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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Portable Life Support Subsystem Thermal Hydraulic Performance Analysis

This paper presents the current state of the thermal hydraulic modeling efforts being conducted for the Constellation Space Suit Element (CSSE) Portable Life Support Subsystem (PLSS). The goal of these efforts is to provide realistic simulations of the PLSS under various modes of operation. The PLSS thermal hydraulic model simulates the thermal, pressure, flow characteristics, and human thermal comfort related to the PLSS performance. This paper presents modeling approaches and assumptions as well as component model descriptions. Results from the models are presented that show PLSS operations at steady-state and transient conditions. Finally, conclusions and recommendations are offered that summarize results, identify PLSS design weaknesses uncovered during review of the analysis results, and propose areas for improvement to increase model fidelity and accuracy.

Barnes, Bruce↗
DOE OSTI2020

Driven electrostatic phase space vortices in a 1D weakly dissipative Vlasov–Poisson system

In this paper, the effect of collisions on driven electrostatic phase space vortices is analyzed by means of Eulerian simulation for two different collision models. It was demonstrated recently that in the absence of collisions, at late times, steady state phase space vortices manifest to form a plateau in the resonant region of the particle velocity distribution function, due to trapping of particles supporting multiextrema giant phase space vortices (PSVs). In the presence of collisions, over long time, this multiextrema plateau are found to smooth out, since collisions drive the velocity distribution toward Maxwellian, irrespective of how weak the collisions are as long as they are non-zero. In these conditions, kinetic processes and collisionality are found to be in competition, and the evolution of the plasma is found, therefore, to be a result of nontrivial combination of these two effects. An attempt has been made by means of numerical simulations to study the effect of weak collisionality on the electrostatic driven phase space vortices with two types of collision operators: (1) Bhatnagar–Gross–Krook (Krook) collision operator, where the colliding particles can be treated as isolated pairs and, (2) Fokker–Planck (FP) type collision operator (Zakharov–Karpman) in one dimension, where many weak collisions lead to particle diffusion in velocity space. It is shown that depending on the collision model used, the nature of smoothing in velocity space of giant PSVs results in qualitatively very different phase space structures. However, irrespective of the collision model used, excess density fractions over 10% are retained.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2009

Explicit Finite Element Modeling of Multilayer Composite Fabric for Gas Turbine Engine Containment Systems, Phase II: Material Model Development and Simulation of Experiments - Part 3

A team consisting of Arizona State University, Honeywell Engines, Systems & Services, the National Aeronautics and Space Administration Glenn Research Center, and SRI International collaborated to develop computational models and verification testing for designing and evaluating turbine engine fan blade fabric containment structures. This research was conducted under the Federal Aviation Administration Airworthiness Assurance Center of Excellence and was sponsored by the Aircraft Catastrophic Failure Prevention Program. The research was directed toward improving the modeling of a turbine engine fabric containment structure for an engine blade-out containment demonstration test required for certification of aircraft engines. The research conducted in Phase II began a new level of capability to design and develop fan blade containment systems for turbine engines. Significant progress was made in three areas: (1) further development of the ballistic fabric model to increase confidence and robustness in the material models for the Kevlar(TradeName) and Zylon(TradeName) material models developed in Phase I, (2) the capability was improved for finite element modeling of multiple layers of fabric using multiple layers of shell elements, and (3) large-scale simulations were performed. This report concentrates on the material model development and simulations of the impact tests.

