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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗

DOE OSTI2024

Self-reported health impacts of do-it-yourself air cleaner use in a smoke-impacted community

Smoke exposure from wildfires or residential wood burning for heat is a public health problem for many communities. Do-It-Yourself (DIY) portable air cleaners (PACs) are promoted as affordable alternatives to commercial PACs, but evidence of their effect on health outcomes is limited. Pilot test an evaluation of the effect of DIY PAC usage on self-reported symptoms, and investigate barriers and facilitators of PAC use, among members of a tribal community that routinely experiences elevated concentrations of fine particulate matter (PM 2.5 ) from smoke. We conducted studies in Fall 2021 (“wildfire study”; N = 10) and Winter 2022 (“wood stove study”; N = 17). Each study included four sequential one-to-two-week phases: 1) initial, 2) DIY PAC usage ≥8 h/day, 3) commercial PAC usage ≥8 h/day, and 4) air sensor with visual display and optional PAC use. We continuously monitored PAC usage and indoor/outdoor PM 2.5 concentrations in homes. Concluding each phase, we conducted phone surveys about participants’ symptoms, perceptions, and behaviors. We analyzed symptoms associated with PAC usage and conducted an analysis of indoor PM 2.5 concentrations as a mediating pathway using mixed effects multivariate linear regression. We categorized perceptions related to PACs into barriers and facilitators of use. No association was observed between PAC usage and symptoms, and the mediation analysis did not indicate that small observed trends were attributable to changes in indoor PM 2.5 concentrations. Small sample sizes hindered the ability to draw conclusions regarding the presence or absence of causal associations. DIY PAC usage was low; loud operating noise was a barrier to use. This research is novel in studying health effects of DIY PACs during wildfire and wood smoke exposures. Such research is needed to inform public health guidance. Recommendations for future studies on PAC use during smoke exposure include building flexibility of intervention timing into the study design.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Report for Department of State (DoS) V-Fund Project "Tying Moment Tensor Solutions to Explosive Yield"

The goal of this study was to use U.S. nuclear explosions with known source parameters (yield, depth, shot point material and/or parameters) to determine moment-derived yield estimates. This work has been accomplished by performing full moment tensor solutions using regional network data from the LLNL network, and other regional broadband stations. As part of this study, we have calculated solutions for 130 U.S. nuclear explosions and 12 additional chemical explosions. We then take several approaches to using moment tensors to estimate yield, considering both the full and isotropic moment tensors, doing straight regression analysis on the whole dataset, then successively refining the calibration with additional information about material and overburden. We have also tried a completely new approach of using the seismic moment to help estimate the radiated seismic energy and tying this to yield through a seismic efficiency. Results appear to be promising, but more work might be required to make this more useful in an operational sense.

58 GEOSCIENCES↗

DOE OSTI2022

Sex comparisons in physiological and cognitive performance during hypoxic challenge

Within the tactical aviation community, human performance research lags in considering potential psychophysiological differences between male and female aviators due to little inclusion of females during the design and development of aircraft systems. A poor understanding of how male and female aviators differ with respect to human performance results in unknown potential sex differences on aeromedically relevant environmental stressors, perchance leading to suboptimal performance, safety, and health guidelines. For example, previous hypoxia studies have excluded female participants or lacked a sizeable sample to examine sex comparisons. As such, progress toward sensor development and improving hypoxia familiarization training are stunted due to limited knowledge of how individual differences, including sex, may or may not underlie hypoxia symptoms and performance impairment. Investigating sex differences bridges the gap between aerospace medicine and operational health, and addressing hypoxia is one of many facets yet to be studied. In the current study, we retrospectively examined N = 6 hypoxia studies with male-female participant samples (total, N = 189; male, n = 118; female, n = 71). We explored sex as a predictor of physiological response, sensory deficits, the severity of cognitive performance declines, and symptom manifestation via linear and binary logistic regression models. We found that the female sex predicted lower peripheral oxygen saturation and the likelihood of headache reporting in response to hypoxic challenge, yet explained little variance when combined with age and body mass index. The sensory and cognitive performance models did not converge, suggesting high intra-individual variability. Together, sex, age, and body mass index were not the most robust predictors in responses to hypoxic challenge; we cannot infer this for sensory deficits and cognitive performance within an experimentally induced hypoxic environment. The findings have implications for improving hypoxia familiarization training, monitoring sensor development, and emergency response and recovery protocols in case of a hypoxia occurrence suitable for all aircrew. We recommend continuing to elucidate the impact of sex and intrapersonal differences in hypoxia and other aeromedically relevant stressors in tactical aviation.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2007

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

Saha, Bhaskar↗

DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗

NASA NTRS1996

Determination of Uncertainties for the New SSME Model

This report discusses the uncertainty analysis performed in support of a new test analysis and performance prediction model for the Space Shuttle Main Engine. The new model utilizes uncertainty estimates for experimental data and for the analytical model to obtain the most plausible operating condition for the engine system. This report discusses the development of the data sets and uncertainty estimates to be used in the development of the new model. It also presents the application of uncertainty analysis to analytical models and the uncertainty analysis for the conservation of mass and energy balance relations is presented. A new methodology for the assessment of the uncertainty associated with linear regressions is presented.

