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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A scoping study of far-SOL main-wall protection limiters for steady-state operation of compact pilot plant tokamaks

We present a novel method for handling steady-state heat fluxes incident on the main wall of pilot plant-scale magnetic fusion devices, based on the utilization of protection limiters in the far scrape-off layer (SOL). This method helps avoid large plasma-wall gaps, without excessively compromising blanket performance. We present an optimization algorithm for determining the appropriate size and scale of these protection limiters given (1) probability distributions of SOL plasma parameters and (2) assumed risk tolerance. As part of this optimization, we have developed an analytic description of parallel heat fluxes across limiter shadows, and an objective cost function (the ‘Far-SOL Marginal Cost’) to quantify the impact that different main-wall thermal management design choices have on reactor capital cost. Applying the model to a midscale fusion pilot plant concept shows that making use of far-SOL protection limiters can reduce capital costs on the order of $500 M, relative to naively increasing the plasma-wall gap. Our analysis demonstrates that the far-SOL power decay length is the highest-leverage plasma assumption for thermal loading of the first wall, and the primary cost driver for main wall thermal management. The relative cost efficiency of protection limiters increases as assumptions on the far-SOL heat flux become more pessimistic. The concepts described in this paper motivate the further development of far-SOL protection limiters as part of larger efforts to design economical core-edge-wall compatible solutions for a fusion pilot plant.

Design under uncertainty↗
NASA NTRS1995

Parallel Monte Carlo Simulation for control system design

The research during the 1993/94 academic year addressed the design of parallel algorithms for stochastic robustness synthesis (SRS). SRS uses Monte Carlo simulation to compute probabilities of system instability and other design-metric violations. The probabilities form a cost function which is used by a genetic algorithm (GA). The GA searches for the stochastic optimal controller. The existing sequential algorithm was analyzed and modified to execute in a distributed environment. For this, parallel approaches to Monte Carlo simulation and genetic algorithms were investigated. Initial empirical results are available for the KSR1.

Schubert, Wolfgang M.↗
NASA NTRS2011

Uncertainty Analysis via Failure Domain Characterization: Polynomial Requirement Functions

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. A Bernstein expansion approach is used to size hyper-rectangular subsets while a sum of squares programming approach is used to size quasi-ellipsoidal subsets. These methods are applicable to requirement functions whose functional dependency on the uncertainty is a known polynomial. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the uncertainty model assumed (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.

Crespo, Luis G.↗
NASA NTRS1990

Ceramics Analysis and Reliability Evaluation of Structures (CARES). Users and programmers manual

This manual describes how to use the Ceramics Analysis and Reliability Evaluation of Structures (CARES) computer program. The primary function of the code is to calculate the fast fracture reliability or failure probability of macroscopically isotropic ceramic components. These components may be subjected to complex thermomechanical loadings, such as those found in heat engine applications. The program uses results from MSC/NASTRAN or ANSYS finite element analysis programs to evaluate component reliability due to inherent surface and/or volume type flaws. CARES utilizes the Batdorf model and the two-parameter Weibull cumulative distribution function to describe the effect of multiaxial stress states on material strength. The principle of independent action (PIA) and the Weibull normal stress averaging models are also included. Weibull material strength parameters, the Batdorf crack density coefficient, and other related statistical quantities are estimated from four-point bend bar or unifrom uniaxial tensile specimen fracture strength data. Parameter estimation can be performed for single or multiple failure modes by using the least-square analysis or the maximum likelihood method. Kolmogorov-Smirnov and Anderson-Darling goodness-of-fit tests, ninety percent confidence intervals on the Weibull parameters, and Kanofsky-Srinivasan ninety percent confidence band values are also provided. The probabilistic fast-fracture theories used in CARES, along with the input and output for CARES, are described. Example problems to demonstrate various feature of the program are also included. This manual describes the MSC/NASTRAN version of the CARES program.

Nemeth, Noel N.↗
NASA NTRS1990

Design of ceramic components with the NASA/CARES computer program

The ceramics analysis and reliability evaluation of structures (CARES) computer program is described. The primary function of the code is to calculate the fast-fracture reliability or failure probability of macro-scopically isotropic ceramic components. These components may be subjected to complex thermomechanical loadings, such as those found in heat engine applications. CARES uses results from MSC/NASTRAN or ANSYS finite-element analysis programs to evaluate how inherent surface and/or volume type flaws component reliability. CARES utilizes the Batdorf model and the two-parameter Weibull cumulative distribution function to describe the effects of multiaxial stress states on material strength. The principle of independent action (PIA) and the Weibull normal stress averaging models are also included. Weibull material strength parameters, the Batdorf crack density coefficient, and other related statistical quantities are estimated from four-point bend bar or uniform uniaxial tensile specimen fracture strength data. Parameter estimation can be performed for a single or multiple failure modes by using a least-squares analysis or a maximum likelihood method. Kolmogorov-Smirnov and Anderson-Darling goodness-to-fit-tests, 90 percent confidence intervals on the Weibull parameters, and Kanofsky-Srinivasan 90 percent confidence band values are also provided. Examples are provided to illustrate the various features of CARES.

