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NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗

NASA NTRS2005

Analysis of Raman Lidar and radiosonde measurements from the AWEX-G field campaign and its relation to Aqua validation

Early work within the Aqua validation activity revealed there to be large differences in water vapor measurement accuracy among the various technologies in use for providing validation data. The validation measurements were made at globally distributed sites making it difficult to isolate the sources of the apparent measurement differences among the various sensors, which included both Raman lidar and radiosonde. Because of this, the AIRS Water Vapor Experiment-Ground (AWEX-G) was held in October - November, 2003 with the goal of bringing validation technologies to a common site for intercomparison and resolution of the measurement discrepancies. Using the University of Colorado Cryogenic Frostpoint Hygrometer (CFH) as the water vapor reference, the AWEX-G field campaign resulted in new correction techniques for both Raman lidar, Vaisala RS80-H and RS90/92 measurements that significantly improve the absolute accuracy of those measurement systems particularly in the upper troposphere. Mean comparisons of radiosondes and lidar are performed demonstrating agreement between corrected sensors and the CFH to generally within 5% thereby providing data of sufficient accuracy for Aqua validation purposes. Examples of the use of the correction techniques in radiance and retrieval comparisons are provided and discussed.

Whiteman, D. N.↗

NASA NTRSDate not supplied

Explore Earth Commercial Smallsat Data Acquisition (CSDA) Program

The European Space Agency (ESA), our international partner in the ESA-NASA Earth Science & Observation Joint Program Planning Group (JPPG), is hosting the VH-RODA (Very High-resolution Radar and Optical Data Assessment Workshop) at ESA/ European Space Research Institute (ERSIN) in Frascati, Italy from November 7-10, 2022. The objective of the VH-RODA workshop is to provide an open forum (for the new space, commercial and institutional space sectors) for presenting and discussing about the current status and future developments related to Earth Observation (EO) data quality, calibration and validation of space-borne very high-resolution Synthetic Aperture Radar (SAR) and Optical sensors and data products, with a dedicated focus on commercial EO data providers and related Calibration/Validation activities, synergies between optical and SAR communities, presentation of standards and best practices for data quality. Additionally, the ESA-NASA Joint Program Planning Group (JPPG) Third Party Mission component will meet to further develop non-binding practices on evaluation, identify common guard rails for comparison of data, and coordinate future schedules to leverage shared knowledge. I serve as the Project Manager for the NASA’s Commercial Smallsat Data Acquisition (CSDA) Program and will participate in the VHRODA workshop on behalf of the program.

Commercial Remote Sensing↗

DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2006

Simulating Visible/Infrared Imager Radiometer Suite Normalized Difference Vegetation Index Data Using Hyperion and MODIS

The success of MODIS (the Moderate Resolution Imaging Spectrometer) in creating unprecedented, timely, high-quality data for vegetation and other studies has created great anticipation for data from VIIRS (the Visible/Infrared Imager Radiometer Suite). VIIRS will be carried onboard the joint NASA/Department of Defense/National Oceanic and Atmospheric Administration NPP (NPOESS (National Polar-orbiting Operational Environmental Satellite System) Preparatory Project). Because the VIIRS instruments will have lower spatial resolution than the current MODIS instruments 400 m versus 250 m at nadir for the channels used to generate Normalized Difference Vegetation Index data, scientists need the answer to this question: how will the change in resolution affect vegetation studies? By using simulated VIIRS measurements, this question may be answered before the VIIRS instruments are deployed in space. Using simulated VIIRS products, the U.S. Department of Agriculture and other operational agencies can then modify their decision support systems appropriately in preparation for receipt of actual VIIRS data. VIIRS simulations and validations will be based on the ART (Application Research Toolbox), an integrated set of algorithms and models developed in MATLAB(Registerd TradeMark) that enables users to perform a suite of simulations and statistical trade studies on remote sensing systems. Specifically, the ART provides the capability to generate simulated multispectral image products, at various scales, from high spatial hyperspectral and/or multispectral image products. The ART uses acquired ( real ) or synthetic datasets, along with sensor specifications, to create simulated datasets. For existing multispectral sensor systems, the simulated data products are used for comparison, verification, and validation of the simulated system s actual products. VIIRS simulations will be performed using Hyperion and MODIS datasets. The hyperspectral and hyperspatial properties of Hyperion data will be used to produce simulated MODIS and VIIRS products. Hyperion-derived MODIS data will be compared with near-coincident MODIS collects to validate both spectral and spatial synthesis, which will ascertain the accuracy of converting from MODIS to VIIRS. MODIS-derived VIIRS data is needed for global coverage and for the generation of time series for regional and global investigations. These types of simulations will have errors associated with aliasing for some scene types. This study will help quantify these errors and will identify cases where high-quality, MODIS-derived VIIRS data will be available.

