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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Findings of a review of spacecraft fire safety needs

Discussions from a workshop to guide UCLA and NASA investigators on the state of knowledge and perceived needs in spacecraft fire safety and its risk management are reviewed, for an introduction to an analytical and experimental project in this field. The report summarizes the workshop discussions and includes the visual aids used in the presentations. Probabilistic Safety Assessment (PSA) methods, which are currently not used, would be of great value to the designs and operation of future human-crew spacecraft. Key points in the discussions were the importance of understanding and testing smoldering as a likely fire scenario in space and the need for smoke damage modeling, since many fire-risk models ignore this mechanism and consider only heat damage.

Apostolakis, G. E.↗
NASA NTRS2003

Application of Probabilistic Analysis to Aircraft Impact Dynamics

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stressstrain behaviors, laminated composites, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the uncertainty in the simulated responses. Several criteria are used to determine that a response surface method is the most appropriate probabilistic approach. The work is extended to compare optimization results with and without probabilistic constraints.

Lyle, Karen H.↗
NASA NTRS2004

Assessment and Control of Spacecraft Charging Risks on the International Space Station

The International Space Station (ISS) operates in the F2 region of Earth's ionosphere, orbiting at altitudes ranging from 350 to 450 km at an inclination of 51.6 degrees. The relatively dense, cool F2 ionospheric plasma suppresses surface charging processes much of the time, and the flux of relativistic electrons is low enough to preclude deep dielectric charging processes. The most important spacecraft charging processes in the ISS orbital environment are: 1) ISS electrical power system interactions with the F2 plasma, 2) magnetic induction processes resulting from flight through the geomagnetic field and, 3) charging processes that result from interaction with auroral electrons at high latitude. Recently, the continuing review and evaluation of putative ISS charging hazards required by the ISS Program Office revealed that ISS charging could produce an electrical shock hazard to the ISS crew during extravehicular activity (EVA). ISS charging risks are being evaluated in an ongoing measurement and analysis campaign. The results of ISS charging measurements are combined with a recently developed model of ISS charging (the Plasma Interaction Model) and an exhaustive analysis of historical ionospheric variability data (ISS Ionospheric Specification) to evaluate ISS charging risks using Probabilistic Risk Assessment (PRA) methods. The PRA combines estimates of the frequency of occurrence and severity of the charging hazards with estimates of the reliability of various hazard controls systems, as required by NASA s safety and risk management programs, to enable design and selection of a hazard control approach that minimizes overall programmatic and personnel risk. The PRA provides a quantitative methodology for incorporating the results of the ISS charging measurement and analysis campaigns into the necessary hazard reports, EVA procedures, and ISS flight rules required for operating ISS in a safe and productive manner.

Koontz, Steve↗
NASA NTRS2010

Affordability Engineering: Bridging the Gap Between Design and Cost

Affordability is a commonly used term that takes on numerous meanings depending on the context used. Within conceptual design of complex systems, the term generally implies comparisons between expected costs and expected resources. This characterization is largely correct, but does not convey the many nuances and considerations that are frequently misunderstood and underappreciated. In the most fundamental sense, affordability and cost directly relate to engineering and programmatic decisions made throughout development programs. Systems engineering texts point out that there is a temporal aspect to this relationship, for decisions made earlier in a program dictate design implications much more so than those made during latter phases. This paper explores affordability engineering and its many sub-disciplines by discussing how it can be considered an additional engineering discipline to be balanced throughout the systems engineering and systems analysis processes. Example methods of multidisciplinary design analysis with affordability as a key driver will be discussed, as will example methods of data visualization, probabilistic analysis, and other ways of relating design decisions to affordability results.

Reeves, J. D.↗
NASA NTRS2003

Assessment and Control of Spacecraft Charging Risks on the International Space Station

