competitively accurate and feasible. Compared with the state-of-the-art studies, the proposed approach improves the simulation accuracy by 7.75%.

Impact of Automation on Enhancing Energy Quality in Grid-Connected Photovoltaic Systems

Rapid growth in the integration of new consumers into the electricity sector, particularly in the industrial sector, has necessitated better control of the electricity supply and of the users’ op-erating conditions to guarantee an adequate quality of service as well as the unregulated dis-turbances that have been generated in the electrical network that can cause significant failures, breakdowns and interruptions, causing considerable expenses and economic losses. This research examines the characteristics of electrical variations in equipment within a company in the industrial sector, analyzes the impact generated within the electrical system according to the need for operation in manufacturing systems, and proposes a new solution through automation of the regulation elements to maintain an optimal system quality and prevent damage and equipment failures while offering a cost-effective model. The proposed solution is evaluated through a reliable simulation in ETAP (Energy Systems Modeling, Analysis and Optimization) software, which emulates the interaction of control elements and simulates the design of electric flow equipment operation. The results demonstrate an improvement in system performance in the presence of disturbances when two automation schemes are applied as well as the exclusive operation of the capacitor bank, which improves the total system current fluctuations and improves the power factor from 85.83% to 93.42%. Such a scheme also improves the waveform in the main power system; another improvement result is when simultaneously operating the voltage and current filter together with the PV system, further improving the current fluctuations, improving the power factor from 85.83% to 94.81%, achieving better stability and improving the quality of the waveform in the main power grid.

Gel point investigation of liquid silicone rubber using rheological approaches

Liquid silicone rubber (LSR) is becoming increasingly popular due to its chemical, UV, and heat resistance as well as its excellent optical properties. However, there is currently a lack of research and understanding of the rheological properties of the material. During the processing of liquid silicone rubber, the material can assume different rheological states. In the present work, both the physical and the chemical gel behavior are investigated rheologically. For this purpose, measurements are carried out with a rotational rheometer and a high-pressure capillary rheometer. Depending on the temperature and pre-shear rate, times are given for the physical gel point, after which a significantly destroyed physical network is recovered. Investigations at higher temperatures, which lead to chemical crosslinking and, therefore, to a chemical gel point, resulted in a representation of the temperature-dependent chemical gel point. The presented results serve as a material database for reliable simulations.

FE2 Computations with Deep Neural Networks: Algorithmic Structure, Data Generation, and Implementation

Multiscale FE2 computations enable the consideration of the micro-mechanical material structure in macroscopical simulations. However, these computations are very time-consuming because of numerous evaluations of a representative volume element, which represents the microstructure. In contrast, neural networks as machine learning methods are very fast to evaluate once they are trained. Even the DNN-FE2 approach is currently a known procedure, where deep neural networks (DNNs) are applied as a surrogate model of the representative volume element. In this contribution, however, a clear description of the algorithmic FE2 structure and the particular integration of deep neural networks are explained in detail. This comprises a suitable training strategy, where particular knowledge of the material behavior is considered to reduce the required amount of training data, a study of the amount of training data required for reliable FE2 simulations with special focus on the errors compared to conventional FE2 simulations, and the implementation aspect to gain considerable speed-up. As it is known, the Sobolev training and automatic differentiation increase data efficiency, prediction accuracy and speed-up in comparison to using two different neural networks for stress and tangent matrix prediction. To gain a significant speed-up of the FE2 computations, an efficient implementation of the trained neural network in a finite element code is provided. This is achieved by drawing on state-of-the-art high-performance computing libraries and just-in-time compilation yielding a maximum speed-up of a factor of more than 5000 compared to a reference FE2 computation. Moreover, the deep neural network surrogate model is able to overcome load-step size limitations of the RVE computations in step-size controlled computations.

Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems.

