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NASA NTRS1971

Optimum radars and filters for the passive sphere system

Studies have been conducted to determine the influence of the tracking radar and data reduction technique on the accuracy of the meteorological measurements made in the 30 to 100 kilometer altitude region by the ROBIN passive falling sphere. A survey of accuracy requirements was made of agencies interested in data from this region of the atmosphere. In light of these requirements, various types of radars were evaluated to determine the tracking system most applicable to the ROBIN, and methods were developed to compute the errors in wind and density that arise from noise errors in the radar supplied data. The effects of launch conditions on the measurements were also examined. Conclusions and recommendations have been made concerning the optimum tracking and data reduction techniques for the ROBIN falling sphere system.

Luers, J. K.↗

DOE OSTI2020

GIS-Based Modeling of Contaminated Soil Volumes at Multiple Sites in the Formerly Utilized Sites Remedial Action Program - 20149

The remediation of hazardous, toxic, and radioactive waste (HTRW) sites produces cost-related risks associated with the estimation of contaminated soil or debris volumes. Historical risk-management techniques include cost contingencies to cover volume uncertainties that affect project budgeting and decision-making. The Buffalo District teamed with project partners to lessen volume uncertainty and reduce project risks at multiple HTRW sites managed under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Historical remedial investigations under FUSRAP commonly identified the presence of radiological material in site media, the associated human health risk, and then areas of remediation. To manage remedial execution and reduce risk, pre-design or remediation-phase sampling essentially 'chased' contamination, which was not conducive to efficient predictive budgeting derived from Feasibility Study (FS) cost analyses. The Buffalo District first optimized their approach to better understand volume uncertainty by utilizing the Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software [1]. BAASS processed soft data (e.g., gamma walk-over data) and spatial sampling data to estimate the lateral extent of contaminated soil irrespective of depth (i.e., gross contamination extent) and define areas of contaminant uncertainty. The software performed a binary transformation of contaminant concentrations at all sampling points based upon remedial action goals or a sum of ratios approach (i.e., clean, impacted, or range of impacts in soil). The model produced two-dimensional (horizontal) contaminant probability contours and statistical uncertainty in the sampling coverage and resulting contaminant extents. This method was translated vertically by partitioning the sampling data into depth brackets that produced a stacked representation of contaminant extents and uncertainty in the subsurface (i.e., similar to construction lifts). The results commonly led to a better understanding of project uncertainty and the need for sampling strategies that produce high-confidence soil volumes, which control costs. The BAASS-based delineations were eventually replaced by Empirical Bayesian Kriging (EBK) methods available in ArcGIS Spatial or 3D Analysts [2]. The EBK method calculates contaminant probability zones derived from user-controlled semivariograms of the spatial datasets. The resulting probability zones (e.g., 50% or 80% of contaminant probability) represent the two-dimensional surface delineation of the overall horizontal remedial area, similarly to BAASS. However, unlike BAASS, the vertical sampling data within these probability zones became vertical control points to contour a subterranean surface that connects subsurface points to the land-surface delineations of contamination. The resulting representation of horizontal and vertical impacts within an enclosed envelop (volume) of soil included uncertainty distributions that are used to plan uncertainty-reduction sampling. These data-driven and math-based models of three-dimensional sampling results produced well-bounded remedial volumes for project planning and better uncertainty predictions during project budgeting. The EBK method was applied to several FUSRAP sites managed by the Buffalo District and compared to less rigorously modeled sites previously remediated by the District. The comparison of modeled to actual remediated volumes provide a basis for validating the volume-estimation method. This comparison is important to ensure modeled volumes match physical boundaries of site remediation. FUSRAP sites with denser investigative sampling and lesser volume uncertainty proved useful in remedial planning and contracting. The Buffalo District noted that historical sites with sparser sampling arrays had greater disparity between estimated volumes and final remedial volumes. The benefit achieved over the cost of detailed soil sampling appears positive for FUSRAP projects, especially where impacts vary widely and appear unbounded by investigation-phase sampling. The subsequent Empirical Bayesian Kriging of contamination coupled with vertical contouring for soil estimations reduces uncertainty in soil volumes or indicates where sampling is required to reduce uncertainty, which together optimize remedial planning and budgeting. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2011

Application of Temperature Sensitivities During Iterative Strain-Gage Balance Calibration Analysis

