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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 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Performance Impact and Trade-Offs for Tuning Key Architectural Parameters on CPU+GPU Systems

In this work, we performed an initial design space exploration of an accelerated processing unit (APU)—a hybrid CPU+GPU architecture that integrates both compute units (CUs) and memory into a unified system. This integration aims to reduce data movement, enhance memory locality, and improve energy efficiency by enabling the CPU and GPU to share memory directly. This effort focused on the interplay of key design components—cache line size, the number of CUs, and main memory technology—and the trade-offs of each configuration were analyzed. This paper highlights the various configurations’ impact on memory accesses, data reuse, and power utilization. The results provide valuable insights that can be leveraged to optimize APU architectures for high-performance and energy-efficient computing and thus create a balanced architecture. This optimization can be achieved by adopting dynamic cache management, runtime CU scaling, and advanced memory integration, highlighting the potential of APUs to address critical challenges in compute, data movement, and memory power consumption.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗

DOE OSTI2012

Current status of the advanced high temperature reactor

The Advanced High Temperature Reactor (AHTR) is a design concept for a central station type [1500 MW(e)] Fluoride salt-cooled High-temperature Reactor (FHR) that is currently under development by Oak Ridge National Laboratory for the U. S. Dept. of Energy, Office of Nuclear Energy's Advanced Reactor Concepts program. FHRs, by definition, feature low-pressure liquid fluoride salt cooling, coated-particle fuel, a high-temperature power cycle, and fully passive decay heat rejection. The overall goal of the AHTR development program is to demonstrate the technical feasibility of FHRs as low-cost, large-size power producers while maintaining full passive safety. The AHTR design option exploration is a multidisciplinary design effort that combines core neutronic and fuel configuration evaluation with structural, thermal, and hydraulic analysis to produce a reactor and vessel concept and place it within a power generation station. The AHTR design remains at the notional level of maturity, as key technologies require further development and a logically complete integrated design has not been finalized. The present design space exploration, however, indicates that reasonable options exist for the AHTR core, primary heat transport path, and fuel cycle provided that materials and systems technologies develop as anticipated. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2005

Innovative Robot Archetypes for In-Space Construction and Maintenance

The space environment presents unique challenges and opportunities in the assembly, inspection and maintenance of orbital and transit spaceflight systems. While conventional Extra-Vehicular Activity (EVA) technology, out of necessity, addresses each of the challenges, relatively few of the opportunities have been exploited due to crew safety and reliability considerations. Extra-Vehicular Robotics (EVR) is one of the least-explored design spaces but offers many exciting innovations transcending the crane-like Space Shuttle and International Space Station Remote Manipulator System (RMS) robots used for berthing, coarse positioning and stabilization. Microgravity environments can support new robotic archetypes with locomotion and manipulation capabilities analogous to undersea creatures. Such diversification could enable the next generation of space science platforms and vehicles that are too large and fragile to launch and deploy as self-contained payloads. Sinuous manipulators for minimally invasive inspection and repair in confined spaces, soft-stepping climbers with expansive leg reach envelopes and free-flying nanosatellite cameras can access EVA worksites generally not accessible to humans in spacesuits. These and other novel robotic archetypes are presented along with functionality concepts

Rehnmark, Fredrik↗

NASA NTRS2012

Environmental Controls and Life Support System Design for a Space Exploration Vehicle

Engineers at Johnson Space Center (JSC) are developing an Environmental Control and Life Support System (ECLSS) design for the Space Exploration Vehicle (SEV). The SEV will aid to expand the human exploration envelope for Geostationary Transfer Orbit (GEO), Near Earth Object (NEO), or planetary missions by using pressurized surface exploration vehicles. The SEV, formerly known as the Lunar Electric Rover (LER), will be an evolutionary design starting as a ground test prototype where technologies for various systems will be tested and evolve into a flight vehicle. This paper will discuss the current SEV ECLSS design, any work contributed toward the development of the ECLSS design, and the plan to advance the ECLSS design based on the SEV vehicle and system needs.

Stambaugh, Imelda C.↗

DOE OSTI2022

Parallel simulated annealing with embedded machine learning and multifidelity models for reactor core design

This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multi-constrained combinatorial optimization with the aim of embedding multi-fidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multi-fidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multi-fidelity optimization methods, called ML-PSA, was demonstrated on a continuous multi-fidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multi-fidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Scaling Graph Community Detection on the Tilera Many-core Architecture

In an era when power constraints and data movement are proving to be significant barriers for the application of high-end computing, the Tilera many-core architecture offers a low-power platform exhibiting many important characteristics of future systems, including a large number of simple cores, a sophisticated network-on-chip, and fine-grained control over memory and caching policies. While this emerging architecture has been previously studied for structured compute-intensive kernels, benchmarking the platform for data-bound, irregular applications present significant challenges that have remained unexplored. Community detection is an advanced prototypical graph-theoretic operation with applications in numerous scientific domains including life sciences, cyber security, and power systems. In this work, we explore multiple design strategies toward developing a scalable tool for community detection on the Tilera platform. Using several memory layout and work scheduling techniques we demonstrate speedups of up to 46x on 36 cores of the Tilera TileGX36 platform over the best serial implementation, and also show results that have comparable quality and performance to mainstream x86 platforms. To the best of our knowledge this is the first work addressing graph algorithms on the Tilera platform. This study demonstrates that through careful design space exploration, low-power many-core platforms like Tilera can be effectively exploited for graph algorithms that that embody all the essential characteristics of an irregular application.

community detection↗

DOE OSTI2022

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗

DOE OSTI2023

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Rao, Nageswara↗

NASA NTRS2010

Environmental Controls and Life Support System (ECLSS) Design for a Space Exploration Vehicle (SEV)

