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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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Variational Coupled Loads Analysis using the Hybrid Parametric Variation Method

Time-domain coupled loads analysis (CLA)is used to determine the response of a launch vehicle and payload system to transient forces, such as liftoff, engine ignitions and shutdowns, jettison events, and atmospheric flight loads, such as buffet. CLA, using Hurty/Craig-Bampton (HCB)component models, is the accepted method for the establishment of design-level loads for launch systems. However, uncertainty in the component models flows into uncertainty in predicted system results. Uncertainty in the structural responses during launch is a significant concern because small variations in launch vehicle and payload mode shapes and their interactions can result in significant variations in system loads. Uncertainty quantification (UQ)is used to determine statistical bounds on prediction accuracy based on model uncertainty. In this paper uncertainty is treated at the HCB component-model level. In an effort to account for model uncertainties and statistically bound their effect on CLA predictions, this work combines CLA with UQ in a process termed variational coupled loads analysis (VCLA). The modeling of uncertainty using a parametric approach, in which input parameters are represented by random variables, is common, but its major drawback is the resulting uncertainty is limited to the form of the nominal model. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. Model-form uncertainty can be represented using a nonparametric approach based on random matrix theory (RMT). In this work, UQ is performed using the hybrid parametric variation (HPV)method, which combines parametric with nonparametric uncertainty at the HCB component model level. The HPV method requires the selection of dispersion values for the HCB fixed-interface (FI)eigenvalues, and the HCB mass and stiffness matrices. The dispersions are based upon component test-analysis modal correlation results. During VCLA, random component models are assembled into an ensemble of random systems using a Monte Carlo (MC)approach. CLA is applied to each of the ensemble members to produce an ensemble of system-level responses for statistical analysis. The proposed methodology is demonstrated through its application to a buffet loads analysis of NASA’s Space Launch System (SLS)during the transonic regime fifty seconds after liftoff. Core stage (CS)section shears and moments are recovered, and statistics are computed.

Uncertainty Quantification↗
DOE OSTI2023

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

PET/CT in brachytherapy early response evaluation of pancreatic ductal adenocarcinoma xenografts: comparison with apparent diffusion coefficient from diffusion-weighted MR imaging

Objective: To evaluate the feasibility of using PET/CT and diffusion-weighted magnetic resonance imaging (DW-MRI) to monitor the early response of pancreatic ductal adenocarcinoma (PDAC) xenografts to brachytherapy, and to determine whether maximum standardized uptake value (SUVmax) correlate with apparent diffusion coefficient (ADC). Materials and Methods: SW1990 human PDAC were subcutaneously implanted in 20 nude mice. They were randomly divided into 125-Iodine ({sup 125}I) seeds and blank seeds group. PET/CT and DW-MRI were performed at pretreatment and 5 days after therapy. SUVmax and ADC values were calculated, respectively. The correlation between SUVmax and ADC values was analyzed by the Pearson correlation test. Results: The SUVmax were significantly decreased between pretreatment and 5 days after {sup 125}I seeds treatment (p < 0.001) and between two groups (p < 0.001). And the ADC values were significantly increased between pretreatment and 5 days after {sup 125}I seeds treatment (p < 0.001) and between two groups (p < 0.001). While in the bank seeds group, there were no significantly difference between pretreatment and after treatment in SUVmax and ADC values (p = 0.057; p = 0.397). SUVmax and ADC correlated significantly and negatively before treatment in both groups (r = − 0.964, R{sup 2} = 0.929, p < 0.001; r = − 0.917, R{sup 2} = 0.841, p < 0.001) and after treatment in the blank seeds group (r = − 0.944, R{sup 2} = 0.891, p < 0.001). But after {sup 125}I seeds treatment there was no significant correlation between SUVmax and ADC (r = − 0.388, R{sup 2} = 0.151, p = 0.268). Conclusion: The PET/CT and DW-MRI are capable of monitoring the early response of PDAC xenografts to brachytherapy. The significantly inverse correlation between pretreatment SUVmax and ADC suggests that PET/CT and DW-MRI might play complementary roles for therapy assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

PARIS: Predicting application resilience using machine learning

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. Furthermore, while analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-Resolution Comonomer Sequencing of Blocky Brominated Syndiotactic Polystyrene Copolymers Using 13C NMR Spectroscopy and Computer Simulations

