Advanced Modeling and Simulation in Engineering Sciences最新文献_第5页

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders. 利用深度空间和时间卷积自动编码器为随机大规模和时间依赖性流动问题进行降阶建模。

IF 2

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-01 Epub Date: 2023-05-19 DOI: 10.1186/s40323-023-00244-0

Azzedine Abdedou, Azzeddine Soulaimani

{"title":"Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders.","authors":"Azzedine Abdedou, Azzeddine Soulaimani","doi":"10.1186/s40323-023-00244-0","DOIUrl":"10.1186/s40323-023-00244-0","url":null,"abstract":"A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow problems. The objective is to perform accurate and rapid uncertainty analyses of the flow outputs of interest for which the input parameters are deemed uncertain. The data are constituted from a set of high-fidelity snapshots, collected using an inhouse high-fidelity flow solver, which correspond to a sample of the uncertain input parameters. The method uses a 1D-convolutional autoencoder to reduce the spatial dimension of the unstructured meshes used by the flow solver. Another convolutional autoencoder is used for the time compression. The encoded latent vectors, generated from the two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron. The proposed model allows for rapid predictions for unseen parameter values, allowing the output statistical moments to be computed efficiently. The accuracy of the proposed approach is compared to that of the linear reduced-order technique based on an artificial neural network through two benchmark tests (the one-dimensional Burgers and Stoker's solutions) and a hypothetical dam break flow problem, with an unstructured mesh and over a complex bathymetry river. The numerical results show that the proposed methods present strong predictive capabilities to accurately approximate the statistical moments of the outputs. In particular, the predicted statistical moments are oscillations-free, unlike those obtained with the traditional proper orthogonal decomposition method. The proposed reduction framework is simple to implement and can be applied to other parametric and time-dependent problems governed by partial differential equations, which are commonly encountered in many engineering and science problems.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"10 1","pages":"7"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9506104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A "data-driven uncertainty" computational method to model and predict instabilities of a frictional system. 一种“数据驱动的不确定性”计算方法来模拟和预测摩擦系统的不稳定性。

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-01 DOI: 10.1186/s40323-023-00241-3

Farouk Maaboudallah, Noureddine Atalla

{"title":"A \"data-driven uncertainty\" computational method to model and predict instabilities of a frictional system.","authors":"Farouk Maaboudallah, Noureddine Atalla","doi":"10.1186/s40323-023-00241-3","DOIUrl":"https://doi.org/10.1186/s40323-023-00241-3","url":null,"abstract":"Most of the recently developed methods for predicting instabilities of frictional systems couple stochastic algorithms with the finite element method (FEM). They use random variables to model the uncertainty of input parameters through standard probability laws. Regardless of the fact that advanced numerical schemes are available nowadays, a systematic and accurate method to describe finely the uncertainties upstream the model, and thus predict its response is still missing. In this contribution, we present a data-driven stochastic finite element scheme to predict the dynamic behavior of a rubbing system. The proposed framework relies on data-driven approach and uses four steps. In the first, the measured data are integrated directly, for the uncertainty quantification, by means of the random balance design (RBD). In the second step, the generated stochastic data are evaluated in an iterative way to solve friction-induced vibration problem. In the third step, the resulted data are reordered in such a way that the corresponding values of each measured input parameters are ranked in ascending order. Finally, the Fourier spectrum is introduced on the reordered results to compute the sensitivity indices. Thus, instead of Monte Carlo-based formalism or Fourier Amplitude Sensitivity Test (FAST), the computational cost of the proposed method is kept down to <math><mrow><mi>O</mi> <mo>(</mo> <mi>N</mi> <mo>)</mo></mrow> </math> with N the number of samples. We investigate the efficiency of the suggested solver on a reduced brake system. Altogether, the suggested procedure achieves excellent accuracy at a much reduced computational time compared to the methods available in the literature.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"10 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9925556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10824088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems. 时变流问题中外推预测的深度卷积架构。

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-01 Epub Date: 2023-11-30 DOI: 10.1186/s40323-023-00254-y

Pratyush Bhatt, Yash Kumar, Azzeddine Soulaïmani

{"title":"Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems.","authors":"Pratyush Bhatt, Yash Kumar, Azzeddine Soulaïmani","doi":"10.1186/s40323-023-00254-y","DOIUrl":"10.1186/s40323-023-00254-y","url":null,"abstract":"Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep learning techniques developed especially for time-series forecasts, such as LSTM and TCN, or for spatial-feature extraction such as CNN, are employed to model the system dynamics for advection-dominated problems. This paper proposes a Convolutional Autoencoder(CAE) model for compression and a CNN future-step predictor for forecasting. These models take as input a sequence of high-fidelity vector solutions for consecutive time steps obtained from the PDEs and forecast the solutions for the subsequent time steps using auto-regression; thereby reducing the computation time and power needed to obtain such high-fidelity solutions. Non-intrusive reduced-order modeling techniques such as deep auto-encoder networks are utilized to compress the high-fidelity snapshots before feeding them as input to the forecasting models in order to reduce the complexity and the required computations in the online and offline stages. The models are tested on numerical benchmarks (1D Burgers' equation and Stoker's dam-break problem) to assess the long-term prediction accuracy, even outside the training domain (i.e. extrapolation). The most accurate model is then used to model a hypothetical dam break in a river with complex 2D bathymetry. The proposed CNN future-step predictor revealed much more accurate forecasting than LSTM and TCN in the considered spatiotemporal problems.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"10 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Pseudo-Direct Numerical Simulation Method considered as a Reduced Order Model 作为降阶模型的伪直接数值模拟方法

