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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

System impacts of solar dynamic and growth power systems on space station

Concepts for the 1990's space station envision an initial operational capability with electrical power output requirements of approximately 75 kW and growth power requirements in the range of 300 kW over a period of a few years. Photovoltaic and solar dynamic power generation techniques are contenders for supplying this power to the space station. A study was performed to identify growth power subsystem impacts on other space station subsystems. Subsystem interactions that might suggest early design changes for the space station were emphasized. Quantitative analyses of the effects of power subsystem mass and projected area on space station controllability and reboost requirements were conducted for a range of growth station configurations. Impacts on space station structural dynamics as a function of power subsystem growth were also considered.

Farmer, J. T.↗
DOE OSTI2023

Approximation of nearly-periodic symplectic maps via structure-preserving neural networks

A continuous-time dynamical system with parameter ε is nearly-periodic if all its trajectories are periodic with nowhere-vanishing angular frequency as ε approaches 0. Nearly-periodic maps are discrete-time analogues of nearly-periodic systems, defined as parameter-dependent diffeomorphisms that limit to rotations along a circle action, and they admit formal U(1) symmetries to all orders when the limiting rotation is non-resonant. For Hamiltonian nearly-periodic maps on exact presymplectic manifolds, the formal U(1) symmetry gives rise to a discrete-time adiabatic invariant. In this paper, we construct a novel structure-preserving neural network to approximate nearly-periodic symplectic maps. This neural network architecture, which we call symplectic gyroceptron, ensures that the resulting surrogate map is nearly-periodic and symplectic, and that it gives rise to a discrete-time adiabatic invariant and a long-time stability. This new structure-preserving neural network provides a promising architecture for surrogate modeling of non-dissipative dynamical systems that automatically steps over short timescales without introducing spurious instabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Girsanov's transformation based variance reduced Monte Carlo simulation schemes for reliability estimation in nonlinear stochastic dynamics

The study considers the problem of simulation based time variant reliability analysis of nonlinear randomly excited dynamical systems. Attention is focused on importance sampling strategies based on the application of Girsanov's transformation method. Controls which minimize the distance function, as in the first order reliability method (FORM), are shown to minimize a bound on the sampling variance of the estimator for the probability of failure. Two schemes based on the application of calculus of variations for selecting control signals are proposed: the first obtains the control force as the solution of a two-point nonlinear boundary value problem, and, the second explores the application of the Volterra series in characterizing the controls. The relative merits of these schemes, vis-à-vis the method based on ideas from the FORM, are discussed. Illustrative examples, involving archetypal single degree of freedom (dof) nonlinear oscillators, and a multi-degree of freedom nonlinear dynamical system, are presented. The credentials of the proposed procedures are established by comparing the solutions with pertinent results from direct Monte Carlo simulations. - Highlights: • The distance minimizing control forces minimize a bound on the sampling variance. • Establishing Girsanov controls via solution of a two-point boundary value problem. • Girsanov controls via Volterra's series representation for the transfer functions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Draper Station Analysis Tool

Draper Station Analysis Tool (DSAT) is a computer program, built on commercially available software, for simulating and analyzing complex dynamic systems. Heretofore used in designing and verifying guidance, navigation, and control systems of the International Space Station, DSAT has a modular architecture that lends itself to modification for application to spacecraft or terrestrial systems. DSAT consists of user-interface, data-structures, simulation-generation, analysis, plotting, documentation, and help components. DSAT automates the construction of simulations and the process of analysis. DSAT provides a graphical user interface (GUI), plus a Web-enabled interface, similar to the GUI, that enables a remotely located user to gain access to the full capabilities of DSAT via the Internet and Webbrowser software. Data structures are used to define the GUI, the Web-enabled interface, simulations, and analyses. Three data structures define the type of analysis to be performed: closed-loop simulation, frequency response, and/or stability margins. DSAT can be executed on almost any workstation, desktop, or laptop computer. DSAT provides better than an order of magnitude improvement in cost, schedule, and risk assessment for simulation based design and verification of complex dynamic systems.