Coleman, Hugh W.↗

NASA NTRS2017

Accuracy/Computation Performance of a New Trilateration Scheme for GPS-Style Localization

We recently introduced a new geometric trilateration (GT) method for GPS-style positioning. Preliminary singlepoint analysis using simplistic error assumptions indicates that the new scheme delivers almost indistinguishable localization accuracy as the traditional Newton-Raphson (NR) approach. Also, the same computation procedure can be used to perform high-accuracy relative positioning between a reference vehicle and an arbitrary number of target vehicles. This scheme has the potential to enable a) new mission concepts in collaborative science, b) in-situ navigation services for human Mars missions, and c) lower cost and faster acquisition of GPS signals for consumer-grade GPS products. The new GT scheme differs from the NR scheme as follows: 1. The new scheme is derived from Pythagoras Theorem, whereas the NR method is based on the principle of linear regression. 2. The NR method uses the absolute locations (xi, yi, zi)’s of the GPS satellites as input to each step of the localization computation. The GT method uses the Directional Cosines Ui’s from Earth’s center to the GPS satellite Si. 3. Both the NR method and the GT method iterate to converge to a localized solution. In each iteration step, multiple matrix operations are performed. The NR method constructs a different matrix in each iterative step, thus requires performing a new set of matrix operations in each step. The GT scheme uses the same matrix in each iteration, thus requiring computing the matrix operations only once for all subsequent iterations. In this paper, we perform an in-depth comparison between the GT scheme and the NR method in terms of a) GPS localization accuracy in the GPS operation environment, b) its sensitivity with respect to systematic errors and random errors, and c) computation load required to converge to a localization solution.

Lee, Charles↗

DOE OSTI2020

Correction for Neutral Pressure-Driven Signal in Radiated Power Measurements on Proto-MPEX

A pinhole camera using four-channel resistive bolometers was designed and implemented on the Prototype Material Plasma Exposure eXperiment (Proto-MPEX) to measure the plasma radiated power. Quantification of the radiated power is required for comprehensive power balance assessments but faces unique challenges on linear plasma devices due to weak absolute signal, compared to typical use in toroidal fusion plasmas, and high neutral pressure changes. Around 1 mTorr, the pressure-driven signal contribution was shown to overwhelm the radiated power signal in Proto-MPEX, found to be ~3 μW . Since the pressure-driven signals could not be predicted from first principle arguments, the signals were corrected using empirical models that take two experimental pressure measurements and the raw bolometer signal to reveal the radiated power signals. Four “gas-only” Proto-MPEX shots were taken, without the radio frequency (rf) plasma discharge, to train, optimize, and validate empirical models. The data included two pressure measurements at different locations along with the bolometer signal. Multiple models were tested; partial least-squares regression with a cross correlation method to account for the time offset in the pressure measurements provided the best combination of accuracy and stability. The model was validated to have a modeling error of 0.0036 V, which was lower than 10% of the expected radiated power signal. The model was tested on a different “gas-only” shot with a complete time-series data. For plasma operation shots, the corrected bolometer measurements were compared with an absolute extreme ultraviolet photodiode measurement and similarities and differences are discussed.

Lee, Seungsup↗

DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS1976

Digital controllers for VTOL aircraft

Using linear-optimal estimation and control techniques, digital-adaptive control laws have been designed for a tandem-rotor helicopter which is equipped for fully automatic flight in terminal area operations. Two distinct discrete-time control laws are designed to interface with velocity-command and attitude-command guidance logic, and each incorporates proportional-integral compensation for non-zero-set-point regulation, as well as reduced-order Kalman filters for sensor blending and noise rejection. Adaptation to flight condition is achieved with a novel gain-scheduling method based on correlation and regression analysis. The linear-optimal design approach is found to be a valuable tool in the development of practical multivariable control laws for vehicles which evidence significant coupling and insufficient natural stability.

