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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

An efficient formulation of the coupled finite element-integral equation technique for solving large 3D scattering problems

It is often desirable to calculate the electromagnetic fields inside and about a complicated system of scattering bodies, as well as in their far-field region. The finite element method (FE) is well suited to solving the interior problem, but the domain has to be limited to a manageable size. At the truncation of the FE mesh one can either impose approximate (absorbing) boundary conditions or set up an integral equation (IE) for the fields scattered from the bodies. The latter approach is preferable since it results in higher accuracy. Hence, the two techniques can be successfully combined by introducing a surface that encloses the scatterers, applying a FE model to the inner volume and setting up an IE for the tangential fields components on the surface. Here the continuity of the tangential fields is used bo obtain a consistent solution. A few coupled FE-IE methods have recently appeared in the literature. The approach presented here has the advantage of using edge-based finite elements, a type of finite elements with degrees of freedom associated with edges of the mesh. Because of their properties, they are better suited than the conventional node based elements to represent electromagnetic fields, particularly when inhomogeneous regions are modeled, since the node based elements impose an unnatural continuity of all field components across boundaries of mesh elements. Additionally, our approach is well suited to handle large size problems and lends itself to code parallelization. We will discuss the salient features that make our approach very efficient from the standpoint of numerical computation, and the fields and RCS of a few objects are illustrated as examples.

Cwik, T.↗
NASA NTRS1994

Comparing the effects of two in-flight aerobic exercise protocols on standing heart rates and VO(2peak) before and after space flight

The effects of regular aerobic exercise on orthostatic tolerance have been the subject of a long-standing controversy that will influence the use of exercise during space flight. To examine these effects, astronauts performed continuous (CE) aerobic exercise (n = 8), interval (IE) aerobic exercise (n = 4), or no (NE) exercise (n = 5) during flights of 7 to 11 days. Heart rate (HR) responses to an orthostatic challenge (stand test) were measured 10 days before flight and on landing day. VO(2peak) (graded treadmill exercise) was measured 7 to 21 days before and 2 days after flight. No significant differences across the groups were observed in standing HRs before or after flight. However, the within-group mean HRs significantly increased in the NE (71-89 beats/min) and CE (60-85 beats/min) groups after space flight. The HRs for the IE group did not significantly increase (75-86 beats/min) after space flight. VO(2peak) decreased (P < .05) in the NE (-9.5%) group, but did not change in the CE (-2.4%) and IE (1%) groups. The relationship (r = 0.237) between the delta HR and delta VO(2peak) was not significant. These preliminary results indicate that: (1) continuous exercise does not affect the orthostatic HR response after space flight; (2) interval exercise may minimize an increase in the postflight orthostatic HR; and (3) both exercise protocols can maintain VO(2peak).

NASA Discipline Musculoskeletal↗
NASA NTRS2021

On Synthetic Absorption Line Profiles of Thermally Driven Winds from Active Galactic Nuclei

The warm absorbers observed in more than half of all nearby active galactic nuclei are tracers of ionized outflows located at parsec-scale distances from the central engine. If the smallest inferred ionization parameters correspond to plasma at a few 104 K, then the gas undergoes a transition from being bound to unbound, provided it is further heated to ∼106 K at larger radii. Dannen et al. recently discovered that, under these circumstances, thermally driven wind solutions are unsteady and even show very dense clumps due to thermal instability. To explore the observational consequences of these new wind solutions, we compute line profiles based on the one-dimensional simulations of Dannen et al. We show how the line profiles from even a simple steady-state wind solution depend on the ionization energy (IE) of absorbing ions, which is a reflection of the wind ionization stratification. To organize the diversity of the line shapes, we group them into four categories: weak Gaussians, saturated boxy profiles with and without an extended blue wing, and broad weak profiles. The lines with profiles in the last two categories are produced by ions with the highest IE that probe the fastest regions. Their maximum blueshifts agree with the highest flow velocities in thermally unstable models, both steady-state and clumpy versions. In contrast, the maximum blueshifts of the highest-IE lines in thermally stable models can be less than half of the actual solution velocities. Clumpy solutions can additionally imprint distinguishable absorption troughs at widely separated velocities.