Bedrossian, Nazareth↗
DOE OSTI2016

Toward more realistic projections of soil carbon dynamics by Earth system models

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool-and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. Furthermore, we recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Energy consumption and CO{sub 2} emissions in Iran, 2025

Climate change and global warming as the key human societies' threats are essentially associated with energy consumption and CO{sub 2} emissions. A system dynamic model was developed in this study to model the energy consumption and CO{sub 2} emission trends for Iran over 2000–2025. Energy policy factors are considered in analyzing the impact of different energy consumption factors on environmental quality. The simulation results show that the total energy consumption is predicted to reach 2150 by 2025, while that value in 2010 is 1910, which increased by 4.3% yearly. Accordingly, the total CO{sub 2} emissions in 2025 will reach 985 million tonnes, which shows about 5% increase yearly. Furthermore, we constructed policy scenarios based on energy intensity reduction. The analysis show that CO{sub 2} emissions will decrease by 12.14% in 2025 compared to 2010 in the scenario of 5% energy intensity reduction, and 17.8% in the 10% energy intensity reduction scenario. The results obtained in this study provide substantial awareness regarding Irans future energy and CO{sub 2} emission outlines. - Highlights: • Creation of an energy consumption model using system dynamics. • The effect of different policies on energy consumption and emission reductions. • An ascending trend for the environmental costs caused by CO{sub 2} emissions is observed. • An urgent need for energy saving and emission reductions in Iran.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

The application of model-referenced adaptive control to robotic manipulators

The achievement of quality dynamic performance in manipulator systems is difficult using conventional control methods because of both the inherent geometric nonlinearities of these systems and the dependence of the system dynamics on the characteristics of manipulated objects. A model-referenced adaptive control law is developed for maintaining uniformly good performance over a wide range of motions and payloads. The effectiveness of the approach is demonstrated in several simulations and the system stability as a function of input is investigated. Also developed is a 'learning signal' approach designed to minimize initial transients arising from abrupt changes in the inertial payload.

Dubowsky, S.↗
DOE OSTI2021

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea . We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py . Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Modelling new particle formation events in the South African savannah

Africa is one of the less studied continents with respect to atmospheric aerosols. Savannahs are complex dynamic systems sensitive to climate and land-use changes, but the interaction of these systems with the atmosphere is not well understood. Atmospheric particles, called aerosols, affect the climate on regional and global scales, and are an important factor in air quality. In this study, measurements from a relatively clean savannah environment in South Africa were used to model new particle formation and growth. There already are some combined long-term measurements of trace gas concentrations together with aerosol and meteorological variables available, but to our knowledge this is the first detailed simulation that includes all the main processes relevant to particle formation. The results show that both of the particle formation mechanisms investigated overestimated the dependency of the formation rates on sulphuric acid. From the two particle formation mechanisms tested in this work, the approach that included low volatile organic compounds to the particle formation process was more accurate in describing the nucleation events than the approach that did not. To obtain a reliable estimate of aerosol concentration in simulations for larger scales, nucleation mechanisms would need to include organic compounds, at least in southern Africa. This work is the first step in developing a more comprehensive new particle formation model applicable to the unique environment in southern Africa. Such a model will assist in better understanding and predicting new particle formation – knowledge which could ultimately be used to mitigate impacts of climate change and air quality.

