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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiphysics Design Optimization and Additive Manufacturing of Nuclear Components (Final CRADA Report - Executive Summary)

Westinghouse Electric Company (WEC) actively participated in the advancement of the nuclear fuel and reactor design space and requested the help of Oak Ridge National Laboratory (ORNL) in the creation of a new design tool set. This report details the creation of a collection of software tool sets that are linked together to collectively assist WEC design engineers in developing novel ideas outside the normal scope of traditional nuclear fuel and reactor design formulas. Specifically, Siemens HEEDS, a design space exploration and parametric optimization software, monitored and changed parameters in a collection of softwares to meet the team’s objective. The HEEDS parametric optimization method, SHERPA, was developed to control the Siemens NX CAD platform to adjust the native CAD of a hexahedral spacer grid. This new geometry can be used to execute a topological design optimization by the NX Topology software add-in. The resulting geometry is additively manufacturable. This topological optimization occurred twice—once on the spacer grid’s spring, and once on the dimple geometry. These new geometries were imported by Siemens’ STAR-CCM+, a multiphysics structural and fluid dynamic computational solver in which the spring geometry is deflected to match the rod insertion configuration. Along with the dimple geometry, this new deflected spring was used to complete a hydraulic assessment of a single-unit cell comprising one rod, one spring, and two dimples. The HEEDS SHERPA algorithm ranks the design based on the final mass of the unit cell and the hydraulic pressure drop performance. The ORNL team demonstrated the ability to use this software and provided engineering judgement to apply modern aerospace aerodynamic design. The effort has been focused on thinking outside the conventional design space and redesigning a spacer grid to perform beyond the WEC set objectives. Furthermore, the ORNL team also demonstrated that the HEEDS optimization routine can independently develop a design that meets the WEC design goals. Although these designs were at a low technology readiness level, their demonstration confirmed the team’s capability to create novel advanced nuclear concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

The Power and Energy Storage Systems Toolbox–PSTess (V1.0)

This document describes the Power and Energy Storage Systems Toolbox for MATLAB, abbreviated as PSTess. This computing package is a fork of the Power Systems Toolbox (PST). PST was originally developed at Rensselaer Polytechnic Institute (RPI) and later upgraded by Dr. Graham Rogers at Cherry Tree Scientific Software. While PSTess shares a common lineage with PST Version 3.0, it is a substantially different application. This document supplements the main PST manual by describing the features and models that are unique to PSTess. As the name implies, the main distinguishing characteristic of PSTess is its ability to model inverter-based energy storage systems (ESS). The model that enables this is called ess.m , and it serves the dual role of representing ESS operational constraints and the generator/converter interface. As in the WECC REGC_A model, the generator/converter interface is modeled as a controllable current source with the ability to modulate both real and reactive current. The model ess.m permits four-quadrant modulation, which allows it to represent a wide variety of inverter-based resources beyond energy storage when paired with an appropriate supplemental control model. Examples include utility-scale photovoltaic (PV) power plants, Type 4 wind plants, and static synchronous compensators (STATCOM). This capability is especially useful for modeling hybrid plants that combine energy storage with renewable resources or FACTS devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

QRCODE: Massively parallelized real-time time-dependent density functional theory for periodic systems

We present a new software module, QRCODE (Quantum Research for Calculating Optically Driven Excitations), for massively parallelized real-time time-dependent density functional theory (RT-TDDFT) calculations of periodic systems in the open-source Qbox software package. Our approach utilizes a custom implementation of a fast Fourier transformation scheme that significantly reduces inter-node message passing interface (MPI) communication of the major computational kernel and shows impressive scaling up to 16,344 CPU cores. In addition to improving computational performance, QRCODE contains a suite of various time propagators for accurate RT-TDDFT calculations. As benchmark applications of QRCODE, we calculate the current density and optical absorption spectra of hexagonal boron nitride (h-BN) and photo-driven reaction dynamics of the ozone-oxygen reaction. We also calculate the second and higher harmonic generation of monolayer and multi-layer boron nitride structures as examples of large material systems. Our optimized implementation of RT-TDDFT in QRCODE enables large-scale calculations of real-time electron dynamics of chemical and material systems with enhanced computational performance and impressive scaling across several thousand CPU cores.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Validation of Phasor-Domain Transmission and Distribution Co-simulation Against Electromagnetic Transient Simulation

The rapid deployment of renewable energy resources has led to the widespread use of power electronics in modern power systems. As these systems transition from being dominated by large synchronous machines to increasingly incorporating inverter-based resources (IBRs), traditional methods are becoming inadequate. Addressing this challenge, this paper introduces a scalable phasor-domain T\&D co-simulation framework based on open-source software. It focuses on the framework's validation against the PSCAD Electromagnetic Transient (EMT) analysis tool. The validation results demonstrate the framework's high-fidelity and a computational time speed-up of 60 to 100 times, marking a pioneering validation effort in T\&D co-simulation research.

Inverter-based resources, co-simulation, Electroma↗
DOE OSTI2024

Lamellar: A Rust-based Asynchronous Tasking and PGAS Runtime for High Performance Computing

Cybersecurity is one of the largest concerns in modern computing, impacting and dictating how governments, private corporations, and individuals interact with and live in an increasingly digital world. The NSA has recently released a memo [ 1] on “Software Memory Safety” where they highlight that both Microsoft and Google have stated around 70% of software vulnerabilities were due to memory safety issues. Although languages such as C and C++ provide freedom and flexibility with memory management, guaran- teeing safety falls mostly on the developer. The NSA recommends using “memory safe” languages whenever possible. In this paper we introduce Lamellar, an asynchronous tasking and PGAS HPC runtime written in Rust, one such "memory safe" language. We describe the entire Lamellar stack, from network interfaces to high- level abstractions such as distributed LamellarArrays and Active Messages. We conclude by showing comparable performance to legacy PGAS runtimes (e.g. OpenSHMEM) on a subset of the BALE kernel suite while maintaining strong memory safety principles.

