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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Finite element methods in probabilistic mechanics

Probabilistic methods, synthesizing the power of finite element methods with second-order perturbation techniques, are formulated for linear and nonlinear problems. Random material, geometric properties and loads can be incorporated in these methods, in terms of their fundamental statistics. By construction, these methods are applicable when the scale of randomness is not too large and when the probabilistic density functions have decaying tails. By incorporating certain computational techniques, these methods are shown to be capable of handling large systems with many sources of uncertainties. Applications showing the effects of combined random fields and cyclic loading/stress reversal are studied and compared with Monte Carlo simulation results.

Liu, Wing Kam↗
NASA NTRS1993

Probabilistic simulation of multi-scale composite behavior

A methodology is developed to computationally assess the probabilistic composite material properties at all composite scale levels due to the uncertainties in the constituent (fiber and matrix) properties and in the fabrication process variables. The methodology is computationally efficient for simulating the probability distributions of material properties. The sensitivity of the probabilistic composite material property to each random variable is determined. This information can be used to reduce undesirable uncertainties in material properties at the macro scale of the composite by reducing the uncertainties in the most influential random variables at the micro scale. This methodology was implemented into the computer code PICAN (Probabilistic Integrated Composite ANalyzer). The accuracy and efficiency of this methodology are demonstrated by simulating the uncertainties in the material properties of a typical laminate and comparing the results with the Monte Carlo simulation method. The experimental data of composite material properties at all scales fall within the scatters predicted by PICAN.

Liaw, D. G.↗
NASA NTRS1974

Design of state-feedback controllers including sensitivity reduction, with applications to precision pointing

The design procedure of feedback controllers was described and the considerations for the selection of the design parameters were given. The frequency domain properties of single-input single-output systems using state feedback controllers are analyzed, and desirable phase and gain margin properties are demonstrated. Special consideration is given to the design of controllers for tracking systems, especially those designed to track polynomial commands. As an example, a controller was designed for a tracking telescope with a polynomial tracking requirement and some special features such as actuator saturation and multiple measurements, one of which is sampled. The resulting system has a tracking performance comparing favorably with a much more complicated digital aided tracker. The parameter sensitivity reduction was treated by considering the variable parameters as random variables. A performance index is defined as a weighted sum of the state and control convariances that sum from both the random system disturbances and the parameter uncertainties, and is minimized numerically by adjusting a set of free parameters.

Hadass, Z.↗
NASA NTRS1975

Error Analysis of the Shuttle Orbital Maneuvering System P-V-T Propellant Gaging Module. Mission Planning, Mission Analysis and Software Formulation

An investigation of the shuttle orbital maneuvering system (OMS) pressure-volume-temperature (P-V-T) propellant gaging module has revealed that the gaging errors due to the combined effects of random instrumentation measurement errors, propellant loading uncertainties, and simplifying assumptions in the software are non-linear over the range of the usable propellant quantity gage (0-100%), with the largest error being at the zero point. When the OMS propellant tanks in the orbiter vehicle pods are filled to contain 100% of the maximum usable propellant, the gaging error at the zero point was determined to be 9.5% for the fuel and 9.5% for the oxidizer. When the OMS propellant tanks initially contain 50% of the maximum usable propellant, the largest gaging error is still 9.5% for the fuel and 9.5% for the oxidizer.

Duhon, D. D.↗
NASA NTRS1991

Simulation of probabilistic wind loads and building analysis

Probabilistic wind loads likely to occur on a structure during its design life are predicted. Described here is a suitable multifactor interactive equation (MFIE) model and its use in the Composite Load Spectra (CLS) computer program to simulate the wind pressure cumulative distribution functions on four sides of a building. The simulated probabilistic wind pressure load was applied to a building frame, and cumulative distribution functions of sway displacements and reliability against overturning were obtained using NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), a stochastic finite element computer code. The geometry of the building and the properties of building members were also considered as random in the NESSUS analysis. The uncertainties of wind pressure, building geometry, and member section property were qualified in terms of their respective sensitivities on the structural response.

