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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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NASA NTRS1982

A high speed implementation of the random decrement algorithm

The algorithm is useful for measuring net system damping levels in stochastic processes and for the development of equivalent linearized system response models. The algorithm works by summing together all subrecords which occur after predefined threshold level is crossed. The random decrement signature is normally developed by scanning stored data and adding subrecords together. The high speed implementation of the random decrement algorithm exploits the digital character of sampled data and uses fixed record lengths of 2(n) samples to greatly speed up the process. The contributions to the random decrement signature of each data point was calculated only once and in the same sequence as the data were taken. A hardware implementation of the algorithm using random logic is diagrammed and the process is shown to be limited only by the record size and the threshold crossing frequency of the sampled data. With a hardware cycle time of 200 ns and 1024 point signature, a threshold crossing frequency of 5000 Hertz can be processed and a stably averaged signature presented in real time.

Kiraly, L. J.↗

NASA NTRS1983

A high-speed implementation of the random decrement algorithm

Kiraly, L. J.↗

NASA NTRS1978

A method for determining the weak statistical stationarity of a random process

A method for determining the weak statistical stationarity of a random process is presented. The core of this testing procedure consists of generating an equivalent ensemble which approximates a true ensemble. Formation of an equivalent ensemble is accomplished through segmenting a sufficiently long time history of a random process into equal, finite, and statistically independent sample records. The weak statistical stationarity is ascertained based on the time invariance of the equivalent-ensemble averages. Comparison of these averages with their corresponding time averages over a single sample record leads to a heuristic estimate of the ergodicity of a random process. Specific variance tests are introduced for evaluating the statistical independence of the sample records, the time invariance of the equivalent-ensemble autocorrelations, and the ergodicity. Examination and substantiation of these procedures were conducted utilizing turbulent velocity signals.

Sadeh, W. Z.↗

NASA NTRS1974

Random-access technique for modular bathymetry data storage in a continental shelf wave refraction program

A study was conducted of an alternate method for storage and use of bathymetry data in the Langley Research Center and Virginia Institute of Marine Science mid-Atlantic continental-shelf wave-refraction computer program. The regional bathymetry array was divided into 105 indexed modules which can be read individually into memory in a nonsequential manner from a peripheral file using special random-access subroutines. In running a sample refraction case, a 75-percent decrease in program field length was achieved by using the random-access storage method in comparison with the conventional method of total regional array storage. This field-length decrease was accompanied by a comparative 5-percent increase in central processing time and a 477-percent increase in the number of operating-system calls. A comparative Langley Research Center computer system cost savings of 68 percent was achieved by using the random-access storage method.

Poole, L. R.↗

DOE OSTI2020

Method for conducting in situ high-temperature digital image correlation with simultaneous synchrotron measurements under thermomechanical conditions

This work presents a novel method of obtaining in situ strain measurements at high temperature by simultaneous digital image correlation (DIC), which provides the total strain on the specimen surface, and synchrotron x-ray diffraction (XRD), which provides lattice strains of crystalline materials. DIC at high temperature requires specialized techniques to overcome the effects of increased blackbody radiation that would otherwise overexpose the images. The technique presented herein is unique in that it can be used with a sample enclosed in an infrared heater, remotely and simultaneously with synchrotron XRD measurements. The heater included a window for camera access, and the light of the heater lamps is used as illumination. High-temperature paint is used to apply a random speckle pattern to the sample to allow the tracking of displacements and the calculation of the DIC strains. An inexpensive blue theatrical gel filter is used to block interfering visible and infrared light at high temperatures. This technique successfully produces properly exposed images at 870 °C and is expected to perform similarly at higher temperatures. The average strains measured by DIC were validated by an analytical calculation of the theoretical strain. Simultaneous DIC and XRD strain measurements of Inconel 718 (IN718) tensile test specimens were performed under thermal and mechanical loads and evaluated. Finally, this approach uses the fact that with DIC, the total strain is measured, including plastic strain, while with XRD, only elastic strain is captured. The observed differences were discussed with respect to the effective deformation mechanisms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

Prediction of space sickness in astronauts from preflight fluid, electrolyte, and cardiovascular variables and Weightless Environmental Training Facility (WETF) training

