Connected and Autonomous Vehicle Cohort Speed Control Optimization via Neuroevolution
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42 ENGINEERING↗
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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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Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks
Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.
97 MATHEMATICS AND COMPUTING↗
Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage
The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.
97 MATHEMATICS AND COMPUTING↗
Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022
Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.
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Collaborative Defense of Transmission and Distribution Protection and Control Devices Against Cyber Attacks (CODEF) DE-OE0000674. ABB Inc. Final Scientific/Technical Report
This report summarizes the activities conducted under the DOE-OE funded project DEOE0000674, where ABB Inc. (ABB), in collaboration with University of Illinois at Urbana-Champaign (UIUC), Bonneville Power Administration (BPA), and Ameren-Illinois (Ameren-IL) pursued the development of a system of collaborative defense of electrical substation’s intelligent electronic devices against cyber-attacks (CODEF). An electrical substation with CODEF features will be more capable of mitigating cyber-attacks especially those that seek to control switching devices. It leverages the security extensions of IEC 61850 to empower existing devices to collaborate in identifying and blocking malicious intents to trip circuit breakers, mis-coordinate devices settings, even though the commands and the measurements comply with correct syntax. The CODEF functions utilize the physics of electromagnetic systems, electric power engineering principles, and computer science to bring more in depth cyber defense closer to the protected substation devices.
97 MATHEMATICS AND COMPUTING↗
Embedded Model Error Representation for Bayesian Model Calibration
Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy, is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters,within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors’ contributions to predictive uncertainty. The framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Embedded Model Error Representation for Bayesian Model Calibration
Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy,is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters, within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors' contributions to predictive uncertainty. As a result, the framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem
97 MATHEMATICS AND COMPUTING↗
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.
97 MATHEMATICS AND COMPUTING↗
Spectral Advanced Radiological Computer System Applications
Explore the source record for details and available documents.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Spectral Advanced Radiological Computer System Applications
Explore the source record for details and available documents.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists
Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.
Computer Science↗
Biological and Environmental Research Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Biological and Environmental Research, March 28-31, 2016, Rockville, Maryland
Understanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOE began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.
54 ENVIRONMENTAL SCIENCES↗
Learning to Scale the Summit: AI for Science on a Leadership Supercomputer
The Summit system at Oak Ridge National Lab-oratory (ORNL) has been the world's top AI for science su-percomputer for several years, ranked world's fastest computer at its 2018 launch and currently top system in the US and #2 on the TOP5OO list. Summit's purposeful design to handle both conventional modeling and simulation science and emerging AI workloads has made it a leading destination for AI-powered computational science. We report here on AI for science usage on Summit near the midpoint of its lifespan. We review AI usage across the many science projects that have used Summit. We then examine in detail a set of applications scaling AI to full system as well as projects implementing AI-coordinated science discovery workflows on Summit. Finally, we offer some observations regarding the future of advancing scientific knowledge and understanding via AI, especially in the context of leadership-class scientific computing.
Joubert, Wayne↗
Nuclear Science User Facilities High Performance Computing: Provide a Science Gateway for HPC Users
Idaho National Laboratory (INL), supported by the Department of Energy Office of Nuclear Energy (DOE-NE) through the Nuclear Science User Facilities (NSUF), provides direct access to the Barracuda Virtual Reactor and 18 Multiphysics Object-Oriented Simulation Environment (MOOSE) applications via a web-based science gateway developed using Open Ondemand on the INL high performance computing (HPC) systems. This gateway features the computational tools of the Nuclear Computational Resource Center (NCRC) and the computing resources of the INL high performance computing systems. These computational tools are a key foundation of collaboration and innovation in nuclear energy systems research. High performance computing resources and INL staff directly support the mission and objectives of DOE-NE. The Barracuda Virtual Reactor was the first science gateway deployed in Jan 2021 to support NSUF users. In July 2021, the science gateway was expanded to support access to NCRC codes for use across all supported INL HPC systems. The HPC science gateway currently supports 20 total applications. The gateway also includes access to training resources specific to NCRC tools.
99 GENERAL AND MISCELLANEOUS↗
An Introduction to the E3SM Special Collection: Goals, Science Drivers, Development, and Analysis
Not Available
54 ENVIRONMENTAL SCIENCES↗
Ensemble Simulations on Leadership Computing Systems
Scientific productivity can be enhanced through workflow management tools, relieving large High Performance Computing (HPC) system users from the tedious tasks of scheduling and designing the complex computational execution of scientific applications. This paper presents a study on the usage of ensemble workflow tools to accelerate science using the Summit and Frontier supercomputing systems. The research aims to connect science domain simulations using Oak Ridge Leadership Computing Facility (OLCF) supercomputing platforms with ensemble workflow methods in order to accelerate HPC-enabled discovery and boost scientific impact. We present the coupling, porting and optimization of Radical-Cybertools on three applications: Chroma, NAMD and LAMMPS. The tools augment traditional HPC monolithic runs with a pilot scheduler. Lessons-learned are discussed for physics, biology and materials science applications. We discuss intrinsic limitations of coupling and porting ensemble workflow tools to applications that run on large HPC systems. The origins of technical challenges and their solutions developed during the implementation process are discussed. Data management strategies, OLCF’s policies for ensembles, and natively supported workflow tools are also summarized.
Georgiadou, Antigoni [ORNL] (ORCID:000000020977631↗
The screening effects of the screened exchange hybrid functional in surface systems: A case study on the CO/Pt(111) problem
The screened exchange (sX) hybrid functional has been widely used in computational material science. Although it has widely been studied in bulk systems, less is known about its functional behavior in surface systems which are crucial to many technologies such as materials synthesis and nano-electronic devices. Assessing the screening dependent functional behaviors in the surface systems is therefore important for its application in such systems. In this work, we investigate the screening effects of the sX in CO adsorption on Pt(111) surface. The differences between the sX and Heyd-Scuseria-Ernzerhof (HSE06) hybrid functionals, and the effects of screening parameters are studied. The screening has two effects: first, the HOMO-LUMO gap is screening dependent. This affects the site preference most significantly. In this work, atop adsorption of CO/Pt(111) is predicted by the hybrid functionals with screened exchange potential. The sX(1.44) gives the largest HOMO-LUMO gap for the isolated CO molecule. The adsorption energy difference between the atop and fcc site is also the largest by the sX(1.44) which is explained by the reduced metal d states to the CO 2π* state back-donation, with stronger effect for the fcc adsorption than for the atop adsorption; second, the adsorption energy is screening dependent. This can be seen by comparing the sX(2.38) and HSE06 which have different screening strengths. They show similar surface band structures for the CO adsorption but different adsorption energies, which is explained by the stronger CO 5σ state to the metal d states donation or the effectively screened Pauli repulsion. This work underlines the screening strength as a main difference between sX and HSE06, as well as an important hybrid functional parameter for surface calculation.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
ASC/NGC Gitlab Tutorial
This document summarizes version control systems and the source code it manages.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