Simulation Engine Research Articles

A new paradigm for the efficient inclusion of stochasticity in engineering simulations: Time-separated stochastic mechanics

AbstractAs a physical fact, randomness is an inherent and ineliminable aspect in all physical measurements and engineering production. As a consequence, material parameters, serving as input data, are only known in a stochastic sense and thus, also output parameters, e.g., stresses, fluctuate. For the estimation of those fluctuations it is imperative to incoporate randomness into engineering simulations. Unfortunately, incorporating uncertain parameters into the modeling and simulation of inelastic materials is often computationally expensive, as many individual simulations may have to be performed. The promise of the proposed method is simple: using extended material models to include stochasticity reduces the number of needed simulations to one. This single computation is cheap, i.e., it has a comparable numerical effort as a single standard simulation. The extended material models are easily derived from standard deterministic material models and account for the effect of uncertainty by an extended set of deterministic material parameters. The time-dependent and stochastic aspects of the material behavior are separated, such that only the deterministic time-dependent behavior of the extended material model needs to be simulated. The effect of stochasticity is then included during post-processing. The feasibility of this approach is demonstrated for three different and highly non-linear material models: viscous damage, viscous phase transformations and elasto-viscoplasticity. A comparison to the Monte Carlo method showcases that the method is indeed able to provide reliable estimates of the expectation and variance of internal variables and stress at a minimal fraction of the computation cost.

Visual Exploration and Analysis of Simulation and Testing Data in Motor Engineering.

End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods (e.g., data-driven approaches) face challenges due to the limited availability of real data from failed motors. Simulated data, though beneficial, lacks the complexity of real motors, impacting the performance of these methods when applied to actual observations. To tackle this challenge, we introduce a visual analysis tool designed to facilitate the analysis of measured and simulated data, presented in the form of time series data. This tool helps identify domain-invariant features and evaluate simulation data accuracy, assisting in selecting training data for reliable automated defect detection in real-world scenarios. The main contribution of this work is a design proposal based on visual design principles, specifically tailored to address the unique requirements of electric motor professionals. The visual design is validated by findings from a think-aloud study with specialized engineers.

Augmented Reality Applications in Mechanical System Design and Prototyping

With the integration of computer-generated images and data into the real world, augmented reality (AR) is a game-changing technology that improves users' perceptions and interactions with their surroundings. This technology, which can overlay digital data on real-world environments, has great potential for use in several fields, such as mechanical system design and prototyping. The use of AR in this industry is not just a theoretical idea; rather, it is a rapidly developing reality with enormous potential for creativity, effectiveness, and accuracy. Augmented Reality (AR) is a technology that combines the physical and digital worlds to provide a composite vision that improves the user's sense of reality. Augmented Reality (AR) superimposes digital data in real-time over the actual world, in contrast to Virtual Reality (VR), which submerges users in a fully digital environment. Usually, gadgets like tablets, smartphones, AR glasses, and head-mounted displays (HMDs) help with this integration. The term "mechanical system design" describes the entire process of imagining, creating, and perfecting mechanical parts and systems; it includes everything from rough concept drawings to intricate engineering models and simulations. Before moving on with full-scale manufacturing, prototyping entails building early models or samples of a product to test and verify design concepts.

Research on simulation accuracy of equivalent aggregate electromagnetic model optimization for large renewable energy stations

Abstract The traditional power grid is rapidly transforming into a new power system with high penetration of new energy. Simulation modeling plays an important role in supporting the cognition and research of the new power system. A high proportion of renewable energy is connected to the grid through power electronic components, which puts forward a higher demand for research on the accuracy and stability of power grid simulation modeling. Renewable energy plants usually have a large number of single units, and equivalent aggregation modeling is the usual way to simplify the modeling of renewable energy plants. However, the transmission line power multiplier equivalent module inevitably introduces series impedance into the network, resulting in errors between simulation and actual engineering. Therefore, the study of lossless multiplier elements based on Modified Nodal Analysis (MNA) can improve the simulation accuracy, and it is verified with the simulation results of detailed models in actual scenarios, proving that MNA lossless multiplier elements can optimize the accuracy and stability of equivalent aggregation modeling of large-scale multi-unit renewable energy stations.