Shalini Ganguly↗
DOE OSTI2017

QUANTIFICATION OF FUNCTIONAL IMPACT CLASSIFICATION ON THE CURRENT US NUCLEAR FLEET

Many plants experience multiple failure events each year, and while none of these events result in core damage further consideration for nuclear power plant (NPP) type and its role in an event needs to be considered. Specifically herein initiating events (IE) are considered these are unplanned events that occur while a nuclear plant is in critical operation, and requires that the plant be shut down to achieve a stable state. Breaking IE failure events into groups results in the classification of functional impact (FI) groups. A FI group is a risk-significant event category that could impact the ability of a NPP to remove decay heat from the reactor. For an event to be considered a FI, it must be associated with a manual or automatic reactor trip, irrespective of the order of events. In addition, the plant must be at or above the point of adding heat and the event must happen before or shortly after a reactor trip. One or more FIs may be identified in any individual reactor trip sequence. An example of a FIs is the loss of offsite power (LOOP) from a bird interaction with the power lines. This results in a failure to transfer power to an emergency source, such as a generator, producing a turbine and reactor trip and then a closure of main steam isolation valves (MSIVS). The FI applicable to this reactor trip sequence would be loss of offsite power and closure of at least one MSIV in each main steam line. The results presented are focused on the analysis and evaluation of FI event categories that can be found in IE studies [1], [2] and the Initialing Events Spreadsheet [3] and is a historical review of Licensee.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Summary Report on Synthetic Time History Development and Deployment

This report summarizes research work performed at the Colorado School of Mines (CSM) and George Washington University (GWU) in support of the DOE Office of Nuclear Energy Integrated Energy Systems (IES) program. In particular, the work focuses on improving and strengthening two fundamental steps in the mathematical and computational framework that is currently being developed to optimize the economic performance of Integrated Energy Systems. One of the innovative approaches taken by the IES program is that, rather than optimizing the system for a specific year or week or day, the optimization is performed using stochastic synthetic data. Synthetic data is generated by training artificial intelligence (AI) using available but limited data sets. The synthetic data should possess the same patterns as the original set and the same statistical properties, but never repeat the same set of values exactly. The work from CSM focuses on this first step; namely, investigating a methodology to determine the quality of the synthetic data and the right settings for properly training the AI. Once a large set of trustworthy data can be generated the problem of stochastic optimization can be tackled. The optimal IES configuration is highly dependent on the rare high peaks or deep depths of the net demand (grid electricity demand less variable renewable energy [VRE] production). Using an unbiased random sampling of the synthetic data, these extremes are very difficult to generate and therefore the optimization can be unrepresentative and inaccurate. GWU has started an approach to increase the likelihood of generating these rare events with the proper associated probabilistic weight. Both of these projects represent a step forward in achieving the capability to properly size the IESs and to properly capture the value that these systems can provide to the grid by absorbing volatility of net demand without disproportionately increasing the cost of maintaining grid reliability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Market-Integrated Optimization of Wind-Battery-Hydrogen Hybrids for Peaking Capacity via Storage

As Integrated Energy Systems (IES) combine multiple energy and storage technologies to provide potentially more value and less risk via resource diversification, complementary overbuild, increased flexibility, and revenue-stacking, IES value is dependent on electricity market dispatch and grid interactions should play an important role in IES design and operation. This study hybridizes and retrofits wind and combustion turbine plants to study the impacts of replacing gas generation capacity with wind, battery, PEM electrolysis, hydrogen tanks and hydrogen turbines. The optimized design is co-simulated in a production cost model with different bidding strategies in order to compare performance and highlight the importance of grid-interactions. We analyze the revenue and dispatch changes as well as the price and cost implications of wind-battery-hydrogen IESs.

electrolysis↗
DOE OSTI2023

Nuclear-Renewable-Storage Systems: Enhancing Planning and Operations of Integrated Energy Systems

Nuclear-renewable-storage integrated energy systems (IES) are multi-carrier energy systems that include not only electricity but also other forms of demands. Because individual IES components must observe their thermo-physical limits, including ramp rates, start-up, and shut-down time, we formulate operations of the IES as an optimization model by minimizing the total operations costs subject to physical limits of all constituent components. In addition, we develop a data-driven approach to improve the computational performance of the economic dispatch model by using reinforcement learning, where an agent is rewarded by meeting demands and penalized otherwise when shifting to the next state.

