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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Composite load spectra for select space propulsion structural components

The objective of this program is to develop generic load models to simulate the composite load spectra (CLS) that are induced in space propulsion system components representative of the space shuttle main engines (SSME). These models are being developed through describing individual component loads with an appropriate mix of deterministic and state-of-the-art probabilistic models that are related to key generic variables. Combinations of the individual loads are used to synthesize the composite loads spectra. A second approach for developing the composite loads spectra load model simulation, the option portion of the contract will develop coupled models which combine the individual load models. Statistically varying coefficients of the physical models will be used to obtain the composite load spectra.

Newell, J. F.↗
NASA NTRS1988

Continuous fiber ceramic matrix composites for heat engine components

High strength at elevated temperatures, low density, resistance to wear, and abundance of nonstrategic raw materials make structural ceramics attractive for advanced heat engine applications. Unfortunately, ceramics have a low fracture toughness and fail catastrophically because of overload, impact, and contact stresses. Ceramic matrix composites provide the means to achieve improved fracture toughness while retaining desirable characteristics, such as high strength and low density. Materials scientists and engineers are trying to develop the ideal fibers and matrices to achieve the optimum ceramic matrix composite properties. A need exists for the development of failure models for the design of ceramic matrix composite heat engine components. Phenomenological failure models are currently the most frequently used in industry, but they are deterministic and do not adequately describe ceramic matrix composite behavior. Semi-empirical models were proposed, which relate the failure of notched composite laminates to the stress a characteristic distance away from the notch. Shear lag models describe composite failure modes at the micromechanics level. The enhanced matrix cracking stress occurs at the same applied stress level predicted by the two models of steady state cracking. Finally, statistical models take into consideration the distribution in composite failure strength. The intent is to develop these models into computer algorithms for the failure analysis of ceramic matrix composites under monotonically increasing loads. The algorithms will be included in a postprocessor to general purpose finite element programs.

Tripp, David E.↗
NASA NTRS1995

Software For Computing Reliability Of Other Software

Computer Aided Software Reliability Estimation (CASRE) computer program developed for use in measuring reliability of other software. Easier for non-specialists in reliability to use than many other currently available programs developed for same purpose. CASRE incorporates mathematical modeling capabilities of public-domain Statistical Modeling and Estimation of Reliability Functions for Software (SMERFS) computer program and runs in Windows software environment. Provides menu-driven command interface; enabling and disabling of menu options guides user through (1) selection of set of failure data, (2) execution of mathematical model, and (3) analysis of results from model. Written in C language.

Nikora, Allen↗
NASA NTRS1995

Subband Image Coding with Jointly Optimized Quantizers

An iterative design algorithm for the joint design of complexity- and entropy-constrained subband quantizers and associated entropy coders is proposed. Unlike conventional subband design algorithms, the proposed algorithm does not require the use of various bit allocation algorithms. Multistage residual quantizers are employed here because they provide greater control of the complexity-performance tradeoffs, and also because they allow efficient and effective high-order statistical modeling. The resulting subband coder exploits statistical dependencies within subbands, across subbands, and across stages, mainly through complexity-constrained high-order entropy coding. Experimental results demonstrate that the complexity-rate-distortion performance of the new subband coder is exceptional.

Kossentini, Faouzi↗
DOE OSTI2023

Self-diagnosis of model suitability for continuous measurements of stream-dissolved organic carbon derived from in situ UV–visible spectroscopy

Application of high-frequency monitoring of dissolved organic carbon (DOC) is difficult in instances where training datasets are challenging to develop (e.g., remote locations) and the relationship between optical features and DOC concentration changes due to environmental or landscape shifts (e.g., climate or land-use change). We developed and compared three partial least squares (PLS) models using in situ water level measurements, conductivity, and UV–Vis spectral attenuation to predict DOC. Two site-specific models were developed using data from a hillslope-dominated forest or a low-relief wetland-pond-dominated stream catchment. The third model, using data from both sites, exhibited the best performance (DOC range = 4–15.5 mg C L −1 , mean = 8.38 mg C L −1 , training RMSE = 0.34 mg C L −1 , internal validation RMSE = 0.50 mg C L −1 , external validation RMSE = 2.43 mg C L −1 ). We further demonstrate using PLS model statistics to monitor performance and elucidate when and how models should be updated. These statistics, Hotelling's T 2 and squared prediction errors, are useful consistency checks for the predictions made and detect underlying inconsistencies that, if undetected, can reduce the robustness of DOC prediction. For example, via the T 2 statistic, we identified the summer–autumn transition as a period when DOC composition differed from what was represented in the training dataset. We also determined that elevated SUVA 254 values contributed to the overall bias observed in predictions made during the subsequent year as part of the external validation. This enabled the application of a bias correction that reduced the RMSE from 2.43 to 0.89 mg C L −1 . The method presented here could be applied to future monitoring programs enabling model updates to monitor DOC fluxes accurately from optical datasets (e.g., attenuance or fluorescence) in the face of developing datasets in remote locations or environmental change. In conclusion, implementation of this approach may also identify possible regime shifts or landscape and hydrologic change associated with climate and other environmental changes relevant to terrestrial to aquatic fluxes.

