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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Proxy Applications for Converged Workloads: DMC LDRD Initiative

Modern scientific applications are complicated and require coordination of several components. Proxy application driven software-hardware co-design plays a vital role in driving innovation among the developments of applications, software infrastructure and hardware architecture. Proxy applications are self-contained and simplified codes that are intended to model the performance-critical computations within applications. Applications executing on modern High Performance Computing (HPC) systems are susceptible to network congestion, insufficient memory bandwidth within and across compute nodes, and inadvertent loss of performance due to bugs and unoptimized programming models. Modern numerical simulations and machine learning models play a critical role in studying physical phenomenon under myriad uncertainties. Such applications often exhibit irregular computation and memory accesses at specific regions of the application code, which can contribute to various performance bottlenecks at scale. To mitigate such issues and prepare the next generation hardware for a variety of computation and data movement contingencies, a well-known practice is to consider "proxy" applications as representative motifs for various classes of scientific applications. While there is disagreement in the HPC community on the mechanisms of construction of the proxy applications, there is a strong consensus on their positive impact in co-design. Proxy Applications for Converged Workloads (PACER) is about facilitating software-hardware co-design through proxy applications with the goal of improving the performance of converged science workflows on heterogeneous systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Research Program for Vibration Control in Structures

Purpose of program to apply control theory to large space structures (LSS's) and design practical compensator for suppressing vibration. Program models LSS as distributed system. Control theory applied to produce compensator described by functional gains and transfer functions. Used for comparison of robustness of low- and high-order compensators that control surface vibrations of realistic wrap-rib antenna. Program written in FORTRAN for batch execution.

Mingori, D. L.↗
NASA NTRS2018

WFIRST Coronagraph Detector Trap Modeling Results and Improvements

The WFIRST coronagraph is being designed to detect and characterize mature exoplanets through the starlight reflected from their surfaces and atmospheres. The light incident on the detector from these distant exoplanets is estimated to be on the order of a few photons per pixel per hour. To measure such small signals, the project has baselined the CCD201 detector made by e2v, a low-noise and high-efficiency electron-multiplying charge-coupled device (EMCCD), and has instituted a rigorous test and modeling program to characterize the device prior to flight. An important consideration is detector performance degradation over the proposed mission lifetime due to radiation exposure in space. To quantify this estimated loss in performance, the project has built a detector trap model that takes into account detailed trap interactions at the sub-pixel level, including stochastic trap capture and release, and the deferment of charge into subsequent pixels during parallel and serial clocking of the pseudo-two-phase CCD201 device. This paper describes recent detector trap model improvements and modeling results.

Holland, Andrew↗
DOE OSTI2018

A Decentralized Bi-Level Fuzzy Two-Stage Decision Model for Flood Management

Flood, as a serious worldwide environment problem, can lead to detrimental economic losses and fatalities. Effective flood control is desired to mitigate the adverse impacts of flooding and the associated flood risk through development of cost-effective and efficient flood management decisions and policies. A bi-level fuzzy two-stage stochastic programming model, named BIFS model is developed in this study to provide decision support for economic analysis of flood management. The BIFS model is capable of not only addressing the sequential decision making issue involving the two-level decision makers, but also correcting the pre-regulated flood management decisions before the occurrence of a flood event in the two-stage environment. The probabilistic and non-probabilistic uncertainties expressed as probability density functions and fuzzy sets are quantitatively analyzed. The overall satisfaction solution is obtained for meeting the goals of the two-level decision makers by compromising, reflecting the tradeoffs among various decision makers in the two decision-making levels. The results of application of the BIFS model to a representative case study indicate informed decision strategies for flood management. Tradeoffs between economic objectives and uncertainty-averse attitudes of decision makers are quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Implementing Arbitrary/Common Concurrent Writes of CRCW PRAM

The Parallel Random Access Machines (PRAM) abstraction is the simplest and most elegant algorithmic model for the design and analysis of parallel algorithms. It consists of different models categorized based on the underlying memory access mode used, the most powerful of which is the Concurrent Read Concurrent Write (CRCW) model. A PRAM algorithm describes a series of rounds, each of which consists of a collection of operations that can be executed concurrently within the same time step. However, the lack of support for concurrent memory accesses and the prevalence of asynchronous programming models led to the belief that implementing CRCW PRAM algorithms is unattainable and prompted many to avoid this model except for theoretical studies of optimal performance.In this work, we study the arbitrary and common concurrent writes in the CRCW PRAM model and explore implementation challenges on general-purpose systems. Moreover, we examine current practices for implementing common/arbitrary concurrent writes and propose a new efficient lightweight and thread-safe method to implement concurrent writes through leveraging atomic instructions. To demonstrate the efficacy of our method, we developed OpenMP kernels for classical CRCW PRAM algorithms and provide experimental results and comparisons based on run time performance measured over the x86 multicore architecture. Our results show a performance speedup compared to current practices up to 4.5x across all our benchmarks.