25 ENERGY STORAGE↗
DOE OSTI2025

Beyond Price Taker: Optimizing Integrated Energy Systems Considering Market/Grid Interactions

Integrated Energy Systems (IES) combine two or more processes to increase the efficiency, flexibility of operation, and the overall reliability. However, analyzing IESs in volatile electricity markets is challenging, since the volatility in electricity prices makes the conventional levelized cost-type analysis less realistic. This work presents two approaches to address the challenge: price-taker and a surrogates-based approach for incorporating market interactions. The price-taker approach formulates a multiperiod optimization problem that takes the time-varying electricity prices into account, and solves the optimization problem to determine the optimal operational schedule that maximizes the chosen economic metric. This approach is successfully applied to investigate the performance of flexible power and hydrogen co-production systems. The market surrogates approach trains a machine learning model to predict the market behavior as a function of the characteristics of the IES. The trained surrogate model is used to optimize the design and operation of the given IES in an electricity market. This approach is demonstrated on a case study involving a nuclear power plant retrofitted with a low-temperature electrolysis unit to co-produce power and hydrogen.

beyond price taker↗
DOE OSTI2025

Identification of Important Phenomena for Light Water Reactors During Heat Transport System Failure Events in Integrated Energy Systems

This work adapts historical literature and existing phenomena identification and ranking tables (PIRT) to be applicable to a novel nuclear power plant (NPP) and chemical or thermal process integrated energy system (IES), particularly focusing on the process heat and heat transport system failure events that are not a concern during normal NPP operation but become vital when an IES is considered. Nuclear energy has been suggested to go beyond base-load applications and be used for hydrogen co-generation systems, amongst other IESs. Prior to the implementation of nuclear IESs, sufficient analysis must be performed on accident events to ensure public safety. The events considered were deemed important because of their potential to damage systems, structures, and components (SSCs). Process thermal events of concern include loss of heat load and temperature transient events. Loss of heat load events were characterized as having high importance and being well understood. Temperature transient events may be further categorized by the cyclic loading and harmonics phenomena. Cyclic loading issues were classified as medium to high importance with knowledge gaps existing regarding fatigue and low power operation, while harmonics phenomena were classified as low importance and are well understood. Heat transport system failure events of concern include intermediate and process heat exchanger failures, mass addition to reactor coolant, ingress of material from thermal manifold/energy storage, and loss of intermediate fluid. Furthermore, these events tended to be of high or medium importance, with some knowledge gaps needing to be filled for individual reactor systems due to unique designs.

Integrated Energy System (IES)↗
DOE OSTI2019

Measured and Calculated DC Arc-Flash Incident Energy in a Large-Scale Photovoltaic Plant

There is a growing amount of high-power dc equipment being deployed, including solar photovoltaic (PV) plants, but very few studies have measured and quantified dc arc-flash risks. Most dc arc-flash literature is based on the theory and, when their theoretical calculations are applied to real-world conditions, can significantly deviate from actual measured values. For PV, the nonlinearity of the power source (i.e., PV modules) creates an additional level of uncertainty for theoretical approaches. This paper quantifies the thermal hazards from arc flash in a real-world 1000-kWdc PV plant by measuring arc current, arc voltage, and incident energy (IE) and then compares results against existing IE models. The study reveals that none of the available models adequately represent real-world hazards. It is also found that a PV source acts as a sustainable constant-current source during an arcflash event. As a result, this paper is an important first step toward developing an improved model that more accurately assesses dc arc-flash risk in a PV system.

14 SOLAR ENERGY↗
DOE OSTI2020

Integrated Energy System Investigation for the Eastman Chemical Company, Kingsport, TN Facility

The industrial manufacturing industry is looking to implement new methods of energy production to ensure a consistent energy supply while reducing economic costs and environmental impacts. Much of the manufacturing industry relies on fossil fuels—primarily coal and natural gas—of which there are finite resources subject to price volatility due to an inelastic demand. These resources also come with significant negative environmental impacts related to emissions. While complete independence from fossil fuels is not immediately realistic, options are available to significantly reduce dependency on fossil fuel supplies, including integrated energy systems (IESs) which tightly couple nuclear energy source generators with energy consumers (i.e., industrial factories) to fulfill power and energy requirements. Once realized, optimized IESs may yield significant economic and environmental benefits over traditionally isolated generator and consumer facilities. This report presents a feasibility study for siting an IES to meet the steam and electricity needs of Eastman Chemical Company’s facility in Kingsport, Tennessee. This study explored reactor technology options, evaluation,and optimization,with a focus on meeting the facility’s operational and reliability requirements. This work is part of an ongoing effort by Eastman to be good environmental stewards and meet the demands of their customers by shifting to more environmentally sustainable solutions for their energy needs. Outcomes of this report are also of general interest and value to any design or development team seeking to deploy an IES,as many topics described herein are generally applicable to any nuclear power facility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Review of Hazards and Accidentology of an Integrated Energy Systems to Nuclear Power Plant Safety