Electrical interactions between the F2 region ionospheric plasma and the 160V photovoltaic (PV) electrical power system on the International Space Station (ISS) can produce floating potentials (FP) on the ISS conducting structure of greater magnitude than are usually observed on spacecraft in low-Earth orbit. Flight through the geomagnetic field also causes magnetic induction charging of ISS conducting structure. Charging processes resulting from interaction of ISS with auroral electrons may also contribute to charging albeit rarely. The magnitude and frequency of occurrence of possibly hazardous charging events depends on the ISS assembly stage (six more 160V PV arrays will be added to ISS), ISS flight configuration, ISS position (latitude and longitude), and the natural variability in the ionospheric flight environment. At present, ISS is equipped with two plasma contactors designed to control ISS FP to within 40 volts of the ambient F2 plasma. The negative-polarity grounding scheme utilized in the ISS 160V power system leads, naturally, to negative values of ISS FP. A negative ISS structural FP leads to application of electrostatic fields across the dielectrics that separate conducting structure from the ambient F2 plasma, thereby enabling dielectric breakdown and arcing. Degradation of some thermal control coatings and noise in electrical systems can result. Continued review and evaluation of the putative charging hazards, as required by the ISS Program Office, revealed that ISS charging could produce a risk of electric shock to the ISS crew during extra vehicular activity. ISS charging risks are being evaluated in ongoing ISS charging measurements and analysis campaigns. The results of ISS charging measurements are combined with a recently developed detailed model of the ISS charging process and an extensive analysis of historical ionospheric variability data, to assess ISS charging risks using Probabilistic Risk Assessment (PRA) methods. The PRA analysis (estimated frequency of occurrence and severity of the charging hazards) are then used to select the hazard control strategy that provides the best overall safety and mission success environment for ISS and the ISS crew. This paper presents: 1) a summary of ISS spacecraft charging analysis, measurements, observations made to date, 2) plans for future ISS spacecraft charging measurement campaigns, and 3) a detailed discussion of the PRA strategy used to assess ISS spacecraft charging risks and select charging hazard control strategies

Koontz, Steve↗
NASA NTRS2012

Probabilistic Sensitivity Analysis for Launch Vehicles with Varying Payloads and Adapters for Structural Dynamics and Loads

This paper examines Probabilistic Sensitivity Analysis (PSA) methods and tools in an effort to understand their utility in vehicle loads and dynamic analysis. Specifically, this study addresses how these methods may be used to establish limits on payload mass and cg location and requirements on adaptor stiffnesses while maintaining vehicle loads and frequencies within established bounds. To this end, PSA methods and tools are applied to a realistic, but manageable, integrated launch vehicle analysis where payload and payload adaptor parameters are modeled as random variables. This analysis is used to study both Regional Response PSA (RRPSA) and Global Response PSA (GRPSA) methods, with a primary focus on sampling based techniques. For contrast, some MPP based approaches are also examined.

McGhee, David S.↗
NASA NTRS2003

Probabilistic Unsteady Aerodynamic Analysis

Probabilistic CFD design is needed because we are asked to do more with less. To cost effectively accomplish the design task, we need to formally quantify the effect of uncertainties (variables) in the design. Probabilistic design is one effective method to formally quantify the effect of uncertainties. Our objective is to establish a revolutionary new early design process, by developing non-deterministic physics-based probabilistic design tools, which will include all the life cycle processes. This work was concerned with the usefulness of parametric optimization method coupled with a Navier-Stokes analysis code for the aero-thermodynamic design of turbomachinery combustor liner. The interconnection between the CFD code and NESSUS codes will facilitate the coupling between the thermal profiles and structural design. We have developed new concepts for reducing the computational cost of unsteady, three-dimensional, compressible aerodynamic analyses for multistage turbomachinery flows. The flow was modeled by the three-dimensional Favre-Reynolds-averaged Navier-Stokes equations using the k-E turbulence closure, which was integrated using an implicit third-order upwind solver. The methodology developed in this work is expected to lead to the design optimization of turbomachinery blades.

Gorla, Rama S. R.↗
DOE OSTI2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Review of the probabilistic failure analysis methodology and other probabilistic approaches for application in aerospace structural design

Probabilistic structural analyses and design methods are steadily gaining acceptance within the aerospace industry. The safety factor approach to design has long been the industry standard, and it is believed by many to be overly conservative and thus, costly. A probabilistic approach to design may offer substantial cost savings. This report summarizes several probabilistic approaches: the probabilistic failure analysis (PFA) methodology developed by Jet Propulsion Laboratory, fast probability integration (FPI) methods, the NESSUS finite element code, and response surface methods. Example problems are provided to help identify the advantages and disadvantages of each method.