Nine preflight variables related to fluid, electrolyte, and cardiovascular status from 64 first-time Shuttle crewmembers were differentially weighted by discrimination analysis to predict the incidence and severity of each crewmember's space sickness as rated by NASA flight surgeons. The nine variables are serum uric acid, red cell count, environmental temperature at the launch site, serum phosphate, urine osmolality, serum thyroxine, sitting systolic blood pressure, calculated blood volume, and serum chloride. Using two methods of cross-validation on the original samples (jackknife and a stratefied random subsample), these variables enable the prediction of space sickness incidence (NONE or SICK) with 80 percent sickness and space severity (NONE, MILD, MODERATE, of SEVERE) with 59 percent success by one method of cross-validation and 67 percent by another method. Addition of a tenth variable, hours spent in the Weightlessness Environment Training Facility (WETF) did not improve the prediction of space sickness incidences but did improve the prediction of space sickness severity to 66 percent success by the first method of cross-validation of original samples and to 71 percent by the second method. Results to date suggest the presence of predisposing physiologic factors to space sickness that implicate fluid shift etiology. The data also suggest that prior exposure to fluid shift during WETF training may produce some circulatory pre-adaption to fluid shifts in weightlessness that results in a reduction of space sickness severity.

Simanonok, K.↗

NASA NTRS1978

Simplified data compressor

Image data are compressed for transmission by simple, economical circuitry. Compressor handles data in blocks of 64 samples. Mean from previous line is subtracted, and pseudo-random sequence of positive and negative 1's multiplies samples of each block. Hadamard transform applied to blocks yields 64 transform coefficients. Each coefficient is compared with approximation to corresponding coefficient of previous line, and difference is quantized. Values are transmitted or stored. Procedure is reversed to reproduce image.

Rice, R. F.↗

DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Single-frame far-field diffractive imaging with randomized illumination

Contains two transmission ptychography datasets, one collected from a Siemens star, and the second from an Fe/Gd multilayer. Both samples are under illumination from the same randomized zone plate. Each dataset has a single diffraction pattern removed, and these patterns are reported in a separate cxi file. Either ptychography dataset can be used for calibration of the probe function, and the remaining diffraction patterns can be reconstructed via randomized probe imaging.

BESSY II↗

NASA NTRS1987

A quantitative approach to the topology of large-scale structure

A quantitative measure of the topology of large-scale structure: the genus of density contours in a smoothed density distribution, is described and applied. For random phase (Gaussian) density fields, the mean genus per unit volume exhibits a universal dependence on threshold density, with a normalizing factor that can be calculated from the power spectrum. If large-scale structure formed from the gravitational instability of small-amplitude density fluctuations, the topology observed today on suitable scales should follow the topology in the initial conditions. The technique is illustrated by applying it to simulations of galaxy clustering in a flat universe dominated by cold dark matter. The technique is also applied to a volume-limited sample of the CfA redshift survey and to a model in which galaxies reside on the surfaces of polyhedral 'bubbles'. The topology of the evolved mass distribution and 'biased' galaxy distribution in the cold dark matter models closely matches the topology of the density fluctuations in the initial conditions. The topology of the observational sample is consistent with the random phase, cold dark matter model.

Gott, J. Richard, III↗

NASA NTRS2020

Recommendations for Using Noise Monitors to Estimate Noise Exposure During X-59 Community Tests

A low fidelity simulation approach is used to explore how to place and use noise monitors during X-59 QueSST community tests, where people’s annoyance to the noise produced by the X-59 aircraft will be gathered. Several recommendations are provided including: 1) the desired number of sparsely spaced noise monitor sites within the survey area, 2) whether to group and average measurements across multiple noise monitors located at a site, 3) what spacing should be used if grouped noise monitors are used, 4) an approach to mitigate ambient noise contamination at the measurement sites, 5) a method to combine empirical and predicted dose estimates to provide a single dose estimate for respondents, and 6) assessing how changes in turbulence intensity and array configuration affect dose uncertainty. To make these recommendations, the error that is expected when fitting contrived, smoothly varying sonic boom “reference exposure surfaces” is studied when a spatially sparse and scattered set of samples is used as responses for the fit. The reference exposure surfaces mimic the sonic boom exposure at ground level that might be expected in the X-59 survey area in the absence of atmospheric turbulence, ambient noise, and other localized effects. The spatial extent of these surfaces varies and is representative of the different survey area sizes that might be expected during future X-59 community overflight tests. These contrived reference surfaces are sampled, and those reference samples are then perturbed to mimic atmospheric turbulence, ambient noise and other localized effects that might affect noise monitor measurements within overflown communities. Two different surface fitting methods are investigated when fitting these perturbed samples to approximate the reference surface. The first method uses interpolation between the perturbed data at the scattered sites to compute the fit. The second method fits a polynomial surface model to the perturbed data using ordinary least squares regression analysis. For both fitting methods, the root mean square fit error is computed from the pointwise difference between the fit surface and the reference surface as the count and configuration of the sites is varied while also averaging the error across many different realizations of both the smooth variation of the reference exposure surface and the random, localized perturbations at the sample sites. Different site configurations are compared using this error statistic to make the recommendations noted above. Additionally, the two fitting approaches (interpolation vs linear regression) are compared based on the fit error observed in these simulations. These analyses, comparisons, and recommendations should inform future decisions on the noise monitor placement and the methods used to analyze the noise monitor data that is collected during X-59 community overflights.