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-10-28 DOI: 10.1186/s40323-022-00235-7

S. Idelsohn, J. M. Giménez, N. Nigro

引用次数: 1

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review 用数据、机器学习和人工智能赋能工程：一篇简短的介绍性综述

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-10-27 DOI: 10.1186/s40323-022-00234-8

F. Chinesta, E. Cueto

引用次数: 8

A deformation-dependent coupled Lagrangian/semi-Lagrangian meshfree hydromechanical formulation for landslide modeling 滑坡模型的变形相关耦合拉格朗日/半拉格朗日无网格流体力学公式

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-09-30 DOI: 10.1186/s40323-022-00233-9

Jonghyuk Baek, Ryan T. Schlinkman, Frank N. Beckwith, Jiun-Shyan Chen

引用次数: 3

A multi-point constraint unfitted finite element method 多点约束非拟合有限元法

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-09-21 DOI: 10.1186/s40323-022-00232-w

B. Freeman

引用次数: 2

Thermo-mechanical simulations of powder bed fusion processes: accuracy and efficiency 粉末床熔化过程的热机械模拟：准确性和效率

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-09-12 DOI: 10.1186/s40323-022-00230-y

C. Burkhardt, P. Steinmann, J. Mergheim

引用次数: 8

A partitioned material point method and discrete element method coupling scheme 提出了分块质点法与离散元法的耦合方案

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-08-16 DOI: 10.1186/s40323-022-00229-5

Singer, Veronika, Sautter, Klaus B., Larese, Antonia, Wüchner, Roland, Bletzinger, Kai-Uwe

{"title":"A partitioned material point method and discrete element method coupling scheme","authors":"Singer, Veronika, Sautter, Klaus B., Larese, Antonia, Wüchner, Roland, Bletzinger, Kai-Uwe","doi":"10.1186/s40323-022-00229-5","DOIUrl":"https://doi.org/10.1186/s40323-022-00229-5","url":null,"abstract":"Mass-movement hazards involving fast and large soil deformation often include huge rocks or other significant obstacles increasing tremendously the risks for humans and infrastructures. Therefore, numerical investigations of such disasters are in high economic demand for prediction as well as for the design of countermeasures. Unfortunately, classical numerical approaches are not suitable for such challenging multiphysics problems. For this reason, in this work we explore the combination of the Material Point Method, able to simulate elasto-plastic continuum materials and the Discrete Element Method to accurately calculate the contact forces, in a coupled formulation. We propose a partitioned MPM-DEM coupling scheme, thus the solvers involved are treated as black-box solvers, whereas the communication of the involved sub-systems is shifted to the shared interface. This approach allows to freely choose the best suited solver for each model and to combine the advantages of both physics in a generalized manner. The examples validate the novel coupling scheme and show its applicability for the simulation of large strain flow events interacting with obstacles.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"29 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Lagrangian–Eulerian procedure for the coupled solution of the Navier–Stokes and shallow water equations for landslide-generated waves 滑坡产生波的Navier-Stokes方程和浅水方程耦合解的拉格朗日-欧拉过程

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-07-30 DOI: 10.1186/s40323-022-00225-9

Masó, Miguel, Franci, Alessandro, de-Pouplana, Ignasi, Cornejo, Alejandro, Oñate, Eugenio

{"title":"A Lagrangian–Eulerian procedure for the coupled solution of the Navier–Stokes and shallow water equations for landslide-generated waves","authors":"Masó, Miguel, Franci, Alessandro, de-Pouplana, Ignasi, Cornejo, Alejandro, Oñate, Eugenio","doi":"10.1186/s40323-022-00225-9","DOIUrl":"https://doi.org/10.1186/s40323-022-00225-9","url":null,"abstract":"This work presents a partitioned method for landslide-generated wave events. The proposed strategy combines a Lagrangian Navier Stokes multi-fluid solver with an Eulerian method based on the Boussinesq shallow water equations. The Lagrangian solver uses the Particle Finite Element Method to model the landslide runout, its impact against the water body and the consequent wave generation. The results of this fully-resolved analysis are stored at selected interfaces and then used as input for the shallow water solver to model the far-field wave propagation. This one-way coupling scheme reduces drastically the computational cost of the analyses while maintaining high accuracy in reproducing the key phenomena of the cascading natural hazard. Several numerical examples are presented to show the accuracy and robustness of the proposed coupling strategy and its applicability to large-scale landslide-generated wave events. The validation of the partitioned method is performed versus available results of other numerical methods, analytical solutions and experimental measures.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"29 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2