Townsend, J.↗
NASA NTRS2002

Probabilistic Study of Fluid Structure Interaction

Probabilistic CFD design is needed because we are asked to do more with less. To cost effectively accomplish the design task, we need to formally quantify the effect of uncertainties (variables) in the design. Probabilistic design is one effective method to formally quantify the effect of uncertainties. Our objective is to establish a revolutionary new early design process, by developing non-deterministic physics-based probabilistic design tools, which will include all the life cycle processes. Breakthroughs will be sought in speed, accuracy, intelligence, and usability of the system. This paper is concerned with the usefulness of parametric optimization method coupled with a Navier-Stokes analysis code for the aero-thermodynamic design of turbomachinery combustor liner. The interconnection between the CFD code and NESSUS codes facilitated the coupling between the thermal profiles and structural design. We have developed new concepts for reducing the computational cost of unsteady, three-dimensional, compressible aerodynamic analyses for multistage turbomachinery flows. The flow was modeled by the three-dimensional Favre-Reynolds-averaged Navier-Stokes equations using the k-epsilon turbulence closure, which was integrated using an implicit third-order upwind solver. The methodology developed in this paper is expected to lead to the design optimization of turbomachinery blades.

Gorla, Rama S. R.↗
DOE OSTI2020

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities

Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Incorporating finite element analysis into component life and reliability

A method for calculating a component's design survivability by incorporating finite element analysis and probabilistic material properties was developed. The method evaluates design parameters through direct comparisons of component survivability expressed in terms of Weibull parameters. The analysis was applied to a rotating disk with mounting bolt holes. The highest probability of failure occurred at, or near, the maximum shear stress region of the bolt holes. Distribution of failure as a function of Weibull slope affects the probability of survival. Where Weibull parameters are unknown for a rotating disk, it may be permissible to assume Weibull parameters, as well as the stress-life exponent, in order to determine the disk speed where the probability of survival is highest.

August, Richard↗
NASA NTRS1992

Fatigue criterion to system design, life and reliability: A primer

A method for estimating a component's design survivability by incorporating finite element analysis and probabilistic material properties was developed. The method evaluates design parameters through direct comparisons of component survivability expressed in terms of Weibull parameters. The analysis was applied to a rotating disk with mounting bolt holes. The highest probability of failure occurred at, or near, the maximum shear stress region of the bolt holes. Distribution of material failure as a function of Weibull slope affects the probability of survival. Where Weibull parameters are unknown for a rotating disk, it may be permissible to assume Weibull parameters, as well as the stress-life exponent, in order to determine the qualitative effect of disk speed on the probability of survival.

Zaretsky, Erwin V.↗
DOE OSTI2024

Endogenizing Probabilistic Resource Adequacy Risks in Deterministic Capacity Expansion Models

In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.

capacity expansion modeling↗
DOE OSTI2025

Dominant balance-based adaptive mesh refinement for incompressible fluid flows

This work introduces a novel adaptive mesh refinement (AMR) method that utilizes dominant balance analysis (DBA) for efficient and accurate grid adaptation in computational fluid dynamics (CFD) simulations. The proposed method leverages a Gaussian mixture model (GMM) to classify grid cells into active and passive regions based on the dominant physical interactions within the equation space. By modeling truncation error probabilistically from discretized terms, the method identifies regions of high interaction where numerical accuracy is most sensitive to resolution. Unlike traditional AMR strategies, this approach does not rely on heuristic-based sensors or user-defined thresholds, providing a fully automated and problem-independent framework for AMR. Applied to the incompressible Navier-Stokes equations for steady and unsteady flow past a cylinder, the DBA-based AMR method achieves comparable accuracy to high-resolution grids while reducing computational costs by up to 70 %. The validation highlights the method’s effectiveness in capturing complex flow features while minimizing grid cells, directing computational resources toward regions with the most critical dynamics. This modular and scalable strategy is adaptable to a wide range of applications, presenting a promising tool for efficient high-fidelity simulations in CFD and other multiphysics domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1993

Reliability based structural optimization - A simplified safety index approach

A probabilistic optimal design methodology for complex structures modelled with finite element methods is presented. The main emphasis is on developing probabilistic analysis tools suitable for optimization. An advanced second-moment method is employed to evaluate the failure probability of the performance function. The safety indices are interpolated using the information at mean and most probable failure point. The minimum weight design with an improved safety index limit is achieved by using the extended interior penalty method of optimization. Numerical examples covering beam and plate structures are presented to illustrate the design approach. The results obtained by using the proposed approach are compared with those obtained by using the existing probabilistic optimization techniques.

Reddy, Mahidhar V.↗
NASA NTRS1990

The simulation of fluid dynamic uncertainties in the SSME turbopump

The aerodynamic uncertainties in the reusable rocket engine turbopumps due to randomness in their cryogenic environment are investigated. The probabilistic simulation of the turbopumps' aerodynamic response was accomplished using the panel method coupled with Fast Probability Integration methods. The results are presented for the probabilistic rotor blades and splitter loading and their sensitivity to specified flow coefficient and rotor preswirl variance.

Hamed, Awatef↗