sonic boom↗

NASA NTRS1987

Measurement of the absorption coefficient of acoustical materials using the sound intensity method

In this study the possibility of using the two-microphone sound intensity technique to measure the normal incidence and the random incidence sound absorption coefficient was investigated. The normal incidence absorption coefficient was determined by measuring the intensity incidence on the sample and the intensity reflected by the sample placed in an anechoic chamber. The random incidence absorption coefficient was determined by measuring the intensity incident on the sample and the intensity reflected by the sample placed in a reverberation chamber. Absorption coefficient results obtained by the sound intensity technique were compared with standard techniques, namely the reverberation chamber and the standing wave tube. The major advantages of using the sound intensity technique are that it permits 'in situ' measurements and the absorption coefficient for a large range of frequencies can be obtained from a single measurement.

Atwal, Mahabir S.↗

NASA NTRS2007

Thermal Conductivity of Ethylene Vinyl Acetate Copolymer/Nanofiller Blends

To reduce weight and increase the mobility, comfort, and performance of future spacesuits, flexible, thermally conductive fabrics and plastic tubes are needed for the Liquid Cooling and Ventilation Garment. Such improvements would allow astronauts to operate more efficiently and safely for extended extravehicular activities. As an approach to raise the thermal conductivity (TC) of an ethylene vinyl acetate copolymer (Elvax(TM)260), it was compounded with three types of carbon based nanofillers: multi-walled carbon nanotubes (MWCNTs), vapor grown carbon nanofibers (CNFs), and expanded graphite (EG). In addition, other nanofillers including metallized CNFs, nickel nanostrands, boron nitride, and powdered aluminum were also compounded with Elvax(TM) 260 in the melt at various loading levels. In an attempt to improve compatibility between Elvax 260 and the nanofillers, MWCNTs and EG were modified by surface coating and through noncovalent and covalent attachment of organic molecules containing alkyl groups. Ribbons of the nanocomposites were extruded to form samples in which the nanofillers were aligned in the direction of flow. Samples were also fabricated by compression molding to yield nanocomposites in which the nanofillers were randomly oriented. Mechanical properties of the aligned samples were determined by tensile testing while the degree of dispersion and alignment of nanoparticles were investigated using high-resolution scanning electron microscopy. TC measurements were performed using a laser flash (Nanoflash(TM)) technique. TC of the samples was measured in the direction of, and perpendicular to, the alignment direction. Additionally, tubing was also extruded from select nanocomposite compositions and the TC and mechanical flexibility measured.