Ghanim, Fady↗
NASA NTRS1992

Small Business Innovations (Helicopters)

The amount of engine power required for a helicopter to hover is an important, but difficult, consideration in helicopter design. The EHPIC program model produces converged, freely distorted wake geometries that generate accurate analysis of wake-induced downwash, allowing good predictions of rotor thrust and power requirements. Continuum Dynamics, Inc., the Small Business Innovation Research (SBIR) company that developed EHPIC, also produces RotorCRAFT, a program for analysis of aerodynamic loading of helicopter blades in forward flight. Both helicopter codes have been licensed to commercial manufacturers.

Source record↗
NASA NTRS1975

The use of a model of human thermoregulation during the Apollo and Skylab programs

A model of thermoregulation in man was used operationally during the Apollo and Skylab programs. The evolution of this description of thermoregulation is discussed as well as the supporting research programs that led to its growth in representational detail and operational accuracy. A discussion of the operational use of the model is presented as well as future applications of the model.

Waligora, J. M.↗
DOE OSTI2019

Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy

To address the uncertain renewable energy in the day-ahead optimal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to minimize the expected total costs. The uncertainties over the multiple stages are characterized by a scenario tree and the optimal dispatch scheme is cast as a decision tree which guarantees the flexibility to decide the reasonable outputs of generation and the adequate reserves accounting for different realizations of renewable energy. Most importantly, to deal with the “Curse of Dimensionality” of stochastic programming, stochastic dual dynamic programming (SDDP) is employed, which decomposes the original problem into several sub-problems according to the stages. Specifically, the SDDP algorithm performs forward pass and backward pass repeatedly until the convergence criterion is satisfied. At each iteration, the original problem is approximated by creating a linear piecewise function. Besides, an improved convergence criterion is adopted to narrow the optimization gaps. The results on the IEEE 118-bus system and real-life provincial power grid show the effectiveness of the proposed model and method.

economic dispatch↗
NASA NTRS1979

SCI model structure determination program (OSR) user's guide

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

Source record↗
NASA NTRS1993

Instrumentation and telemetry systems for free-flight drop model testing

This paper presents instrumentation and telemetry system techniques used in free-flight research drop model testing at the NASA Langley Research Center. The free-flight drop model test technique is used to conduct flight dynamics research of high performance aircraft using dynamically scaled models. The free-flight drop model flight testing supplements research using computer analysis and wind tunnel testing. The drop models are scaled to approximately 20 percent of the size of the actual aircraft. This paper presents an introduction to the Free-Flight Drop Model Program which is followed by a description of the current instrumentation and telemetry systems used at the NASA Langley Research Center, Plum Tree Test Site. The paper describes three telemetry downlinks used to acquire the data, video, and radar tracking information from the model. Also described are two telemetry uplinks, one used to fly the model employing a ground-based flight control computer and a second to activate commands for visual tracking and parachute recovery of the model. The paper concludes with a discussion of free-flight drop model instrumentation and telemetry system development currently in progress for future drop model projects at the NASA Langley Research Center.

Hyde, Charles R.↗
NASA NTRS2008

Cryogenic Pressure Control Modeling for Ellipsoidal Space Tanks in Reduced Gravity

A computational fluid dynamics (CFD) model is developed to simulate pressure control of an ellipsoidal-shaped liquid hydrogen tank under external heating in low gravity. Pressure control is provided by an axial jet thermodynamic vent system (TVS) centered within the vessel that injects cooler liquid into the tank, mixing the contents and reducing tank pressure. The two-phase cryogenic tank model considers liquid hydrogen in its own vapor with liquid density varying with temperature only and a fully compressible ullage. The axisymmetric model is developed using a custom version of the commercially available FLOW-3D software and simulates low gravity extrapolations of engineering checkout tests performed at Marshall Space Flight Center in 1999 in support of the Solar Thermal Upper Stage Technology Demonstrator (STUSTD) program. Model results illustrate that stable low gravity liquid-gas interfaces are maintained during all phases of the pressure control cycle. Steady and relatively smooth ullage pressurization rates are predicted. This work advances current low gravity CFD modeling capabilities for cryogenic pressure control and aids the development of a low cost CFD-based design process for space hardware.

Hedayat, Ali↗
DOE OSTI2023

Virtual Power Plants and Energy Justice

The Federal Energy Regulatory Commission's (FERC) Order 2222, issued in September 2020, removes barriers for distributed energy resources (DERs) to participate in wholesale energy markets by allowing DERs to aggregate and participate in wholesale markets as a single entity, known as a virtual power plant (VPP). VPPs can provide balancing, reliability, and resiliency grid services and can participate in capacity, energy and ancillary services markets. They can also increase customer energy access and lower electricity bills. This report focuses on VPP business models, including considerations of energy justice (EJ). Through an analysis of the VPP value chain, business models, programs, and pilots, several VPP applications are identified and grouped by their ability to have quantitatively measurable or monetized benefits. Benefits and barriers specific to underserved communities are outlined, and VPP programs with an intentional focus on underserved communities are compared to those without such a focus.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Simultaneous Process and Heat Exchanger Network Synthesis Using a Discrete Temperature Grid

