Towards interactive simulation in automotive design

Abstract

One of the important tasks in Mechanical Engineering is to increase the safety of the vehicle and decrease its production costs. This task is typically solved by means of Multiobjective Optimization, which formulates the problem as a mapping from the space of design variables to the space of target criteria and tries to find an optimal region in these multidimensional spaces. Due to high computational costs of numerical simulations, the sampling of this mapping is usually very sparse and scattered. Combining design of experiments methods, metamodeling, new interpolation schemes and innovative graphics methods, we enable the user to interact with simulation parameters, optimization criteria, and come to a new interpolated crash result within seconds. We denote this approach as Simulated Reality, a new concept for the interplay between simulation, optimization and interactive visualization. In this paper we show the application of Simulated Reality for solution of real life car design optimization problems

Interactive visual analysis of large simulation ensembles

“…Analyzing multidimensional parameter spaces has long been of interest to visualization researchers. Sampling of parameter spaces has helped to solve many high-dimensional domain problems, for example, in aircraft design (Shaffer, Knill, and Watson 1998), or in engineering (Stork, Thole, Klimenko, Nikitin, Nikitina, and Astakhov 2008). Some of the authors deal with a large number of simpler responses (1D and 2D), and some focus on 3D responses (Demir, Dick, and Westermann 2014).…”

Metamodel-Based Simulation Optimization: A Systematic Literature Review

“…Many authors have also studied MBSO techniques to optimize product designs. Examples include applications related to the automotive (Fu and Sahin, 2004;Ivanova and Kuhnt, 2014;Stork et al, 2008), energy (Sharif and Hammad 2019;Storti et al, 2019), hydropower (Bananmah et al, 2020;Mooselu et al, 2019), and transit infrastructure (Yin et al, 2016) sectors. Many authors have also studied MBSO applied to resource allocation problems (e.g., (Coelho and Pinto 2018;Song et al, 2005;Yousefi and Yousefi 2019;Zeinali et al, 2015), to synthetic mathematical functions (e.g., Baquela and Olivera, 2019;Kim and Boukouvala, 2020;Gonzalez et al, 2020;Wang et al, 2020), and to urban traffic issues Chong and Osorio 2018;Osorio and Bierlaire 2013;Osorio and Chong 2015).…”

“…Furthermore, 3.0% used metamodeling to provide quick answers to operational problems requiring short-term decision-making time frames (e.g., Dunke and Nickel, 2020;Calahorrano et al, 2016). Metamodeling was also used to perform joint optimizations taking data from several models (e.g., Bartz-Beielstein et al, 2018), to quickly generate entire solution spaces (e.g., Stork et al, 2008), and better understand optimization processes (e.g., Huyet and Paris, 2001).…”

Interactive interaction plot

“…The data set describes Olympic medals won since 2000, sorted by countries [27]. In this case, the control parameters are Country/Continent, Age and Season.…”