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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Processing unit performance projection using dynamic hardware behaviors

Methods for estimating accelerator performance for dynamic hardware behaviors are disclosed. Computer program code to be executed on a first processing unit is received, and an execution of the computer code on the first processing unit is monitored to determine a plurality of performance characteristics. A plurality of dynamic hardware behaviors is determined by applying a clustering algorithm to the performance characteristics, and an equivalent accelerator portion of computer code to be executed on a second processing unit is generated by translating a set of instructions in a first portion of computer code corresponding to a first one of the plurality of dynamic hardware behaviors to an equivalent set of instructions to be executed on the second processing unit. An estimated measure of performance for executing the equivalent accelerator portion on the second processing unit is determined for the first one of the plurality of dynamic hardware behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud

Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

Samuel, David↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2021

Additive Manufacturing Case Study

Geothermal technologies include an extremely wide range of products required for well construction, completion, production, intervention and surface energy conversion activities. Many of these products are geometrically complex, require multi-step and highly specialized fabrication processes, and are expensive due to the low production numbers typically associated with the geothermal market. These challenges along with the high temperature demands of the geothermal environment have also hindered the adoption of many tools routinely used in the oil & gas industry.Recent advancements in Additive Manufacturing (AM) materials of construction, build volumes and part quality have transitioned the technology from primarily cosmetic prototyping applications to the point where AM can be used to make production parts, even for the most demanding applications. These improved AM capabilities along with the inherent ability of AM to produce complex parts and, in some cases, geometries that cannot be manufactured using conventional casting, machining and joining fabrication approaches motivate an exploration of its potential to positively impact geothermal well construction and operations technologies.Sandia National Labs collaborated with Oak Ridge National Laboratory on a case study examining additive manufacturing opportunities for Geothermal applications. The study focused on designing components with improved performance characteristics that cannot be fabricated conventionally. A rotor for a downhole motor was chosen based on the potential for improving its rotational dynamics. Topology optimization was used as a design method to reduce the rotational inertia of the part while preserving sufficient rotational stiffness to transmit the torque required for the drilling application. The optimization resulted in a nearly 50% reduction in polar moment of inertia while maintaining other desired performance characteristics. The design developed using the topology optimization approach was fabricated using additive manufacturing and cannot be fabricated conventionally. This paper will discuss the design approach, performance improvements and manufacturing methods used to produce the part.

Polsky, Yarom↗
DOE OSTI2020

Performance and wake flow characterization of a 1:8.7-scale reference USDOE MHKF1 hydrokinetic turbine to establish a verification and validation test database

As hydrokinetic turbine technologies continue to advance towards commercialization, public datasets on the performance characteristics for these devices and their flow field effects are invaluable to advance our understanding of these technologies and to validate analytical and numerical models. The Applied Research Laboratory at The Pennsylvania State University (ARL Penn State) collaborated with Sandia National Laboratories and the University of California at Davis to design, fabricate (at a 1:8.7 scale), and experimentally test a novel hydrokinetic turbine rotor design to provide an open platform and dataset for further study and development. The water tunnel test of this three-bladed, horizontal-axis rotor recorded power production, blade loading, the near-wake flow, cavitation effects, and noise generation. These state-of-the-art measurements demonstrate much of the complex physics associated with the flow through an unducted, horizontal-axis turbine, and they elucidate the performance characteristics and flow field effects at an unprecedented fidelity, accuracy and resolution. Measurements of powering coefficients (power, torque and thrust) as a function of tip-speed-ratio were performed. The dataset also includes unsteady measurements of driveshaft loading, blade strain, tower pressures, and radiated noise. Detailed flow mapping using laser Doppler velocimetry, and planar and stereo particle image velocimetry includes measurements of mean velocity and Reynolds stresses. Although the wake measurements are limited to less than half a diameter, they reveal the complex flow patterns in the near-wake structure of the rotor. The full database, available at the United States Department of Energy’s marine and hydrokinetic data repository, includes tunnel and model Computer Aided Design geometry files and inflow data sufficient for a “Model-the-Test” computational Verification and Validation study.