Ross, Kenton W.↗

NASA NTRS1999

Synthetic Scene Generation of Stennis Target Range for the Calibration of Remote Sensing Systems

The validation and verification (V&V) target range developed at Stennis Space Center is a useful tool for the calibration of remote sensing systems. In this paper, we present an algorithm for generating synthetic scenes or digital models of this target range. The atmospheric radiative transfer code Modtran 4 is used in simulating at-sensor radiance for a given sensor. Software is developed to generate the scene with different spatial and spectral resolutions using the at-sensor radiance values. Synthetic scenes provide a cost-effective way for in-flight validation for the spatial and radiometric accuracy of the data. Other applications include mission planning, sensor simulation, and trade-off analysis in sensor design. The radiometric and spatial accuracy of the simulation is evaluated by comparing simulated scenes with AVIRIS acquired values.

Cao, Chang-Yong↗

NASA NTRSDate not supplied

Distributed Target Tracking With Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using dynamic information fusion from multi-modal sensors with geodiversity. First, the algorithm execution location is determined using an optimal data migration strategy, next the sensors information is dynamically fused at each estimation instance using validity flag for each sensor reading, finally the target estimation is updated based on the fused innovation vector. The approach is applied to synthetic data generated from the radar and camera models located on the ground for the simulated target flight in Reflection simulation environment.

Distributed sensing↗

NASA NTRS2001

A Spatio-Temporal Approach for Global Validation and Analysis of MODIS Aerosol Products

With the launch of the MODIS sensor on the Terra spacecraft, new data sets of the global distribution and properties of aerosol are being retrieved, and need to be validated and analyzed. A system has been put in place to generate spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of the MODIS aerosol parameters over more than 100 validation sites spread around the globe. Corresponding statistics are also computed from temporal subsets of AERONET-derived aerosol data. The means and standard deviations of identical parameters from MOMS and AERONET are compared. Although, their means compare favorably, their standard deviations reveal some influence of surface effects on the MODIS aerosol retrievals over land, especially at low aerosol loading. The direction and rate of spatial variation from MODIS are used to study the spatial distribution of aerosols at various locations either individually or comparatively. This paper introduces the methodology for generating and analyzing the data sets used by the two MODIS aerosol validation papers in this issue.

Ichoku, Charles↗

NASA NTRS2007

Overview of the MEDLI Project

The Mars Science Laboratory Entry, Descent, and Landing Instrumentation (MEDLI) Project's objectives are to measure aerothermal environments, sub-surface heatshield material response, vehicle orientation, and atmospheric density for the atmospheric entry and descent phases of the Mars Science Laboratory (MSL) entry vehicle. The flight science objectives of MEDLI directly address the largest uncertainties in the ability to design and validate a robust Mars entry system, including aerothermal, aerodynamic and atmosphere models, and thermal protection system (TPS) design. The instrumentation suite will be installed in the heatshield of the MSL entry vehicle. The acquired data will support future Mars entry and aerocapture missions by providing measured atmospheric data to validate Mars atmosphere models and clarify the design margins for future Mars missions. MEDLI thermocouple and recession sensor data will significantly improve the understanding of aeroheating and TPS performance uncertainties for future missions. MEDLI pressure data will permit more accurate trajectory reconstruction, as well as separation of aerodynamic and atmospheric uncertainties in the hypersonic and supersonic regimes. This paper provides an overview of the project including the instrumentation design, system architecture, and expected measurement response.

