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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Results of wind tunnel tests of an ASRM configured 0.03 scale Space Shuttle integrated vehicle model (47-OTS) in the AEDC 16-foot Transonic wind tunnel (IA613A), volume 1

An experimental Aerodynamic and Aero-Acoustic loads data base was obtained at transonic Mach numbers for the Space Shuttle Launch Vehicle configured with the ASRM Solid Rocket Boosters as an increment to the current flight configuration (RSRB). These data were obtained during transonic wind tunnel tests (IA 613A) conducted in the Arnold Engineering Development Center 16-Foot transonic propulsion wind tunnel from March 27, 1991 through April 12, 1991. This test is the first of a series of two tests covering the Mach range from 0.6 to 3.5. Steady state surface static and fluctuating pressure distributions over the Orbiter, External Tank and Solid Rocket Boosters of the Shuttle Integrated Vehicle were measured. Total Orbiter forces, Wing forces and Elevon hinge moments were directly measured as well from force balances. Two configurations of Solid Rocket Boosters were tested, the Redesigned Solid Rocket Booster (RSRB) and the Advanced Solid Rocket Motor (ASRM). The effects of the position (i.e. top, bottom, top and bottom) of the Integrated Electronics Assembly (IEA) box, mounted on the SRB attach ring, were obtained on the ASRM configured model. These data were obtained with and without Solid Plume Simulators which, when used, matched as close as possible the flight derived pressures on the Orbiter and External Tank base. Data were obtained at Mach numbers ranging from 0.6 to 1.55 at a Unit Reynolds Number of 2.5 million per foot through model angles of attack from -8 to +4 degrees at sideslip angles of 0, +4 and -4 degrees.

Marroquin, J.↗
DOE OSTI2022

Allometric, Growth, and Biomass Estimation Models for Acacia dealbata Link.: A Case Study in Serra da Estrela Natural Park (Portugal)

Allometric, growth, and biomass estimation models for the quantification of missing parameters in trees are widely used to estimate the productivity of a stand. However, regarding species with lower economic interest, or exotic invasive species, the creation of these tools did not occur in the same way as for other species, lacking the development of relationships to allow a deeper study of these species. Thus, data were collected in a settlement of Acacia dealbata Link. in Portugal, in an area known for the current infestation of this species. After a bibliographic review, some of the identified models were tested to select those who best fit the characteristics of this stand, which were used to develop relationships that, based on the data collected, would be able to estimate parameters, such as height, volume, or mass. It was found that the models established, at least for the initial 20 years of the life of the trees, followed close to a linear model. However, the model still presents some weaknesses. For being considered an invasive species in Portugal, A. dealbata is frequently controlled, and for this reason it is very difficult to find trees older than 20 years.

Nunes, Leonel J. R. (ORCID:0000000154048163)↗
DOE OSTI2026

A filter-dependent granular temperature model from large-scale CFD-DEM data

The computational study of strongly-coupled, gas–solid flows at scales relevant to most environmental and engineering applications requires the use of ‘coarse-grained’ methodologies such as the two-fluid model, particle-in-cell approach or the multiphase Reynolds Averaged Navier–Stokes equations. While these strategies enable computations at desirable length- and time-scales, they rely heavily on models to capture important flow physics that occur at scales smaller than the mesh. To date, the models that do exist are based on a limited set of flow conditions, such as very dilute particle phase. To this end, we leverage a large-scale repository of CFD-DEM data to develop filter-size dependent models for the mean variance in particle volume fraction, a quantity commonly used to assess the degree of clustering, and the granular temperature, a key quantity for accurately predicting gas–solid flows. In conclusion, because of its filter-size dependence, the granular temperature model can be directly translated to coarse-grained approaches and tied directly to grid size.

AMReX↗
DOE OSTI2024

Contrastive Machine Learning with Gamma Spectroscopy Data Augmentations for Detecting Shielded Radiological Material Transfers

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

Methods and models for predicting fatigue crack growth under random loading

Papers are presented in the volume summarizing the baseline data, methodology, procedures, and results of a round-robin analysis which was conducted to predict the fatigue crack growth in 2219-T851 aluminum center-cracked specimens subjected to flight loading in random cycle-by-cycle format. The objective of the analysis was to assess whether data from constant-amplitude fatigue crack growth tests on center-cracked specimens can be used to predict fatigue crack growth lives under random loading. The following approaches are discussed in detail: a root-mean-square approach, a crack-closure model, a multi-parameter yield zone model, and a load-interaction model.