HPC Software Systems, Rust Programming Language, P↗
DOE OSTI2020

Choppy Lite

The existing methodologies for gathering and computing zonal statistics from the combination of a raster image and a shapefile either require advanced programming knowledge in at least one language or the use of proprietary software such as ArcGIS. The software “Choppy Lite” offers several solutions for acquiring zonal statistics that can be used without proprietary software or programming knowledge. This software provides a Docker-based ecosystem, allowing users of any experience level to simply run a single line command in a terminal prompt independent of the operating system. Intermediate to advanced users can import the class provided by the software package into their own Python scripts.

Grant, JoshuaN.↗
DOE OSTI2022

Lighting application efficacy: A framework for holistically measuring lighting use in buildings

Lighting consumes significant energy in buildings, but current measurements of lighting efficiency are inadequate in capturing the spatial and temporal effectiveness of light. The most widely used lighting energy efficiency measure, luminous efficacy, quantifies the amount of light generated by individual light sources. While luminous efficacy provides manufacturers and designers with a useful metric for quantifying the energy efficiency of individual lighting devices, it does not fully address the use of lighting in architectural spaces. Instead, application efficacy, the relationship between the electrical power consumed by lighting hardware and the amount of light that contributes to the visual perception of building occupants, must be measured. Here, we propose a framework for calculating lighting application efficacy (LAE) based on the primary pathway of light in architectural settings: the generation and emission of light from a luminaire, the travel of the light throughout the space and into occupants’ eyes, and the process of visual perception. The LAE framework will include the development of computational methods for estimating the proportion of emitted light that is directed to task areas and areas corresponding to occupants’ visual fields using ray-tracing lighting design software. Future modeling and validation work will quantify energy efficiency of buildings.

Durmus, Dorukalp↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PHASM: A Toolkit for Creating AI Surrogate Models within Legacy Codebases

PHASM (“Parallel Hardware viA Surrogate Models”) is a software toolkit for creating AI-based surrogate models of scientific code. AI-based surrogate models are widely used for creating fast and inverse simulations. PHASM anticipates an additional future use case: adapting legacy code to modern hardware. While data centers are investing in heterogeneous hardware such as GPUs and FPGAs, many established scientific codebases remain unable to take advantage of the hardware’s higher parallelism without undergoing a costly rewrite. An alternative is to train a AI-based surrogate model to mimic computationally intensive functions in the code, and run the surrogate instead. PHASM formalizes a development lifecycle for such surrogate models, including discovering functions amenable to replacement with a surrogate model, predicting the resulting performance, identifying the function’s space of inputs and outputs, binding the model to the code, and managing model versions. A suite of software tools for facilitating these steps was written and validated against a set of model problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗
DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗
DOE OSTI2022

Thermal-Hydraulics Modeling and Simulations of Hot Pool Using the SAS-CFD Coupled Code

The main goal of this activity is to test the dynamic coupling of the SAS4A/SASSYS-1 (SAS) and CFD models, using a recently patched version of the SAS code intended to address an undocumented limitation that hindered the Versatile Test Reactor (VTR) simulation efforts in FY21. As described in previous VTR calculation reports, the undocumented limitation in SAS v5.4 does not allow the user to activate the CFD coupling option during restart calculations. Since the analysts were unaware of this limitation, prior SAS-CFD simulation results for the protected station blackout (PSBO) transient were erroneous. Root-cause analysis was performed to determine the cause of this undocumented limitation in SAS v5.4, the SAS software was updated in a new patch, and the SAS-CFD simulations were repeated with this patched software. The results of the SAS-CFD simulations documented in this report show that the software patch does address the cited issue, and that the patched software indeed supports the activation of the CFD coupling model in restart calculations. The SAS development team will determine the schedule for implementing the patch in an official software release. This report documents updated SAS-CFD simulations of the PSBO transient response in the VTR. The hot pool is modeled with the CFD code STAR-CCM+, which is coupled at the flow boundaries to the SAS model of the primary heat transport system. SAS computes the mass flow rate and temperature at each core subassembly outlet, the thermal insulation cavity bypass, and the IHX inlet windows. CFD in turn computes the absolute pressure and temperature at each of these boundaries. The SAS code will ignore the temperature data at flow boundaries where flow is directed into the hot pool, i.e., at the core subassembly outlets unless flow reversal occurs. Similarly, CFD will ignore temperature data at boundaries where the flow is directed out of the hot pool, i.e., at the IHX inlets except under flow reversal. The focus of this work is to ensure that the SAS software patch addresses the undocumented limitation described in prior VTR calculation reports, rather than the accurate assessment of thermal stratification in the VTR during protected transients. This motivates the development of a new, simplified CFD model with a coarser mesh to accelerate the testing process. The updated model, and simplifying assumptions, are documented in this report. In future work, the thermal stratification assessment should be performed in more detail. The simplified CFD model can be improved by performing grid convergence studies sensitivity studies of turbulence parameters (e.g., Prandtl number, turbulence production and dissipation parameters) on temperature distributions and thermal stratification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Fast and accurate metagenotyping of the human gut microbiome with GT-Pro

Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Here, using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