We present mixed-integer linear programming models for utility targeting and heat exchanger network synthesis that account for variable stream temperatures and flow rates as well as unclassified streams and thus can be combined with process synthesis optimization models. By projecting temperature information onto a fixed discrete temperature grid, the model remains linear even under variable temperatures and flow rates, leading to a more tractable optimization model. The proposed model is well suited to address problems in the area of process intensification where the close interaction between different phenomena naturally leads to process synthesis problems with unclassified process streams. Several extensions are presented, including nonisothermal mixing as well as phase changes. Moreover, we show how a nonuniform grid can be adopted to reduce the complexity of the model for medium and large-size problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Optimizing the Performance of Sparse-Matrix Vector Products on Next-Generation Processors

Matrix-vector products are ubiquitous in high-performance scientific applications and have a growing set of occurrences in advanced data analysis activities. Achieving high performance for these kernels is therefore paramount, in part, because these operations can consume vast amounts of application execution time. In this report we document the development of several sparse-matrix vector product kernel implementations using a variety of programming models and approaches. Each kernel is run on a broad set of matrices selected to demonstrate the wide variety of matrix structure and sparsity that is possible with a single, generic kernel. For benchmarking and performance analysis, we utilize leading computing architectures for the NNSA/ASC program including Intel's Knights Landing processor and IBM's POWER8.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

2016 High Performance Computing Annual Report

Sandia National Laboratories’ California site is celebrating its 60th anniversary (1956 to 2016), and high performance computing has been a key enabler for its scientists and engineers throughout much of its history. Since its founding, Sandia California has helped pioneer the use of HPC platforms including hosting Sandia’s first Cray-1 supercomputer in the 1970s and supporting development of scalable cluster computing platforms to create a new paradigm for cost-effective supercomputing in the 1990s. Recent decades of investment in creation of scalable application frameworks for scientific computing have also enabled new generations of modeling and simulation codes. These resources have facilitated computational analysis of complex phenomena in diverse applications spanning national defense, energy, and homeland security. Today, Sandia California researchers work with partners in academia, industry, and national labs to evolve the state-of-the-art in HPC, modeling, and data analysis (including foundational capabilities for exascale computing platforms) and apply them in transformational ways. Research efforts include mitigating the effects of silent hardware failures that can jeopardize the results of large-scale computations, developing exascale-capable asynchronous task-parallel programming models and runtime systems, formulating new techniques to better explore and analyze extreme-scale data sets, and increasing our understanding of materials and chemical sciences which has applications spanning nuclear weapons stockpile stewardship to more efficient automobile engines. The following section highlights some of these research and applications projects and further illustrates the breadth of our HPC capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

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

Lessons from Artist in Residence Program Design and Impact

As a national laboratory, Pacific Northwest National Laboratory (PNNL) seeks to be on the leading edge of public interest research and deliver innovation, a core value of the institution. Through the support of the U.S. Department of Energy’s Water Power Technologies Office, PNNL has convened a workshop entitled Advancing Energy Futures through Art in Seattle, Washington, on August 19th and 20th, 2024. An explicit focus on Artist in Residence (AiR) programs initiates the discussion, as this is one of the few clear places where public interest research, artistic engagement, and energy futures have aligned. The workshop proposes to hear from program managers of successful AiR programs, scientists, and artists who work in the energy futures space, to discuss AiR program models and successful modalities for art and storytelling. This paper is provided as background context for workshop participants.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Verifying MCNP Models of the TEX High 240 Plutonium Benchmark

Computational modeling programs are invaluable tools that allow us to understand systems, safely develop new processes, and make reliable predictions about future designs. However, the effectiveness of these codes is limited by the degree to which their parameters match the real world. In the field of nuclear engineering, cross section data is one of these vital parameters. Accurate cross section data on important fissile and fissionable isotopes promotes the design of safer and more efficient fabrication, transportation, storage, and stockpiling of nuclear fuel. Unfortunately, there are knowledge gaps in data on key isotopes. In 2011, a multinational meeting hosted by the US Department of Energy Nuclear Criticality Safety Program ranked the priority of certain cross section data needs. In response, Lawrence Livermore National Lab (LLNL) designed the Thermal and Epithermal eXperiment (TEX) series of benchmark experiments. Benchmark experiments are used to validate current cross section data. They validate data by comparing the results of an actual experiment to the predicted results from a computational model. The data a benchmark applies to depends on the isotope and energy range the experiment’s neutron multiplication factor ( k eff ) is most sensitive to. The development and testing of the TEX High 240 Plutonium Benchmark will help validate 240 Pu cross section data. The configuration and materials of this benchmark are designed to be most sensitive to 240 Pu's intermediate energy range (from 0.625 ev to 100 keV ). MCNP® models of the assembly have been developed by LLNL and the results have been written in the final design report. In order for the discrepancies between benchmark models and experiments to be attributed to cross section inaccuracies, the accuracy of the models needs to be verified. The goal of this project is to verify of the results of LLNL's modeling by creating a new set of MCNP models and comparing the results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