Shah, Ashwin R.↗
NASA NTRS1992

A statistical model for evaluating GOPEX uplink performance

This article describes a statistical model to analyze the signal intensity received at the solid-state imaging (SSI) camera of the Galileo optical communications system from an Earth-based transmitter (GOPEX) demonstration. The analytical model assumes that the optical beam possesses a Gaussian profile and the communication channel has a log-normal scattering characteristic. The atmospheric-induced jitter is modelled as two independent zero mean Gaussian random variables. By modelling the system parameters as a set of independent and identically distributed (iid) random variables, the combined impact of uncertainties due to system parameters and the turbulent atmosphere can be approximated by a log-normal distributed signal intensity at the spacecraft. A Monte-Carlo software simulation package was also developed to compute the confidence interval probabilities for general optical beam profiles. Numerical results show that the approximation is valid for a wide range of operation scenarios.

Kiesaleh, K.↗
NASA NTRS1994

Physical Models of Cognition

This paper presents and discusses physical models for simulating some aspects of neural intelligence, and, in particular, the process of cognition. The main departure from the classical approach here is in utilization of a terminal version of classical dynamics introduced by the author earlier. Based upon violations of the Lipschitz condition at equilibrium points, terminal dynamics attains two new fundamental properties: it is spontaneous and nondeterministic. Special attention is focused on terminal neurodynamics as a particular architecture of terminal dynamics which is suitable for modeling of information flows. Terminal neurodynamics possesses a well-organized probabilistic structure which can be analytically predicted, prescribed, and controlled, and therefore which presents a powerful tool for modeling real-life uncertainties. Two basic phenomena associated with random behavior of neurodynamic solutions are exploited. The first one is a stochastic attractor ; a stable stationary stochastic process to which random solutions of a closed system converge. As a model of the cognition process, a stochastic attractor can be viewed as a universal tool for generalization and formation of classes of patterns. The concept of stochastic attractor is applied to model a collective brain paradigm explaining coordination between simple units of intelligence which perform a collective task without direct exchange of information. The second fundamental phenomenon discussed is terminal chaos which occurs in open systems. Applications of terminal chaos to information fusion as well as to explanation and modeling of coordination among neurons in biological systems are discussed. It should be emphasized that all the models of terminal neurodynamics are implementable in analog devices, which means that all the cognition processes discussed in the paper are reducible to the laws of Newtonian mechanics.

Zak, Michail↗
NASA NTRS1996

Validation of UARS Microwave Limb Sounder ClO Measurements

Validation of stratospheric ClO measurements by the Microwave Limb Sounder (MLS) on the Upper Atmosphere Research Satellite (UARS) is described. Credibility of the measurements is established by (1) the consistency of the measured ClO spectral emission line with the retrieved ClO profiles and (2) comparisons of ClO from MLS with that from correlative measurements by balloon-based, ground-based, and aircraft-based instruments. Values of "noise" (random), "scaling" (multiplicative), and "bias" (additive) uncertainties are determined for the Version 3 data, in the first version public release of the known artifacts in these data are identified. Comparisons with correlative measurements indicate agreement to within the combined uncertainties expected for MLS and the other measurements being compared. It is concluded that MLS Version 3 ClO data, with proper consideration of the uncertainties and "quality" parameters produced with these data, can be used for scientific analyses at retrieval surfaces between 46 and 1 hPa (approximately 20-50 km in height). Future work is planned to correct known problems in the data and improve their quality.

Waters, J. W.↗
NASA NTRS2017

Advanced Characterisation of Aerosol Size Properties from Measurements of Spectral Optical Depth Using the GRASP Algorithm