13 HYDRO ENERGY↗
DOE OSTI2023

Additive Manufacturing Case Study Test Report

Sandia National Labs collaborated with Oak Ridge National Laboratory on a case study examining additive manufacturing opportunities for Geothermal applications. The study focused on designing components with improved performance characteristics that cannot be fabricated conventionally. A rotor for a downhole motor was chosen based on the potential for improving its rotational dynamics. Topology optimization was used as a design method to reduce the rotational inertia of the part while preserving sufficient rotational stiffness to transmit the torque required for the drilling application. The optimization resulted in a nearly 50% reduction in polar moment of inertia while maintaining other desired performance characteristics. The design developed using the topology optimization approach was fabricated using additive manufacturing and cannot be fabricated conventionally.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Frontier Job-Centric Telemetry Dataset

Comprehensive analysis of high-performance computing (HPC) systems requires linking workload execution to system behavior. This kind of analysis is vital for diagnosing performance issues, managing capacity, detecting anomalous workloads, and understanding how applications interact with system hardware. This job-centric telemetry dataset unifies scheduler job records with node-level measurements, enabling direct association between workloads and their corresponding power, thermal, and performance characteristics. It contains sanitized, scheduler related metadata for 152,400 individual jobs that ran on the Frontier supercomputer and ended on selected days throughout 2024 and 2025, a subpopulation of ~6.8% of the total number of allocated jobs with non-zero run time on the system over that same period. Each is linked with files that contain telemetry time series records of the power utilization and temperature behavior of its allocated nodes and their processors during the run time of the job. Where available, a portion of the job files also contain network performance time series. Jobs are sampled from select days that reflect normal levels of user activity and possess job size distributions with large numbers of leadership class jobs (>20% of Frontier nodes). Jobs in this dataset attempt to best represent successful user workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Access Patterns and Performance Behaviors of Multi-layer Supercomputer I/O Subsystems under Production Load

Scientific computing workloads at HPC facilities have been shifting from traditional numerical simulations to AI/ML applications for training and inference while processing and producing ever-increasing amounts of scientific data. To address the growing need for increased storage capacity, lower access latency, and higher bandwidth, emerging technologies such as non-volatile memory are integrated into supercomputer I/O subsystems. With these emerging trends, we need a better understanding of the multilayer supercomputer I/O systems and ways to use these subsystems efficiently. In this work, we study the I/O access patterns and performance characteristics of two representative supercomputer I/O subsystems. Through an extensive analysis of year-long I/O logs on each system, we report new observations in I/O reads and writes, unbalanced use of storage system layers, and new trends in user behaviors at the HPC I/O middleware stack.

Bez, JL↗
DOE OSTI2023

An Appraisal of the Performance and Characteristics of Summary Rays Calculated for the SALSA3D Traveltime Dataset

The SALSA3D tomographic model provides a crucial community resource for improving the quality (in terms of both accuracy and precision) of predictions of the traveltimes of seismic waves and therefore improving our ability to locate anthropogenic or natural seismic events. Constructing the requisite tomographic model requires addressing the challenges implied by a massive and growing dataset of traveltime measurements. This study explores one approach to tackle this challenge: the use of summary rays, which average traveltime measurements from sources within evenly spaced cells, thereby eliminating redundant data.

58 GEOSCIENCES↗
DOE OSTI2021

Advanced Multi-Tube Mixer Combustion for 65% Efficiency (Final Report)