Gierens, Rosa↗
DOE OSTI2017

Simulation Exploration through Immersive Parallel Planes

We present a visualization-driven simulation system that tightly couples systems dynamics simulations with an immersive virtual environment to allow analysts to rapidly develop and test hypotheses in a high-dimensional parameter space. To accomplish this, we generalize the two-dimensional parallel-coordinates statistical graphic as an immersive 'parallel-planes' visualization for multivariate time series emitted by simulations running in parallel with the visualization. In contrast to traditional parallel coordinate's mapping the multivariate dimensions onto coordinate axes represented by a series of parallel lines, we map pairs of the multivariate dimensions onto a series of parallel rectangles. As in the case of parallel coordinates, each individual observation in the dataset is mapped to a polyline whose vertices coincide with its coordinate values. Regions of the rectangles can be 'brushed' to highlight and select observations of interest: a 'slider' control allows the user to filter the observations by their time coordinate. In an immersive virtual environment, users interact with the parallel planes using a joystick that can select regions on the planes, manipulate selection, and filter time. The brushing and selection actions are used to both explore existing data as well as to launch additional simulations corresponding to the visually selected portions of the input parameter space. As soon as the new simulations complete, their resulting observations are displayed in the virtual environment. This tight feedback loop between simulation and immersive analytics accelerates users' realization of insights about the simulation and its output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologiesone to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimizationcan provide a computational means for high-throughput hypothesis testing, and eventually, optimization.ResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.ConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

Agent-based model↗
NASA NTRS1980

Dynamics of collisionless systems

The three dimensional dynamics of rotating stellar systems were studied. A comparison of various mathematical models of flat galaxies is presented. The effects of self-gravity upon a flat galaxy undergoing a tidal encounter with another galaxy were investigated.

Zang, T. A.↗
DOE OSTI2014

The Development of an INL Capability for High Temperature Flow, Heat Transfer, and Thermal Energy Storage with Applications in Advanced Small Modular Reactors, High Temperature Heat Exchangers, Hybrid Energy Systems, and Dynamic Grid Energy Storage C

The overall goal of this project is to support Idaho National Laboratory in developing a new advanced high temperature multi fluid multi loop test facility that is aimed at investigating fluid flow and heat transfer, material corrosion, heat exchanger characteristics and instrumentation performance, among others, for nuclear applications. Specifically, preliminary research has been performed at The Ohio State University in the following areas: 1. A review of fluoride molten salts’ characteristics in thermal, corrosive, and compatibility performances. A recommendation for a salt selection is provided. Material candidates for both molten salt and helium flow loop have been identified. 2. A conceptual facility design that satisfies the multi loop (two coolant loops [i.e., fluoride molten salts and helium]) multi purpose (two operation modes [i.e., forced and natural circulation]) requirements. Schematic models are presented. The thermal hydraulic performances in a preliminary printed circuit heat exchanger (PCHE) design have been estimated. 3. An introduction of computational methods and models for pipe heat loss analysis and cases studies. Recommendations on insulation material selection have been provided. 4. An analysis of pipe pressure rating and sizing. Preliminary recommendations on pipe size selection have been provided. 5. A review of molten fluoride salt preparation and chemistry control. An introduction to the experience from the Molten Salt Reactor Experiment at Oak Ridge National Laboratory has been provided. 6. A review of some instruments and components to be used in the facility. Flowmeters and Grayloc connectors have been included. This report primarily presents the conclusions drawn from the extensive review of literatures in material selections and the facility design progress at the current stage. It provides some useful guidelines in insulation material and pipe size selection, as well as an introductory review of facility process and components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Stochastic equation of motion approach to fermionic dissipative dynamics. I. Formalism

In this work, we establish formally exact stochastic equation of motion (SEOM) theory to describe the dissipative dynamics of fermionic open systems. The construction of the SEOM is based on a stochastic decoupling of the dissipative interaction between the system and fermionic environment, and the influence of environmental fluctuations on the reduced system dynamics is characterized by stochastic Grassmann fields. Meanwhile, numerical realization of the time-dependent Grassmann fields has remained a long-standing challenge. To solve this problem, we propose a minimal auxiliary space (MAS) mapping scheme with which the stochastic Grassmann fields are represented by conventional c-number fields along with a set of pseudo-levels. This eventually leads to a numerically feasible MAS-SEOM method. The important properties of the MAS-SEOM are analyzed by making connection to the well-established time-dependent perturbation theory and the hierarchical equations of motion theory. The MAS-SEOM method provides a potentially promising approach for the accurate and efficient simulation of fermionic open systems at ultra-low temperatures.

Han, Lu (ORCID:0000000339002225)↗
DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