Gazarik, Michael J.↗

NASA NTRS2008

Overview of the MEDLI Project

The Mars Science Laboratory Entry, Descent, and Landing Instrumentation (MEDLI) Project s objectives are to measure aerothermal environments, sub-surface heatshield material response, vehicle orientation, and atmospheric density for the atmospheric entry and descent phases of the Mars Science Laboratory (MSL) entry vehicle. The flight science objectives of MEDLI directly address the largest uncertainties in the ability to design and validate a robust Mars entry system, including aerothermal, aerodynamic and atmosphere models, and thermal protection system (TPS) design. The instrumentation suite will be installed in the heatshield of the MSL entry vehicle. The acquired data will support future Mars entry and aerocapture missions by providing measured atmospheric data to validate Mars atmosphere models and clarify the design margins for future Mars missions. MEDLI thermocouple and recession sensor data will significantly improve the understanding of aeroheating and TPS performance uncertainties for future missions. MEDLI pressure data will permit more accurate trajectory reconstruction, as well as separation of aerodynamic and atmospheric uncertainties in the hypersonic and supersonic regimes. This paper provides an overview of the project including the instrumentation design, system architecture, and expected measurement response.

Gazarik, Michael J.↗

DOE OSTI2025

Design and Analysis of a Mutual Inductance Level Sensor for Liquid Metals

Here, this article describes the design and analysis of an electromagnetic level sensor for use in high-temperature liquid metal systems. The mutual inductance level sensor (MILS) described in this work was fabricated using two single-conductor mineral insulated cables wrapped in a bifilar fashion around a stainless steel tube core and was housed in an isolating thimble that preserved the pressure boundary of the test vessel. Two sensor variations were fabricated that differ only in active length, 1016 and 1778 mm. Experimental data were collected using the 1016-mm sensor (MILS-MKII-040) that demonstrated a sensitivity of 9.2 μ V/mm in a room temperature testing stand that used solid aluminum as a surrogate for liquid metal. Experimental data were collected using the 1778-mm sensor (MILS-MKII-070) that demonstrated a sensitivity of 6.9 μ V/mm in the high-temperature (300 ° C) liquid sodium environment at the mechanisms engineering test loop (METL) of Argonne National Laboratory. The sensor performance was found to be repeatable over the course of several months, with roughly ±1% deviation from nominal output. Finite element models were developed in COMSOL Multiphysics that fully describe each test setup, and the models were validated using experimental data. The validated COMSOL models were used to perform an array of analyses that examined the performance of the sensor in differing environments. Maximizing the coil diameter inside the isolating thimble was found to maximize the signal and sensitivity of the sensor. An optimal operating frequency was found near 1000 Hz using both experimental data and COMSOL. The influence of a metallic thimble surrounding the sensor and a metallic sensor core was quantified and found to be negligible at the optimal operating frequency. The sensitivity of the sensor was quantified when monitoring the level of additional liquid metals. These include lead, lead-bismuth eutectic (LBE), sodium-potassium alloy (NaK), and lithium (in addition to sodium). The sensitivities were quantified using liquid metal properties at 350 ° C and 650 ° C. The geometry of the test stand model, all material properties used in the model, and the results are presented in a manner that allows the reader can replicate the model and perform additional analyses.

COMSOL↗

DOE OSTI2018

Highly Selective and Stable Multivariable Gas Sensors for Enhanced Robustness and Reliability of SOFC Operation

