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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC). The outputs (targets) of interest from these simulations included five metrics related to engine performance and emissions. Over 2000 samples generated from CFD were then used to train an ML model that could predict these five targets based on the nine input features. A robust super learner approach was employed to build the ML model, where results from a collection of different ML algorithms were pooled together. Thereafter, a stochastic global optimization genetic algorithm (GA) was used, with the ML model as the objective function, to optimize the input parameters based on a merit function so as to minimize fuel consumption while satisfying CO and NOx emissions constraints. The optimized configuration from ML-GA was found to be very close to that obtained from a sequentially performed CFD-GA approach, where a CFD simulation served as the objective function. In addition, the overall turnaround time was (at least) 75% lower with the ML-GA approach, as the training data was generated from concurrent CFD simulations and employing the ML model as the objective function significantly accelerated the GA optimization. Finally, this study demonstrates the potential of ML-GA and high-performance computing (HPC) to reduce the number of CFD simulations to be performed for optimization problems without loss in accuracy, thereby providing significant cost savings compared to traditional approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

SBIR PHASE I FINAL REPORT: Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures

Many US manufacturing companies have moved fabrication and production facilities off shore because of cheaper labor costs. A key aspect in bringing these jobs back to the US is the use of technology to render US-made fabrications more efficient overall with higher quality. A new initiative of the current administration has the goal of enhancing competitiveness to retain manufacturing jobs in the US. One significant competitive advantage that has emerged in the US over the last two decades is the use of virtual design for fabrication of large structures in the light and heavy materials industries. Industries that have used virtual design and analysis tools have reduced material parts size, developed environmentally-friendly fabrication processes, improved product quality and performance, and reduced manufacturing costs. Indeed, Caterpillar Inc. (CAT), one of the partners in this effort, continues to have a large fabrication presence in the US because of the use of weld fabrication modeling to optimize fabrications by controlling weld residual stresses and distortions and improving fatigue, corrosion, and fracture performance. This report describes Engineering Mechanics Corporation of Columbus (Emc2's) DOE SBIR Phase I results which extended an existing, state-of-the-art software code, VFT, currently used to design and model large welded structures prior to fabrication - to a broader range of products with widespread applications for small and medium-sized enterprises (SMEs). VFT helps control distortion, can minimize and/or control residual stresses, control welding microstructure, and pre-determine welding parameters such as weld-sequencing, pre-bending, thermal-tensioning, etc. VFT uses material properties, consumable properties, etc. as inputs. Through VFT, manufacturing companies can avoid costly design changes after fabrication. This leads to the concept of joint design/fabrication where these important disciplines are intimately linked to minimize fabrication costs. VFT currently is tied to a commercial solver which makes it prohibitively expensive for use by SMEs, as there is a significant licensing cost for the solver - over and above for the relatively minimal cost for VFT. Emc2 developed this software code over a number of years in close cooperation with CAT (Peoria, IL), who currently uses this code exclusively for worldwide fabrication, product design and development activities. The use of VFT has allowed CAT to move directly from design to product fabrication and helped eliminate (to a large extent) new product prototyping and subsequent testing. Additionally, CAT has been able to eliminate/reduce costly one-of-a-kind appliances used to reduce distortion effects due to fabrication. In this context, SMEs can realize the same kind of improved product quality and reduced cost through adoption of the adapted version of VFT for design and subsequent manufacture of new products. Emc2's DOE SBIR Phase I effort successfully adapted VFT so that SMEs have access to this sophisticated and proven methodology that is quick, accurate and cost effective and available on-demand to address weld-simulation and fabrication problems prior to manufacture. The open source code, WARP3D, a high performance finite element code mainly used in fracture and damage assessment of structures, was modified so that computational weld problems can be solved efficiently on multiple processors and threads with VFT. The thermal solver for VFT, based on a series of closed form solution approximations, was enhanced for solution on multiple processors greatly increasing overall speed. In addition, the graphical user interface (GUI) has been tailored to integrate these solutions with WARP3D. The GUI is used to define all the weld pass descriptions, number of passes, material properties, consumable properties, weld speed, etc. for the structure to be modeled. The GUI was improved to make it user-friendly for engineers that are not experts in finite element modeling. Finally, a plan for porting VFT onto the Ohio Supercomputer Center (OSC) through its hosted Manufacturing and Polymer Portal has been developed. This access route will permit SMEs to perform weld modeling to improve their competitiveness at a reasonable cost. All of these improvements are detailed in this repo

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing

High-performance computing (HPC) and the cloud have evolved independently, specializing their innovations into performance or productivity. Acceleration as a Service (XaaS) is a recipe to empower both fields with a shared execution platform that provides transparent access to computing resources, regardless of the underlying cloud or HPC service provider. Bridging HPC and cloud advancements, XaaS presents a unified architecture built on performance-portable containers. Here, our converged model concentrates on low-overhead, high-performance communication and computing, targeting resource-intensive workloads from climate simulations to machine learning. XaaS lifts the restricted allocation model of Function as a Service (FaaS), allowing users to benefit from the flexibility and efficient resource utilization of serverless computing while supporting long-running and performance-sensitive workloads from HPC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The Role of National Research and Education Networks Providing Connectivity and Advanced Network Services to Virtual Communities in Collaborative R&E Projects. CUDI: The Mexican Case Part 1, , In: ISUM 2015: High Performance Computer Applications

