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Results for “Machining simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. Here, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring the Feasibility of INCONEL® ALLOY 740H® for Power Plant Headers: Integrating Machine Learning with Computational Fluid Dynamics (CFD)

This keynote presentation explores the behavior of headers—essential components of pipeline systems—using ANSYS simulation software and machine learning techniques. The study aims to predict the thermal and mechanical performance of headers under diverse conditions through both steady-state and transient simulations. We investigate critical parameters such as heat transfer coefficient, fluid velocity, and temperature to optimize header design. Conducted as part of a DOE project led by NCAT in collaboration with UNC Charlotte, this research encompasses multiple key topics. The initial section focuses on the behavior of header systems under steady-state conditions using ANSYS simulation. It underscores the importance of headers in industrial infrastructure, especially in the energy sector, and examines the implications of material selection and flow direction on heat transfer dynamics. Methodologically, we employ Computational Fluid Dynamics (CFD) analysis through ANSYS, detailing the development of models, material properties, geometry specifications, boundary conditions, and meshing strategies. Our simulations explore various operational parameters, including temperature and mass flow rates, crucial for predicting heat transfer coefficients and enhancing header design. Results from the study include parametric investigations into mesh sensitivity, viscosity model evaluations, and the effects of heat transfer locations, all validated against theoretical calculations. We conclude with insights on mesh optimization, the suitability of viscosity models, and recommendations for future research aimed at improving header system efficiency and sustainability in industrial applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer [Slides]

Project Goal: Use parametric Machine Learning methods in order to reduce memory requirements at checkpointing & restarting in the IMC simulations of Thermal Radiative Transfer using: Expectation Maximization and Weighted Gaussian Mixture Model-based approach for `particle-data compression', introduced in Plasma Physics to model Maxwellian particle distributions by Luis Chacon and Guangye Chen; Expectation Maximization with Weighted Hyper-Erlang Model in order to compress isotropic IMC particle data in the frequency domain; and Expectation Maximization and von Mises-Fisher Mixture Model for compression of anisotropic IMC particle data in the angular domain (work-in-progress).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Support Vector Machines for Estimating Decision Boundaries with Numerical Simulations

Many engineering design problems can be formulated as decisions between two possible options. This is the case, for example, when a quantity of interest must be maintained below or above some threshold. The threshold thereby determines which input parameters lead to which option, and creates a boundary between the two options known as the decision boundary. This report details a machine learning approach for estimating decision boundaries, based on support vector machines (SVMs), that is amenable to large scale computational simulations. Because it is computationally expensive to evaluate each training sample, the approach iteratively estimates the decision boundary in a manner that requires relatively few training samples to glean useful estimates. The approach is then demonstrated on three example problems from structural mechanics and heat transport.

25 ENERGY STORAGE↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗
DOE OSTI2023

Deducing neutron star equation of state from telescope spectra with machine-learning-derived likelihoods

The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star's interior is not directly observable, its pressure and density determine the star's macroscopic structure which affects the spectra observed in telescopes. The relationship between the observations and the internal state is complex and partially intractable, presenting difficulties for inference. Previous work has focused on the regression from stellar spectra of parameters describing the internal state. We demonstrate a calculation of the full likelihood of the internal state parameters given observations, accomplished by replacing intractable elements with machine learning models trained on samples of simulated stars. Our machine-learning-derived likelihood allows us to perform maximum a posteriori estimation of the parameters of interest, as well as full scans. We demonstrate the technique by inferring stellar mass and radius from an individual stellar spectrum, as well as equation of state parameters from a set of spectra. Our results are more precise than pure regression models, reducing the width of the parameter residuals by 11.8% in the most realistic scenario. The neural networks will be released as a tool for fast simulation of neutron star properties and observed spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Large Scale MD to Predict Epitope Regions in HIV Env [Slides]

Highly dense carbohydrates located on the surface of the HIV Env protein play a key role in immune evasion. Such evolutionary adaptation hampers any attempt to obtain a full mechanistic understanding of the role played by the glycans in protecting the virus against an effective immune response. Moreover, and due to their chemical variability, an accurate molecular understanding of the so called “glycan shield” is still limited by the lack of effective resolution of state-of-the-art experimental technics. Here, we have used extensive computational modelling in order to fill this gap, addressing the presence of a large glycan variability as observed experimentally. Based on an automated pipeline, we were able to assemble, set-up and simulate via Molecular dynamics hundreds of different glycosylated Env variants at nearly atomic resolution, recapitulating the glycosylation distributions observed experimentally. Results from these simulations were subjected to machine learning and very accurate prediction of simulation derived glycan shielding areas of each glycan as a function of static sequence features. Such predictive models of per-glycan shielding, incorporating both glycan dynamics and heterogeneity, were used to develop a novel sequence-based glycan shield mapping strategy. Parallel to these studies, we also developed an accurate machine learning approach to predict glycan heterogeneity data using sequence features and found good prediction accuracy.

59 BASIC BIOLOGICAL SCIENCES↗