Search NASA⌕ Search

SEARCH · Search NASA

Results for “OSR”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

geothermal_osr (Energy Predictor for Geothermal Open Source Reservoir) [SWR-22-23]

This is a repository with the tools developed for predicting energy produced at Open-Source Reservoir (OSR). It includes both simulation data for OSR, as well as Jupyter notebooks for training and evaluating prediction models. OSR was constructed based on the data from Brady Hot Springs reservoir (Nevada, USA) but has a number of sufficiently modified characteristics and does not disclose any sensitive data.

Duplyakin, Dmitry↗
DOE OSTI2022

SCO#1197 Addendum #4 OSR Characterization Database (Test Report)

A test plan was developed and approved in June of 2022. Testing was successfully performed to verify the functions of Version 2.0 of the OSR Characterization database. Checks verified that data remains consistent to tests previously performed using Microsoft Access® 2003, 2010 and 2016.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

SCI model structure determination program (OSR) user's guide

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

Source record↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
NASA NTRS1979

Investigation of electrostatic discharge phenomena on conductive and non-conductive solar reflectors

A sample composed of non conductive optical solar reflectors (OSR) was irradiated with low energy electrons at the DERTS facility to study the effects of charge build up on thermal control coating materials. The degration effects on this panel due to electrostatic discharges were investigated to find possible alternatives to limit the amount of damage. The following systems are evaluated: (1) non conductive OSR (non conductive adhesive) (2) non conductive OSR (conductive adhesive); and conductive OSR (conductive adhesive).

S J Bosma↗
DOE OSTI2021

Isolating Detrital and Diagenetic Signals in Magnetic Susceptibility Records From Methane‐Bearing Marine Sediments

Abstract Volume‐dependent magnetic susceptibility (κ) is commonly used for paleoenvironmental reconstructions in both terrestrial and marine sedimentary environments where it reflects a mixed signal between primary deposition and secondary diagenesis. In the marine environment, κ is strongly influenced by the abundance of ferrimagnetic minerals regulated by sediment transport processes. Post‐depositional alteration by H 2 S, however, can dissolve titanomagnetite, releasing reactive Fe that promotes pyritization and subsequently decreases κ. Here, we provide a new approach for isolating the detrital signal in κ and identifying intervals of diagenetic alteration of κ driven by organoclastic sulfate reduction (OSR) and the anaerobic oxidation of methane (AOM) in methane‐bearing marine sediments offshore India. Using the correlation of a heavy mineral proxy from X‐ray fluorescence data (Zr/Rb) and κ in unaltered sediments, we predict the primary detrital κ signal and identify intervals of decreased κ, which correspond to increased total sulfur content. Our approach is a rapid, high‐resolution method that can identify overprinted κ resulting from pyritization of titanomagnetite due to H 2 S production in marine sediments. In addition, total organic carbon, total sulfur, and authigenic carbonate δ 13 C measurements indicate that both OSR and AOM can drive the observed κ loss, but AOM drives the greatest decreases in κ. Overall, our approach can enhance paleoenvironmental reconstructions and provide insight into paleo‐positions of the sulfate‐methane transition zone, past enhancements of OSR or paleo‐methane seepage, and the role of detrital iron oxide minerals on the marine sediment sulfur sink, with consequences influencing the development of chemosynthetic biological communities at methane seeps.

58 GEOSCIENCES↗
DOE OSTI2021

Proposed Longitudinal Profile Diagnostics for Optical Stochastic Cooling of Stored Electrons in the IOTA Ring