Ghose, Sayata↗

NASA NTRS2007

Thermal Conductivity of Ethylene Vinyl Acetate Copolymer/Nanofiller Blends

To reduce weight and increase the mobility, comfort, and performance of future spacesuits, flexible, thermally conductive fabrics and plastic tubes are needed for the Liquid Cooling and Ventilation Garment. Such improvements would allow astronauts to operate more efficiently and safely for extended extravehicular activities. As an approach to raise the thermal conductivity (TC) of an ethylene vinyl acetate copolymer (Elvax 260), it was compounded with three types of carbon based nanofillers: multi-walled carbon nanotubes (MWCNTs), vapor grown carbon nanofibers (CNFs), and expanded graphite (EG). In addition, other nanofillers including metallized CNFs, nickel nanostrands, boron nitride, and powdered aluminum were also compounded with Elvax 260 in the melt at various loading levels. In an attempt to improve compatibility between Elvax 260 and the nanofillers, MWCNTs and EG were modified by surface coating and through noncovalent and covalent attachment of organic molecules containing alkyl groups. Ribbons of the nanocomposites were extruded to form samples in which the nanofillers were aligned in the direction of flow. Samples were also fabricated by compression molding to yield nanocomposites in which the nanofillers were randomly oriented. Mechanical properties of the aligned samples were determined by tensile testing while the degree of dispersion and alignment of nanoparticles were investigated using high-resolution scanning electron microscopy. TC measurements were performed using a laser flash (Nanoflash ) technique. TC of the samples was measured in the direction of, and perpendicular to, the alignment direction. Additionally, tubing was also extruded from select nanocomposite compositions and the TC and mechanical flexibility measured.

Ghose, S.↗

NASA NTRS2007

Thermal Conductivity of Ethylene Vinyl Acetate Copolymer/Carbon Nanofiller Blends

To reduce weight and increase the mobility, comfort, and performance of future spacesuits, flexible, thermally conductive fabrics and plastic tubes are needed for the Liquid Cooling and Ventilation Garment. Such improvements would allow astronauts to operate more efficiently and safely for extended extravehicular activities. As an approach to raise the thermal conductivity (TC) of an ethylene vinyl acetate copolymer (Elvax 260), it was compounded with three types of carbon based nanofillers: multi-walled carbon nanotubes (MWCNTs), vapor grown carbon nanofibers (CNFs), and expanded graphite (EG). In addition, other nanofillers including metallized CNFs, nickel nanostrands, boron nitride, and powdered aluminum were also compounded with Elvax 260 in the melt at various loading levels. In an attempt to improve compatibility between Elvax 260 and the nanofillers, MWCNTs and EG were modified by surface coating and through noncovalent and covalent attachment of organic molecules containing alkyl groups. Ribbons of the nanocomposites were extruded to form samples in which the nanofillers were aligned in the direction of flow. Samples were also fabricated by compression molding to yield nanocomposites in which the nanofillers were randomly oriented. Mechanical properties of the aligned samples were determined by tensile testing while the degree of dispersion and alignment of nanoparticles were investigated using high-resolution scanning electron microscopy. TC measurements were performed using a laser flash (Nanoflash ) technique. TC of the samples was measured in the direction of, and perpendicular to, the alignment direction. Additionally, tubing was also extruded from select nanocomposite compositions and the TC and mechanical flexibility measured.

Ghose, S.↗

NASA NTRS1993

Computational methods for efficient structural reliability and reliability sensitivity analysis

This paper presents recent developments in efficient structural reliability analysis methods. The paper proposes an efficient, adaptive importance sampling (AIS) method that can be used to compute reliability and reliability sensitivities. The AIS approach uses a sampling density that is proportional to the joint PDF of the random variables. Starting from an initial approximate failure domain, sampling proceeds adaptively and incrementally with the goal of reaching a sampling domain that is slightly greater than the failure domain to minimize over-sampling in the safe region. Several reliability sensitivity coefficients are proposed that can be computed directly and easily from the above AIS-based failure points. These probability sensitivities can be used for identifying key random variables and for adjusting design to achieve reliability-based objectives. The proposed AIS methodology is demonstrated using a turbine blade reliability analysis problem.

Wu, Y.-T.↗

DOE OSTI2023

On unifying randomized methods for inverse problems

This work unifies the analysis of various randomized methods for solving linear and nonlinear inverse problems with Gaussian priors by framing the problem in a stochastic optimization setting. By doing so, we show that many randomized methods are variants of a sample average approximation (SAA). More importantly, we are able to prove a single theoretical result that guarantees the asymptotic convergence for a variety of randomized methods. Additionally, viewing randomized methods as an SAA enables us to prove, for the first time, a single non-asymptotic error result that holds for randomized methods under consideration. Another important consequence of our unified framework is that it allows us to discover new randomization methods. Here, we present various numerical results for linear, nonlinear, algebraic, and PDE-constrained inverse problems that verify the theoretical convergence results and provide a discussion on the apparently different convergence rates and the behavior for various randomized methods.

42 ENGINEERING↗