This project targeted advanced low NOx combustion for advanced gas turbines capable of 65%, or greater, efficiency in combined cycle application. This technology advancement has further potential to benefit gas turbines used in coal based IGCC applications with pre-combustion carbon capture and hydrogen as the resulting fuel. The program developed and synthesized the most advanced combustion system capable of achieving low NOX emissions up to turbine inlet temperatures of 3100F while also supporting the load-following needs of a modern grid. The combustion system contributes to the overall gas turbine efficiency goal by setting the maximum cycle temperature achievable for a given NOX level and by minimizing the through-combustor air flow pressure drop. Focus areas for this project targeted maximizing the turbine inlet temperature entitlement, as constrained by emissions considerations. The design also minimized parasitic air flow pressure drop by using advanced cooling techniques and performance materials selections and by minimizing hot surface area. These two technology objectives (maximum, emissions-compliant cycle temperature and minimum air flow pressure drop) were integrated into a prototype design. The primarily analytical project sought to identify the most promising technologies to meet these objectives. Additional critical “jugular” data were obtained from multi-tube mixer tests to realize the potential of leveraging “micro flames” for minimizing overall hot surface area. This data was used, in conjunction with an understanding of advanced material and cooling design technologies, to analytically develop multiple design concepts. Phase I focused on in-depth engineering analysis and design, with minimal supporting laboratory testing to enable a selection of the top three combustion architectures for achieving these overall objectives. Phase II of the program developed the selected design through a combination of sub-scale testing and analytical efforts. Early tests included a cold-flow cascade to establish aerodynamic performance characteristics and a sub-scale fired test at GE Global Research in Niskayuna, NY, to establish cooling and heat transfer characteristics in conjunction with combustion performance. The data from these tests validated the analytical models to ultimately design a full-scale, test article to evaluate at prototypical pressure and temperature conditions at GE Gas Power’s Gas Turbine Technology Laboratory in Greenville, SC. GE Gas Power also developed, tested, and recommended a suitable seal design to be applied to the unique features of the combustor. To assess the technology challenges from prospective future production of the combustor from a ceramic matrix composite material, screening tests of Environmental Barrier Coatings were completed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Defining Detection Limits for Continuous Monitoring Systems for Methane Emissions at Oil and Gas Facilities

Networks of fixed-point continuous monitoring systems are becoming widely used in the detection and quantification of methane emissions from oil and gas facilities in the United States. Regulatory agencies and operators are developing performance metrics for these systems, such as minimum detection limits. Performance characteristics, such as minimum detection limits, would ideally be expressed in emission rate units; however, performance parameters such as detection limits for a continuous monitoring system (CMS) will depend on meteorological conditions, the characteristics of emissions at the site where the CMS is deployed, the positioning of CMS devices in relation to the emission sources, and the amount of time allowed for the CMS to detect an emission source. This means that certifying the performance of a CMS will require test protocols with well-defined emission rates and durations; initial protocols are now being used in field tests. Field testing results will vary, however, depending on meteorological conditions and the time allowed for detection. This work demonstrates methods for evaluating CMS performance characteristics using dispersion modeling and defines an approach for normalizing test results to standard meteorological conditions using dispersion modeling.

Chen, Qining (ORCID:0000000316908091)↗
DOE OSTI2023

Evaluating Awkward Arrays, uproot, and coffea as a query platform for High Energy Physics Data

Query languages for High Energy Physics (HEP) are an ever present topic within the field. A query language that can efficiently represent the nested data structures that encode the statistical and physical meaning of HEP data will help analysts by ensuring their code is more clear and pertinent. As the result of a multi-year effort to develop an in-memory columnar representation of high energy physics data, the NumPy, Awkward Array, and uproot Python packages present a mature and efficient interface to HEP data. Atop that base, the coffea package adds functionality to launch queries at scale, manage and apply experiment-specific transformations to data, and present a rich object-oriented columnar data representation to the analyst. Recently, a set of Analysis Description Language (ADL) benchmarks has been established to compare HEP queries in multiple languages and frameworks. In this paper we present these benchmark queries implemented within the coffea framework and discuss their readability and performance characteristics. We find that the columnar queries perform as well or better than the implementations given in previous studies.

Gray, L.↗
DOE OSTI2020

The Kokkos Ecosystem [Brief]