This paper describes the role of National Research and Education Networks (NRENs) and their aim to provide a seamless, large scale, high-speed, low-latency network to accelerate scientific and education discovery for national, regional or global collaborative projects that are dependent on the use of modern high performance distributed computing systems (HPDCS) to process mathematical models, produce new knowledge and drive innovation when resolving complex problems. NRENs have become the mainstream of communication and collaboration to integrate the efforts of large geographically distributed teams of Virtual Communities (VC) that - train, teach and learn-, -create, use and share- knowledge, data, information and communications technology resources (e.g. computer and information systems, middleware, tools, instruments, data sets, repositories, applications and services) building e-infrastructures necessary to meet the collaborative projects’ goals. The paper focuses on the current status of the Mexican NREN: CUDI, its future organization and political support, necessary to provide seamless, large scale, high-speed, low latency and secure national backbone and international links providing connectivity, advanced network services and technical support to VCs and their e-infrastructures in collaborative R&E projects within the country, the LAC region and worldwide.

Computer Science↗
DOE OSTI2025

A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows

Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

high-performance computing↗
DOE OSTI2012

Using High Performance Computing to Understand Roles of Labile and Nonlabile U(VI) on Hanford 300 Area Plume Longevity

Evolution of a hexavalent uranium [U(VI)] plume at the Hanford 300 Area bordering the Columbia River is investigated to evaluate the roles of labile and nonlabile forms of U(VI) on the longevity of the plume. A high fidelity, three-dimensional, field-scale, reactive flow and transport model is used to represent the system. Richards equation coupled to multicomponent reactive transport equations are solved for times up to 100 years taking into account rapid fluctuations in the Columbia River stage resulting in pulse releases of U(VI) into the river. The peta-scale computer code PFLOTRAN developed under a DOE SciDAC-2 project is employed in the simulations and executed on ORNL's Cray XT5 supercomputer Jaguar. Labile U(VI) is represented in the model through surface complexation reactions and its nonlabile form through dissolution of metatorbernite used as a surrogate mineral. Initial conditions are constructed corresponding to the U(VI) plume already in place to avoid uncertainties associated with the lack of historical data for the waste stream. The cumulative U(VI) flux into the river is compared for cases of equilibrium and multirate sorption models and for no sorption. The sensitivity of the U(VI) flux into the river on the initial plume configuration is investigated. The presence of nonlabile U(VI) was found to be essential in explaining the longevity of the U(VI) plume and the prolonged high U(VI) concentrations at the site exceeding the EPA MCL for uranium.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Ad Hoc File Systems for High-Performance Computing

Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 “Challenges and Opportunities of User-Level File Systems for HPC” and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Physics-based seismic hazard analysis on petascale heterogeneous supercomputers, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

We have developed a highly scalable and efficient GPU-based finite-difference code (AWP) for earthquake simulation that implements high throughput, memory locality, communication reduction and communication/computation overlap and achieves linear scalability on Cray XK7 Titan at ORNL and NCSA's Blue Waters system. We simulate realistic 0-10 Hz earthquake ground motions relevant to building engineering design using high-performance AWP. Moreover, we show that AWP provides a speedup by a factor of 110 in key strain tensor calculations critical to probabilistic seismic hazard analysis (PSHA). These performance improvements to critical scientific application software, coupled with improved co-scheduling capabilities of our workflow-managed systems, make a statewide hazard model a goal reachable with existing supercomputers. The performance improvements of GPU-based AWP are expected to save millions of core-hours over the next few years as physics-based seismic hazard analysis is developed using heterogeneous petascale supercomputers.

Computer Science↗
DOE OSTI2022

Data Storage for HEP Experiments in the Era of High-Performance Computing

As particle physics experiments push their limits on both the energy and the intensity frontiers, the amount and complexity of the produced data are also expected to increase accordingly. With such large data volumes, next-generation efforts like the HL-LHC and DUNE will rely even more on both high-throughput (HTC) and high-performance (HPC) computing clusters. Full utilization of HPC resources requires scalable and efficient data-handling and I/O. For the last few decades, ROOT has been used by most HEP experiments to store data. However, other storage technologies like HDF5 may perform better in HPC environments. Initial explorations with HDF5 have begun using ATLAS, CMS and DUNE data; the DUNE experiment has also adopted HDF5 for its data-acquisition system. This paper presents the future outlook of the HEP computing and the role of HPC, and a summary of ongoing and future works to use HDF5 as a possible data storage technology for the HEP experiments to use in HPC environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

HipGISAXS : a high-performance computing code for simulating grazing-incidence X-ray scattering data

This article describes the development of a flexible grazing-incidence small-angle X-ray scattering (GISAXS) simulation code in the framework of the distorted wave Born approximation that effectively utilizes the parallel processing power provided by graphics processors and multicore processors. The code, entitled High-Performance GISAXS , computes the GISAXS image for any given superposition of user-defined custom shapes or morphologies in a material and for various grazing-incidence angles and sample orientations. These capabilities permit treatment of a wide range of possible sample structures, including multilayered polymer films and nanoparticles on top of or embedded in a substrate or polymer film layers. In cases where the material displays regions of significant refractive index contrast, an algorithm has been implemented to perform a slicing of the sample and compute the averaged refractive index profile to be used as the reference geometry of the unperturbed system. A number of case studies are presented, which demonstrate good agreement with the experimental data for a variety of polymer and hybrid polymer/nanoparticle composite materials. The parallelized simulation code is well suited for addressing the analysis efforts required by the increasing amounts of GISAXS data being produced by high-speed detectors and ultrafast light sources.

Chemistry↗