A new method is discussed that may be used to correct wind tunnel strain-gage balance load predictions for the influence of residual temperature effects at the location of the strain-gages. The method was designed for the iterative analysis technique that is used in the aerospace testing community to predict balance loads from strain-gage outputs during a wind tunnel test. The new method implicitly applies temperature corrections to the gage outputs during the load iteration process. Therefore, it can use uncorrected gage outputs directly as input for the load calculations. The new method is applied in several steps. First, balance calibration data is analyzed in the usual manner assuming that the balance temperature was kept constant during the calibration. Then, the temperature difference relative to the calibration temperature is introduced as a new independent variable for each strain--gage output. Therefore, sensors must exist near the strain--gages so that the required temperature differences can be measured during the wind tunnel test. In addition, the format of the regression coefficient matrix needs to be extended so that it can support the new independent variables. In the next step, the extended regression coefficient matrix of the original calibration data is modified by using the manufacturer specified temperature sensitivity of each strain--gage as the regression coefficient of the corresponding temperature difference variable. Finally, the modified regression coefficient matrix is converted to a data reduction matrix that the iterative analysis technique needs for the calculation of balance loads. Original calibration data and modified check load data of NASA's MC60D balance are used to illustrate the new method.

Ulbrich, N.↗

DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗

DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1974

Digital computer modeling of the process of reduction of redundancy in multichannel telemetry information by the method of adaptive discretization with associative sorting

Digital computer modeling of the process of adaptive discretization with associative sorting of actual multichannel telemetry information is discussed. The main task in modeling is production of initial data for determination of dependences describing the operation of the on-board information compression device. Conclusions are presented including the shortcomings of telemetric information used in modeling.

Tolmadzheva, T. A.↗

DOE OSTI2024

Microstructural Coarsening Kinetics and Mechanical Property Changes in Long-Term Aged Sn–Pb–Sb Solder Joints

Tin-lead-antimony (50Sn–47Pb–3Sb wt.%) soldered assemblies were mechanically tested approximately 30 years after initial production and found to have solder joints of reduced strength. The microstructure of this solder alloy exhibits a ternary eutectic structure with Sn-rich, Pb-rich, and SnSb phases. Accelerated aging was performed to evaluate solder microstructural coarsening and associated strength of laboratory solder joints to correlate these properties to the “naturally aged” solder joints. Isothermal aging was conducted at room temperature, 55, 70, 100, and 135 °C and aging times that ranged from 0.1 to 365 days. The coarsening kinetics of the Pb-rich phase were determined through optical microscopy and image analysis methods established in previous studies on binary Sn–Pb solder. A kinetic equation was developed with time exponent n of 0.43 and activation energy of 24000 J/mol, suggesting grain boundary diffusion or other fast diffusion pathways controlling the microstructural evolution. Compression testing and Vickers microhardness showed significant strength loss within the first 20–30 days after soldering; then, the microstructure and mechanical properties changed more slowly over long periods of time. Further, by combining accelerated aging data and the microstructure-based kinetics, strength predictions were made that match well with the properties of the actual soldered assemblies naturally aged for 30 years. However, aging at the highest temperature of 135 °C produced anomalous behavior suggesting that extraneous aging mechanisms are active. Therefore, data obtained at this temperature or higher should not be used. Overall, the combined microstructural and mechanical property methods used in this study confirmed that the observed reduction in strength of ~ 30-year-old solder joints can be accounted for by the microstructural coarsening that takes place during long-term solid-state aging.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Electrochemical Salt Wasteform Development: A Review of Salt Treatment and Immobilization Options

Electrochemical reprocessing, also referred to as pyroprocessing, is a technique for recycling actinides from used nuclear fuel (UNF) to produce fuel for future reactors. Here, UNF is dissolved in a molten salt (e.g., LiCl-KCl eutectic) within an electrorefiner. After UNF dissolution, fission products are released into the electrolyte salt and converted to chlorides. This paper discusses wasteform options for processing the base electrolyte salt with the fission product salts as well as just the rare-earth fission products (as RECl 3 , REOCl, or REO x ) with the intent of finding optimal methods for reducing total waste salt volumes or partitioning the salt for alternate wasteform options. Furthermore, two of the more detailed partitioning options discussed herein include halide removal from the salt (dehalogenation), which accounts for more than half of the salt on a molar basis, and RE fission product removal for wasteforms with high-RE loadings. Wasteform properties are compared with emphasis on wasteform volume starting from a given amount of (1) total salt cations or (2) RE cations. Comparisons are also made of wasteform chemical durabilities, with the data available from like testing methods. A main conclusion from this work is the justification of subsequent salt processing after electrorefiner operations for achieving significant wasteform volume reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗

NASA NTRS1976

Positive ion densities and mobilities in the upper stratosphere and mesosphere

A brief sketch of the theory concerning the use of the Gerdien condenser as a mobility spectrometer is presented. Data reduction of three parachute borne Gerdien condenser probes is given, as well as that of one blunt conductivity probe. Comparisons of concentrations calculated by two different methods indicate consistency of results. Mobility profiles demonstrating remarkable fine structure are discussed in detail. Finally, theoretical implications of the results on ionospheric structure, including possible night-day differences and latitudinal variations, are considered.

