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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

A Systems Model for System-Wide Safety Safety Demonstrator (SD-1): Wildfire Response Operations

The aim of this internship-based project was to contribute to the ongoing development of a systems model for System-Wide Safety’s first Technical Challenge 5 (TC5) series Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System (IASMS) in emerging wildfire response operations. Using Models-Based Systems Engineering (MBSE) principles to develop the model, I organized and traced previously collected stakeholder needs from the Spring 2022 NASA System-Wide Safety Wildland Firefighting Operations Virtual Workshop (https://nari.arc.nasa.gov/sws-wildfire) to system elements, creating connections which can be used in the future by the project engineers to identify and address requirements gaps throughout the system design process. I also identified and modeled preliminary use case scenarios for aerial assets in the demonstrator and, building on previously produced preliminary high-level models of the 8 SD-1 Services, Functions, and Capabilities (SFCs) and their IASMS data flows, worked to model the Real-Time Risk Assessment (RTRA) tool as an implementation of Risk Assessment and Management that can take in multiple sets of data monitored by SFCs. Project deliverables include stakeholder requirements tables and matrices and systems model diagrams produced with MagicDraw software in the SysML Systems Modeling Language, with eventual plans to connect model diagrams to a Department of Defense Operational Viewpoint (OV-1) graphic, a high-level operational concept graphic that will be used to visualize the SD-1 scenarioin a future phase. The system model serves to provide a common understanding of the scope of and activities necessary for the completion of SD-1,and traces how stakeholder needs are to be addressed.

model-based systems engineering↗
NASA NTRS1987

Temporal and contextual knowledge in model-based expert systems

A basic paradigm that allows representation of physical systems with a focus on context and time is presented. Paragon provides the capability to quickly capture an expert's knowledge in a cognitively resonant manner. From that description, Paragon creates a simulation model in LISP, which when executed, verifies that the domain expert did not make any mistakes. The Achille's heel of rule-based systems has been the lack of a systematic methodology for testing, and Paragon's developers are certain that the model-based approach overcomes that problem. The reason this testing is now possible is that software, which is very difficult to test, has in essence been transformed into hardware.

Toth-Fejel, Tihamer↗
DOE OSTI2018

The effects of microstructure and porosity on the competing fatigue failure mechanisms in powder metallurgy Ti-6Al-4V

In near-net-shape manufacturing methods, such as powder metallurgy, additive manufacturing, and metal injection molding, porosity has historically been viewed as the sole limiting factor for fatigue life. This is because pores tend to act as stress concentrators. However, in this work, a fractographic analysis of Ti-6Al-4V produced through several powder metallurgy techniques has shown that microstructural faceting due to slip can cause fatigue failure, even in the presence of porosity. The likelihood of pore related failure was found to be dependent on microstructure size and morphology. Further, a minimum pore size threshold was found to exist for each microstructure, under which pores will not cause fatigue failure. A simple model was developed to determine this threshold based on the microstructural characteristics of the material. This model was then compared to experimental data and properly predicted the fatigue failure mechanism in over 99% of the samples examined.

36 MATERIALS SCIENCE↗
NASA NTRS1980

Mathematical modeling of moving boundary problems in thermal energy storage

The capability for predicting the performance of thermal energy storage (RES) subsystems and components using PCM's based on mathematical and physical models is developed. Mathematical models of the dynamic thermal behavior of (TES) subsystems using PCM's based on solutions of the moving boundary thermal conduction problem and on heat and mass transfer engineering correlations are also discussed.

Solomon, A. D.↗
NASA NTRS1980

Software cost/resource modeling

The Putnam method for planning the time/work effort component of software development is evaluated. This software costing model, based on Norden-Rayleigh product life cycle concepts, was applied to four development projects. The time/effort tradeoff leading to savings attainable when development time on a project is extended by a few months is demonstrated. The software equation which relates the principal parameters of development time, total effort, system size, and the development environment is presented.

Golden, J. R.↗
NASA NTRS1988

A relational data-knowledge base system and its potential in developing a distributed data-knowledge system

A new approach used in constructing a rational data knowledge base system is described. The relational database is well suited for distribution due to its property of allowing data fragmentation and fragmentation transparency. An example is formulated of a simple relational data knowledge base which may be generalized for use in developing a relational distributed data knowledge base system. The efficiency and ease of application of such a data knowledge base management system is briefly discussed. Also discussed are the potentials of the developed model for sharing the data knowledge base as well as the possible areas of difficulty in implementing the relational data knowledge base management system.