This study evaluates the potential of using aerosol optical depth (τ (sub a)) measurements to characterise the microphysical and optical properties of atmospheric aerosols. With this aim, we used the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties) code for numerical testing of six different aerosol models with different aerosol loads. The direct numerical simulations (self-consistency tests) indicate that the GRASP-AOD retrieval provides modal aerosol optical depths (fine and coarse) to within 0.01 of the input values. The retrieval of the fine-mode radius, width and volume concentration are stable and precise if the real part of the refractive index is known. The coarse-mode properties are less accurate, but they are significantly improved when additional a priori information is available. The tests with random simulated errors show that the uncertainty in the bimodal log-normal size distribution parameters increases as the aerosol load decreases. Similarly, the reduction in the spectral range diminishes the stability of the retrieved parameters. In addition to these numerical studies, we used optical depth observations at eight AERONET locations to validate our results with the standard AERONET inversion products. We found that bimodal log-normal size distributions serve as useful input assumptions, especially when the measurements have inadequate spectral coverage and/or limited accuracy, such as moon photometry. Comparisons of the mode median radii between GRASP-AOD and AERONET indicate average differences of 0.013µm for the fine mode and typical values of 0.2–0.3µm for the coarse mode. The dominant mode (i.e. fine or coarse) indicates a 10 % difference in mode radii between the GRASP-AOD and AERONET inversions, and the average of the difference in volume concentration is around 17 % for both modes. The retrieved values of the fine-mode τ(sub a)(500) using GRASP-AOD are generally between those values obtained by the standard AERONET inversion and the values obtained by the AERONET spectral deconvolution algorithm (SDA), with differences typically lower than 0.02 between GRASP-AOD and both algorithms. Finally, we present some examples of application of GRASP-AOD inversion using moon photometry and the airborne PLASMA sun photometer during the ChArMEx summer 2013 campaign in the western Mediterranean.

Torres, Benjamin↗
NASA NTRSDate not supplied

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗
NASA NTRS2004

Probability Distribution Estimated From the Minimum, Maximum, and Most Likely Values: Applied to Turbine Inlet Temperature Uncertainty

Modern engineering design practices are tending more toward the treatment of design parameters as random variables as opposed to fixed, or deterministic, values. The probabilistic design approach attempts to account for the uncertainty in design parameters by representing them as a distribution of values rather than as a single value. The motivations for this effort include preventing excessive overdesign as well as assessing and assuring reliability, both of which are important for aerospace applications. However, the determination of the probability distribution is a fundamental problem in reliability analysis. A random variable is often defined by the parameters of the theoretical distribution function that gives the best fit to experimental data. In many cases the distribution must be assumed from very limited information or data. Often the types of information that are available or reasonably estimated are the minimum, maximum, and most likely values of the design parameter. For these situations the beta distribution model is very convenient because the parameters that define the distribution can be easily determined from these three pieces of information. Widely used in the field of operations research, the beta model is very flexible and is also useful for estimating the mean and standard deviation of a random variable given only the aforementioned three values. However, an assumption is required to determine the four parameters of the beta distribution from only these three pieces of information (some of the more common distributions, like the normal, lognormal, gamma, and Weibull distributions, have two or three parameters). The conventional method assumes that the standard deviation is a certain fraction of the range. The beta parameters are then determined by solving a set of equations simultaneously. A new method developed in-house at the NASA Glenn Research Center assumes a value for one of the beta shape parameters based on an analogy with the normal distribution (ref.1). This new approach allows for a very simple and direct algebraic solution without restricting the standard deviation. The beta parameters obtained by the new method are comparable to the conventional method (and identical when the distribution is symmetrical). However, the proposed method generally produces a less peaked distribution with a slightly larger standard deviation (up to 7 percent) than the conventional method in cases where the distribution is asymmetric or skewed. The beta distribution model has now been implemented into the Fast Probability Integration (FPI) module used in the NESSUS computer code for probabilistic analyses of structures (ref. 2).

Holland, Frederic A., Jr.↗
NASA NTRS1993

Confidence bounds on structural reliability

Different approaches for quantifying physical, statistical, and model uncertainties associated with the distribution parameters which are aimed at determining structural reliability are described. Confidence intervals on the distribution parameters of the input random variables are estimated using four algorithms to evaluate uncertainty of the response. Design intervals are evaluated using either Monte Carlo simulation or an iterative approach. A first order approach can be used to compute a first approximation of the design interval, but its accuracy is not satisfactory. The regression approach which combines the iterative approach with Monte Carlo simulation is capable of providing good results if the performance function can be accurately represented using regression analysis. It is concluded that the design interval-based approach seems to be quite general and takes into account distribution and model uncertainties.

Mehta, S. R.↗
NASA NTRS1987

Numerical simulations and the problem of periodicity in the cratering record

Ages of craters in the record of impacts on earth may be uniformly periodic, totally random, or a mixture of the two. These alternatives are studied through numerical simulation wherein time-series analysis is performed on real and simulated sequences to which random noise has been added to represent age-dating uncertainty. It is concluded that the real record is most likely to have been generated by a mixture of random and periodic impacts, with the random events constituting the majority.

