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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Perturbation theory under the truncated Wigner approximation: How system-environment entanglement formation drives quantum decoherence

In discrete quantum systems, quantum decoherence is the disappearance of simple phase relations as a result of interactions with an environment. For many applications, the question is not necessarily how to avoid (inevitable) system-environment interactions, but rather how to design environments that optimally preserve a system's phase relations in spite of such interactions. The formation of system-environment entanglement is a major driving mechanism for decoherence, and a detailed understanding of this process could inform strategies for conserving coherence optimally. This requires scalable, flexible, and systematically improvable quantum dynamical methods that retain detailed information about the entanglement properties of the environment, yet very few current methods offer this combination of features. Here, in this work, we address this need by introducing a theoretical framework wherein we combine the truncated Wigner approximation with standard time-dependent perturbation theory, allowing for computing expectation values of operators in the combined system-environment Hilbert space. We demonstrate the utility of this framework by applying it to the spin-boson model, representative of qubits and simple donor-acceptor systems. For this model, our framework provides an analytical description of perturbative contributions to expectation values. We monitor how quantum decoherence at zero temperature is accompanied by entanglement formation with individual environmental degrees of freedom. Based on this entanglement behavior, we find that the selective suppression of low-frequency environmental modes is particularly effective for mitigating quantum decoherence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2020

In Situ Reliability Investigation of All-Vanadium Redox Flow Batteries by a Stable Reference Electrode

Redox flow batteries (RFBs) have been studied over the past several decades as a promising candidate for stationary energy storage applications. It is therefore important to understand the reliability of RFBs and the mechanisms that cause degradation with time. Contributions from individual electrodes (cathode or anode) are difficult to separate especially for long-term cycle testing, due to the lack of a stable reference electrode. In our work, the reliability and degradation mechanisms of an all-vanadium RFB were investigated by a stable reference electrode based on the dynamic hydrogen electrode (DHE). With factor exploration and design optimization, the newly developed DHE reference electrode demonstrated high accuracy and long-term stability that enables in-situ monitoring of individual electrode signals (potentials and impedances) over hundreds of cycles in a vanadium RFB. This approach enables the full cell degradation to be separated and the degradation attributed to contributions from the cathode and anode. The cathode and anode were found to play quite different roles in the increase in overpotential of the vanadium RFB during long-term cycling. The anode reaction limited both the charge and discharge capacity over 100 cycles. In addition, the negative side also appeared to be the rate limiting factor throughout cycling as determined by EIS measurement. The cathode contributed to the performance degradation as cycling exceeded 50 cycles.

Huang, Qian↗
DOE OSTI2021

Initial use of Nek5000/Cardinal to improve closure models in Pronghorn

Heat transfer coefficient closure models for pebble bed reactors are built using a data-driven approach by leveraging the capabilities of an Evolutionary Algorithm entitled Particle Swarm Optimization (PSO). In the present work, the Computational Fluid Dynamics code nekRS was used in order to collect the high-fidelity flow data for a core with 1,568 pebbles. To characterize the heat transfer, multiple concentric regions were considered to extract the physical quantities of interest, e.g./ the Reynolds number. The PSO algorithm is employed as part of an inverse problem targeting determine what are the coefficients for a Nusselt number correlation to match the collected data. Such correlation should follow any given format that is defined a priori. Finally, two correlations are proposed, one with an implicit dependence on the pebbles’ wall temperatures and another expressed as a fully explicit correlation depending on the flow conditions and the position within the core. Anyway, given the generic nature of the proposed approach, correlations following different formats could be tested. Preliminary results for the high-fidelity simulation of a fast MSR core are presented. The target Reynolds number is currently 20K, with the expectation that this will increase, pending the availability of further computational resources. These simulations will be used to inform lower fidelity models, including a coarse CFD turbulence model in Pronghorn. Additionally, they will serve as a reference for the RANS models in Nek5000/NekRS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Syntrophic Co-Cultures of Clostridium Organisms to Produce Higher Alcohols & Other C6-C8 Metabolites (Final Report)

The goal of this project was to advance the systems biology understanding and predictive modeling of synthetic & syntrophic Clostridium microbial consortia, focusing on elucidation of metabolic networks and environmental signals in the consortia. The project has direct applicability to lignocellulosic-biomass based production of higher alcohols as advanced biofuels and C6-C8 metabolites, that can be used as chemicals or serve as biofuel precursors. This project studied and optimized three synthetic syntrophic systems. It examined the population dynamics using flow-cytometry, time-lapse microscopy and PCR analysis. 13C-based tracer analyses was used to examine the metabolite exchange between the syntrophic cell populations and the impact of those interactions on the transcriptome of the individual populations. To enhance our analytical and predictive capabilities, genome-scale models (GSMs) for these syntrophies was developed. RNAseq data for these syntrophic coculture systems were acquired to enable a molecular level understanding of the syntrophies aiming to identify the genetic networks of each organism in the co-culture and compare those against the networks of pure cultures.