With the interconnectivity of industrial processes in a nuclear integrated energy system (IES) the primary issue becomes the safety of the nuclear power plant (NPP). The industrial processes being supported are assumed to have been in colocation prior to the addition of an NPP. Do the hazards from each process reach beyond the perimeter of the plant to affect the NPP? This paper reviews hazards and accident statistics from processes in an IES as a first step to answer this question. Hazards and accidentology discussed cover hydrogen, ammonia, syngas, methanol, synthetic fuels, and oil refineries. They include mainly fire, explosion, and toxicity in varying degrees. Historical accident statistics are given whenever available, and accident causes are evaluated and ranked based on their frequencies. Lessons learned from previous accidents are presented. Future work will focus on utilizing the data collected for accidentology of the industrial processes to perform Failure Modes and Effects Analysis and analyze the impacts of additional hazards on the colocated existing nuclear power plant or newly built advanced nuclear reactor. The analysis will provide the frequency and consequence of external events that can affect the NPP.

08 - HYDROGEN↗
DOE OSTI2017

Evaluation of aromatic radiobromination by nucleophilic substitution using diaryliodonium salt precursors

Radiobromine-labeled compounds can be used for positron emission tomography (PET) imaging (ie, 76 Br) and for radiation therapy (ie, 77 Br). However, the commonly used electrophilic substitution reaction using no-carrier-added radiobromide does not always afford the desired product due to the high reactivity of the brominating intermediate. A nucleophilic substitution by bromide, such as radiobromination of iodonium precursors, provides an alternative route for the synthesis of bromo-radiopharmaceuticals. The applicability of aromatic radiobromination by nucleophilic substitution using diaryliodonium salt precursors was evaluated using iodonium model compounds and [ 76 Br]/[ 77 Br]bromide. Radiobromination was observed under all conditions tested, in up to quantitative yields. A QMA cartridge treatment method and a base-free method have been developed, and no extra base is needed for either methods. The base-free conditions are mild and afford much cleaner reactions. Up to 20% water is tolerated in the reactions without reducing the radiochemical yields. No-carrier-added and carrier-added reactions afforded similar results. 4-Bromobenzaldehyde and 4-bromobenzoate have been radiosynthesized reliably and in good yields. Finally, these results indicate that this method is robust and efficient and thus will provide a route for radiobromination of electron-deficient arenes and an alternative route for the synthesis of bromo-radiopharmaceuticals for biological evaluations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

Robust control of a family of uncertain nonminimum‐phase systems via continuous‐time and sampled‐data output feedback

Summary The problem of global robust stabilization is studied by both continuous‐time and sampled‐data output feedback for a family of nonminimum‐phase nonlinear systems with uncertainty. The uncertain nonlinear system considered in this paper has an interconnect structure consisting of a driving system and a possibly unstable zero dynamics with uncertainty, ie, the uncertain driven system. Under a linear growth condition on the uncertain zero dynamics and a Lipschitz condition on the driving system, we show that it is possible to globally robustly stabilize the family of uncertain nonminimum‐phase systems by a single continuous‐time or a sampled‐data output feedback controller. The sampled‐data output feedback controller is designed by using the emulated versions of a continuous‐time observer and a state feedback controller, ie, by holding the input/output signals constant over each sampling interval. The design of either continuous‐time or sampled‐data output compensator uses only the information of the nominal system of the uncertain controlled plant. In the case of sampled‐data control, global robust stability of the hybrid closed‐loop system with uncertainty is established by means of a feedback domination method together with the robustness of the nominal closed‐loop system if the sampling time is small enough.

Lin, Wei↗
DOE OSTI2017

Context-aided analysis of community evolution in networks

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