Land carbon dynamics in temperate and boreal ecosystems are sensitive to environmental change. Accurately simulating gross primary productivity (GPP) and its seasonality is key for reliable carbon cycle projections. However, significant biases have been found in early spring GPP simulations of northern forests, where observations often suggest a later resumption of photosynthetic activity than predicted by models. Here, we used eddy covariance-based GPP estimates from 39 forest sites that differ by their climate and dominant plant functional types. We used a mechanistic and an empirical light use efficiency (LUE) model to investigate the magnitude and environmental controls of delayed springtime photosynthesis resumption (DSPR) across sites. We found DSPR reduced ecosystem LUE by 30-70% at many, but not all site-years during spring. A significant depression of LUE was found not only in coniferous but also at deciduous forests and was related to combined high radiation and low minimum temperatures. By embedding cold-acclimation effects on LUE that considers the delayed effects of minimum temperatures, initial model bias in simulated springtime GPP was effectively resolved. This provides an approach to improve GPP estimates by considering physiological acclimation and enables more reliable simulations of photosynthesis in northern forests and projections in a warming climate.

Analysis of Structural Parameters of Steel-NC-UHPC Composite Beams.

The cracking of the negative moment area of steel-normal concrete (NC) composite bridges is common owning to the low tensile strength of concrete. In order to solve the problem, Ultra High Performance Concrete (UHPC) is used to enhance the tensile performance of the negative moment area. This paper conducted interface experiments to study the bonding behaviour of the UHPC-NC interface. The design parametric analysis of steel-NC-UHPC composite bridges was carried out based on the interface experimental results. Firstly, slant shear tests and flexural shear tests were carried out to study the rationality of the interface handling methods. Then, the finite element model was used to analyze the state of every component in the composite beams based on experimental results, such as the stress of UHPC, concrete and steel plate. Finally, the calculation results of finite analysis were compared and summarized. It is concluded that (1) the chiseling interface can meet the utilization requirements of physical bridges. The average shear stress and flexural tensile strength of the chiseling interface are 10.29 MPa and 1.93 MPa, respectively. In the failure state, a slight interface damage occurs for specimens with a chiseling interface. (2) The influence on overall performance is different for changes in different design parameters. The thickness of concrete has a significant influence on the stress distribution of composite slabs. (3) Reliable interface simulation is conducted in the finite element models based on interface test results. The stress variation patterns are reflected in the change of design parameters.

Simulation of Water Quality in a River Network with Time-Varying Lateral Inflows and Pollutants

Non-point source pollution inflow is one of the main causes of water quality decline in urban river networks. In this paper, aiming at the problem of non-point source pollutant transport in river network, the lateral outflow term in the Saint-Venant equation is improved from the previous constant to the time-varying flow process, and a mathematical model considering the time-varying source and sink term is established. Based on the initial rainfall intensity, surface confluence and non-point source pollutant concentration, a method for calculating the time-varying lateral pollutant input of nodes and tributaries with linear increase and exponential decay in the initial rainfall period is proposed. Based on the principle of proximity, the watershed is divided into districts. According to the principle of elevation, the non-point source pollutants are allocated to the calculation nodes of adjacent rivers in a certain proportion and incorporated into the model calculation so as to improve the mathematical model of river network water quality and apply it to the simulation of river network water quality in Maozhou River Basin. Verified by the measured data, the NSE values of the improved model are 0.805 and 0.851, respectively, indicating that the model has reliable hydrodynamic and water quality simulation accuracy, indicating that the model can be applied to the calculation of non-point source pollutants in the basin. Based on the improved model, the variation of COD concentration in the Maozhou River of Shenzhen before and after optimized water replenishment was calculated, and the time variation and spatial distribution law of the sudden drop of water quality in the river network caused by the inflow of non-point source pollution in the initial rainfall runoff and the rapid recovery after optimized water replenishment were revealed.

