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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 253 records · Page 14

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NASA NTRS1983

Integrated flight/propulsion control system architectures for a high speed aircraft

The present investigation is concerned with the impact of integrated flight, inlet, and engine control system architectures on system effectiveness and life cycle cost (LCC). The aircraft employed in the investigation is a modified YF-12 with a cruise Mach number of 2.5. The aircraft has two advanced technology aircraft JT-69 afterburning, low-bypass turbofan engines. Advanced technologies suitable for use in future flight/propulsion control systems are related to distributed computer networks, fault tolerant computers and software, analytical redundancy, self-checking microprocessor pairs, advanced direct drive actuators, fiber optic data buses, VLSI microcircuits, and skewed sensors. The integrated architectures were found to have fewer sensors, actuators, and power sources than the nonintegrated architectures.

Bangert, L. H.↗

NASA NTRS1986

An improved multilevel optimization approach for the design of complex engineering systems

Multilevel optimization methods are being considered for the design of complex systems on distributed networks of computers or even parallel processors. An obstacle to the use of multilevel methods is that they can be computationally expensive because of the cycling necessary to account for the coupling between the subproblems. This research effort aims at increasing the efficiency of multilevel optimization by adapting two techniques that are widely used in conventional one-level optimization: constraint approximation and temporary constraint deletion. These improvements are implemented and tested on three-, ten- and 52-bar planar truss designs. The results show that for larger problems (approximately 100 design variables and larger), the cost of analysis dominates the total cost so that multilevel optimization is no more expensive than one-level optimization. If parallel processing is used or the analysis process itself is decomposed, then multilevel optimization stands to become more economical than one-level optimization.

Barthelemy, J.-F. M.↗

NASA NTRS1990

Analyzing Distributed Processing For Electric Utilities

Distributed Processing Trade-Off Model for Electric Utility Operation computer program based upon study performed at California Institute of Technology for NASA's Jet Propulsion Laboratory. Study presented technique addressing question of tradeoffs between expanding communications network or expanding capacity of distributed computers in energy-management systems (EMS) of electric utility. Gives EMS planners macroscopic tool for evaluation of architectures of distributed-processing systems and major technical and economic tradeoffs as well as interactions within systems.

Klein, Stanley A.↗

NASA NTRS1990

Automation in the Space Station module power management and distribution Breadboard

The Space Station Module Power Management and Distribution (SSM/PMAD) Breadboard, located at NASA's Marshall Space Flight Center (MSFC) in Huntsville, Alabama, models the power distribution within a Space Station Freedom Habitation or Laboratory module. Originally designed for 20 kHz ac power, the system is now being converted to high voltage dc power with power levels on a par with those expected for a space station module. In addition to the power distribution hardware, the system includes computer control through a hierarchy of processes. The lowest level process consists of fast, simple (from a computing standpoint) switchgear, capable of quickly safing the system. The next level consists of local load center processors called Lowest Level Processors (LLP's). These LLP's execute load scheduling, perform redundant switching, and shed loads which use more than scheduled power. The level above the LLP's contains a Communication and Algorithmic Controller (CAC) which coordinates communications with the highest level. Finally, at this highest level, three cooperating Artificial Intelligence (AI) systems manage load prioritization, load scheduling, load shedding, and fault recovery and management. The system provides an excellent venue for developing and examining advanced automation techniques. The current system and the plans for its future are examined.

Walls, Bryan↗

NASA NTRS1991

Augmentation of the space station module power management and distribution breadboard

The space station module power management and distribution (SSM/PMAD) breadboard models power distribution and management, including scheduling, load prioritization, and a fault detection, identification, and recovery (FDIR) system within a Space Station Freedom habitation or laboratory module. This 120 VDC system is capable of distributing up to 30 kW of power among more than 25 loads. In addition to the power distribution hardware, the system includes computer control through a hierarchy of processes. The lowest level consists of fast, simple (from a computing standpoint) switchgear that is capable of quickly safing the system. At the next level are local load center processors, (LLP's) which execute load scheduling, perform redundant switching, and shed loads which use more than scheduled power. Above the LLP's are three cooperating artificial intelligence (AI) systems which manage load prioritizations, load scheduling, load shedding, and fault recovery and management. Recent upgrades to hardware and modifications to software at both the LLP and AI system levels promise a drastic increase in speed, a significant increase in functionality and reliability, and potential for further examination of advanced automation techniques. The background, SSM/PMAD, interface to the Lewis Research Center test bed, the large autonomous spacecraft electrical power system, and future plans are discussed.

Walls, Bryan↗

NASA NTRS1990

Telescience - Optimizing aerospace science return through geographically distributed operations

The paper examines the objectives and requirements of teleoperations, defined as the means and process for scientists, NASA operations personnel, and astronauts to conduct payload operations as if these were colocated. This process is described in terms of Space Station era platforms. Some of the enabling technologies are discussed, including open architecture workstations, distributed computing, transaction management, expert systems, and high-speed networks. Recent testbedding experiments are surveyed to highlight some of the human factors requirements.

Rasmussen, Daryl N.↗

NASA NTRS1992

High order parallel numerical schemes for solving incompressible flows

The use of parallel computers for numerically solving flow fields has gained much importance in recent years. This paper introduces a new high order numerical scheme for computational fluid dynamics (CFD) specifically designed for parallel computational environments. A distributed MIMD system gives the flexibility of treating different elements of the governing equations with totally different numerical schemes in different regions of the flow field. The parallel decomposition of the governing operator to be solved is the primary parallel split. The primary parallel split was studied using a hypercube like architecture having clusters of shared memory processors at each node. The approach is demonstrated using examples of simple steady state incompressible flows. Future studies should investigate the secondary split because, depending on the numerical scheme that each of the processors applies and the nature of the flow in the specific subdomain, it may be possible for a processor to seek better, or higher order, schemes for its particular subcase.