Aero-engine performance evaluation model in the entire flight envelope

Abstract An aero-engine performance characteristics mathematical model in the entire flight envelope is proposed based on the stratification analysis method and multi-condition test data. The thrust, fuel flow, low/high-pressure rotor speed, exhaust temperature, and other engine output parameters can be calculated in the entire flight envelope, according to the engine operating characteristic model and input parameters of aircraft flight altitude, velocity, ambient temperature, throttle angle, etc. Based on the evaluation model proposed in this paper, the evaluation data of an aero-engine is consistent with the test data, and the error is less than 5%. The research and model can support flight performance calculation and mission planning and can be applied to flight simulator engine modules.

Error Analysis of the Classical Artificial Diffusion Weak Galerkin Finite Element Method for the steady state- convection diffusion-reaction Equation in 2-D

In this study, we modified the error in the weak Galerkin method when solving problems in which diffusion is the dominant convection( h) in two dimensions. This is done by adding the artificial diffusion term (-δΔw, where δ=h-ϵ). The finite element method for discrete functions using a weakly defined gradient operator is presented in this study. The concept of the weak discrete gradient is introduced, which plays an important role when using numerical methods to solve partial differential equations. The goal of this study is to enhance the accuracy and stability of the solutions by studying the ellipticity and stability properties of the method, which works to ensure that the numerical method retains the properties of the original equation while reducing the fluctuations occurring with the weak galerkin finite element method. Specific theories have been used to estimate the error in parameters -norm, and . Practical examples demonstrate how this method improves the handling of partial equations characterized by convection-dominated diffusion, enhancing its potential for advancing numerical simulations in engineering and physics.

Mathematical modeling and numerical simulation in sciences and engineering

Mathematical modeling and numerical simulation in sciences and engineering

Engine & vehicle modeling for fuel assessment under local driving conditions

Automotive biofuels offer a promising alternative to traditional fossil fuels. Accurate evaluation of combustion and emissions in IC engines and vehicles is crucial. This research aimed at developing and validating an engine and vehicle simulation methodology to assess the fuel effect on vehicle consumption and emissions considering different driving cycles and the road slope (barely evaluated for fuels widely used in emerging markets). Two blends were tested: 20 % biodiesel (B20) and 20 % hydrotreated vegetable oil (HVO20) with Ultra-Low-Sulfur Diesel (ULSD). A light-duty diesel vehicle model was developed in GTSuite®, using emission maps from a calibrated steady-state engine model. Good agreement with experiments was found. Road slope in local DC significantly increased fuel consumption and CO, CO2, NOx, and PN emissions, reducing HC. Compared to ULSD, B20 reduced PN and HC by 27–35 % and 12–22.5 %, respectively. HVO20 had a smaller effect on PN but reduced HC emissions by up to 19.5 %. Neither blend significantly affected CO and CO2. B20 slightly increased NOx and fuel consumption, while HVO20 had no significant impact on these.

Investigation of the bedding effect on coal rock under Brazilian splitting tests

This contribution focuses on understanding the bedding effect of coal rocks under the Brazilian splitting test. First, multiple Brazilian splitting tests were performed on coal rocks with various bedding angles to systematically investigate the influence of stratification. Subsequently, numerical models with stratified structures were constructed, and a continuous–discontinuous numerical analysis method based on the cohesive zone model (CZM) was employed to conduct the corresponding numerical investigations. Results indicate that the load–displacement curves of coal rock specimens with different bedding angles can be classified into four stages: initial compaction stage, elastic deformation stage, crack rapid coalescence stage, and final destruction stage. With increase in the bedding angle, the failure patterns of coal rock specimens can be categorized into three groups: 1) stretching damage along bedding planes; 2) mixed tension and shear failure along the bedding planes and the coal matrix; and 3) stretching failure passing through the coal matrix. Furthermore, the tensile strength and cumulative acoustic emission (AE) energy–displacement relations are significantly influenced by the bedding angle. The numerical model can effectively predict the mechanical responses and fracture behavior of coal rock specimens, providing empirical parameters for the simulation of similar rock engineering.

