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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Bloom Modeling and Prediction of the Harmful Algae Alexandrium in Bellingham Bay, WA

Bellingham, Washington is located near the American-Canadian border in the northwestern part of Washington state, and is bordered on its west side by the Lummi reservation. Between Lummi and Bellingham lies Bellingham bay, which has a history of harmful algae related closures dating back to 1978. The subject of this work is a genus of dinoflagellates: Alexandrium, within which many species have the capacity to produce a suite of toxins known as saxitoxin. These toxins bioaccumulate in bivalves, which in turn cause paralytic shellfish poisoning in marine consumers (mammals, birds, and fish), including humans. Symptoms in humans can range from tingling and numbness to difficulty or inability to breathe, resulting in death. Because of the longstanding history of shellfish gathering among Salish tribes and the fact that Washington is the leading U.S. producer of farmed bivalves, harmful algae blooms impact both native and non-native peoples living in the Salish Sea area negatively. The objective of this work is to identify factors which influence Alexandrium blooms in Bellingham Bay, as well as predict Alexandrium blooms in the future. The method of doing so involves two processes: an initial statistical modeling phase to find in situ and remote sensing observations correlated to bloom density (including, but not limited to: water temperature, chlorophyll-a, salinity, color dissolved organic matter, and discharge rate of local rivers), followed by use of that data as a training set for a recursive neural network. This predictive capacity may inform future closures, help ensure the safety of shellfish consumers, and act as a baseline for future modeling efforts in the region.

Harmful↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines

We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Summary of studies for a solar optical telescope in space: 1968-1976

The primary objective of this review is to tabulate the basic recommendations of several solar telescope studies. A primary matrix, listing some of the basic optical parameters, was compiled and forms the basis for a table. From this table it is apparent that a strong consensus exists on the configuration of the telescope and on its fundamental dimensionless parameters. Other tables presented in this document address the basic approach of each study to the telescope design as well as to the design of critical subsystems. These subsystem problems include the material, coating, configuration, mounting, launch locks, and thermal control of the primary mirror, the structure of the main telescope and the instrument bay, the mechanisms for radiation rejection, thermal control, and meteoroid shielding, and methods of maintaining image quality by proper alignment and by image motion compensation.

Bremer, J.↗
NASA NTRS1992

Metabolic response of environmentally isolated microorganisms to industrial effluents: Use of a newly described cell culture assay

An environmental application using a microtiter culture assay to measure the metabolic sensitivity of microorganisms to petrochemical effluents will be tested. The Biomedical Operations and Research Branch at NASA JSC has recently developed a rapid and nondestructive method to measure cell growth and metabolism. Using a colorimetric procedure the uniquely modified assay allows the metabolic kinetics of prokaryotic and eukaryotic cells to be measured. Use of such an assay if adapted for the routine monitoring of waste products, process effluents, and environmentally hazardous substances may prove to be invaluable to the industrial community. The microtiter method as described will be tested using microorganisms isolated from the Galveston Bay aquatic habitat. The microbial isolates will be identified prior to testing using the automated systems available at JSC. Sodium dodecyl sulfate (SDS), cadmium, and lead will provide control toxic chemicals. The toxicity of industrial effluent from two industrial sites will be tested. An effort will be made to test the efficacy of this assay for measuring toxicity in a mixed culture community.

Ferebee, Robert N.↗
NASA NTRS1999

Polymers Erosion and Contamination Experiment Being Developed

The Polymers Erosion and Contamination Experiment (PEACE) is currently being developed at the NASA Lewis Research Center by the Electro-Physics Branch in conjunction with students and faculty from Hathaway Brown School in Cleveland. The experiment is a Get Away Special Canister shuttle flight experiment sponsored by the American Chemical Society. The two goals of this experiment are (1) to measure ram atomic oxygen erosion rates of approximately 40 polymers that have potential use in space applications and (2) to validate a method for identifying sources of silicone contamination that occur in the shuttle bay. Equipment to be used in this flight experiment is shown in the schematic diagram. Spacecraft materials subjected to attack by atomic oxygen in the space environment experience significant degradation over the span of a typical mission. Therefore, learning the rates of atomic oxygen erosion of a wide variety of polymers would be of great benefit to future missions. PEACE will use two independent techniques to determine the atomic oxygen erosion rates of polymers. Large (1-in.-diameter) samples will be used for obtaining mass loss. Preflight and postflight dehydrated masses will be obtained, and the mass lost during flight will be determined. Small (0.5-in.-diameter) samples will be protected with isolated particles (such as NaCl crystals) and then exposed to the space environment. After flight, the protective particles will be removed (washed off) and atomic force microscopy (AFM) will be used to measure the erosion depth from protected mesas. Erosion depth measurements are more sensitive than traditional mass measurements and are very useful for materials with low erosion yields or with very low fluence missions.

deGroh, Kim K.↗
DOE OSTI2024

Regularized Differentiation for Bioburden Density Estimation in Planetary Protection

In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Experimental results of active control on a large structure to suppress vibration

Three design methods, Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR), H-infinity, and mu-synthesis, are used to obtain compensators for suppressing the vibrations of a 10-bay vertical truss structure, a component typical of what may be used to build a large space structure. For the design process the plant dynamic characteristics of the structure were determined experimentally using an identification method. The resulting compensators were implemented on a digital computer and tested for their ability to suppress the first bending mode response of the 10-bay vertical truss. Time histories of the measured motion are presented, and modal damping obtained during the experiments are compared with analytical predictions. The advantages and disadvantages of using the various design methods are discussed.