Nemeth, Noel N.↗
NASA NTRS1993

Advanced Multi-Phase Flow CFD Model Development for Solid Rocket Motor Flowfield Analysis

It is known that the simulations of solid rocket motor internal flow field with AL-based propellants require complex multi-phase turbulent flow model. The objective of this study is to develop an advanced particulate multi-phase flow model which includes the effects of particle dynamics, chemical reaction and hot gas flow turbulence. The inclusion of particle agglomeration, particle/gas reaction and mass transfer, particle collision, coalescence and breakup mechanisms in modeling the particle dynamics will allow the proposed model to realistically simulate the flowfield inside a solid rocket motor. The Finite Difference Navier-Stokes numerical code FDNS is used to simulate the steady-state multi-phase particulate flow field for a 3-zone 2-D axisymmetric ASRM model and a 6-zone 3-D ASRM model at launch conditions. The 2-D model includes aft-end cavity and submerged nozzle. The 3-D model represents the whole ASRM geometry, including additional grain port area in the gas cavity and two inhibitors. FDNS is a pressure based finite difference Navier-Stokes flow solver with time-accurate adaptive second-order upwind schemes, standard and extended k-epsilon models with compressibility corrections, multi zone body-fitted formulations, and turbulence particle interaction model. Eulerian/Lagrangian multi-phase solution method is applied for multi-zone mesh. To simulate the chemical reaction, penalty function corrected efficient finite-rate chemistry integration method is used in FDNS. For the AL particle combustion rate, the Hermsen correlation is employed. To simulate the turbulent dispersion of particles, the Gaussian probability distribution with standard deviation equal to (2k/3)(exp 1/2) is used for the random turbulent velocity components. The computational results reveal that the flow field near the juncture of aft-end cavity and the submerged nozzle is very complex. The effects of the turbulent particles affect the flow field significantly and provide better prediction of the ASRM performance. The multi-phase flow analysis using the FDNS code in the present research can be used as a design tool for solid rocket motor applications.

Liaw, Paul↗
DOE OSTI2025

Structure–Activity Relationships in Ether-Functionalized Solid-State Metal–Organic Framework Electrolytes

The structure–property relationships of metal–organic framework (MOF)-based solid-state electrolytes are not well understood. Herein, a systematic investigation of 12 Zr(IV)-based UiO-66 MOFs with varying ether-chain functional groups was carried out to elucidate the critical microscopic interactions that facilitate improved solid-state electrolyte performance. Enhanced sampling molecular dynamics (MD) simulations were employed and revealed a three-tier ion hopping mechanism: linker–linker hopping, linker-counterion hopping, and counterion-counterion hopping. Detailed structural analysis of the MD trajectories revealed that the chemistry and morphology of the linker groups affect the relative stability and population distribution of the electrolyte components, such that crown-ether-based linker groups enhance the probability of extended, low-barrier ion percolation pathways. As a result, we were able to tune the ionic conductivities by rationally manipulating the counterion distributions, linker binding strengths, and the configurational entropy (multivariability of the linkers). The resulting performance of these MOF-based solid-state electrolytes was significantly enhanced, with a methoxy-functionalized framework (UiO-66-L1 100 ) achieving high ionic conductivities of 2.32 × 10 –4 S/cm and 2.07 × 10 –3 S/cm at 30 °C and 90 °C, respectively, an order of magnitude greater than other all-solid-state MOF electrolyte systems. The electrolyte stability was evaluated with LiIn|LPSCl|MOF:LiTFSI|LPSCl|LiIn symmetric cells, showing excellent Li plating/stripping processes for over 2 months.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Success potential of automated star pattern recognition

A quasi-analytical model is presented for calculating the success probability of automated star pattern recognition systems for attitude control of spacecraft. The star data is gathered by an imaging star tracker (STR) with a circular FOV capable of detecting 20 stars. The success potential is evaluated in terms of the equivalent diameters of the FOV and the target star area ('uniqueness area'). Recognition is carried out as a function of the position and brightness of selected stars in an area around each guide star. The success of the system is dependent on the resultant pointing error, and is calculated by generating a probability distribution of reaching a threshold probability of an unacceptable pointing error. The method yields data which are equivalent to data available with Monte Carlo simulatins. When applied to the recognition system intended for use on the Space IR Telescope Facility it is shown that acceptable pointing, to a level of nearly 100 percent certainty, can be obtained using a single star tracker and about 4000 guide stars.