Zylon↗
NASA NTRS2005

Hybrid Verification of an Air Traffic Operational Concept

A concept of operations for air traffic management consists of a set of flight rules and procedures aimed to keep aircraft safely separated. This paper reports on the formal verification of separation properties of the NASA's Small Aircraft Transportation System, Higher Volume Operations (SATS HVO) concept for non-towered, non-radar airports. Based on a geometric description of the SATS HVO air space, we derive analytical formulas to compute spacing requirements on nominal approaches. Then, we model the operational concept by a hybrid non-deterministic asynchronous state transition system. Using an explicit state exploration technique, we show that the spacing requirements are always satisfied on nominal approaches. All the mathematical development presented in this paper has been formally verified in the Prototype Verification System (PVS). Keywords. Formal verification, hybrid systems, air traffic management, theorem proving

Munoz, Cesar A.↗
DOE OSTI2022

Benchmarking intra-nuclear cascade models for neutrino scattering with relativistic optical potentials

The description of final-state interactions (FSI) in the large phase space probed in neutrino experiments poses a great challenge. In neutrino experiments, which operate under semi-inclusive conditions, cascade models are commonly used for this task, while under exclusive conditions FSI can be treated with relativistic optical potentials (ROP). We formulate conditions under which the ROP approach and cascade model can be directly compared. We feed the NEUT cascade with events from a relativistic distorted-wave impulse approximation calculation that uses the real part of an optical potential. Cuts on the missing energy of the resulting events are applied to define a set of events that can be directly compared to RDWIA calculations with the full optical potential. The NEUT cascade and ROP agree for proton kinetic energies $T_p > 150$ MeV for carbon, oxygen and calcium nuclei when a realistic nuclear density is used to introduce events in the cascade. For $T_p < 100$ MeV the ROP and NEUT cross sections differ in shape and differences in magnitude are larger than 50 %. Single transverse variables allow to distinguish different approaches to FSI, but due to a large non-QE contribution the comparison to T2K data does not give an unambiguous view of FSI. We discuss electron scattering and argue that with a cut in missing energy FSI can be studied with minimal confounding factors in e.g. $e4\nu$. The agreement of the ROP and NEUT for T2K conditions lends confidence to these models as a tool in oscillation analyses for sufficiently large nucleon kinetic energies. These results urge for caution when a cascade model is applied for small nucleon energies. The assessment of model assumptions relevant to this region are strongly encouraged. This paper provides novel constraints on cascade models from proton-nucleus scattering that can be easily applied to other neutrino event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2013

Tethered Satellites as an Enabling Platform for Operational Space Weather Monitoring Systems

Tethered satellites offer the potential to be an important enabling technology to support operational space weather monitoring systems. Space weather "nowcasting" and forecasting models rely on assimilation of near-real-time (NRT) space environment data to provide warnings for storm events and deleterious effects on the global societal infrastructure. Typically, these models are initialized by a climatological model to provide "most probable distributions" of environmental parameters as a function of time and space. The process of NRT data assimilation gently pulls the climate model closer toward the observed state (e.g., via Kalman smoothing) for nowcasting, and forecasting is achieved through a set of iterative semi-empirical physics-based forward-prediction calculations. Many challenges are associated with the development of an operational system, from the top-level architecture (e.g., the required space weather observatories to meet the spatial and temporal requirements of these models) down to the individual instruments capable of making the NRT measurements. This study focuses on the latter challenge: we present some examples of how tethered satellites (from 100s of m to 20 km) are uniquely suited to address certain shortfalls in our ability to measure critical environmental parameters necessary to drive these space weather models. Examples include long baseline electric field measurements, magnetized ionospheric conductivity measurements, and the ability to separate temporal from spatial irregularities in environmental parameters. Tethered satellite functional requirements are presented for two examples of space environment observables.

Gilchrist, Brian E.↗
NASA NTRSDate not supplied

EARLY INFORMATION PARAMETER-SET ANALYSIS FOR SATELLITE CLOSE APPROACHES USING MACHINE LEARNING

In spaceflight navigation applications, understanding and accurately applying orbital mechanics by leveraging force models for trajectory predictions will always remain an important aspect in space mission design and operations. In the process of capturing the dynamics and perturbations in the space environment, the force models are not all encompassing in that these models are subject to errors, commonly referred to as process noise. Therefore, in predicting state vectors of space objects such as spacecraft or debris over long periods of time, these errors in the process noise tend to grow over time.

machine learning↗
NASA NTRS2026

Measure sea level air pressure from space to improve knowledge and forecasting of the atmospheric state

Modern numerical weather prediction (NWP) and analysis models require globally-observed meteorological data including sea-level pressure (SLP) for accurate operations. Up until now, SLP has only been measured by in situ instruments from ships, buoys, and ocean platforms. These measurements are sparse with large gaps, leaving models starved of this critical information to constrain the atmospheric state. Recent advancements in differential absorption radar (DAR) provide a path to close this critical observation gap through spaceborne observations in the coming decade, improving the analysis models relied upon for atmospheric research and the weather forecasts depended upon daily for public safety and commerce.

