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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Ontogenesis of mammals in microgravity

This report is an English translation of a Russian report prepared by a group of authors from the USSR, Bulgaria, Hungary, the GDR, Poland, Czechoslovakia, France, and the USA. It presents results of the first microgravity experiment on mammalian embryology performed during the flight of the biosatellite Cosmos-1514 and in ground-based simulation studies. An overview is provided of the data available about the role of gravity in animal growth and development, and future studies into this problem are discussed. A new introduction has been provided for the English version.

Gazenko, O. G.↗
NASA NTRS1994

The sea state bias in altimeter estimates of sea level from collinear analysis of TOPEX data

The wind speed and significant wave height (H(sub 1/3)) dependencies of the sea state bias in altimeter estimates of sea level, expressed in the form (Delta)h(sub SSB) = bH(sub 1/3), are examined from least squares analysis of 21 cycles of collinear TOPEX data. The bias coefficient b is found to increase in magnitude with increasing wind speed up to about 12 m/s and decrease monotonically in magnitude with increasing H(sub 1/3). A parameterization of b as a quadratic function of wind speed only, as in the formation used to produce the TOPEX geophysical data records (GDRs), is significantly better than a parameterization purely in terms of H(sub 1/3). However, a four-parameter combined wind speed and wave height formulation for b (quadratic in wind speed plus linear in H(sub 1/3)) significantly improves the accuracy of the sea state bias correction. The GDR formulation in terms of wind speed only should therefore be expanded to account for a wave height dependence of b. An attempt to quantify the accuracy of the sea state bias correction (Delta)h(sub SSB) concludes that the uncertainty is a disconcertingly large 1% of H(sub 1/3).

Chelton, Dudley B.↗
NASA NTRS2000

Verification and Improvement of ERS-1/2 Altimeter Geophysical Data Records for Global Change Studies

This Final Technical Report summarizes the research work conducted under NASA's Physical Oceanography Program entitled, Verification And Improvement Of ERS-112 Altimeter Geophysical Data Recorders For Global Change Studies, for the time period from January 1, 2000 through June 30, 2000. This report also provides a summary of the investigation from July 1, 1997 - June 30, 2000. The primary objectives of this investigation include verification and improvement of the ERS-1 and ERS-2 radar altimeter geophysical data records for distribution of the data to the ESA-approved U.S. ERS-1/-2 investigators for global climate change studies. Specifically, the investigation is to verify and improve the ERS geophysical data record products by calibrating the instrument and assessing accuracy for the ERS-1/-2 orbital, geophysical, media, and instrument corrections. The purpose is to ensure that the consistency of constants, standards and algorithms with TOPEX/POSEIDON radar altimeter for global climate change studies such as the monitoring and interpretation of long-term sea level change. This investigation has provided the current best precise orbits, with the radial orbit accuracy for ERS-1 (Phases C-G) and ERS-2 estimated at the 3-5 cm rms level, an 30-fold improvement compared to the 1993 accuracy. We have finalized the production and verification of the value-added ERS-1 mission (Phases A, B, C, D, E, F, and G), in collaboration with JPL PODAAC and the University of Texas. Orbit and data verification and improvement of algorithms led to the best data product available to-date. ERS-2 altimeter data have been improved and we have been active on Envisat (2001 launch) GDR algorithm review and improvement. The data improvement of ERS-1 and ERS-2 led to improvement in the global mean sea surface, marine gravity anomaly and bathymetry models, and a study of Antarctica mass balance, which was published in Science in 1998.

Shum, C. K.↗
NASA NTRS2003

TOPEX Software Document Series: Rev. 1 - Volume 5

This document is a compendium of the WFF TOPEX Software Development Team's knowledge regarding Geophysical Data Record (GDR) Processing. It includes many elements of a requirements document, a software specification document, a software design document, and a user's manual. In the more technical sections, this document assumes the reader is familiar with TOPEX and instrument files.

