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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 1,279 records · Page 71

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NASA NTRS2017

Minerva: User-Centered Science Operations Software Capability for Future Human Exploration

In June of 2016, the Biologic Analog Science Associated with Lava Terrains (BASALT) research project conducted its first field deployment, which we call BASALT-1. BASALT-1 consisted of a science-driven field campaign in a volcanic field in Idaho as a simulated human mission to Mars. Scientists and mission operators were provided a suite of ground software tools that we refer to collectively as Minerva to carry out their work. Minerva provides capabilities for traverse planning and route optimization, timeline generation and display, procedure management, execution monitoring, data archiving, visualization, and search. This paper describes the Minerva architecture, constituent components, use cases, and some preliminary findings from the BASALT-1 campaign.

Deans, Matthew↗

NASA NTRSDate not supplied

Development of a Lunar Lander Modeling and Simulation Capability

The development and validation of a capability to perform quantitative, physics-based analysis of alternatives of conceptual unmanned, reusable lunar cargo lander architectures is discussed. The lander is conceptualized as a suite of disciplines that need to be sized, synthesized, and optimized for a mission. As such, models of these disciplines were developed and integrated in a recently-developed multi-disciplinary analysis and optimization framework for space systems. The development of the disciplines, as well as how they are integrated into the framework, is discussed. Additionally, validation studies were performed against the Descent Module of the Altair lunar lander. Finally, an example problem for an uncrewed, reusable lander is presented.

Eugina Mendez Ramos↗

NASA NTRSDate not supplied

Development of a Lunar Lander Modeling and Simulation Capability

Eugina Mendez Ramos↗

NASA NTRSDate not supplied

Development of a Lunar Lander Modeling and Simulation Capability

Eugina Mendez Ramos↗

DOE OSTI2016

Operations DER-CAM based Microgrid Controller, v1.0

The ODC Microgrid Controller is an optimization-based model predicative microgrid controller (MPMC) to minimize operation cost (and/or CO2 emissions) in a microgrid in the grid-connected mode. It is composed of several modules, including a) forecasting, b) optimization, c) data exchange and d) power balancing modules. In the presence of a multi-layered control system architecture, these modules will reside in the supervisory control layer.

STADLER, MICHAEL↗

DOE OSTI2015

Enabling Structured Exploration of Workflow Performance Variability in Extreme-Scale Environments

Workflows are taking an Workflows are taking an increasingly important role in orchestrating complex scientific processes in extreme scale and highly heterogeneous environments. However, to date we cannot reliably predict, understand, and optimize workflow performance. Sources of performance variability and in particular the interdependencies of workflow design, execution environment and system architecture are not well understood. While there is a rich portfolio of tools for performance analysis, modeling and prediction for single applications in homogenous computing environments, these are not applicable to workflows, due to the number and heterogeneity of the involved workflow and system components and their strong interdependencies. In this paper, we investigate workflow performance goals and identify factors that could have a relevant impact. Based on our analysis, we propose a new workflow performance provenance ontology, the Open Provenance Model-based WorkFlow Performance Provenance, or OPM-WFPP, that will enable the empirical study of workflow performance characteristics and variability including complex source attribution.

Enabling structured workflows↗

DOE OSTI2016

MILC staggered conjugate gradient performance on Intel KNL

We review our work done to optimize the staggered conjugate gradient (CG) algorithm in the MILC code for use with the Intel Knights Landing (KNL) architecture. KNL is the second gener- ation Intel Xeon Phi processor. It is capable of massive thread parallelism, data parallelism, and high on-board memory bandwidth and is being adopted in supercomputing centers for scientific research. The CG solver consumes the majority of time in production running, so we have spent most of our effort on it. We compare performance of an MPI+OpenMP baseline version of the MILC code with a version incorporating the QPhiX staggered CG solver, for both one-node and multi-node runs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

Blum, David↗

DOE OSTI2025

Integrating Safety, Security, and Nuclear Operations for Advanced Reactors

The traditional separation between safety, security, and operations teams has created significant barriers to achieving optimal outcomes. When security considerations are introduced late in the design process, they often conflict with already-established architectural, operational, or engineering parameters. Retrofitting security measures can lead to increased costs, schedule delays, and compromises in security effectiveness. For instance, the need to retrofit physical barriers or surveillance systems often results in trade-offs that could have been avoided with earlier input from security professionals. Delayed integration can also affect regulatory processes and result in licensing delays. Security reviews conducted at later stages frequently identify gaps that necessitate significant redesign efforts, impacting not only scope, schedule, and budget, but also adding risk and lowering stakeholder confidence in the project. This paper aims to address these challenges by identifying practical opportunities for integrating security considerations seamlessly with design and operations teams throughout the entire lifecycle of nuclear facilities—from conceptual design to commissioning and beyond. The research emphasizes the value of early and continuous collaboration among stakeholders to ensure that security measures are robust, operationally effective, and cost-efficient. By examining case studies, analyzing past incidents, and leveraging best practices from other high-security industries, this study highlights actionable strategies for bridging the gap between safety, security, and operations teams.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗

NASA NTRS1996

Distributed Large Data-Object Environments: End-to-End Performance Analysis of High Speed Distributed Storage Systems in Wide Area ATM Networks

We have developed and deployed a distributed-parallel storage system (DPSS) in several high speed asynchronous transfer mode (ATM) wide area networks (WAN) testbeds to support several different types of data-intensive applications. Architecturally, the DPSS is a network striped disk array, but is fairly unique in that its implementation allows applications complete freedom to determine optimal data layout, replication and/or coding redundancy strategy, security policy, and dynamic reconfiguration. In conjunction with the DPSS, we have developed a 'top-to-bottom, end-to-end' performance monitoring and analysis methodology that has allowed us to characterize all aspects of the DPSS operating in high speed ATM networks. In particular, we have run a variety of performance monitoring experiments involving the DPSS in the MAGIC testbed, which is a large scale, high speed, ATM network and we describe our experience using the monitoring methodology to identify and correct problems that limit the performance of high speed distributed applications. Finally, the DPSS is part of an overall architecture for using high speed, WAN's for enabling the routine, location independent use of large data-objects. Since this is part of the motivation for a distributed storage system, we describe this architecture.

Johnston, William↗

DOE OSTI2022

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

This work develops and implements an NMPC control system to facilitate fuel-optimal platooning of Class 8 vehicles over challenging terrain. Prior research has shown that Cooperative Adaptive Cruise Control (CACC), which allows multiple Class 8 vehicles to follow in close succession, can save between 3 and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g. rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of Nonlinear Model Predictive Control (NMPC) with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. Furthermore, this comparison yields the comparative fuel savings and energy efficiency benefit of NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

SU-F-T-256: 4D IMRT Planning Using An Early Prototype GPU-Enabled Eclipse Workstation

Purpose: True 4D IMRT planning, based on simultaneous spatiotemporal optimization has been shown to significantly improve plan quality in lung radiotherapy. However, the high computational complexity associated with such planning represents a significant barrier to widespread clinical deployment. We introduce an early prototype GPU-enabled Eclipse workstation for inverse planning. To our knowledge, this is the first GPUintegrated Eclipse system demonstrating the potential for clinical translation of GPU computing on a major commercially-available TPS. Methods: The prototype system comprised of four NVIDIA Tesla K80 GPUs, with a maximum processing capability of 8.5 Tflops per K80 card. The system architecture consisted of three key modules: (i) a GPU-based inverse planning module using a highly-parallelizable, swarm intelligence-based global optimization algorithm, (ii) a GPU-based open-source b-spline deformable image registration module, Elastix, and (iii) a CUDA-based data management module. For evaluation, aperture fluence weights in an IMRT plan were optimized over 9 beams,166 apertures and 10 respiratory phases (14940 variables) for a lung cancer case (GTV = 95 cc, right lower lobe, 15 mm cranio-caudal motion). Sensitivity of the planning time and memory expense to parameter variations was quantified. Results: GPU-based inverse planning was significantly accelerated compared to its CPU counterpart (36 vs 488 min, for 10 phases, 10 search agents and 10 iterations). The optimized IMRT plan significantly improved OAR sparing compared to the original internal target volume (ITV)-based clinical plan, while maintaining prescribed tumor coverage. The dose-sparing improvements were: Esophagus Dmax 50%, Heart Dmax 42% and Spinal cord Dmax 25%. Conclusion: Our early prototype system demonstrates that through massive parallelization, computationally intense tasks such as 4D treatment planning can be accomplished in clinically feasible timeframes. With further optimization, such systems are expected to enable the eventual clinical translation of higher-dimensional and complex treatment planning strategies to significantly improve plan quality. This work was partially supported through research funding from National Institutes of Health (R01CA169102) and Varian Medical Systems, Palo Alto, CA, USA.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Bimetallic NiCo boride nanoparticles confined in a MXene network enable efficient ambient ammonia electrosynthesis