Matthew L Walker McLinden↗
NASA NTRS2007

Using Arctic Data for Magnetospheric Modeling: Current State and Future Perspectives

Geomagnetic storms are the most dramatic manifestations of solar influences on the magnetosphere. Storms are accompanied by substantial Space Weather effects. For these, as well as for scientific reasons, storms have been a special target of magnetospheric modeling for quite some time. Accordingly, there has been considerable interest and success in improving the application of magnetospheric models to storm-time dynamics, an to compare to data from arctic observatories. In this presentation, we present an overview of CCMC-studies of storm-time model capabilities, and we will provide an overall assessment of capabilities. In addition, we will present an analysis of future challenges, and we will suggest strategies for future model development.

Hesse, Michael↗
NASA NTRS1998

On-Orbit Model Refinement for Controller Redesign

High performance control design for a flexible space structure is challenging since high fidelity plant models are difficult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. A new procedure for refining a multivariable open loop plant model based on closed-loop response data is presented. Using a minimal representation of the state space dynamics, a least squares prediction error method is employed to estimate the plant parameters. This control-relevant system identification procedure stresses the joint nature of the system identification and control design problem by seeking to obtain a model that minimizes the difference between the predicted and actual closed-loop performance. This paper presents an algorithm for iterative closed-loop system identification and controller redesign along with illustrative examples.

Whorton, Mark S.↗
NASA NTRS2005

Diagnostic instrumentation aboard ISS: just-in-time training for non-physician crewmembers

INTRODUCTION: The performance of complex tasks on the International Space Station (ISS) requires significant preflight crew training commitments and frequent skill and knowledge refreshment. This report documents a recently developed "just-in-time" training methodology, which integrates preflight hardware familiarization and procedure training with an on-orbit CD-ROM-based skill enhancement. This "just-in-time" concept was used to support real-time remote expert guidance to complete ultrasound examinations using the ISS Human Research Facility (HRF). METHODS: An American and Russian ISS crewmember received 2 h of "hands on" ultrasound training 8 mo prior to the on-orbit ultrasound exam. A CD-ROM-based Onboard Proficiency Enhancement (OPE) interactive multimedia program consisting of memory enhancing tutorials, and skill testing exercises, was completed by the crewmember 6 d prior to the on-orbit ultrasound exam. The crewmember was then remotely guided through a thoracic, vascular, and echocardiographic examination by ultrasound imaging experts. RESULTS: Results of the CD-ROM-based OPE session were used to modify the instructions during a complete 35-min real-time thoracic, cardiac, and carotid/jugular ultrasound study. Following commands from the ground-based expert, the crewmember acquired all target views and images without difficulty. The anatomical content and fidelity of ultrasound video were adequate for clinical decision making. CONCLUSIONS: Complex ultrasound experiments with expert guidance were performed with high accuracy following limited preflight training and multimedia based in-flight review, despite a 2-s communication latency. In-flight application of multimedia proficiency enhancement software, coupled with real-time remote expert guidance, facilitates the successful performance of ultrasound examinations on orbit and may have additional terrestrial and space applications.