The Method of Elementary Solvers in SPICE

Circuit simulators are fundamentally used for solving electric circuit problems with different degrees of complexity in which node voltages and branch currents are the unknowns. This is fully understandable since they were originally created for this specific task. However, behind the curtains, powerful simulation engines based on a variety of numerical techniques operate so as to always comply with Kirchhoff’s current and voltage laws. In this paper, it is shown how a simple circuital configuration, referred to as the elementary solver, consistent in two behavioral current sources in series, can be used to solve mathematical problems that go beyond electronics. Of course, the intention is not to substitute mathematical packages with well-proven calculus capacity but to increase the scope of circuit simulators for their application in other areas of research or simply for educational purposes. It is worth mentioning that no special programming skills are required (except a basic knowledge of the available tools and language) and, furthermore, that the user can operate exclusively in a graphical environment. It is shown, throughout a series of selected examples, how the method of elementary solvers (MES) works, providing a new and practical dimension to the applicability of circuit simulators.

Adaptive Synchronization and Pacing Control for Visual Interactive Simulation

Parallel and distributed computing enable the execution of large and complex simulations. Yet, the usual separation of (headless) simulation execution and (subsequent, offline) output analysis often renders the simulation endeavor long and inefficient. Recently, Visual Interactive Simulation (VIS) tools and methods that address this end-to-end efficiency are gaining relevance, offering in-situ visualization, real-time debugging, and computational steering. Here, the typically distributed computing nature of the simulation execution poses synchronization challenges between the headless simulation engine and the user-facing frontend required for Visual Interactive Simulation. To the best of our knowledge, state-of-the-art synchronization approaches fall short due to their rigidity and inability to adapt to real-time user-centric changes. This paper introduces a novel adaptive algorithm to dynamically adjust the simulation’s pacing through a buffer-based framework, informed by predictive workload analysis. Our extensive experimental evaluation across diverse synthetic scenarios illustrates our method’s effectiveness in enhancing runtime efficiency and synchronicity, significantly reducing end-to-end time while minimizing user interaction delays, thereby addressing key limitations of existing synchronization strategies.

Impact of well completion types on oil drainage area in reservoirs

Well completion design plays a crucial role in oil production and reservoir management. This study examines the influence of various well completion types on the oil drainage area within reservoirs, with the goal of enhancing our understanding of how completion methods affect hydrocarbon recovery. Using advanced reservoir engineering techniques and simulations, this research investigates the dynamic interaction between well completion and the accessibility of oil resources. The study provides a comprehensive analysis of different well completion types, including open-hole completions, cased-hole completions, and multilateral completions, and evaluates their collective impact on the drainage area. It also explores the interplay between well completion strategies and reservoir properties, such as permeability, lithology, and fluid characteristics. The findings of this study reveal that the choice of well completion type can significantly influence the drainage area by affecting the flow paths and reservoir contact. Different completion methods can extend or limit the reach of hydrocarbon recovery, impacting oil production rates. The research underscores the importance of optimizing well completion design to maximize reservoir performance. Furthermore, the study addresses operational considerations related to well completion, including equipment selection, production optimization, and workover requirements. It offers insights into the economic and operational implications of different well completion strategies, providing valuable guidance for reservoir management. The outcomes of this research contribute to a deeper understanding of how well completion types impact the oil drainage area in reservoirs. This knowledge is valuable for reservoir engineers, operators, and industry professionals involved in oil production, assisting them in making informed decisions to optimize oil recovery, reservoir performance, and well completion strategies.

Subspace graph networks for real-time granular flow simulation with applications to machine-terrain interactions

Granular flows interacting with rigid bodies are key to machine-terrain interactions in robotics and related fields, and still pose several open problems. Continuum methods and deep learning, separately and in combination, show promise. This research advances machine learning methods for modeling rigid body-driven granular flows, for terrestrial industrial machine and planetary rover (where gravity is an important factor) applications. The original contribution of this research is the application of a subspace machine learning simulation approach for these engineering problems. To generate training datasets, a high-fidelity continuum method, the material point method (MPM) is utilized. Principal component analysis (PCA) is used to reduce the dimensionality of data. It is shown that the first few (8 in our datasets) principal components of our high-dimensional data keep almost the entire variance in data. A graph network simulator (GNS) is trained to learn the underlying subspace dynamics. The learned GNS is then able to predict particle positions and interaction forces with good accuracy, averaging 6×10−5 and 3×10−6 mean squared position error and 7% and 17% mean percentage force error, for excavation and wheel data, respectively. More importantly, PCA significantly enhances the time and memory efficiency of GNS in both training and rollout. This enables GNS to be trained using a single desktop graphics processing unit (GPU) with moderate video random-access memory (VRAM). This also makes the GNS real-time on large-scale 3D physics configurations (700x faster than our continuum method), which is the first demonstration of such significantly accelerated high-fidelity granular flow engineering simulations.