Chang, J. B.↗
NASA NTRS1978

An analysis of short haul air passenger demand, volume 2

Several demand models for short haul air travel are proposed and calibrated on pooled data. The models are designed to predict demand and analyze some of the motivating phenomena behind demand generation. In particular, an attempt is made to include the effects of competing modes and of alternate destinations. The results support three conclusions: (1) the auto mode is the air mode's major competitor; (2) trip time is an overriding factor in intermodal competition, with air fare at its present level appearing unimportant to the typical short haul air traveler; and (3) distance appears to underly several demand generating phenomena, and therefore, must be considered very carefully to any intercity demand model. It may be the cause of the wide range of fare elasticities reported by researchers over the past 15 years. A behavioral demand model is proposed and calibrated. It combines the travel generating effects of income and population, the effects of modal split, the sensitivity of travel to price and time, and the effect of alternative destinations satisfying the trip purpose.

Blumer, T. P.↗
NASA NTRS1989

A reduced version of the NMC DERF 2 data set

The National Meterological Center (NMC) Dynamical Extended Range Forecast (DERF 2) data represents a major computational effort to better ascertain the potential for extended range forecasts and to develop a strategy for performing operational extended range forecasts using dynamical models. A major stumbling block for using this data has been the sheer volume of data that must be processed to perform even simple calculations. The product of the data reduction described is a manageable data set that fits comfortably on five magnetic tapes or on one compact disc. The document outlines the data reduction process of the second phase of DERF data. It contains the description of the fields and the resolution of both the original and final fields. In order to assist the users of this data set, maps of selected fields, using both the original truncation at rhomboidal 30 and the truncation of the final data at triangular 20, are displayed.

Schubert, Siegfried↗
NASA NTRS1990

Structural properties of laminated Douglas fir/epoxy composite material

This publication contains a compilation of static and fatigue strength data for laminated-wood material made from Douglas fir and epoxy. Results of tests conducted by several organizations are correlated to provide insight into the effects of variables such as moisture, size, lamina-to-lamina joint design, wood veneer grade, and the ratio of cyclic stress to steady stress during fatigue testing. These test data were originally obtained during development of wood rotor blades for large-scale wind turbines of the horizontal-axis (propeller) configuration. Most of the strength property data in this compilation are not found in the published literature. Test sections ranged from round cylinders 2.25 in. in diameter to rectangular slabs 6 by 24 in. in cross section and approximately 30 ft. long. All specimens were made from Douglas fir veneers 0.10 in. thick, bonded together with the WEST epoxy system developed for fabrication and repair of wood boats. Loading was usually parallel to the grain. Size effects (reduction in strength with increase in test volume) are observed in some of the test data, and a simple mathematical model is presented that includes the probability of failure. General characteristics of the wood/epoxy laminate are discussed, including features that make it useful for a wide variety of applications.

Spera, David A.↗
NASA NTRS1993

Aerospace devices for magnetic replicas

Retained persistent magnetic field has been studied and improved in the superconductor YBa2Cu3O7 (Y123). During the study, trapped magnetic field, B(t), has been increased by over a factor of 10(exp 5). Methods used to improve magnetic field trapping were principally: (1) the adoption of the Melt Texturing process to increase grain size; (2) the addition of excess Y to disperse deposits of Y2BaCuO5 (Y211) and again increase grain size; (3) irradiation with high energy particles including 1H+, 3He++, 4He++, and fission fragments; and (4) utilizing temperatures below 77 K has also been quantified as a way to increase trapped field. In addition, in our study of B(t), we have found laws governing creep, activation, temperature dependence, creep vs. current flow, etc. In the range 20 K less than or equal to T less than or equal to 65 K, and for B less than 10 Tesla, a simple empirical relationship was found: B(trap) (T2) = B(trap) (T1) ((Tc - T2)/(Tc - T1))squared where Tc is the critical temperature. The highest experimental trapped field was B(trap) = 3.96 Tesla, at 65 K. We believe this to be the highest persistent field ever produced, by any method. A two component model of the persistent currents has been developed. This accurately reproduces the data, using as parameters only the magnitude of a constant surface current, J(s), and a constant volume current J(v). The model successfully predicts B(t) (xyz) for the case of maximum trapped field, for all samples observed. It has also been extended to describe the unsaturated case either zero field cooled, or field cooled. Loss of strap with time has been studied for the critical state (Bt,max), and non critical state (Bt less than Bt,max), for times from a few minutes to a few months, for unirradiated material, for irradiation by 1H+, 3He++, 4He++, high z projectiles, and neutrons, and for all materials used in the overall study. We conclude that: (1) multi Tesla trapped fields are attained; (2) fields over 10 T are achievable; (3) creep is not a large problem; (4) application is feasible to motors, generators, magnets for particle beam optics, separators, levitating bearings, energy storage, shielding, and transportation.