Lee, Jeffrey↗
NASA NTRS2006

13 Years of TOPEX/POSEIDON Precision Orbit Determination and the 10-fold Improvement in Expected Orbit Accuracy

Launched in the summer of 1992, TOPEX/POSEIDON (T/P) was a joint mission between NASA and the Centre National d Etudes Spatiales (CNES), the French Space Agency, to make precise radar altimeter measurements of the ocean surface. After the remarkably successful 13-years of mapping the ocean surface T/P lost its ability to maneuver and was de-commissioned January 2006. T/P revolutionized the study of the Earth s oceans by vastly exceeding pre-launch estimates of surface height accuracy recoverable from radar altimeter measurements. The precision orbit lies at the heart of the altimeter measurement providing the reference frame from which the radar altimeter measurements are made. The expected quality of orbit knowledge had limited the measurement accuracy expectations of past altimeter missions, and still remains a major component in the error budget of all altimeter missions. This paper describes critical improvements made to the T/P orbit time series over the 13-years of precise orbit determination (POD) provided by the GSFC Space Geodesy Laboratory. The POD improvements from the pre-launch T/P expectation of radial orbit accuracy and Mission requirement of 13-cm to an expected accuracy of about 1.5-cm with today s latest orbits will be discussed. The latest orbits with 1.5 cm RMS radial accuracy represent a significant improvement to the 2.0-cm accuracy orbits currently available on the T/P Geophysical Data Record (GDR) altimeter product.

Lemoine, F. G.↗
NASA NTRS2006

Towards the GEOSAT Follow-On Precise Orbit Determination Goals of High Accuracy and Near-Real-Time Processing

The US Navy's GEOSAT Follow-On spacecraft (GFO) primary mission objective is to map the oceans using a radar altimeter. Satellite laser ranging data, especially in combination with altimeter crossover data, offer the only means of determining high-quality precise orbits. Two tuned gravity models, PGS7727 and PGS7777b, were created at NASA GSFC for GFO that reduce the predicted radial orbit through degree 70 to 13.7 and 10.0 mm. A macromodel was developed to model the nonconservative forces and the SLR spacecraft measurement offset was adjusted to remove a mean bias. Using these improved models, satellite-ranging data, altimeter crossover data, and Doppler data are used to compute both daily medium precision orbits with a latency of less than 24 hours. Final precise orbits are also computed using these tracking data and exported with a latency of three to four weeks to NOAA for use on the GFO Geophysical Data Records (GDR s). The estimated orbit precision of the daily orbits is between 10 and 20 cm, whereas the precise orbits have a precision of 5 cm.

Lemoine, Frank G.↗
NASA NTRS2021

Accuracy Assessment of Global Internal-Tide Models Using Satellite Altimetry Models Using Satellite Altimetry

Altimeter measurements are corrected for several geophysical parameters in order to access ocean signals of interest, like mesoscale or sub-mesoscale variability. The ocean tide is one of the most critical corrections due to the amplitude of the tidal elevations and to the aliasing phenomena of high-frequency signals into the lower-frequency band, but the internal-tide signatures at the ocean surface are not yet corrected globally. Internal tides can have a signature of several centimeters at the surface with wavelengths of about 50–250 km for the first mode and even smaller scales for higher-order modes. The goals of the upcoming Surface Water Ocean Topography (SWOT) mission and other high-resolution ocean measurements make the correction of these small-scale signals a challenge, as the correction of all tidal variability becomes mandatory to access accurate measurements of other oceanic signals. In this context, several scientific teams are working on the development of new internal-tide models, taking advantage of the very long altimeter time series now available, which represent an unprecedented and valuable global ocean database. The internal-tide models presented here focus on the coherent internal-tide signal and they are of three types: empirical models based upon analysis of existing altimeter missions, an assimilative model and a three-dimensional hydrodynamic model. A detailed comparison and validation of these internal-tide models is proposed using existing satellite altimeter databases. The analysis focuses on the four main tidal constituents: M2, K1, O1 and S2. The validation process is based on a statistical analysis of multi-mission altimetry including Jason-2 and Cryosphere Satellite-2 data. The results show a significant altimeter variance reduction when using internal-tide corrections in all ocean regions where internal tides are generating or propagating. A complementary spectral analysis also gives some estimation of the performance of each model as a function of wavelength and some insight into the residual non-stationary part of internal tides in the different regions of interest. This work led to the implementation of a new internal-tide correction (ZARON'one) in the next geophysical data records version-F (GDR-F) standards.

Loren Carrere↗
DOE OSTI2025

Testing the DAE LLRF system with a PIP-II SSR2 Cavity

The PIP-II linac is an international collaboration project with in kind contributions of key subsystems from multiple countries including India (DAE). In the research and development phase of the project, the LLRF and resonance control systems were jointly developed by BARC and Fermilab and were delivered to Fermilab for testing and validation. Initial testing of the LLRF system was carried out using Fermilabs analog cavity emulator. Following successful emulator testing, the LLRF system was deployed at STC on a PIP-II 325 MHz SSR2 cavity. The cavity was operated in both SEL and GDR modes at a gradient of 5 MV/m. The results of the testing are presented here.

Nasery, Radhika [Bhabha Atomic Res. Ctr.]↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

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DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

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