An Improved Onboard Adaptive Aero-Engine Model Based on an Enhanced Neural Network and Linear Parameter Variance for Parameter Prediction

Achieving measurable and unmeasurable parameter prediction is the key process in model-based control, for which an accurate onboard model is the most important part. However, neither nonlinear models like component level models or LPV models, nor linear models like state–space models can fully meet the requirements. Hence, an original ENN-LPV linearization strategy is proposed to achieve the online modelling of the state–space model. A special network structure that has the same format as the state–space model’s calculation was applied to establish the state–space model. Importantly, the network’s modelling ability was improved through applying multiple activation functions in the single hidden layer and an experience pool that records data of past sampling instants, which strengthens the ability to capture the engine’s strongly nonlinear dynamics. Furthermore, an adaptive model, consisting of a component-level model with adaptive factors, a linear Kalman filter, a predictive model, an experience pool, and two ENN-LPV networks, was developed using the proposed linearization strategy as the core process to continuously update the Kalman filter and the predictive model. Simulations showed that the state space model built using the ENN-LPV linearization strategy had a better model identification ability in comparison with the model built using the OSELM-LPV linearization strategy, and the maximum output error between the ENN-LPV model and the simulated engine was 0.1774%. In addition, based on the ENN-LPV linearization strategy, the adaptive model was able to make accurate predictions of unmeasurable performance parameters such as thrust and high-pressure turbine inlet temperature, with a maximum prediction error within 0.5%. Thus, the effectiveness and the advantages of the proposed method are demonstrated.

BnglViz: online visualization of rule-based models.

Rule-based modeling is a powerful method to describe and simulate interactions among multi-site molecules and multi-molecular species, accounting for the internal connectivity of molecules in chemical species. This modeling technique is implemented in BioNetGen software that is used by various tools and software frameworks, such as BioNetGen stand-alone software, NFSim simulation engine, Virtual Cell simulation and modeling framework, SmolDyn and PySB software tools. These tools exchange models using BioNetGen scripting language (BNGL). Until now, there was no online visualization of such rule-based models. Modelers and researchers reading the manuscripts describing rule-based models had to learn BNGL scripting or master one of these tools to understand the models. Here, we introduce bnglViz, an online platform for visualizing BNGL files as graphical cartoons, empowering researchers to grasp the nuances of rule-based models swiftly and efficiently, and making the exploration of complex biological systems more accessible than ever before. The produced visualizations can be used as supplemental figures in publications or as a way to annotate BNGL models on web repositories. Available at https://bnglviz.github.io/.

Engineering of the start condensation domain with improved N-decanoyl catalytic activity for daptomycin biosynthesis.

Daptomycin, a lipopeptide comprising an N-decanoyl fatty acyl chain and a peptide core, is used clinically as an antimicrobial agent. The start condensation domain (dptC1) is an enzyme that catalyzes the lipoinitiation step of the daptomycin synthesis. In this study, we integrated enzymology, protein engineering, and computer simulation to study the substrate selectivity of the start condensation domain (dptC1) and to screen mutants with improved activity for decanoyl loading. Through molecular docking and computer simulation, the fatty acyl substrate channel and the protein-protein interaction interface of dptC1 are analyzed. Key residues at the protein-protein interface between dptC1 and the acyl carrier were mutated, and a single-point mutant showed more than three-folds improved catalytic efficiency of the target n-decanoyl substrate in comparing with the wild type. Moreover, molecular dynamics simulations suggested that mutants with increased catalytic activity may correlated with a more "open" and contracted substrate binding channel. Our work provides a new perspective for the elucidation of lipopeptide natural products biosynthesis, and also provides new resources to enrich its diversity and optimize the production of important components.