This work demonstrates the first high-resolution comonomer sequencing of Blocky brominated syndiotactic polystyrene (sPS-co-sPS-Br) copolymers based on pentad assign- ments of the quaternary carbon region of the nuclear magnetic resonance spectrum. Copolymers containing p-bromostyrene (Br-Sty) units were prepared in matched sets using postpolymerization bromination methods carried out in the heterogeneous gel state (Blocky) and homogeneous solution state (Random). Quantitative information from the quaternary carbon spectra, heteronuclear multiple bond correlation spectroscopy, electronic structure calculations, and simulated statistically random copolymers was correlated to confirm the carbon resonance assignments for all 20 possible pentad comonomer sequences. Using the experimental pentad sequence prevalences, a computer code was developed to simulate chains with microstructures typical of each sample as a means to visually represent the copolymer blockiness with quantitative precision. Based on the microstructure and distribution of run lengths in these chains, the simulations revealed that the Blocky copolymers contain a high degree of blockiness. By comparing the run lengths in the simulated chains to the average number of styrene units in a crystalline segment of sPS (found by small-angle X-ray scattering), copolymer crystallizability was predicted. For the simulated Blocky B-21% (21 mol % Br-Sty) chain, the probability of randomly selecting a styrene unit in a crystallizable block was 25.8%, while that in the simulated Random R-18% was zero, in excellent agreement with the experimental crystallization behavior measured by differential scanning calorimetry. Additionally, these predictions confirmed that the simulated chains accurately represent the ensemble of chains in their respective copolymer samples. Furthermore, each simulated Blocky chain contained one or more long sPS blocks that paralleled the measured 38-40 styrene units spanning a crystalline segment within the sPS/CCl4 gel. This finding affirmed that the long sPS segments originated from the precise lamellar structure within the heterogeneous gel morphology (i.e., block length is correlated with lamellar thickness). Overall, the ability to tailor the copolymer microstructure through control of the semicrystalline gel morphology opens the door to synthesizing ordered copolymers by postpolymerization functionalization processes with unprecedented levels of compositional control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Spherulite-enhanced macroscopic polarization in molecular ferroelectric films from vacuum deposition

Proton-transfer type molecular ferroelectrics hold significant promise for practical application due to their large spontaneous polarizations, high Curie temperatures, and small switching fields. However, it remains puzzling that preparation of quasi-2D films exhibiting macroscopic ferroelectric behaviors has been reported in only few molecular ferroelectrics. To address this puzzle, we studied the impact of microstructures on the macroscopic ferroelectric properties of 5,6-dichloro-2-methylbenzimidazole (DC-MBI) films grown using the low-temperature deposition followed by the restrained crystallization (LDRC) method. Our findings revealed a competition between dense spherulites and porous microstructures containing randomly oriented nanograins in the as-grown films. Post-growth annealing at moderate temperature promotes the formation of spherulites, leading to macroscopic ferroelectric polarization switching. These results underscore the critical role of microstructure density in determining macroscopic ferroelectric properties, potentially resolving the puzzle for the absence of such behavior in many molecular ferroelectric films. Here, we anticipate that the approach proposed in this study to enhance microstructure density will significantly advance the fabrication of quasi-2D molecular ferroelectric films and unlock their potential in device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Futility

The Futility package contains the following: 1) Definition of the size of integers and real numbers; 2) A generic Unit test harness; 3) Definitions for some basic extensions to the Fortran language: arbitrary length strings, a parameter list construct, exception handlers, command line processor, timers; 4) Geometry definitions: point, line, plane, box, cylinder, polyhedron; 5) File wrapper functions: standard Fortran input/output files, Fortran binary files, HDF5 files; 6) Parallel wrapper functions: MPI, and Open MP abstraction layers, partitioning algorithms; 7) Math utilities: BLAS, Matrix and Vector definitions, Linear Solver methods and wrappers for other TPLs (PETSC, MKL, etc), preconditioner classes; 8) Misc: random number generator, water saturation properties, sorting algorithms.