Van Bezooijen, R. W. H.↗
NASA NTRS2000

Airborne Coherent Lidar for Advanced In-Flight Measurements (ACLAIM) Flight Testing of the Lidar Sensor

The purpose of the ACLAIM program is ultimately to establish the viability of light detection and ranging (lidar) as a forward-looking sensor for turbulence. The goals of this flight test are to: 1) demonstrate that the ACLAIM lidar system operates reliably in a flight test environment, 2) measure the performance of the lidar as a function of the aerosol backscatter coefficient (beta), 3) use the lidar system to measure atmospheric turbulence and compare these measurements to onboard gust measurements, and 4) make measurements of the aerosol backscatter coefficient, its probability distribution and spatial distribution. The scope of this paper is to briefly describe the ACLAIM system and present examples of ACLAIM operation in flight, including comparisons with independent measurements of wind gusts, gust-induced normal acceleration, and the derived eddy dissipation rate.

Soreide, David C.↗
NASA NTRS2002

NESTEM-QRAS: A Tool for Estimating Probability of Failure

An interface between two NASA GRC specialty codes, NESTEM and QRAS has been developed. This interface enables users to estimate, in advance, the risk of failure of a component, a subsystem, and/or a system under given operating conditions. This capability would be able to provide a needed input for estimating the success rate for any mission. NESTEM code, under development for the last 15 years at NASA Glenn Research Center, has the capability of estimating probability of failure of components under varying loading and environmental conditions. This code performs sensitivity analysis of all the input variables and provides their influence on the response variables in the form of cumulative distribution functions. QRAS, also developed by NASA, assesses risk of failure of a system or a mission based on the quantitative information provided by NESTEM or other similar codes, and user provided fault tree and modes of failure. This paper will describe briefly, the capabilities of the NESTEM, QRAS and the interface. Also, in this presentation we will describe stepwise process the interface uses using an example.

Patel, Bhogilal M.↗
NASA NTRS2008

Reliability and Confidence Interval Analysis of a CMC Turbine Stator Vane

High temperature ceramic matrix composites (CMC) are being explored as viable candidate materials for hot section gas turbine components. These advanced composites can potentially lead to reduced weight, enable higher operating temperatures requiring less cooling and thus leading to increased engine efficiencies. However, these materials are brittle and show degradation with time at high operating temperatures due to creep as well as cyclic mechanical and thermal loads. In addition, these materials are heterogeneous in their make-up and various factors affect their properties in a specific design environment. Most of these advanced composites involve two- and three-dimensional fiber architectures and require a complex multi-step high temperature processing. Since there are uncertainties associated with each of these in addition to the variability in the constituent material properties, the observed behavior of composite materials exhibits scatter. Traditional material failure analyses employing a deterministic approach, where failure is assumed to occur when some allowable stress level or equivalent stress is exceeded, are not adequate for brittle material component design. Such phenomenological failure theories are reasonably successful when applied to ductile materials such as metals. Analysis of failure in structural components is governed by the observed scatter in strength, stiffness and loading conditions. In such situations, statistical design approaches must be used. Accounting for these phenomena requires a change in philosophy on the design engineer s part that leads to a reduced focus on the use of safety factors in favor of reliability analyses. The reliability approach demands that the design engineer must tolerate a finite risk of unacceptable performance. This risk of unacceptable performance is identified as a component's probability of failure (or alternatively, component reliability). The primary concern of the engineer is minimizing this risk in an economical manner. The methods to accurately determine the service life of an engine component with associated variability have become increasingly difficult. This results, in part, from the complex missions which are now routinely considered during the design process. These missions include large variations of multi-axial stresses and temperatures experienced by critical engine parts. There is a need for a convenient design tool that can accommodate various loading conditions induced by engine operating environments, and material data with their associated uncertainties to estimate the minimum predicted life of a structural component. A probabilistic composite micromechanics technique in combination with woven composite micromechanics, structural analysis and Fast Probability Integration (FPI) techniques has been used to evaluate the maximum stress and its probabilistic distribution in a CMC turbine stator vane. Furthermore, input variables causing scatter are identified and ranked based upon their sensitivity magnitude. Since the measured data for the ceramic matrix composite properties is very limited, obtaining a probabilistic distribution with their corresponding parameters is difficult. In case of limited data, confidence bounds are essential to quantify the uncertainty associated with the distribution. Usually 90 and 95% confidence intervals are computed for material properties. Failure properties are then computed with the confidence bounds. Best estimates and the confidence bounds on the best estimate of the cumulative probability function for R-S (strength - stress) are plotted. The methodologies and the results from these analyses will be discussed in the presentation.