Trefil, J. S.↗
NASA NTRS1994

System Design under Uncertainty: Evolutionary Optimization of the Gravity Probe-B Spacecraft

This paper discusses the application of evolutionary random-search algorithms (Simulated Annealing and Genetic Algorithms) to the problem of spacecraft design under performance uncertainty. Traditionally, spacecraft performance uncertainty has been measured by reliability. Published algorithms for reliability optimization are seldom used in practice because they oversimplify reality. The algorithm developed here uses random-search optimization to allow us to model the problem more realistically. Monte Carlo simulations are used to evaluate the objective function for each trial design solution. These methods have been applied to the Gravity Probe-B (GP-B) spacecraft being developed at Stanford University for launch in 1999, Results of the algorithm developed here for GP-13 are shown, and their implications for design optimization by evolutionary algorithms are discussed.

Pullen, Samuel P.↗
NASA NTRS2011

Quantifying Errors in TRMM-Based Multi-Sensor QPE Products Over Land in Preparation for GPM

Determining uncertainties in satellite-based multi-sensor quantitative precipitation estimates over land of fundamental importance to both data producers and hydro climatological applications. ,Evaluating TRMM-era products also lays the groundwork and sets the direction for algorithm and applications development for future missions including GPM. QPE uncertainties result mostly from the interplay of systematic errors and random errors. In this work, we will synthesize our recent results quantifying the error characteristics of satellite-based precipitation estimates. Both systematic errors and total uncertainties have been analyzed for six different TRMM-era precipitation products (3B42, 3B42RT, CMORPH, PERSIANN, NRL and GSMap). For systematic errors, we devised an error decomposition scheme to separate errors in precipitation estimates into three independent components, hit biases, missed precipitation and false precipitation. This decomposition scheme reveals hydroclimatologically-relevant error features and provides a better link to the error sources than conventional analysis, because in the latter these error components tend to cancel one another when aggregated or averaged in space or time. For the random errors, we calculated the measurement spread from the ensemble of these six quasi-independent products, and thus produced a global map of measurement uncertainties. The map yields a global view of the error characteristics and their regional and seasonal variations, reveals many undocumented error features over areas with no validation data available, and provides better guidance to global assimilation of satellite-based precipitation data. Insights gained from these results and how they could help with GPM will be highlighted.

Peters-Lidard, Christa D.↗
NASA NTRS1990

Radiometric properties of the NS001 Thematic Mapper Simulator aircraft multispectral scanner

Laboratory tests of the NS001 TM are described emphasizing absolute calibration to determine the radiometry of the simulator's reflective channels. In-flight calibration of the data is accomplished with the NS001 internal integrating-sphere source because instabilities in the source can limit the absolute calibration. The data from 1987-89 indicate uncertainties of up to 25 percent with an apparent average uncertainty of about 15 percent. Also identified are dark current drift and sensitivity changes along the scan line, random noise, and nonlinearity which contribute errors of 1-2 percent. Uncertainties similar to hysteresis are also noted especially in the 2.08-2.35-micron range which can reduce sensitivity and cause errors. The NS001 TM Simulator demonstrates a polarization sensitivity that can generate errors of up to about 10 percent depending on the wavelength.

Markham, Brian L.↗
NASA NTRS2018

Stochastic Reduced Order Models with Python (SROMPy)

Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach.

Warner, James E.↗
NASA NTRS1974

Remote sounding of stratospheric temperatures using high resolution radiance measurements from the IRIS-D

Remote sounding of stratospheric temperatures up to 3.2 millibars is attempted using high resolution (unapodized) radiance measurements in the 15 micron CO2 band from the Infrared Interferometer Spectrometer on Nimbus 4. Inversions are performed using the Chahine relaxation technique. Radiance data and simultaneous in situ temperature profiles are obtained from the Rocket/Nimbus Sounder Comparison. Numerical tests with synthetic radiance data show that the uncertainty in the retrieved temperatures due to random instrument noise is about 1.1 K when averaged over layers about 10 km thick. However, comparison of the measured radiances with the radiances calculated from the in situ profiles show the calculated radiances to be systematically higher than the measured radiances. The evidence indicates that systematic errors exist in both the radiance and the in situ measurements.

Gallery, W. O.↗