Weinstein, Roy↗
NASA NTRS2007

Estimating Dust and Water Ice Content of the Martian Atmosphere From THEMIS Data

Researchers at JPL and Arizona State University conducted a comparative study of three candidate algorithms for estimating components of the Martian atmosphere, using raw (uncalibrated) data collected by the Thermal Emission Imaging System (THEMIS). THEMIS is an instrument onboard the Mars Odyssey spacecraft that acquires image data in five visible and nine infrared (IR) wavelength bands. The algorithms under study used data collected from eight of the nine IR bands to estimate the dust and water ice content of the atmosphere. Such an algorithm could be used in onboard data processing to trigger other algorithms that search for features of scientific interest and to reduce the volume of data transmitted to Earth. The algorithms studied were based on regression models. In the study, the optical depths estimated by these algorithms were compared with optical depths estimated in ground-based processing using fully calibrated data from both THEMIS and the Thermal Emission Spectrometer (TES). TES is an instrument onboard the Mars Global Surveyor spacecraft that also observes the planet at infrared wavelengths, but at a lower spatial resolution than THEMIS does. Of the algorithms studied, the one that performed best was based on a Gaussian Support Vector Machine regression model. The test results indicated that this algorithm, operating on the raw data, had error rates that were within the uncertainty associated with the estimates obtained by the groundbased analysis of the fully calibrated data. This level of fidelity demonstrates that these algorithms are sufficiently accurate for use in an onboard setting.

Bandfield, Joshua↗
NASA NTRSDate not supplied

Spacesuit Reach Capability and Work Volume Assessment

Work volumes and reach envelopes define the locations where work objects or interfaces can be placed around a person to reduce ergonomic risk. Spacesuits, however, show unique reach patterns, due to the stiffness and mechanical constraints from pressurization. This study measured the reach motion patterns and capabilities from subjects wearing a pressurized spacesuit using a 3D motion capture system. The subjects performed arm and hand motions, focusing on extreme lateral and vertical positions. These motions defined the maximum ranges. They also performed on-suit reaches to measure where they could or couldn’t reach on the suit surface to determine where critical controls and devices could be placed on the suit. A parametric model of the reach volume was statistically built using the collected hand position data. The observed shape and size of the work volume substantially vary by persons, depending on anthropometry and strength capability. It was also indicated that the traditional metrics (e.g., simple spherical or cylindrical representations) estimated from “unsuited” persons or computer simulations may not accurately capture such variations. The results of this study can help to design and optimize space hardware and task activities, to protect crewmembers from overexertion or injuries.

Han Kim↗
DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1990

Studies in astronomical time series analysis. IV - Modeling chaotic and random processes with linear filters

While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.

Scargle, Jeffrey D.↗
DOE OSTI2022

SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability

Advanced reactors often involve complicated thermal-fluid (T-F) phenomena. Modeling such phenomena with the traditional one-dimensional (1-D) system code is a challenging task. The System Analysis Module (SAM), a modern nuclear system code, has developed a coarse mesh multi-dimensional (multi-D) flow model to capture the spatial effect of T-F phenomena in advanced reactors. As a coarse mesh solver, constitutive relations are required for SAM's multi-D model for unresolved fine-scale physics, such as turbulence. Here this work presents a novel approach that integrates neural networks as data-driven closure for SAM's multi-D flow model. The data-driven closure is trained with fine-resolution data to ensure its accuracy while maintaining a coarse mesh setup to ensure its efficiency and consistency with SAM. We demonstrate the applicability of this SAM-ML capability in an open volume thermal stratification problem, where a neural network model serves as the eddy viscosity closure. A customized interface between the neural network and SAM is developed to ensure flexible and efficient data exchange. The SAM-ML results demonstrate superior performance compared to SAM's built-in zero-equation eddy viscosity closure. The case study shows that although the generalization capability of the data-driven closure still needs to be improved for different transient case or different geometric setup, SAM -ML demonstrates good potential for challenging simulation problems with improved accuracy and computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Although accurately classifying signals from earthquakes and explosions at local distance (<250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.

58 GEOSCIENCES↗
DOE OSTI2022

Extreme variation in fatigue: Fatigue life prediction and dependence on build volume location in laser powder bed fusion of 17-4 stainless steel

Laser powder bed fusion (LPBF), a metal additive manufacturing technology, is well-suited for design optimization but fatigue life is limited by manufacturing defects. In this work, 17-4 stainless steel components were manufactured in densely populated build volumes, simulating at-scale LPBF production. Tests revealed extreme variability in fatigue life data, analyzed via rigorous statistical tools. The El-Haddad model, modified for finite-life, enabled defect-based life prediction. Specimen location within the build volume correlated to life, which was heteroscedastic. Investigating defect concentration over the build volume explained typical life and scatter. Finally, these findings argue for qualification approaches which acknowledge high material lot variability.

36 MATERIALS SCIENCE↗