Discrete element simulation study of mixing process of Guizhi Fuling Capsules: parameter calibration

The mixing process is a critical link in the formation of oral solid preparations of traditional Chinese medicine. This paper took the extract powder of Guizhi Fuling Capsules and Paeonol powder as research objects. The angle of repose, loose packing density, and particle size of the two powders were measured to calibrate discrete element simulation parameters for the mixing process. The discrete element method was used to calibrate the simulated solid density of Paeonol powder and extract powder of Guizhi Fuling Capsules based on the Hertz-Mindlin with JKR V2 contact model and particle scaling. The Plackett-Burman experimental design was used to screen out the critical contact parameters that had a significant effect on the simulation of the angle of repose. The regression model between the critical contact parameters and the simulated angle of repose was established by the Box-Behnken experimental design, and the critical contact parameters of each powder were optimized based on the regression model. The best combination of critical contact parameters of the extract powder of Guizhi Fuling Capsules was found to be 0.51 for particle-particle static friction coefficient, 0.31 for particle-particle rolling friction coefficient, and 0.64 for particle-stainless steel static friction coefficient. For Paeonol powder, the best combination of critical contact parameters was 0.4 for particle-particle static friction coefficient and 0.19 for particle-particle rolling friction coefficient. The best combination of contact parameters between Paeonol powder and extract powder of Guizhi Fuling Capsules was 0.27 for collision recovery coefficient, 0.49 for static friction coefficient, and 0.38 for rolling friction coefficient. The verification results show that the relative error between the simulated value and the measured value of the angle of repose of the two single powders is less than 1%, while the relative error between the simulated value and the measured value of the angle of repose of the mixed powder with a mass ratio of 1∶1 is less than 4%. These research results provide reliable physical property simulation data for the mixed simulation experiment of extract powder of Guizhi Fuling Capsules and Paeonol powder.

Performance Evaluation of 802.11ax OFDMA Through Theoretical Analysis and Simulations

With the introduction of Orthogonal Frequency Division Multiple Access (OFDMA) in 802.11ax, the role of the Access Point (AP) in Wi-Fi networks changes significantly, thanks to the opportunity of implementing more complex scheduling logic to handle Downlink (DL) traffic flows and simultaneously act as coordinator of Multi User (MU) Uplink (UL) transmissions. In this context, it becomes necessary to develop reliable network analysis and simulation tools that allow for an in-depth investigation of the trade-offs involved in the usage of OFDMA, especially considering that the standard leaves much of the actual scheduling algorithmic details to vendor-specific implementation. In this work we present a series of results highlighting how several network settings have an impact on throughput and Head-of-Line Delay, in a network that employs multiple 802.11ax features such as OFDMA and the MU Enhanced Distributed Channel Access (EDCA) Parameter Set, while also containing legacy devices. The results are obtained via both the newly re-designed ns-3 wifi module and an original analytical framework, based on the well-established Bianchi 802.11 model.

Uncertainty quantification for mineral precipitation and dissolution in fractured porous media

In this work we present an uncertainty quantification analysis to determine the influence and importance of some physical parameters in a reactive transport model in fractured porous media. An accurate description of flow and transport in the fractures is key to obtain reliable simulations, however, fractures geometry and physical characteristics pose several challenges from both the modeling and implementation side. We adopt a mixed-dimensional approximation, where fractures and their intersections are represented as objects of lower dimension. To simplify the presentation, we consider only two chemical species: one solute, transported by water, and one precipitate attached to the solid skeleton. A global sensitivity analysis to uncertain input data is performed exploiting the Polynomial Chaos expansion along with spectral projection methods on sparse grids.

Versatile and effective design platform for surface acoustic wave accelerometers

Micro-electro-mechanical system (MEMS) accelerometers have great potentials for applications in aerospace, autonomous driving and consumer electronics. However, their working principles are mostly based on capacitive and resistance types, which cannot be easily and effectively used for wireless and passive sensing, while surface acoustic wave (SAW) technology is the key solution for this problem. Due to complex acoustic-electric-mechanical coupling during SAW accelerators’ operations, currently, there needs an accurate, reliable, and efficient design and simulation platform to improve their structure and performance. In this work, we proposed an accurate, reliable, and efficient modeling platform to optimize designs of SAW accelerometers, using a double-ended cantilever beam structure as an example. This model integrated the elastic acoustic effect and the coupled wave equations under both the mechanical and electrical loading using the finite element analysis, and effectively obtained acceleration-frequency responses of the SAW accelerators. We have systematically simulated effects of thickness of piezoelectric film, wavelength, and structural parameters of cantilever beams, and the simulation results are well consistent with the theoretical ones. Finally, using the developed model, we designed a high-G SAW accelerometer (up to 20000 g) with a high sensitivity (−41.8 Hz g−1) and excellent linearity (0.9999), and another one with a high sensitivity (3.02 KHz g−1) and a good linearity (0.9999) over a 100 g acceleration range.