Leiden, S.↗

NASA NTRS1980

A miniature high frequency sphere probe

A spherical probe having five surface mounted silicon pressure sensors is described. The probe is 0.2 inches in diameter yielding a frequency response above 30 kHz. A scheme is described for retrieving from the five pressure signals the stagnation and static pressures, Mach number and flow angles in two planes. The calibrations forming the base for this procedure, obtained from steady state tests, are given in the form of polynomials in the two flow angles. A method for compensating the silicon pressure sensors for thermal effects is given. Typical data obtained in the MIT Blowdown Compressor are presented in raw form and after reduction to flow angles, Mach number and stagnation pressure.

Kerrebrock, J. L.↗

NASA NTRS1982

An initial look at the supersonic aerodynamics of twin-fuselage aircraft concepts

Results of two studies into the supersonic aerodynamics of twin-fuselage aircraft configurations are summarized. In the first study, a set of experimental data was obtained on a simple rectangular-wing twin-fuselage wind-tunnel model; this data was then used to evaluate prediction methods, assess favorable interference effects, and identify any unexpected or unpredictable aerodynamic phenomena. Results are presented which show that significant reductions in wave drag are possible through optimum body positioning and that existing aerodynamic prediction methods are adequate for making preliminary aerodynamic estimates. Several configuration concepts were theoretically explored in the second study, and results are presented which indicate the sensitivity of the twin-fuselage concept to various methods of integrating the aircraft components.

Wood, R. M.↗

NASA NTRS1983

Application of a global variational analysis to quasi three-dimensional temperature retrievals

The Halen and Kalnay (1983) hypothesis that the application of global variational analysis to clear column radiances will result in a reduction of both observational noise and data gaps is tested, together with the hypothesis of these authors that the estimate of clear column radiances furnished by the variational analysis can yield a useful reduction of the data gaps in the retrieved temperatures. In the first of two experiments conducted, attention is given to whether the nonlinearity of the temperature retrieval method is sufficiently strong to result in more accurate temperature retrievals. In the second experiment, realistic subgrid scale cloud fields and observational and temperature errors are included in the simulation system.

Dalcher, A.↗

NASA NTRS1992

Predictive onboard flow control for packet switching satellites

We outline two alternate approaches to predicting the onset of congestion in a packet switching satellite, and argue that predictive, rather than reactive, flow control is necessary for the efficient operation of such a system. The first method discussed is based on standard, statistical techniques which are used to periodically calculate a probability of near-term congestion based on arrival rate statistics. If this probability exceeds a present threshold, the satellite would transmit a rate-reduction signal to all active ground stations. The second method discussed would utilize a neural network to periodically predict the occurrence of buffer overflow based on input data which would include, in addition to arrival rates, the distributions of packet lengths, source addresses, and destination addresses.

Bobinsky, Eric A.↗

NASA NTRS1992

Predictive onboard flow control in packet switching satellites

Bobinsky, E. A.↗

NASA NTRS2005

High Speed Research Program Sonic Fatigue

The objective of this sonic fatigue summary is to provide major findings and technical results of studies, initiated in 1994, to assess sonic fatigue behavior of structure that is being considered for the High Speed Civil Transport (HSCT). High Speed Research (HSR) program objectives in the area of sonic fatigue were to predict inlet, exhaust and boundary layer acoustic loads; measure high cycle fatigue data for materials developed during the HSR program; develop advanced sonic fatigue calculation methods to reduce required conservatism in airframe designs; develop damping techniques for sonic fatigue reduction where weight effective; develop wing and fuselage sonic fatigue design requirements; and perform sonic fatigue analyses on HSCT structural concepts to provide guidance to design teams. All goals were partially achieved, but none were completed due to the premature conclusion of the HSR program. A summary of major program findings and recommendations for continued effort are included in the report.

Rizzi, Stephen A.↗

DOE OSTI2022

Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods

Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.

Lu, Dan↗

DOE OSTI2023

Throughfall-Reduction Drying Effects on Soil Moisture and Soil Temperature at 30-Minute Intervals Four Lowland Panamanian Forests

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that drying would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We measured soil CO2 fluxes, soil moisture, soil temperature, and forest floor biomass during wet-dry cycles (2015 – 2022) in four Panamanian forests that vary in rainfall and soil fertility. We also surveyed all tree species and identified to species in 2018 and 2019.Results: We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks (Cusack et al. 2023). Datasets included: Datasets here include .csv and .xls files for volumetric soil moisture (volume/volume) and soil temperature (°C). Data were taken at 30-minute intervals at 5cm and 20cm depths using permanent probes and data loggers (see methods). Data presented are from 2018-2019. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