Collins, BenjaminS.↗
DOE OSTI2018

Metabolic Response of the Immature Right Ventricle to Acute Pressure Overloading

Background Surgical palliation or repair of complex congenital heart disease in early infancy can produce right ventricular (RV) pressure overload, often leading to acute hemodynamic decompensation. The mechanisms causing this acute RV dysfunction remain unclear. We tested the hypothesis that the immature right ventricle lacks the ability to modify substrate metabolism in order to meet increased energy demands induced by acute pressure overloading. Methods and Results Twenty-two infant male mixed breed Yorkshire piglets were randomized to a sham operation (Control) or pulmonary artery banding yielding >2-fold elevation over baseline RV systolic pressure. We used carbon 13 ( 13 C)-labeled substrates and proton nuclear magnetic resonance to assess RV energy metabolism. [Phosphocreatine]/[ATP] was significantly lower after pulmonary artery banding. [Phosphocreatine]/[ATP] inversely correlated with energy demand indexed by maximal sustained RV systolic pressure/left ventricular systolic pressure. Fractional contributions of fatty acids to citric acid cycle were significantly lower in the pulmonary artery banding group than in the Control group (medium-chain fatty acids; 14.5±1.6 versus 8.2±1.0%, long-chain fatty acids; 9.3±1.5 versus 5.1±1.1%). 13 C-flux analysis showed that flux via pyruvate decarboxylation did not increase during RV pressure overloading. Conclusions Acute RV pressure overload yielded a decrease in [phosphocreatine]/[ATP] ratio, implying that ATP production did not balance the increasing ATP requirement. Relative fatty acids oxidation decreased without a reciprocal increase in pyruvate decarboxylation. The data imply that RV inability to adjust substrate oxidation contributes to energy imbalance, and potentially to contractile failure. The data suggest that interventions directed at increasing RV pyruvate decarboxylation flux could ameliorate contractile dysfunction associated with acute pressure overloading.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Utilizing Highly Scattered Light for Intelligence through Aerosols

This communication is the final report for the project Utilizing Highly Scattered Light for Intelligence through Aerosols funded by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories and lasting six months in 2019. Aerosols like fog reduce visibility and cause down-time that for critical systems or operations are unacceptable. Information is lost due to the random scattering and absorption of light by tiny particles. Computational diffuse optical imaging methods show promise for interpreting the light transmitted through fog, enabling sensing and imaging to improve situational awareness at depths 10 times greater than current methods. Developing this capability first requires verification and validation of diffusion models of light propagation in fog. For this reason, analytical models were developed and compared to experimental data captured at the Sandia National Laboratory Fog Chamber facility. A methodology was developed to incorporate the propagation of scattered light through the imaging optics to a pixel array. The diffusion approximation to the radiative transfer equation was found to predict light propagation in fog under the appropriate conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1973

On the improper neglect of certain terms in random function theory

This paper presents some exact solutions of problems in random function theory for the purpose of testing the validity of an approximate method known variously in the many different fields of its application as first order smoothing theory, first order cumulant discard, quasilinear theory, or the adiabatic approximation. The hydromagnetic dynamo equations are used here, as particularly appropriate for such an investigation. The calculations show that in one case the exact and approximate solutions agree. In the other case the approximate solution is wrong. Hence, in the absence of a general criterion for validity, a result based on first order smoothing theory is a conjecture rather than a fact. This impacts strongly on much of the recent work on hydromagnetic dynamos.

Lerche, I.↗
NASA NTRS1976

Data processing/display design for the space shuttle/spacelab Electromagnetic Environment Experiment (EEE)

Methods for data analysis, data compression including universal coding, storage and retrieval on random access storage devices, and display were developed and implemented on the GSFC Interdata computer. The original 64 bit per frequency band representation was reduced to 10 bits through source coding/universal coding, a compression ratio of 6.4, prior to storage. Rapid encoding/decoding was achieved by the algorithms used so that rapid random access is retained.

Davisson, L. D.↗
NASA NTRS1988

Zones of information in the AVIRIS spectra

To make the best use of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data an investigator needs to know the ratio of signal to random variability or noise (S/N ratio). The signal is land-cover dependent and decreases with both wavelength and atmospheric absorption and random noise comprises sensor noise and intra-pixel variability. The three existing methods for estimating the S/N ratio are inadequate as typical laboratory methods inflate, while dark current and image methods deflate the S/N ratio. We propose a new procedure called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intra-pixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season.

Curran, Paul J.↗
NASA NTRS1988

Proposed acceptance, qualification, and characterization tests for thin-film PV modules

Details of a proposed test program for PV thin-film modules which the Department of Energy has directed the Solar Energy Research Institute (SERI) to prepare are presented. Results of one of the characterization tests that SERI has performed are also presented. The objective is to establish a common approach to testing modules that will be acceptable to both users and manufacturers. The tests include acceptance, qualification, and characterization tests. Acceptance tests verify that randomly selected modules have similar characteristics. Qualification tests are based on accelerated test methods designed to simulate adverse conditions. Characterization tests provide data on performance in a predefined environment.

Waddington, D.↗