GE Global Research, in partnership with SUNY Polytechnic Institute (SUNY Poly) and GE–Fuel Cells LLC, designed, built, and tested multi-gas sensor prototypes for in situ monitoring of gases produced during steam reforming in solid oxide fuel cell (SOFC) systems. To be widely adopted and to deliver a lower operating cost for customers, SOFC systems should enhance their operation reliability and improve stack robustness. These needs can be achieved by early diagnostics of potential upsets by using in-situ gas sensors for the real-time monitoring of gas mixture compositions. Thus, this project objective was to achieve selectivity and stability of sensing of gases for SOFC applications by implementing a new generation of gas sensors, known as multivariable sensors. Such sensors are intended to complement the mature traditional analytical instruments based on sensor performance and design characteristics. The key sensor-performance characteristics include accurate detection and quantitation of several gases in their mixtures and stability of operation in unattended applications. The key design characteristics of sensors include their unobtrusive form factor and cost-effectiveness. The duration of the project was 18 months with the project structure that included three technical tasks such as (1) design and laboratory validation of multivariable sensors for selective detection of H 2 and CO gases, (2) laboratory validation of sensor stability over a two-week testing, and (3) field-validation of the developed multivariable sensor system prototype at GE–Fuel Cells factory. Multi-gas detection was achieved with multivariable photonic sensors designed to operate at elevated temperature. The sensor design was based on three-dimensional (3D) bio-inspired photonic nanostructure that was fabricated using materials responsive to gases of interest such as H 2 and CO. The gas discrimination and the rejection of interferences such as CH 4 , H 2 O, and others was performed by optimizing the design parameters of the 3D photonic nanostructures such as their composition and morphology. Planar sol-gel and sputtered sensing films were fabricated as controls. As a result of the fabrication, functionalization, excitation, and performance testing of 3D photonic sensors, we developed initial design rules for high temperature multivariable sensors. These design rules pave the way for the new generation of cost-effective industrial sensors. Stability of sensor operation was tested in the laboratory conditions under variations of the ambient environments. Stability tests were designed in the order of enhanced complexity and realism of sensor operation and included quantitation of a single gas, quantitation of a single gas in the presence of interferences, and quantitation of two and three gases. We have found that conventional machine learning data analysis stools were unable to correct for the sensor drift. Thus, we have developed new methods of machine learning data analysis that provided us with needed performance stability. Such performance improvement in gas sensing was a significant milestone in bringing our multivariable sensors to practical applications. The project culminated with initial field validation of developed multivariable sensor prototypes at GE–Fuel Cells LLC. The in situ data generated by the sensor system in the lab and field conditions provided inputs that advanced our fundamental understanding of gas sensing at high temperatures to enable sensors for SOFC applications and allowed development of the recommendations for further deliverables.

47 OTHER INSTRUMENTATION↗

NASA NTRS2005

Remote Sensing Product Verification and Validation at the NASA Stennis Space Center

Remote sensing data product verification and validation (V&V) is critical to successful science research and applications development. People who use remote sensing products to make policy, economic, or scientific decisions require confidence in and an understanding of the products' characteristics to make informed decisions about the products' use. NASA data products of coarse to moderate spatial resolution are validated by NASA science teams. NASA's Stennis Space Center (SSC) serves as the science validation team lead for validating commercial data products of moderate to high spatial resolution. At SSC, the Applications Research Toolbox simulates sensors and targets, and the Instrument Validation Laboratory validates critical sensors. The SSC V&V Site consists of radiometric tarps, a network of ground control points, a water surface temperature sensor, an atmospheric measurement system, painted concrete radial target and edge targets, and other instrumentation. NASA's Applied Sciences Directorate participates in the Joint Agency Commercial Imagery Evaluation (JACIE) team formed by NASA, the U.S. Geological Survey, and the National Geospatial-Intelligence Agency to characterize commercial systems and imagery.

Stanley, Thomas M.↗

NASA NTRS1998

Real-Time Sensor Validation, Signal Reconstruction, and Feature Detection for an RLV Propulsion Testbed

A real-time system for validating sensor health has been developed in support of the reusable launch vehicle program. This system was designed for use in a propulsion testbed as part of an overall effort to improve the safety, diagnostic capability, and cost of operation of the testbed. The sensor validation system was designed and developed at the NASA Lewis Research Center and integrated into a propulsion checkout and control system as part of an industry-NASA partnership, led by Rockwell International for the Marshall Space Flight Center. The system includes modules for sensor validation, signal reconstruction, and feature detection and was designed to maximize portability to other applications. Review of test data from initial integration testing verified real-time operation and showed the system to perform correctly on both hard and soft sensor failure test cases. This paper discusses the design of the sensor validation and supporting modules developed at LeRC and reviews results obtained from initial test cases.

Jankovsky, Amy L.↗

DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗

NASA NTRS1977

Application of microelectronic technology to general aviation flight control

This paper presents several different methods that can be used to determine the aircraft orientation (attitude) without direct measurement. The methods combine state estimation techniques with measurements from solid state pressure sensors, accelerometers, and magnetometers to determine the aircraft state. The paper demonstrates how the estimation algorithms were validated and compared using flight test data, and it presents results of performance sensitivity analyses of sensor error, modeling inaccuracies, and wind disturbance effects on the attitude estimation errors. If implemented, the methods presented could make it possible to eliminate directional and vertical gyros and to change from many isolated sensors to an integrated, small, reliable sensing package for determining the aircraft state.