Murthy, Pappu L. N.↗
NASA NTRS2012

A Search Model for Imperfectly Detected Targets

Under the assumptions that 1) the search region can be divided up into N non-overlapping sub-regions that are searched sequentially, 2) the probability of detection is unity if a sub-region is selected, and 3) no information is available to guide the search, there are two extreme case models. The search can be done perfectly, leading to a uniform distribution over the number of searches required, or the search can be done with no memory, leading to a geometric distribution for the number of searches required with a success probability of 1/N. If the probability of detection P is less than unity, but the search is done otherwise perfectly, the searcher will have to search the N regions repeatedly until detection occurs. The number of searches is thus the sum two random variables. One is N times the number of full searches (a geometric distribution with success probability P) and the other is the uniform distribution over the integers 1 to N. The first three moments of this distribution were computed, giving the mean, standard deviation, and the kurtosis of the distribution as a function of the two parameters. The model was fit to the data presented last year (Ahumada, Billington, & Kaiwi, 2 required to find a single pixel target on a simulated horizon. The model gave a good fit to the three moments for all three observers.

Ahumada, Albert↗
NASA NTRS1995

Hypervolemia from Drinking Hyperhydration Solutions at Rest and Exercise

The mechanism of muscular fatigue from physical work and exercise (high metabolism) is not clear, but involves disturbances of muscle surface membrane excitation-contraction coupling from changes in sarcoplasmic reticulum Ca2+ release, cell H+ and Pi responses, and carbohydrate metabolism. Fatigue in people at rest (low metabolism) involves both psychological and physiological factors, probably in different proportions. One common factor appears to be the level and distribution of water and electrolytes within muscle cells and other vascular, interstitial, body fluid compartments. The vascular fluid volume, composed of plasma and red blood cells, is a primary regulatory factor for cardiovascular function; reduction of vascular volume (hypovolemia) and total body water (hypohydration) adversely affect exercise performance. Plasma volume and plasma ionic-osmotic constituent concentrations are also regulatory factors for body thermoregulation, which is often compromised from exercise induced hypovolemia and hypohydration. Rehydration of dehydrated people on earth is relatively easy with appropriate food (osmols), fluid, and a restful environment. But ad libitum drinking under stressful conditions; e.g., heat, exercise, or prior dehydration, results in involuntary dehydration defined as the delay in full fluid replacement (euhydration) during and following loss of body fluid. Astronauts, with their reduced total body water are euhydrated while in weightlessness, but become "dehydrated" during reentry and landing. Thus, people subjected to acute or chronic stress are probably somewhat "dehydrated" as well as fatigued. Many rehydration drinks are more concentrated (hypertonic-hyperosmotic) with respect to the normal plasma osmolality of 285 mOsm/kg H2O and more of the drink osmols are contributed by carbohydrates than by ionized substances. There have been few studies on the efficacy of various drink formulations for increasing body fluid compartment volumes, especially plasma volume, in rested hydrated subjects. Recent findings from our laboratory have indicated that drinks containing greater concentrations of ionized substances (Performance 1 and AstroAde) up to 157 mEq/L Na+ induced greater levels of hypervolemia in resting, moderately dehydrated men, and were also better than water for attenuating the characteristic hypovolemia during supine, submaximal, leg ergometer exercise.

Greenleaf, John E.↗
NASA NTRS2010

Characterization of Cloud Water-Content Distribution

The development of realistic cloud parameterizations for climate models requires accurate characterizations of subgrid distributions of thermodynamic variables. To this end, a software tool was developed to characterize cloud water-content distributions in climate-model sub-grid scales. This software characterizes distributions of cloud water content with respect to cloud phase, cloud type, precipitation occurrence, and geo-location using CloudSat radar measurements. It uses a statistical method called maximum likelihood estimation to estimate the probability density function of the cloud water content.

Lee, Seungwon↗
NASA NTRS2015

Calibration of Predictor Models Using Multiple Validation Experiments

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

Crespo, Luis G.↗
DOE OSTI2026

Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

AI/ML↗
NASA NTRS1973

Remote optical measurement of wind speed spectra

The paper presents some new analytical and experimental results on the cross-correlation function and power spectrum for the flow speed by the passive optical crossed-beam correlation technique. Cross-correlation functions of crossed-beam measurements for both intersecting beams and separated beams have been reviewed for the case of varying wind speeds. The corresponding spectral density functions were derived and expressed in terms of the probability density of wind speed variations and the correlation of atmospheric turbulent eddies. The associated spectral density for a special approximate model was derived by assuming a Gaussian distribution of transit times. The theoretical predictions were then compared with actual field test data, and a reasonably good agreement in the valid frequency range was observed.

Su, M.-Y.↗