Rahimian, Eric N.↗
DOE OSTI2012

Strategic Analysis of Complex Security Scenarios

Many advanced technical tools are available to prevent attacks on national infrastructure. Nevertheless, while traditional analyses of security problems have succeeded in producing good technical solutions, they have often ignored the human factor integral to these problems. Human attackers (who may be individuals or state-level attackers) expend substantial effort to breach security because they have the incentive for doing so. People involved in implementing security follow individual incentives, which need not align with global security concerns; consequently, desired security solutions are often implemented poorly, or not at all. This complex interplay between individual incentives and global (organizational and/or national) goals can be modeled and analyzed using game theoretic techniques. By analyzing not only what is possible, but also what is motivated, a holistic approach to security problems can be developed, informing policy and providing tools to policy makers. We study game theoretic models that unify several current incentive-based approaches to security, and develop simulation-based and mathematical optimization methods for analyzing such models that exploit the high-performance computing capabilities at Sandia. Our first model studies security in interdependent settings, offering a scalable local search heuristic to approximate optimal security decisions in general, and a linear programming approach, coupled with simulations of consequences, to optimally compute security in an important special case. Our second class of models addresses security patrolling problems when an adversary gets to observe the patrol location. We present a general framework, based on stochastic games, for computing optimal security policies in such settings, and present more scalable tools that apply in the important special cases. Our third contribution is a model of security that involves many defenders, but only models non-adaptive attackers (or natural disasters, inadvertent errors, etc). In this model, we demonstrate that the security decisions of many players result in global security configuration that is not very far from optimal, and is much more resilient to environment changes that an optimal solution. This positive effect dissipates, however, when the number of decision makers becomes too large.

Vorobeychik, Yevgeniy↗
DOE OSTI2016

A general-purpose machine learning framework for predicting properties of inorganic materials

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Cramer-Rao bounds for signal-to-noise ratio and combiner weight estimation

Cramer-Rao lower bounds on estimator variance are calculated for arbitrary unbiased estimates of signal-to-noise ratio and combiner weight parameters. Estimates are assumed to be based on a discrete set of observables obtained by matched filtering of a biphase modulated signal. The bounds are developed first for a problem model based on one observable per channel symbol period, and then extended to a more general problem in which subperiod observables are also available.

Dolinar, S. J.↗
DOE OSTI2023

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

Improving the long-term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics-based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data-driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data-driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient-based local search algorithm. Two field-scale examples are presented to investigate the performance of the developed data-driven prediction and optimization framework. Furthermore, the prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems

The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.

Failure Analysis↗
NASA NTRSDate not supplied

From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems

The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.

Failure Analysis↗
DOE OSTI2013

Efficient and reliable 3D dose quality assurance for IMRT by combining independent dose calculations with measurements

Purpose: Advanced radiotherapy treatments require appropriate quality assurance (QA) to verify 3D dose distributions. Moreover, increase in patient numbers demand efficient QA-methods. In this study, a time efficient method that combines model-based QA and measurement-based QA was developed; i.e., the hybrid-QA. The purpose of this study was to determine the reliability of the model-based QA and to evaluate time efficiency of the hybrid-QA method. Methods: Accuracy of the model-based QA was determined by comparison of COMPASS calculated dose with Monte Carlo calculations for heterogeneous media. In total, 330 intensity modulated radiation therapy (IMRT) treatment plans were evaluated based on the mean gamma index (GI) with criteria of 3%/3mm and classification of PASS (GI {<=} 0.4), EVAL (0.4 < GI > 0.6), and FAIL (GI {>=} 0.6). Agreement between model-based QA and measurement-based QA was determined for 48 treatment plans, and linac stability was verified for 15 months. Finally, time efficiency improvement of the hybrid-QA was quantified for four representative treatment plans. Results: COMPASS calculated dose was in agreement with Monte Carlo dose, with a maximum error of 3.2% in heterogeneous media with high density (2.4 g/cm{sup 3}). Hybrid-QA results for IMRT treatment plans showed an excellent PASS rate of 98% for all cases. Model-based QA was in agreement with measurement-based QA, as shown by a minimal difference in GI of 0.03 {+-} 0.08. Linac stability was high with an average GI of 0.28 {+-} 0.04. The hybrid-QA method resulted in a time efficiency improvement of 15 min per treatment plan QA compared to measurement-based QA. Conclusions: The hybrid-QA method is adequate for efficient and accurate 3D dose verification. It combines time efficiency of model-based QA with reliability of measurement-based QA and is suitable for implementation within any radiotherapy department.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings

Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. We report the simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control

This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Building a free-energy functional from atomically resolved imaging: Atomic-scale phenomena in La-doped BiFe O 3

Scanning transmission electron microscopy (STEM) has enabled mapping of atomic structures of solids with sub-picometer precision, providing insight to the physics of ferroic phenomena and chemical expansion. Yet, only a subset of information is available, due to projective nature of imaging in the beam direction. Correspondingly, the analysis often relies on the postulated form of macroscopic Landau-Ginzburg energy for the ferroic long-range order parameter, and some predefined relationship between experimentally determined atomic coordinates and the order-parameter field. Here, we propose an approach for exploring the structure of ferroics using reduced order-parameter models constructed based on experimental data only. We develop a four-sublattice model (FSM) for the analytical description of A -cation displacement in (anti)ferroelectric-antiferrodistortive perovskites of ABO 3 type. The model describes the displacements of cation A in four neighboring unit cells and determines the conditions of different structural phases’ appearance and stability in ABO 3 . We show that FSM explains the coexistence of rhombohedral, orthorhombic, and spatially modulated phases, observed by atomic-resolution STEM in La-doped BiFeO 3 . Using this method, we atomically resolve and theoretically model the sublattice asymmetry inherent to the case of the A -site La/Bi cation sublattice in La x Bi 1- x FeO 3 polymorphs. This approach allows the exploration of ferroic behaviors from experimental data only, without additional assumptions on the nature of the order parameter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