Sorensen, J. A.↗

DOE OSTI2013

Rotation-Enabled 7-Degree of Freedom Seismometer for Geothermal Resource Development. Phase 1 Final Report

Under this Department of Energy (DOE) grant, A-Tech Corporation d.b.a. Applied Technology Associates (ATA), seeks to develop a seven-degree-of-freedom (7-DOF) seismic measurement tool for high-temperature geothermal applications. The Rotational-Enabled 7-DOF Seismometer includes a conventional tri-axial accelerometer, a conventional pressure sensor or hydrophone, and a tri-axial rotational sensor. The rotational sensing capability is novel, based upon ATA's innovative research in rotational sensing technologies. The geothermal industry requires tools for high-precision seismic monitoring of crack formation associated with Enhanced Geothermal System (EGS) stimulation activity. Currently, microseismic monitoring is conducted by deploying many seismic tools at different depth levels along a 'string' within drilled observation wells. Costs per string can be hundreds of thousands of dollars. Processing data from the spatial arrays of linear seismometers allows back-projection of seismic wave states. In contrast, a Rotational-Enabled 7-DOF Seismometer would simultaneously measure p-wave velocity, s-wave velocity, and incident seismic wave direction all from a single point measurement. In addition, the Rotational-Enabled 7-DOF Seismometer will, by its nature, separate p- and s-waves into different data streams, simplifying signal processing and facilitating analysis of seismic source signatures and geological characterization. By adding measurements of three additional degrees-of-freedom at each level and leveraging the information from this new seismic observable, it is likely that an equally accurate picture of subsurface seismic activity could be garnered with fewer levels per hole. The key cost savings would come from better siting of the well due to increased information content and a decrease in the number of confirmation wells drilled, also due to the increase in information per well. Improved seismic tools may also increase knowledge, understanding, and confidence, thus removing some current blocks to feasibility and significantly increasing access to potential geothermal sites. During the Phase 1 effort summarized in this final report, the ATA Team modeled and built two TRL 3 proof-of-concept test units for two competing rotational sensor technologies. The two competing technologies were based on ATA's angular rate and angular displacement measurement technologies; Angular rate: ATA's Magnetohydrodynamic Angular Rate Sensor (Seismic MHD); and Angular displacement: ATA's Low Frequency Improved Torsional Seismometer (LFITS). In order to down-select between these two technologies and formulate a go / no go decision, the ATA Team analyzed and traded scientific performance requirements and market constraints against sensor characteristics and components, acquiring field data where possible to validate the approach and publishing results from these studies of rotational technology capability. Based on the results of Phase 1, the ATA Team finds that the Seismic MHD (SMHD) technology is the best choice for enabling rotational seismometry and significant technical potential exists for micro-seismic monitoring using a downhole 7-DOF device based on the SMHD. Recent technical papers and field data confirm the potential of rotational sensing for seismic mapping, increasing confidence that cost-reduction benefits are achievable for EGS. However, the market for geothermal rotational sensing is small and undeveloped. As a result, this report recommends modifying the Phase 2 plan to focus on prototype development aimed at partnering with early adopters within the geothermal industry and the scientific research community. The highest public benefit will come from development and deployment of a science-grade SMHD rotational seismometer engineered for geothermal downhole conditions and an integrated test tool for downhole measurements at active geothermal test sites.

15 GEOTHERMAL ENERGY↗

NASA NTRS1994

OARE flight maneuvers and calibration measurements on STS-58

The Orbital Acceleration Research Experiment (OARE), which has flown on STS-40, STS-50, and STS-58, contains a three axis accelerometer with a single, nonpendulous, electrostatically suspended proofmass which can resolve accelerations to the nano-g level. The experiment also contains a full calibration station to permit in situ bias and scale factor calibration. This on-orbit calibration capability eliminates the large uncertainty of ground-based calibrations encountered with accelerometers flown in the past on the orbiter, thus providing absolute acceleration measurement accuracy heretofore unachievable. This is the first time accelerometer scale factor measurements have been performed on orbit. A detailed analysis of the calibration process is given along with results of the calibration factors from the on-orbit OARE flight measurements on STS-58. In addition, the analysis of OARE flight maneuver data used to validate the scale factor measurements in the sensor's most sensitive range is also presented. Estimates on calibration uncertainties are discussed. This provides bounds on the STS-58 absolute acceleration measurements for future applications.

Blanchard, Robert C.↗