Inservice Training/methods↗
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 OSTI2021

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning

Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data. Typically, models are evaluated on unseen classes and datasets that are defined by the same fundamental task as they are trained for (e.g. category membership). One can also ask how well a model can generalize to fundamentally different tasks within a fixed dataset (for example: moving from category membership to tasks that involve detecting object orientation or quantity). To formalize this kind of shift we define a notion of “independence of tasks” and identify three new sets of labels for established computer vision datasets that test a model's ability to generalize to tasks which draw on orthogonal attributes in the data. We use these datasets to investigate the failure modes of metric-based few-shot models. Based on our findings, we introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data. In particular, FSN models can not only form multiple representations for a given class but can also begin to capture the low-dimensional structure which characterizes class manifolds in the encoded space of deep networks. We show that FSN outperforms state-of-the-art models on the challenging tasks we introduce in this paper while remaining competitive on standard few-shot benchmarks.

deep learning↗
DOE OSTI2023

Reduced-order autodifferentiable ensemble Kalman filters

This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system. Our reduced-order autodifferentiable ensemble Kalman filters (ROAD-EnKFs) learn a latent low-dimensional surrogate model for the dynamics and a decoder that maps from the latent space to the state space. The learned dynamics and decoder are then used within an EnKF to reconstruct and forecast the state. Numerical experiments show that if the state dynamics exhibit a hidden low-dimensional structure, ROAD-EnKFs achieve higher accuracy at lower computational cost compared to existing methods. If such structure is not expressed in the latent state dynamics, ROAD-EnKFs achieve similar accuracy at lower cost, making them a promising approach for surrogate state reconstruction and forecasting.

Mathematics↗
DOE OSTI2022

TAUOLA update for decay channels with e + e – pairs in the final state

With the arrival of high luminosity B-factories like the Belle II experiment, τ decay measurements have become more precise than ever, allowing rarer processes to be explored, and finer details of τ decays to be studied. These are important to understand the spectrum of intermediate particles produced in τ decays. Therefore Monte Carlo generators, like the TAUOLA program, have to facilitate precision analysis as well as confront new models that constantly emerge with the availability of high statistics experimental data. New decay channels and models may lead to large variation of matrix elements size within the available phase space, as in the case when e + e - pairs are present in final state. It requires appropriate presampler of the phase space generator and a proper documentation to help users introduce their own models. While releasing a new update, it is important for the TAUOLA Monte Carlo library to maintain the general structure of the previous versions to preserve backward compatibility. The aim is to minimize changes from the user perspective. Finally, this paper presents a demonstration of new models implementation facilitated by the current update.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equations (NODE) have shown promising results by leveraging the expressive power of neural networks to model unknown dynamics. However, these approaches often suffer from limited labeled training data, leading to poor generalization and suboptimal predictions. On the other hand, semi-supervised algorithms can utilize abundant unlabeled data and have demonstrated good performance in classification and regression tasks. We propose TS-NODE, the first semi-supervised approach to modeling dynamical systems with NODE. TS-NODE explores cheaply generated synthetic pseudo rollouts to broaden exploration in the state space and to tackle the challenges brought by lack of ground-truth system data under a teacher-student model. TS-NODE employs an unified optimization framework that corrects the teacher model based on the student's feedback while mitigating the potential false system dynamics present in pseudo rollouts. TS-NODE demonstrates significant performance improvements over a baseline Neural ODE model on multiple dynamical system modeling tasks.

Wang, Yu↗
NASA NTRS1986

Grain dynamics in zero gravity

The dynamics of granular materials has proved difficult to model, primarily because of the complications arising from inelastic losses, friction, packing, and the effect of many grains being in contact simultaneously. The kinetic model of granular systems is similar to the kinetic theory of gases, except that collisional energy losses are always present in the former and must be treated explicity. Few granular materials on Earth are describable by this limiting model, since gravity tends to collapse the grains into a high density state where Coulombic friction effects are dominant. The planned Space Station offers an unusual opportunity to test the kinetic grain model and to explore its predictions. Without gravity, the regime of low interparticle velocities (where an elastic description of the collision is still valid) can be investigated. This will allow for direct interpretation by dynamical computer simulations as well as by the kinetic theory. The dynamics of spherical grains inside a clear box would be examined. Results would be compared with the predictions of the kinetic theory and computer simulations.

Werner, B. T.↗
NASA NTRS2012

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Armstrong, Jeffrey B.↗