Stengel, R. F.↗

DOE OSTI2023

Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning

Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO 2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO 2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO 2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

25 ENERGY STORAGE↗

NASA NTRS1994

Kinetic Study of Methyl Acetate Oxidation in a Pt/Al2O3 Fixed-Bed Reactor

To support technology development for future long-term missions, a metabolic simulator will be used in a closed chamber to test the functions of a Controlled Ecological Life Support System (CELSS). Methyl acetate (MA) was selected as the fuel because its metabolic respiratory quotient is near that of humans. A kinetic study of the catalytic oxidation of MA over Pt/Al203 was then conducted to support the design and operation of the simulator. Kinetic data were obtained as a conversion percentage of MA versus retention time. The reaction was studied at one atmosphere and temperatures from 220 to 340 deg. C. The inlet MA concentration was varied from 100 to 2000 ppm with retention times from 0.01 to 10 sec. A first-order rate law and a Langmuir-Hinshelwood rate equation were tested by nonlinear regression of the kinetic data to estimate rate constants in the rate law. Regression results of the L-H equation explain the kinetic data better than the results of the first-order rate law. A Taguchi experimental design was used to study the effects of temperature, retention time, and concentrations of MA, CO2, and O2 on the conversion of MA. Results indicate that temperature has greatest effect, followed by retention time, and finally MA concentration. It was further determined that the effects of CO2 and O2 concentrations, and the cross effects, are negligible.

Hoy, Michael↗

NASA NTRS2004

A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

Propulsion ground test facilities face the daily challenges of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Due to budgetary and schedule constraints, NASA and industry customers are pushing to test more components, for less money, in a shorter period of time. As these new rocket engine component test programs are undertaken, the lack of technology maturity in the test articles, combined with pushing the test facilities capabilities to their limits, tends to lead to an increase in facility breakdowns and unsuccessful tests. Over the last five years Stennis Space Center's propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and broken numerous test facility and test article parts. While various initiatives have been implemented to provide better propulsion test techniques and improve the quality, reliability, and maintainability of goods and parts used in the propulsion test facilities, unexpected failures during testing still occur quite regularly due to the harsh environment in which the propulsion test facilities operate. Previous attempts at modeling the lifecycle of a propulsion component test project have met with little success. Each of the attempts suffered form incomplete or inconsistent data on which to base the models. By focusing on the actual test phase of the tests project rather than the formulation, design or construction phases of the test project, the quality and quantity of available data increases dramatically. A logistic regression model has been developed form the data collected over the last five years, allowing the probability of successfully completing a rocket propulsion component test to be calculated. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),..,X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure. Logistic regression has primarily been used in the fields of epidemiology and biomedical research, but lends itself to many other applications. As indicated the use of logistic regression is not new, however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from the models provide project managers with insight and confidence into the affectivity of rocket engine component ground test projects. The initial success in modeling rocket propulsion ground test projects clears the way for more complex models to be developed in this area.

Messer, Bradley P.↗

NASA NTRS2005

Objective Lightning Probability Forecasting for Kennedy Space Center and Cape Canaveral Air Force Station

Five logistic regression equations were created that predict the probability of cloud-to-ground lightning occurrence for the day in the KSC/CCAFS area for each month in the warm season. These equations integrated the results from several studies over recent years to improve thunderstorm forecasting at KSC/CCAFS. All of the equations outperform persistence, which is known to outperform NPTI, the current objective tool used in 45 WS lightning forecasting operations. The equations also performed well in other tests. As a result, the new equations will be added to the current set of tools used by the 45 WS to determine the probability of lightning for their daily planning forecast. The results from these equations are meant to be used as first-guess guidance when developing the lightning probability forecast for the day. They provide an objective base from which forecasters can use other observations, model data, consultation with other forecasters, and their own experience to create the final lightning probability for the 1100 UTC briefing.

Lambert, Winifred↗

NASA NTRS2020

Southern Colorado Disasters: Using NASA Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Project Summary↗

NASA NTRS2020

Southern Colorado Disasters: Using NASA Earth Observations to Map Aspen Extent and Recovery Due to Wildfire

DEVELOP Technical Paper↗

NASA NTRS1988

Satellite/radiosonde comparison

Collocated measurements of radiosonde and TIROS Operational Vertical Sounder (TOVS) data are compared to determine whether significant latitudinal, seasonal, or instrumental biases exist. Long-wave structure determined by each system is being examined for correlation. Because U.S. radiosonde data are archived uncorrected, an effort to correct the measurements is being considered. These corrections will be applied to 7 years of measurements. New comparisons will then be made using corrected radiosonde and TOVS data. A regression method is used to retrieve temperatures, and radiosonde zonal means are used to update the regression coefficients. Preliminary results of auto-correlation analysis indicate that detection of long-wave structure in each set of data is slightly different with some levels and locations experiencing different wave periods or amplitudes. Scatter diagrams and linear regression for each layer show bias in the mean temperatures. Winter-time measurements show poorer correlations due to noisy measurements.

Schmidlin, F. J.↗