Повышение акустической безопасности производственного помещения

Sound-emitting production and technological as well as engineering equipment located within the enclosed space of industrial premises can form paired interacting sound fields with similar values of expressed dominant emission frequencies in the spectra, ultimately causing the formation of a physical process of sound vibration beats. The process manifests in negative psychoacoustic impacts on a human characterized by pulsating sound pressure levels over time with a pulsation frequency equal to the difference between similar frequency values of discrete components of interacting sound fields. The article provides the results of experimental studies to determine the required value of frequency mismatch of sound fields for individual sources of noise emissions mounted in industrial premises. The industrial premises have been presented as a rectangular parallelepiped with rigid sound-reflective fences. Simulation experiments have been carried out using an electroacoustic sound-emitting system represented as two autonomous sound-emitters — electroacoustic loudspeakers. The loudspeakers have simulated noise-generating engineering units with the specified passport steady-state speed operating modes ns and accompanied by the respective established physical processes of sound energy emission whose spectral composition contains specific discrete values of operating dominant functional sound frequencies fs. The technical result of the study is achieved by the implementation of a sufficient degree of frequency mismatch of expressed discrete components of sound emissions of individual autonomous sources of noise preventing the formation of the physical process of generation of intense sound vibration beats. The suggested technical method aiming to increase the acoustic safety of industrial premises assumes that the discrete values of operating dominant functional sound frequencies fs differ from each other by a limiting value Δf exceeding the value of 5 Hz.

Experimental and Engineering Simulation of Loss Characteristics of Silicon Steel Wafers Based on Levenberg-Marquardt Optimization Algorithm

Abstract The loss characteristics of 20WTG1500 silicon steel wafers are simulated and studied by using the Epstein square circle method. First, the loss-measured data are fitted based on the classical Bertotti loss model. When working, a Levenberg-Marquardt (L-M) algorithm is proposed to calculate the parameters of the loss model accurately. For the problem of significant calculation errors of hysteresis loss and stray loss in the classical loss separation model, a variable parameter closely related to the magnetic induction intensity is constructed by using the fixed parameter values of the classical loss model. Then, the loss improvement model is proposed. The accuracy of the improved model is proved by comparing the fitting results of the enhanced model with the measured data.

Relative permeability estimation using mercury injection capillary pressure measurements based on deep learning approaches

Relative permeability (RP) curves which provide fundamental insights into porous media flow behavior serve as critical parameters in reservoir engineering and numerical simulation studies. However, obtaining accurate RP curves remains a challenge due to expensive experimental costs, core contamination, measurement errors, and other factors. To address this issue, an innovative approach using deep learning strategy is proposed for the prediction of rock sample RP curves directly from mercury injection capillary pressure (MICP) measurements which include the mercury injection curve, mercury withdrawal curve, and pore size distribution. To capture the distinct characteristics of different rock samples' MICP curves effectively, the Gramian Angular Field (GAF) based graph transformation method is introduced for mapping the curves into richly informative image forms. Subsequently, these 2D images are combined into three-channel red, green, blue (RGB) images and fed into a Convolutional Long Short-Term Memory (ConvLSTM) model within our established self-supervised learning framework. Simultaneously the dependencies and evolutionary sequences among image samples are captured through the limited MICP-RP samples and self-supervised learning framework. After that, a highly generalized RP curve calculation proxy framework based on deep learning called RPCDL is constructed by the autonomously generated nearly infinite training samples. The remarkable performance of the proposed method is verified with the experimental data from rock samples in the X oilfield. When applied to 37 small-sample data spaces for the prediction of 10 test samples, the average relative error is 3.6%, which demonstrates the effectiveness of our approach in mapping MICP experimental results to corresponding RP curves. Moreover, the comparison study against traditional CNN and LSTM illustrated the great performance of the RPCDL method in the prediction of both So and Sw lines in oil–water RP curves. To this end, this method offers an intelligent and robust means for efficiently estimating RP curves in various reservoir engineering scenarios without costly experiments.

Environmental and Economic Aspects of a Containership Engine Performance in Off-Design Conditions

A comprehensive thermodynamic model of the marine diesel engine in combination with the operating cost assessment is used in the decision-making process regarding the selection of the most favorable slow steaming speed. The influence of the number of cylinders and sailing speed on exhaust emissions, fuel consumption and operating costs is analyzed for the case of a containership sailing on a Trans-Pacific route. The engine simulation model was used for the calculation of engine fuel consumption, NOX and soot emissions. The operating costs and annual income were calculated through a fuel consumption correlation. The benefit of slow steaming is shown through the comparison of calculated data with the data calculated for the six-cylinder engine and the design speed of 23 knots. The highest reduction of 67.2% in CO2 and 93.3% in NOX emissions is achieved with the seven-cylinder engine at 15 knots, but the six-cylinder engine yields the highest increase in income per route of 6.2%. To comply with the proposed regulations for GHG emissions, the sailing speed should be reduced by at least 26%, which results in a decrease in the annual income by 24% compared to the design speed.