Validation of a multi-physics simulation platform for engine emissions modelling

This paper introduces a comprehensive and systematic Design of Experiments based methodology deployed in conjunction with a multi-physics engine air-path and combustion co-simulation, leading to the development of a global transient simulation capability for engine out NOx emissions. The proposed multi-physics engine simulation framework couples a real-time one-dimensional air flow model with a Probability Density Function based Stochastic Reactor Model that accounts for detailed in-cylinder combustion chemistry to predict combustion emissions. The integration challenge stemming from the different computation complexities and time scales required to ensure adequate fidelity levels across multi-physics simulations was addressed through a comprehensive Design of Experiments methodology to develop a reduction of the slower Stochastic Reactor Model simulation to enable a transient simulation focussed on NOx emissions. The Design of Experiments methodology, based on Optimal Latin Hypercube design experiments, was deployed on the multi-physics engine co-simulation platform and systematically validated against both steady state and transient light-duty Diesel engine test data. The surrogate selection process included the evaluation of a range of metamodels, with Kriging metamodels selected based on both the statistical performance criteria and consideration of physical phenomena trends. The transient validation was carried out on a simulated New European Drive Cycle against the experimental data available, showing good capability to capture transient NOx emission behaviour in terms of trends and values. The significance of the results is that it proves the transient and drive cycle capability of the multi-physics simulation platform, suggesting a promising potential applicability for early powertrain development work focussed on drive cycle emissions.

JAX, M.D. A framework for differentiable physics*

We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics. JAX MD includes a number of physics simulation environments, as well as interaction potentials and neural networks that can be integrated into these environments without writing any additional code. Since the simulations themselves are differentiable functions, entire trajectories can be differentiated to perform meta-optimization. These features are built on primitive operations, such as spatial partitioning, that allow simulations to scale to hundreds-of-thousands of particles on a single GPU. These primitives are flexible enough that they can be used to scale up workloads outside of molecular dynamics. We present several examples that highlight the features of JAX MD including: integration of graph neural networks into traditional simulations, meta-optimization through minimization of particle packings, and a multi-agent flocking simulation. JAX MD is available at https://www.github.com/google/jax-md.

Pengembangan Buku Ajar Simulasi dan Pemodelan Fisika dengan Unity3D Berbasis Self Regulated Learning

This study aims to develop a simulation and modeling physics textbook assisted by Unity3D software with a self-regulated learning approach. This research uses Development and Research method through 4D Model. The definition stage is carried out by determining the characteristics of the textbook that will be developed through the initial study. The design stage is done by choosing the format and design of the textbook to produce the first correction (draft). The development stage is carried out to have the second draft, third correction, and the final product of the textbook. The second correction is obtained through the revised expert validation results that have been recommended. The third correction is the acquisition of small group test results by taking them randomly. Based on the results of field tests on students who are taking Physics Simulation and Modeling courses, the final product of the textbook is obtained. One of the textbook features and tools developed is the project assignment feature and the video tutorial tool. Based on the validation of the textbook and its supporting devices, the criteria are very feasible and ready to be used and can guide student learning independently through the features in the textbook. The development of this textbook is expected to assist in developing simulations and modeling in physics and other fields of science, such as engineering and health.

A review on coupled building physics analyses

This paper reviews methods and tools for coupled building physics analyses in the context of Building Performance Simulations (BPS) with a focus on Building Energy Simulations (BES) and Computational Fluid Dynamics (CFD) as a common application. Furthermore, requirements regarding the necessary information for simulations, data models and coupling are identified. Possibilities of automated simulation model generation, data exchange and the performance of existing multi physics simulation models are analysed and limiting factors are discussed.

Daisen: A Framework for Visualizing Detailed GPU Execution

Graphics Processing Units~(GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator-generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator-generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator-generated GPU execution traces. Daisen also includes a web-based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open-sourced implementation of Daisen can be found at gitlab.com/akita/vis. Supplemental materials including a demo video, survey questions, evaluation study guide, and post-study evaluation survey are available at osf.io/j5ghq.