Stambaugh, Imelda↗

NASA NTRS2018

Integrated Optimization of Mars Hybrid Solar-Electric/Chemical Propulsion Trajectories

NASA’s Human Exploration and Operation Mission Directorate is developing a reusable hybrid transportation architecture in which both chemical and solar-electric propulsion systems are used to deliver crew and cargo to the Martian sphere of influence. By combining chemical and solar-electric propulsions into a single spacecraft and applying each where it is the most effective, the hybrid architecture enables a series of Mars trajectories that are more fuel efficient than an all chemical propulsion architecture without significant increase to trip time. Solving the complex problem of low-thrust trajectory optimization coupled with the vehicle sizing requires development of an integrated trajectory analysis frame- work. Previous studies have utilized a more segmented optimization framework due to the limitation of the tools available. A new integrated optimization framework was recently developed to address the deficiencies of the previous methods that enables higher fidelity analysis to be performed and increases the efficiency of large design space explorations.

Chai, Patrick R.↗

NASA NTRSDate not supplied

Parametric Modeling and Mission Performance Analysis of a True Parallel Hybrid Turboprop Aircraft for Freighter Operations

Hybrid-electric propulsion systems for short-haul, cargo carrying aircraft have emerged as promising solutions for an environmentally sustainable future for commercial aviation. The novel propulsion architecture presents significant complexity and requires the development of new methodologies to account for the unique coupling and integration between critical design variables. This paper presents a comprehensive study on the modeling, performance assessment, and design space exploration of a C-130H freighter aircraft retrofitted with a parallel hybrid electric powertrain. Details on the development of parametric models for both the baseline and true parallel hybrid (TPH) aircraft and the integrated electrified aircraft propulsion (EAP) system sizing approach are presented along with detailed performance analyses of payload/range capabilities for short-haul cargo missions. For 2030, 2040, and 2050 EAP technology levels, the TPH C-130H configuration with a ~2.12 MW class EAP system has a range capability of 485-1,028 nautical miles and block fuel savings of 27-44%.

efficiency↗

DOE OSTI2019

Predictive Materials Design of Magnetic Random-Access Memory Based on Nanoscale Atomic Structure and Element Distribution

Magnetic tunnel junctions (MTJs) capable of electrical read and write operations have emerged as a canonical building block for non-volatile memory and logic. However, the cause of the wide-spread device properties found experimentally in various MTJ stacks, including tunneling magnetoresistance (TMR), perpendicular magnetic anisotropy (PMA), and voltage-controlled magnetic anisotropy (VCMA), remains elusive. Here, using high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy, we found that the MTJ crystallization quality, boron diffusion out of the CoFeB fixed layer, and minimal CoFe oxidation correlate with the TMR. As with the CoFeB free layer, seed layer diffusion into the free layer/MgO interface is negatively correlated with the interfacial PMA, while the metal-oxides concentrations in the free layer correlate with the VCMA. Combined with formation enthalpy and thermal diffusion analysis, we further established a predictive materials design framework to guide the complex design space explorations for high-performance MTJs. In this paper, we demonstrate experimentally high PMA and VCMA values of 1.74 mJ/m2 and 115 fJ/V-m with annealing stability above 400 °C.

36 MATERIALS SCIENCE↗

DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Using cylindrical implosions to investigate hydrodynamic instabilities in convergent geometry

Hydrodynamic instabilities such as the Rayleigh–Taylor (RT) and Richtmyer–Meshkov instabilities disrupt inertial confinement fusion (ICF) implosions through the growth of 3D perturbations. Growth of these 3D imperfections at the interfaces of an ICF capsule during implosion lead to mixing between materials that is detrimental to performance. These instabilities have been studied extensively in planar geometry, but such experiments lack the effects of convergence in spherical implosions. While several studies have been performed in spherical geometry, these often lack a direct means to measure perturbation growth. Experiments in cylindrical geometry include convergence effects while maintaining direct diagnostic access. Although cylinders have less compression than spheres, they do provide an excellent platform to validate modeling for convergent geometries. The problem with previous cylindrical implosion experiments was that the convergence ratios were limited to ~4. With the National Ignition Facility (NIF), larger cylindrical targets can be driven to convergences of 10–15 while maintaining a large enough final diameter to measure perturbation growth. This paper reviews the design process used to both benchmark radiation hydrodynamics codes and enable 1D post-processed simulations to explore design space to separate compression effects from acceleration/deceleration RT instability. Results from 1D simulations suggest that cylindrical implosions on the NIF can produce high-convergence experiments to validate RT instability growth for ICF implosions.

36 MATERIALS SCIENCE↗

DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗

DOE OSTI2021

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators

The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion of domain-specific accelerators that could support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a capability to quickly and automatically transition from algorithm definition to hardware implementation and explore design space along a variety of SWaP (size, weight and Power). The software defined architectures (SODA) synthesizer implements a compiler-based modular infrastructure for the end-to-end generation of machine learning accelerators from high-level frameworks to hardware description language. At the same time, neuromorphic computing, by mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies orders of magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lack the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper we discuss the support for such an integrated generation leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect (part of the SODA frontend) that allows expressing spiking neural network features (e.g., available resources, spiking sequences, analog signal reading, etc.) and illustrate how it enables mapping to Spiking Neurons and deployment to the related specialized hardware (which, in the digital domain, could be generated through the other existing layers of the SODA Synthesizer). We then discuss the opportunities for even deeper integration afforded by the hardware compilation infrastructure, providing a path towards the generation of complex heterogeneous artificial intelligence systems.

Curzel, Serena↗

DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗

DOE OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