UV-VIS spectroscopy↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗
DOE OSTI2024

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification

Abstract Conditional independence mixture models (CIMMs) are an important class of statistical models used in many fields of science. We introduce a novel unsupervised machine learning technique called the independent classifier networks (InClass nets) technique for the nonparameteric estimation of CIMMs. InClass nets consist of multiple independent classifier neural networks (NNs), which are trained simultaneously using suitable cost functions. Leveraging the ability of NNs to handle high-dimensional data, the conditionally independent variates of the model are allowed to be individually high-dimensional, which is the main advantage of the proposed technique over existing non-machine-learning-based approaches. Two new theorems on the nonparametric identifiability of bivariate CIMMs are derived in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We use the InClass nets technique to perform CIMM estimation successfully for several examples. We provide a public implementation as a Python package called RainDancesVI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Validation of MCNP Critical Benchmark Models of PU-MET-FAST-016 [Slides]

The validation of PU-MET-FAST-016 contributed to the centralized LANL benchmark repository currently under development. The revisions made the MCNP models statistically, significantly more similar to the benchmark models. The overall impact on the USL is negligible. The revisions to the PU-MET-FAST-016 models provide value to the Los Alamos Benchmark Suite without invalidating past Whisper results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
NASA NTRS1973

Coordination procedure for radio relay and communication satellite services

A global rain rate statistic model is used to link microwave propagation statistics to measurable rain statistics in order to develop international telecommunication site criteria for radio relay and communication satellite services that minimize interference between receivers and transmitters. This rain coordination procedure utilizes a rain storm cell size, a statistical description of the rainfall rate within the cell valid for most of the earth's surface, approximations between Raleigh scatter and constancy of precipitation with altitude, and an analytic relation between radar reflectivity and rain rate.

Eckerman, J.↗
NASA NTRS1985

Magnetospheric plasma modeling (0-100 keV)

Spacecraft surface charging, which is primarily a current balance phenomenon, is in general a function of the dominant currents to and from the vehicle's surface. Within the near-earth magnetosphere the dominant currents to the surface are the ambient space plasma fluxes between approximately 0 and 100 keV. A major effort to understand the near-earth environment was initiated when spacecraft charging became a major issue. The present paper has the objective to summarize the basic features of the models which have resulted from this effort. A description is given of four categories of models, based primarily on the degree of empirical and theoretical input. Types of quantitative models are discussed, taking into account definitions, statistical models, analytic models, static models, and time-dependent models. Engineering models are also considered, giving attention to baseline models and 'worst-case' models.

Garrett, H. B.↗
NASA NTRS1992

Summertime photochemistry of the troposphere at high northern latitudes

Budgets of O3, NO(x), and NO(y), and acetic acid in the Arctic Boundary Layer Expedition (ABLE 3A) flight region are constructed using photochemical model statistics based on aircraft observations. A Lagrangian model is used to reconstruct the photochemical history of two aged biomass fire plumes sampled by the ABLE 3A aircraft. It is shown that anthropogenic influence on O3 levels in the Arctic may manifest itself not by long-range transport of pollution-derived O3, but rather by a decrease of the regional photochemical sink due to the presence of small amounts of NO(x). The low concentrations of NO(x) measured in ABLE 3A were sufficient to reduce the rate of photochemical loss appreciably relative to a NO(x)-free atmosphere, thus increasing the O3 lifetime. It is shown that decomposition of PAN can account for most of the NO(x) measured below 4-km altitude, but for only 20 percent at 6-km altitude. A lifetime of 29 days is estimated for NO(y) in the ABLE 23A flight region.

Jacob, D. J.↗
NASA NTRS1993

Stochastic performance modeling and evaluation of obstacle detectability with imaging range sensors

Statistical modeling and evaluation of the performance of obstacle detection systems for Unmanned Ground Vehicles (UGVs) is essential for the design, evaluation, and comparison of sensor systems. In this report, we address this issue for imaging range sensors by dividing the evaluation problem into two levels: quality of the range data itself and quality of the obstacle detection algorithms applied to the range data. We review existing models of the quality of range data from stereo vision and AM-CW LADAR, then use these to derive a new model for the quality of a simple obstacle detection algorithm. This model predicts the probability of detecting obstacles and the probability of false alarms, as a function of the size and distance of the obstacle, the resolution of the sensor, and the level of noise in the range data. We evaluate these models experimentally using range data from stereo image pairs of a gravel road with known obstacles at several distances. The results show that the approach is a promising tool for predicting and evaluating the performance of obstacle detection with imaging range sensors.

Matthies, Larry↗
NASA NTRS2000

Operational Implementation of a 2-Hour Prebreathe Protocol for International Space Station

Procedures, equipment, and analytical techniques were developed to implement the ground tested 2-hour protocol in-flight operations. The methods are: 1) The flight protocol incorporates additional safety margin over the ground tested protocol. This includes up to 20 min of additional time on enriched O2 during suit purge and pressure check, increased duration of extravehicular activity (EVA) preparation exercise during O2 prebreathing (up to 90 min vs; the tested 24 min), and reduced rates of depressurization. The ground test observations were combined with model projections of the conservative measures (using statistical models from Duke University and NASA JSQ to bound the risk of Type I and Type II decompression sickness (DCS). 2) An inflight exercise device using the in-flight ergometer and elastic tubes for upper body exercise was developed to replicate the dual cycle exercise in the ground trials. 3) A new in-flight breathing system was developed and man-tested. 4) A process to monitor inflight experience with the protocol, including the use of an in-suit Doppler bubble monitor when available, was developed. The results are: 1) The model projections of the conservative factors of the operational protocol were shown to reduce the risk of DCS to levels consistent with the observations of no DCS to date in the shuttle program. 2) Cross over trials of the dual cycle ergometer used in ground tests and the in-flight exercise system verified that02consumption and the % division of work between upper and lower body was not significantly different at the p= 0.05 level. 3) The in-flight breathing system was demonstrated to support work rates generating 75% O2(max) in 95 percentile subjects. 4) An in-flight monitoring plan with acceptance criteria was put in place for the 2-hour prebreathe protocol. And the conclusions are: The 2-hour protocol has been approved for flight, and all implementation efforts are in place to allow use of the protocol as early as flight ISS 7A, now scheduled in November of 2000.

Waligora, James M.↗