Ambient electrocatalytic nitrogen fixation is an emerging technology for green ammonia synthesis, but the absence of optimized, stable and performant catalysts can render its practical application challenging. Herein, bimetallic NiCo boride nanoparticles confined in MXene are shown to accomplish high-performance nitrogen reduction electrolysis. Taking advantage of the synergistic effect in specific compositions with unique electronic d and p orbits and typical architecture of rich nanosized particles embedded in the interconnected conductive network, the synthesized MXene@NiCoB composite demonstrates extensive improvements in nitrogen molecule chemisorption, active area exposure and charge transport. As a result, optimal NH 3 yield rate of 38.7 μg h -1 mgcat. -1 and Faradaic efficiency of 6.92% are acquired in 0.1 M Na 2 SO 4 electrolyte. Moreover, the great catalytic performance can be almost entirely maintained in the cases of repeatedly-cycled and long-term electrolysis. Theoretical investigations reveal that the nitrogen reduction reaction on MXene@NiCoB catalyst proceeds according to the distal pathway, with a distinctly-reduced energy barrier relative to the Co 2 B counterpart. In conclusion, this work may inspire a new route towards the rational catalyst design for the nitrogen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2011

Multidisciplinary Modeling Software for Analysis, Design, and Optimization of HRRLS Vehicles

The concept for Highly Reliable Reusable Launch Systems (HRRLS) under the NASA Hypersonics project is a two-stage-to-orbit, horizontal-take-off / horizontal-landing, (HTHL) architecture with an air-breathing first stage. The first stage vehicle is a slender body with an air-breathing propulsion system that is highly integrated with the airframe. The light weight slender body will deflect significantly during flight. This global deflection affects the flow over the vehicle and into the engine and thus the loads and moments on the vehicle. High-fidelity multi-disciplinary analyses that accounts for these fluid-structures-thermal interactions are required to accurately predict the vehicle loads and resultant response. These predictions of vehicle response to multi physics loads, calculated with fluid-structural-thermal interaction, are required in order to optimize the vehicle design over its full operating range. This contract with ResearchSouth addresses one of the primary objectives of the Vehicle Technology Integration (VTI) discipline: the development of high-fidelity multi-disciplinary analysis and optimization methods and tools for HRRLS vehicles. The primary goal of this effort is the development of an integrated software system that can be used for full-vehicle optimization. This goal was accomplished by: 1) integrating the master code, FEMAP, into the multidiscipline software network to direct the coupling to assure accurate fluid-structure-thermal interaction solutions; 2) loosely-coupling the Euler flow solver FEFLO to the available and proven aeroelasticity and large deformation (FEAP) code; 3) providing a coupled Euler-boundary layer capability for rapid viscous flow simulation; 4) developing and implementing improved Euler/RANS algorithms into the FEFLO CFD code to provide accurate shock capturing, skin friction, and heat-transfer predictions for HRRLS vehicles in hypersonic flow, 5) performing a Reynolds-averaged Navier-Stokes computation on an HRRLS configuration; 6) integrating the RANS solver with the FEAP code for coupled fluid-structure-thermal capability; and 7) integrating the existing NASA SRGULL propulsion flow path prediction software with the FEFLO software for quasi-3D propulsion flow path predictions, 8) improving and integrating into the network, an existing adjoint-based design optimization code.

Spradley, Lawrence W.↗

DOE OSTI2026

Optimal invariant sets for atomistic machine learning

The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

ECP libraries and tools: An overview

The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Improving Lithium Metal Composite Anodes with Seeding and Pillaring Effects of Silicon Nanoparticles

Metallic lithium (Li) anodes are crucial for the development of high specific energy batteries yet plagued by their poor cycling efficiency. Electrode architecture engineering is vital for maintaining a stable anode volume and suppress Li corrosion during cycling. In this paper, a reduced graphene oxide “host” framework for Li metal anodes is further optimized by embedding silicon (Si) nanoparticles between the graphene layers. They serve as Li nucleation seeds to promote Li deposition within the framework even without pre-stored Li. Meanwhile, the LixSi alloy particles serve as supporting “pillars” between the graphene layers, enabling a minimized thickness shrinkage after full stripping of metallic Li. Combined with a Li compatible electrolyte, 99.4% Coulombic efficiency over ~600 cycles is achieved, and stable cycling of a Li||NMC532 full cell for ~380 cycles with negligible capacity decay is realized.

Wang, Hansen↗

DOE OSTI2016

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power.In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation.The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

97 MATHEMATICS AND COMPUTING↗