Lin, Avi↗

NASA NTRS1991

Computer aided reliability, availability, and safety modeling for fault-tolerant computer systems with commentary on the HARP program

Many of the most challenging reliability problems of our present decade involve complex distributed systems such as interconnected telephone switching computers, air traffic control centers, aircraft and space vehicles, and local area and wide area computer networks. In addition to the challenge of complexity, modern fault-tolerant computer systems require very high levels of reliability, e.g., avionic computers with MTTF goals of one billion hours. Most analysts find that it is too difficult to model such complex systems without computer aided design programs. In response to this need, NASA has developed a suite of computer aided reliability modeling programs beginning with CARE 3 and including a group of new programs such as: HARP, HARP-PC, Reliability Analysts Workbench (Combination of model solvers SURE, STEM, PAWS, and common front-end model ASSIST), and the Fault Tree Compiler. The HARP program is studied and how well the user can model systems using this program is investigated. One of the important objectives will be to study how user friendly this program is, e.g., how easy it is to model the system, provide the input information, and interpret the results. The experiences of the author and his graduate students who used HARP in two graduate courses are described. Some brief comparisons were made with the ARIES program which the students also used. Theoretical studies of the modeling techniques used in HARP are also included. Of course no answer can be any more accurate than the fidelity of the model, thus an Appendix is included which discusses modeling accuracy. A broad viewpoint is taken and all problems which occurred in the use of HARP are discussed. Such problems include: computer system problems, installation manual problems, user manual problems, program inconsistencies, program limitations, confusing notation, long run times, accuracy problems, etc.

Shooman, Martin L.↗

NASA NTRS1997

Performance Analysis and Optimization on a Parallel Atmospheric General Circulation Model Code

An analysis is presented of the primary factors influencing the performance of a parallel implementation of the UCLA atmospheric general circulation model (AGCM) on distributed-memory, massively parallel computer systems.

component↗

DOE OSTI2022

A Hybrid Reinforcement Learning-MPC Approach for Distribution System Critical Load Restoration

This paper proposes a hybrid control approach for distribution system critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while MPC models grid operations incorporating RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. We formulate the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The RL algorithm is trained offline using a historical forecast of renewable generation and load demand. The method is tested using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery. Case studies demonstrated that the proposed method outperforms other policies with static operating reserves.

distribution system↗

NASA NTRS1990

A study of fracture mechanisms in ATD roller bearing

The purpose was to investigate how microstructures, especially anisotropy, affects internal stresses and the overall mechanical response of bearings. Samples with the stress axis along the aligned carbide direction possessed high modulus values compared to those with their axis perpendicular to the carbide stringers. The difference in the modulus was found to be more than a factor of two. A series of experiments was conducted on rolled samples to further investigate this effect; the two sets of results were consistent with each other. The degree of anisotropy of the microstructure in terms of the carbide and matrix orientations was determined using x-ray diffraction. The stress state determination was conducted using neutron diffraction. It was found that there was little variation in the distribution of the internal stresses amongst different samples, indicating that small changes in the processing and geometrical conditions did not result in significant variations in the internal stress. A nominal tensile hoop stress of 39 ksi was obtained for the inner raceway. Furthermore, during the course of the investigation, it was apparent that there was a need to determine the stress state induced by the shrink fitting process. Therefore, a series of experiments was conducted using strain gages to identify the stress distribution in a shrink fitting process in three different types of geometries. Correlations were obtained to estimate the highest stress values in the outer and inner groove geometry. A finite element program based on the ANSYS system was developed to compute the stress distribution in the inner raceway geometry. This analysis indicates that the highest tensile stress in the system occurs at the ID of the ring with a stress value of over 5 times that of the applied radial stress. Results from all these facets were correlated with one another. It appears that the material does not fail as a result of any one single factor, but results from a combination of the various factors investigated.

Zee, Ralph H.↗

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 NTRS1988

Kanerva's sparse distributed memory: An associative memory algorithm well-suited to the Connection Machine

The advent of the Connection Machine profoundly changes the world of supercomputers. The highly nontraditional architecture makes possible the exploration of algorithms that were impractical for standard Von Neumann architectures. Sparse distributed memory (SDM) is an example of such an algorithm. Sparse distributed memory is a particularly simple and elegant formulation for an associative memory. The foundations for sparse distributed memory are described, and some simple examples of using the memory are presented. The relationship of sparse distributed memory to three important computational systems is shown: random-access memory, neural networks, and the cerebellum of the brain. Finally, the implementation of the algorithm for sparse distributed memory on the Connection Machine is discussed.

Rogers, David↗

DOE OSTI2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

Mohseni, Masoud↗

NASA NTRS2007

Ground Support for the Space-Based Range Flight Demonstration 2

The primary objective of the NASA Space-Based Range Demonstration and Certification program was to develop and demonstrate space-based range capabilities. The Flight Demonstration 2 flights at NASA Dryden Flight Research Center were conducted to support Range Safety (commanding and position reporting) and high-rate (5 Mbps) Range User (video and data) requirements. Required ground support infrastructure included a flight termination system computer, the ground-data distribution network to send range safety commands and receive range safety and range user telemetry data and video, and the ground processing systems at the Dryden Mission Control Center to process range safety and range user telemetry data and video.

Burkes, Darryl A.↗

DOE OSTI2023

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Marković, Marija (ORCID:0000000247839837)↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