One of the predicted signatures of successful optical stochastic cooling (OSC) in the Fermilab Integrable Optics Test Accelerator (IOTA) ring is reduction of the bunch length. The IOTA OSC experiment is designed for a low nominal beam current (~0.1 microAmps of 100-MeV electrons) to reduce intrabeam scattering (IBS), and during cooling, OSC is expected to reduce the bunch length from ~200 ps to ~130 ps*. These equilibrium bunch lengths can be measured using a streak camera and the optical synchrotron radiation (OSR) generated in a ring dipole by the circulating beam. A similar measurement was previously performed at the Advanced Photon Source with a Hamamatsu C5680 synchroscan streak camera operating at 117.3 MHz**. In this case, synchronous summing of OSR resulted in a bunch length measurement of 276 ± 30 ps using only 57 electrons circulating at 425 MeV. At IOTA, an existing streak camera has been modified to operate at the 11th harmonic of the ring’s revolution frequency of 7.50 MHz and has been installed on an OSR port in support of the OSC experiments. The integrated system will have sufficient sensitivity and resolution for measuring the evolution and equilibrium values of the bunch length during OSC experiments.

Lumpkin, Alex H.↗
NASA NTRS1982

IUS materials outgassing condensation effects on sensitive spacecraft surfaces

Four materials used on the inertial upper state (IUS) were subjected to vacuum conditions and heated to near-operational temperatures (93 to 316 C), releasing volatile materials. A fraction of the volatile materials were collected on 25 C solar cells, optical solar reflectors (OSR's) or aluminized Mylar. The contaminated surfaces were exposed to 26 equivalent sun hours of simulated solar ultraviolet (UV) radiation. Measurements of contamination deposit mass, structure, reflectance and effects on solar cell power output were made before and after UV irradiation. Standard total mass loss - volatile condensible materials (TML - VCM) tests were also performed. A 2500 A thick contaminant layer produced by EPDM rubber motor-case insulation outgassing increased the solar absorptance of the OSR's from 0.07 to 0.14, and to 0.18 after UV exposure. An 83,000 A layer caused an increase from 0.07 to 0.21, and then the 0.46 after UV exposure. The Kevlar-epoxy motor-case material outgassing condensation raised the absorptance from 0.07 to 0.13, but UV had no effect. Outgassing from multilayer insulation and carbon-carbon nozzle materials did not affect the solar absorptance of the OSR's.

Mullen, C. R.↗
DOE OSTI2025

Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition

We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS1979

Materials and techniques for spacecraft static charge control 2

Results of exploratory development on the design, fabrication and testing of transparent conductive coatings, conductive bulk materials and grounding techniques for application to high resistivity spacecraft dielectric materials to obtain control of static charge buildup are presented. Deposition techniques for application of indium oxide, indium/tin oxide and other metal oxide films on Kapton, FEP Teflon, OSR and solar cell coverglasses discussed include RF and Magnetron sputtering and vapor deposition. Development, fabrication and testing of conductive glass tiles for OSR and solar cell coverglass applications is discussed. Several grounding techniques for rapid charge dissipation from the conductively coated polymer and glass dielectrics which were developed and tested in thermal cycled and electron plasma environments are described. The optical and electrical characterization and aging effects of these coatings, bulk materials and grounding techniques are reviewed as they apply to the performance of their design functions in a geosynchronous orbit environment.

Schmidt, R. E.↗
NASA NTRS1980

Dielectric discharge characteristics in a two-electron simulation environment

The results of a series of experiments in which flux levels representative of the space electron environment were used are presented and compared to the results of high flux tests. The simulation approach was to partition the space electron spectrum into two parts, those electrons which do not penetrate a material and therefore contribute to charging and those which completely penetrate the material. The nonpenetrating electrons were simulated using 25 keV electrons and the penetrating electrons by 350 keV electrons. The materials included in this investigation were Kapton, optical solar reflectors (OSRs), and a ground test satellite surface potential monitor which contained Kapton, astroquart, OSRs and teflon.

Treadaway, M.↗
NASA NTRS1985

High temperature, high intensity solar array

The solar array for the Venus Radar Mapper mission will operate in the high temperature, high intensity conditions of a low Venus orbit environment. To fulfill the performance requirements in this environment at minimum cost and mass while maximizing power density and packing factor on the panel surface, several features were introduced into the design. These features included the use of optical surface reflectors (OSR's) to reduce the operating temperature; new adhesives for conductive bonding of OSR's to avoid electrostatic discharges; custom-designed large area cells and novel shunt diode circuit and panel power harness configurations.