In 2016/2017, the field of High-Performance Computing (HPC) entered a new era driven by fundamental physics challenges to produce ever more energy and cost-efficient processors. Since the convergence on the Message-Passing Interface (MPI) standard in the mid-1990s, application developers enjoyed a seemingly static view of the underlying machine — that of a distributed collection of homogeneous nodes executing in collaboration. However, after almost two decades of dominance, the sole use of MPI to derive parallelism acted as a limiter to improved future performance. While MPI is widely expected to continue to function as the basic mechanism for communication between compute nodes for the immediate future, additional parallelism is required on the computing node itself if high performance and efficiency goals are to be realized. When reviewing the architectures of the top HPC systems today, the change in paradigm is clear: the compute nodes of the leading machines in the world are either powered by many-core chips with a few dozen cores each, or use heterogeneous designs, where traditional CPUs marshal work to massively parallel compute accelerators which has as many as 200,000 processing threads in flight simultaneously. Complicating matters further for application developers, each processor vendor has its own preferred way of writing code for their architecture.The Kokkos EcoSystem was released by Sandia in 2017 to address this new era in HPC system design by providing a vendor independent performance portable programming system for scientific, engineering, and mathematical software applications written in the C++ programming language. Using Kokkos, application developers can be more productive because they will not have to create and maintain separate versions of their software for each architecture, nor will they have to be experts in each architecture's peculiar requirements. Instead, they will have a single method of programming for the diverse set of modern HPC architectures. While Kokkos started in 2011 as a programming model only, it soon became clear that complex applications needed more. It is also critical to have a portable mathematical functions and developers need tools to debug their applications, gain insight into the performance characteristics of their codes and tune algorithm performance parameters through automated processes. The Kokkos EcoSystem addresses those needs through its three main components: the Kokkos Core programming model, the Kokkos Kernels math library, and the Kokkos Tools project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cost and Performance Estimates for State-of-the-Art and Advanced 1×1 H-Class Natural Gas-Fired Power Plants

As an extension of NETL's Fossil Energy Baseline for Electricity Generating Units Volume 1: Coal and Natural Gas to Electricity (FEB Rev 4a, this study develops cost and performance estimates for analogous NGCC cases using a state-of-the-art 2023 vintage H-Class CT in a 1×1 configuration, where a single combustion turbine and heat recovery steam generator are coupled to a single steam turbine on a common shaft. These 1×1 H-Class cases are used to develop cost and performance estimates of X-Class 1×1 NGCC cases with advanced performance characteristics, analogous to NETL’s cost and performance projections report.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Electromagnetic device having layered magnetic material components and methods for making same

A micro fabricated electromagnetic device and method for fabricating its component structures, the device having a layered magnetic core of a potentially unlimited number of alternating insulating and magnetic layers depending upon application, physical property and performance characteristic requirements for the device. Methods for fabricating the high performing device permit cost effective, high production rates of the device and its component structures without any degradation in device performance resulting from component layering.

Mehrotra, Vivek↗
DOE OSTI2020

Benchmark for Fuel Shuffling and Depletion for Pebble-Bed Reactors

Pebble bed reactors have specific operational characteristics when their fuel-cycle and fueling operations are considered. They are specifically distinguished by other type of nuclear reactor designs by their online fuel recycling scheme, where the fuel elements that have not yet reached discharge burnup can be reloaded and recycled continuously during normal operation. The fuel in a pebble bed reactor is not stationary and stochastically moves through the core once or several times during its lifetime, which allows them to operate without requiring a large excess reactivity hold for the burnup. However, this characteristic of pebble bed reactors introduces challenges in simulation, as each pebble can take many different trajectories through the core, its composition depends on the details of the irradiation history that is unique to its aggregated path through the core. For predicting the safety performance characteristics, such as source term, maximum fuel temperatures and fuel failure rates, etc., it is important to accurately incorporate the movement of pebbles through the core during their lifetime in a multi-physics simulation together with other phenomena. The equilibrium core analysis for pebble bed reactors are performed with multi-physics tools including fuel depletion in a multi pass reload coupled to the fuel movement. Currently, there are only a few legacy multi-physics simulation tools that can implement the pebble flow characteristics and perform equilibrium core analysis for pebble bed reactors. However, there are development efforts on-going under Department of Energy's Nuclear Energy Advanced Modelling and Simulation program and also in private industry for including these capabilities into their modelling and simulation tools. Any new development in the modelling and simulation tools needs to be validated by using tools such as experiments, analytical solutions or code-to-code benchmarks. In this work, a code-to-code benchmark for the equilibrium core analysis capability of pebble bed reactors was developed. Multiple cases were identified to capture different fuel cycle strategies that can be used in PBRs. The results of each case are presented in terms of overall equilibrium core characteristics: the discharge burnup; spatial burnup distribution; spatial isotopic distributions; axial and radial neutron flux distributions and power history of fuel elements per pass through the core for both a prototypical pebble bed High Temperature Gas-cooled Reactor and a prototypical pebble bed Fluoride-salt cooled High temperature Reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