Dunn, H. J.↗
NASA NTRS2003

Observations and Simulations on the M-I Coupling of Bursty Convection

This final report summarizes the development and application of the first ever method to measure a proxy of the global merging along the solar wind-Earth separatrix. The ultimate aim of the project is to quantify the reconnection potential in the magnetotail and the fraction of it that is due to bursty bulk flows (BBFs). To achieve this goal, we developed the first method that allows the measurement of the distribution of reconnection rate along the polar cap boundary. The method was then applied to substorms and steady magnetospheric convection (labeled as SMC or convection bay) periods in order to quantify the strength of reconnection and the fraction of it that is contributed by BBFs in each case. Results of this research were reported in two manuscripts submitted to the Journal of Geophysical Research and presented at three international conferences. This report is organized as follows. In Section 2 we discuss the conceptual framework that is the foundation of the method. In Section 3 we describe the details of the method. In Section 4 we describe the application of the technique to substorms and SMCs. In Section 5 we summarize the results of this investigation and provide references to the manuscripts and conference presentations that report on the results of this project.

Sanchez, Ennio R.↗
NASA NTRS2004

Space Shuttle UHF Communications Performance Evaluation

An extension boom is to be installed on the starboard side of the Space Shuttle Orbiter (SSO) payload bay for thermal tile inspection and repairing. As a result, the Space Shuttle payload bay Ultra High Frequency (UHF) antenna will be under the boom. This study is to evaluate the Space Shuttle UHF communication performance for antenna at a suitable new location. To insure the RF coverage performance at proposed new locations, the link margin between the UHF payload bay antenna and Extravehicular Activity (EVA) Astronauts at a range distance of 160 meters from the payload bay antenna was analyzed. The communication performance between Space Shuttle Orbiter and International Space Station (SSO-ISS) during rendezvous was also investigated. The multipath effects from payload bay structures surrounding the payload bay antenna were analyzed. The computer simulation tool based on the Geometrical Theory of Diffraction method (GTD) was used to compute the signal strengths. The total field strength was obtained by summing the direct fields from the antennas and the reflected and diffracted fields from the surrounding structures. The computed signal strengths were compared to the signal strength corresponding to the 0 dB link margin. Based on the results obtained in this study, RF coverage for SSO-EVA and SSO- ISS communication links was determined for the proposed payload bay antenna UHF locations. The RF radiation to the Orbiter Docking System (ODS) pyros, the payload bay avionics, and the Shuttle Remote Manipulator System (SRMS) from the new proposed UHF antenna location was also investigated to ensure the EMC/EMI compliances.

Hwu, Shian U.↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Estimating vegetation coverage in St. Joseph Bay, Florida with an airborne multispectral scanner

A four-channel multispectral scanner (MSS) carried aboard an aircraft was used to collect data along several flight paths over St. Joseph Bay, FL. Various classifications of benthic features were defined from the results of ground-truth observations. The classes were statistically correlated with MSS channel signal intensity using multivariate methods. Application of the classification measures to the MSS data set allowed computer construction of a detailed map of benthic features of the bay. Various densities of segrasses, various bottom types, and algal coverage were distinguished from water of various depths. The areal vegetation coverage of St. Joseph Bay was not significantly different from the results of a survey conducted six years previously, suggesting that seagrasses are a very stable feature of the bay bottom.

Savastano, K. J.↗
NASA NTRS1984

Multivariate spline methods in surface fitting

The use of spline functions in the development of classification algorithms is examined. In particular, a method is formulated for producing spline approximations to bivariate density functions where the density function is decribed by a histogram of measurements. The resulting approximations are then incorporated into a Bayesiaan classification procedure for which the Bayes decision regions and the probability of misclassification is readily computed. Some preliminary numerical results are presented to illustrate the method.

Guseman, L. F., Jr.↗
DOE OSTI2021

Analysis and prediction of intersection traffic violations using automated enforcement system data

We report that the automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model’s prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

42 ENGINEERING↗
NASA NTRS1975

Quantitative sediment mapping from remotely sensed multispectral data

Multiple regression techniques were used to correlate ERTS-1 data with measured values of suspended sediment. The resultant equation was applied to a different ERTS scene using a deepwater area for atmospheric calibration. This approach provides greater accuracy than previously reported methods. Suspended sediment contours are plotted for the Potomac River, Virginia, using Chesapeake Bay deep water for atmospheric calibration.

Johnson, R. W.↗
NASA NTRS1979

Linear feature selection with applications

Several ways in which feature selection techniques were used in LACIE are discussed. In all cases, the methods require some a priori information and assumptions; in most, the classification procedure (Bayes optimal) was chosen in advance. The transformations used for dimensionality reduction are linear, that is, the variables in feature space are always linear combinations of the original measurements. Several numerically tractable criteria developed for LACIE, which provide information about the probability of misclassification, are discussed. Recent results on linear feature selection techniques are included. Their use in LACIE is discussed. Related open questions are mentioned.

Decell, H. P., Jr.↗
NASA NTRS1985

Environmental monitoring of the orbiter payload bay and Orbiter Processing Facilities

Contamination control in the Orbiter Processing Facility (OPF) is studied. The clean level required in the OPF is generally clean, which means no residue, dirt, debris, or other extraneous contamination; various methods of maintaining this level of cleanliness are described. The monitoring and controlling of the temperature, relative humidity, and air quality in the OPF are examined. Additional modifications to the OPF to improve contamination control are discussed. The methods used to maintain the payload changeout room at a level of visually clean, no particulates are to be detected by the unaided eye, are described. The payload bay (PLB) must sustain the cleanliness level required for the specific Orbiter's mission; the three levels of clean are defined as: (1) standard, (2) sensitive, and (3) high sensitive. The cleaning and inspection verification required to achieve the desired cleanliness level on a variety of PLB surface types are examined.

Bartelson, D. W.↗
NASA NTRS2012

Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products

This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.

Spruce, J. P.↗