Smith, B. S.↗
NASA NTRS1992

Solar absorptance degradation of optical solar reflector radiators on the Spacenet satellites

Telemetry data are presented for two communications hybrid satellites, Spacenet I and Spacenet II, collected to determine the long-term temperature trend and associated solar absorptance degradation of the optical solar reflectors (OSRs). A thermal model was used to calculate the thermal sensitivity of various OSR components to changes in the solar absorptance and to determine absolute values of solar absorptance. The separation of the data into sunlit and nonsunlit periods made it possible to confirm the hypothesis that degradation occurs under the catalytic activity of direct sunlight on the spacecraft contaminants. The differences found between the degradation rates for Spacenet-I and Spacenet-II satellites and between the present results and published reports on other satellites are considered to be related to variations in the volume of spacecraft contaminants.

Naegeli, Charles R.↗
NASA NTRS2002

Contamination in Orbit of GOES-8

The GOES-8 satellite has lost some of its ability to dissipate heat over time. This is shown by the temperature increases over time of spacecraft and instrument components that are cooled with optical solar reflector (OSR) radiators. Contamination has a significant, well-documented effect on the solar absorptance (a(sub s)) of OSRs. This document attempts to discern how much molecular contamination has collected on the Imager and Sounder radiant coolers by analyzing the increase in temperature of the vacuum cooler housing. In the first part, temperature change is transformed into solar absorptance units by a method devised by ITT. The second part transfomis the solar absorptance gain into a molecular film thickness.

Sanders, Jack T.↗
NASA NTRS2013

Simplified Mantle Architecture and Distribution of Radiogenic Power

The mantle components that represent the source region of ocean island basalts (OIB) and feed hotspot volcanism are predicted to contain 160 +/- 20 (2σ(sub m)) ng/g Th, a heat‐producing element. This critical model composition indicates that the OIB source region (OSR) comprises a significant amount of recycled oceanic crust and constitutes 19(sup +3)(sub −2) (2σ(sub m))% of the mantle by mass. The mass fraction of this reservoir supports a mantle architecture with a basal thermochemical layering at an average depth of 2000 +/- 100 (2σ(sub m)) km or two thermochemical piles that extend up to midmantle levels. The hotspot source described here generates 10 pW/kg of radiogenic heat and supplies 7.3 TW to the planet's total surface heat flux. Given that the silicate portion of the Earth produces some 20.4 TW of radiogenic power, with 7.2 TW derived from the continental crust, the mantle source responsible for mid‐ocean ridge volcanism provides only 5.9 TW of radiogenic power (or <2 pW/kg). As a result, the source of hotspots generates >5x more radiogenic heat than the source of mid‐ocean ridges, thus contributing to the energetics that drive mantle convection and potentially the formation of long‐lived plumes via bottom heating of the modern mantle. The potential for a sequestered or unsampled mantle reservoir would impact the relative mass fractions of the source regions of OIB and mid‐ocean ridge volcanism but not the compositional model of the OSR presented here.

Arevalo, Ricardo, Jr.↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Geometrical and chemical effects on the electrochemistry of single-wall carbon nanotube (SWCNT) network electrodes

Single-wall carbon nanotube (SWCNT) network is a promising electrode material for bio detection. Unfortunately, the associations between their physical as well as chemical properties and observed electrochemical performance are not known. This hinders any systematic optimization of the network properties towards specific analytes. Here we present a consistent physicochemical and electrochemical characterization of differently treated SWCNT networks. The results unambiguously show that (i) even if the electrochemical properties of different electrodes are practically identical when assessed by surface insensitive outer sphere redox (OSR) probes their behavior with inner sphere redox (ISR) probes can be drastically different. Further, (ii) the choice of the modification method (structural, chemical, electrochemical) heavily depends on nature of the target analyte, which are typically ISR probes. Although, (iii) chemical changes in the carbon phase appeared to be minor, effects of different treatments on oxidation states of Fe appeared to have a strong effect on the electrochemical performance of the networks in the case of ISR probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