09 BIOMASS FUELS↗
DOE OSTI2025

FuSED – Users Manual – (V.5.26)

The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics (InverseSD) and thermal physics (InverseAria), a sensor placement optimization tool via Optimal Experimental Design (OED), and a decision boundary tool using SVMs (TRACE). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Synthesis of aircraft structures using integrated design and analysis methods

A systematic research is reported to develop and validate methods for structural sizing of an airframe designed with the use of composite materials and active controls. This research program includes procedures for computing aeroelastic loads, static and dynamic aeroelasticity, analysis and synthesis of active controls, and optimization techniques. Development of the methods is concerned with the most effective ways of integrating and sequencing the procedures in order to generate structural sizing and the associated active control system, which is optimal with respect to a given merit function constrained by strength and aeroelasticity requirements.

Sobieszczanski-Sobieski, J.↗
NASA NTRS1977

Computer assisted thermal-vacuum testing

In testing complex systems and components under dynamic thermal-vacuum environments, it is desirable to optimize the environment control sequence in order to reduce test duration and cost. This paper describes an approach where a computer is utilized as part of the test control operation. Real time test data is made available to the computer through time-sharing terminals at appropriate time intervals. A mathematical model of the test article and environmental control equipment is then operated on using the real time data to yield current thermal status, temperature analysis, trend prediction and recommended thermal control setting changes to arrive at the required thermal condition. The data acquisition interface and the time-sharing hook-up to an IBM-370 computer is described along with a typical control program and data demonstrating its use.

Petrie, W.↗
NASA NTRS1984

Hardware for a real-time multiprocessor simulator

The hardware for a real time multiprocessor simulator (RTMPS) developed at the NASA Lewis Research Center is described. The RTMPS is a multiple microprocessor system used to investigate the application of parallel processing concepts to real time simulation. It is designed to provide flexible data exchange paths between processors by using off the shelf microcomputer boards and minimal customized interfacing. A dedicated operator interface allows easy setup of the simulator and quick interpreting of simulation data. Simulations for the RTMPS are coded in a NASA designed real time multiprocessor language (RTMPL). This language is high level and geared to the multiprocessor environment. A real time multiprocessor operating system (RTMPOS) has also been developed that provides a user friendly operator interface. The RTMPS and supporting software are currently operational and are being evaluated at Lewis. The results of this evaluation will be used to specify the design of an optimized parallel processing system for real time simulation of dynamic systems.

Blech, R. A.↗
NASA NTRS1985

NASA Lewis Research Center/University Graduate Research Program on Engine Structures

NASA Lewis Research Center established a graduate research program in support of the Engine Structures Research activities. This graduate research program focuses mainly on structural and dynamics analyses, computational mechanics, mechanics of composites and structural optimization. The broad objectives of the program, the specific program, the participating universities and the program status are briefly described.

Chamis, C. C.↗
NASA NTRS1983

NASA LeRC/Akron University Graduate Cooperative Fellowship Program and Graduate Student Researchers Program

On June 1, 1980, the University of Akron and the NASA Lewis Research Center (LERC) established a Graduate Cooperative Fellowship Program in the specialized areas of Engine Structural Analysis and Dynamics, Computational Mechanics, Mechanics of Composite Materials, and Structural Optimization, in order to promote and develop requisite technologies in these areas of engine technology. The objectives of this program are consistent with those of the NASA Engine Structure Program in which graduate students of the University of Akron participate by conducting research at Lewis. This report is the second on this grant and summarizes the second and third year research effort, which includes the participation of five graduate students where each student selects one of the above areas as his special field of interest. Each student is required to spend 30 percent of his educational training time at the NASA Lewis Research Center and the balance at the University of Akron. His course work is judiciously selected and tailored to prepare him for research work in his field of interest. A research topic is selected for each student while in residence at the NASA Lewis Research Center, which is also approved by the faculty of the University of Akron as his thesis topic for a Master's and/or a Ph.D. degree.

Fertis, D. G.↗
NASA NTRS1985

NASA Lewis Research Center/university graduate research program on engine structures

NASA Lewis Research Center established a graduate research program in support of the Engine Structures Research activities. This graduate research program focuses mainly on structural and dynamics analyses, computational mechanics, mechanics of composites and structural optimization. The broad objectives of the program, the specific program, the participating universities and the program status are briefly described.

Chamis, C. C.↗
NASA NTRS1987

Approximate Feedback Control for a System With Memory

Report presents algorithm for calculating feedback gain for control of hereditary dynamical systems with control delay. Problem is to approximate optimal feedback gain that minimizes cost function of state and control. Theory applicable to design of controllers for mechanical systems subject to thermal deformation, electrical systems with delay, electrical systems with plasma components, and other systems that exhibit memory.

Milman, Mark H.↗