Novel High-Precision and Efficient Momentum Source Method

Effective and reliable slipstream numerical simulation methods are important for propeller-driven aircraft design. This paper presents a high-precision and efficient momentum source method (HPE-MSM) based on a novel actuator disk load prediction model established by the frozen rotor method and blade element momentum theory. The simulation results of two benchmark test cases of an isolated propeller and a typical turboprop airliner show that the accuracy of the HPE-MSM proposed in this paper is close to the time-averaged unsteady results of the sliding mesh method (SMM), with a maximum error of 5% in aircraft lift and drag coefficients compared with experimental values. Meanwhile, the calculation efficiency of the HPE-MSM is comparable to quasi-steady methods, with only about 3.4% of the computation resources of the SMM in the whole aircraft simulation. This novel approach achieves high-precision and efficient simulation of the slipstream, which has the potential to improve the design level of propeller-driven aircraft.

SimCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data.

Single-cell chromatin accessibility sequencing (scCAS) technology provides an epigenomic perspective to characterize gene regulatory mechanisms at single-cell resolution. With an increasing number of computational methods proposed for analyzing scCAS data, a powerful simulation framework is desirable for evaluation and validation of these methods. However, existing simulators generate synthetic data by sampling reads from real data or mimicking existing cell states, which is inadequate to provide credible ground-truth labels for method evaluation. We present simCAS, an embedding-based simulator, for generating high-fidelity scCAS data from both cell- and peak-wise embeddings. We demonstrate simCAS outperforms existing simulators in resembling real data and show that simCAS can generate cells of different states with user-defined cell populations and differentiation trajectories. Additionally, simCAS can simulate data from different batches and encode user-specified interactions of chromatin regions in the synthetic data, which provides ground-truth labels more than cell states. We systematically demonstrate that simCAS facilitates the benchmarking of four core tasks in downstream analysis: cell clustering, trajectory inference, data integration, and cis-regulatory interaction inference. We anticipate simCAS will be a reliable and flexible simulator for evaluating the ongoing computational methods applied on scCAS data. simCAS is freely available at https://github.com/Chen-Li-17/simCAS.

Sub-Micrometer Phonon Mean Free Paths in Metal-Organic Frameworks Revealed by Machine Learning Molecular Dynamics Simulations.

Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability and hold great potential in various applications, many of which require a proper understanding of the thermal transport properties. Molecular dynamics (MD) simulations play an important role in characterizing the thermal transport properties of various materials. However, due to the complexity of the structures, it is difficult to construct accurate empirical interatomic potentials for reliable MD simulations of MOFs. To this end, we develop a set of accurate yet highly efficient machine-learned potentials for three typical MOFs, including MOF-5, HKUST-1, and ZIF-8, using the neuroevolution potential approach as implemented in the GPUMD package, and perform extensive MD simulations to study thermal transport in the three MOFs. Although the lattice thermal conductivity values of the three MOFs are all predicted to be smaller than 1 W/(m K) at room temperature, the phonon mean free paths (MFPs) are found to reach the sub-micrometer scale in the low-frequency region. As a consequence, the apparent thermal conductivity only converges to the diffusive limit for micrometer single crystals, which means that the thermal conductivity is heavily reduced in nanocrystalline MOFs. The sub-micrometer phonon MFPs are also found to be correlated with a moderate temperature dependence of thermal conductivity between those in typical crystalline and amorphous materials. Both the large phonon MFPs and the moderate temperature dependence of thermal conductivity fundamentally change our understanding of thermal transport in MOFs.

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems.

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.