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NASA NTRS2007

SIM Planetquest Science and Technology: A Status Report

Optical interferometry will open new vistas for astronomy over the next decade. The Space Interferometry Mission (SIM-PlanetQuest), operating unfettered by the Earth's atmosphere, will offer unprecedented astrometric precision that promises the discovery of Earth-analog extra-solar planets as well as a wealth of important astrophysics. Results from SIM will permit the determination of stellar masses to accuracies of 2% or better for objects ranging from brown dwarfs through main sequence stars to evolved white dwarfs, neutron stars, and black holes. Studies of star clusters will yield age determinations and internal dynamics. Microlensing measurements will present the mass spectrum of the Milky Way internal to the Sun while proper motion surveys will show the Sun's orbital radius and speed. Studies of the Galaxy's halo component and companion dwarf galaxies permit the determination of the Milky Way's mass distribution, including its Dark Matter component and the mass distribution and Dark Matter component of the Local Group. Cosmology benefits from precision (1-2%) determination of distances to Cepheid and RR Lyrae standard candles. The emission mechanism of supermassive black holes will be investigated. Finally, radio and optical celestial reference frames will be tied together by an improvement of two orders of magnitude. Optical interferometers present severe technological challenges. The Jet Propulsion Laboratory, with the support of Lockheed Martin Advanced Technology Center (LM ATC) and Northrop Grumman Space Technology (NGST), has addressed these challenges with a technology development program that is now complete. The requirements for SIM have been satisfied, based on outside peer review, using a series of laboratory tests and appropriate computer simulations: laser metrology systems perform with 10 picometer precision; mechanical vibrations have been controlled to nanometers, demonstrating orders of magnitude disturbance rejection; and knowledge of component positions throughout the whole test assembly has been demonstrated to the required picometer level. Technology transfer to the SIM flight team is now well along.

astrometry↗

NASA NTRS2014

A Multi-Parameter Approach for Calculating Crack Instability

An accurate fracture control analysis of spacecraft pressure systems, boosters, rocket hardware and other critical low-cycle fatigue cases where the fracture toughness highly impacts cycles to failure requires accurate knowledge of the material fracture toughness. However, applicability of the measured fracture toughness values using standard specimens and transferability of the values to crack instability analysis of the realistically complex structures is refutable. The commonly used single parameter Linear Elastic Fracture Mechanics (LEFM) approach which relies on the key assumption that the fracture toughness is a material property would result in inaccurate crack instability predictions. In the past years extensive studies have been conducted to improve the single parameter (K-controlled) LEFM by introducing parameters accounting for the geometry or in-plane constraint effects]. Despite the importance of the thickness (out-of-plane constraint) effects in fracture control problems, the literature is mainly limited to some empirical equations for scaling the fracture toughness data] and only few theoretically based developments can be found. In aerospace hardware where the structure might have only one life cycle and weight reduction is crucial, reducing the design margin of safety by decreasing the uncertainty involved in fracture toughness evaluations would result in lighter hardware. In such conditions LEFM would not suffice and an elastic-plastic analysis would be vital. Multi-parameter elastic plastic crack tip field quantifying developments combined with statistical methods] have been shown to have the potential to be used as a powerful tool for tackling such problems. However, these approaches have not been comprehensively scrutinized using experimental tests. Therefore, in this paper a multi-parameter elastic-plastic approach has been used to study the crack instability problem and the transferability issue by considering the effects of geometrical constraints as well as the thickness. The feasibility of the approach has been examined using a wide range of specimen geometries and thicknesses manufactured from 7075-T7351 aluminum alloy.

Zanganeh, M.↗

NASA NTRS2012

Optimize Use of Space Research and Technology for Medical Devices

systems, and cutting-edge component technologies to conduct a wide range of scientific observations and measurements. These technologies are also considered for practical applications that benefit society in remarkable ways. At NASA Goddard, the technology transfer initiative promotes matching technologies from Earth and space science needs to targeted industry sectors. This requires clear knowledge of industry needs and priorities and social demands. The process entails matching mature technologies where there are known innovation challenges and good opportunities for matching technology needs. This requires creative thinking and takes commitment of time and resources. Additionally, we also look at applications for known hot industry or societal needs. Doing so has given us occasion to host discussions with representatives from industry, academia, government organizations, and societal special interest groups about the application of NASA Goddard technologies for devices used in medical monitoring and detection tools. As a result, partnerships have been established. Innovation transpired when new products were enabled because of NASA Goddard research and technology programs.

Optimize↗

NASA NTRS1958

A method of computing the transient temperature of thick walls from arbitrary variation of adiabatic-wall temperature and heat-transfer coefficient

A method of calculating the temperature of thick walls has been developed in which the time series and the response to a unit triangle variation of surface temperature concepts are used, together with essentially standard formulas for transient temperature and heat flow into thick walls. The method can be used without knowledge of the mathematical tools of its development. The method is particularly suitable for determining the wall temperature in one-dimensional thermal problems in aeronautics where there is a continuous variation of the heat-transfer coefficient and adiabatic-wall temperature. The method also offers a convenient means for solving the inverse problem of determining the heat-flow history when temperature history is known.

Hill, P R↗

NASA NTRS2000

NASA Performance Report

Introduction NASA's mission is to advance and communicate scientific knowledge and understanding of Earth, the solar system, and the universe; to advance human exploration, use, and development of space; and to research, develop, verify, and transfer advanced aeronautics, space, and related technologies. In support of this mission, NASA has a strategic architecture that consists of four Enterprises supported by four Crosscutting Processes. The Strategic Enterprises are NASA's primary mission areas to include Earth Science, Space Science, Human Exploration and Development of Space, and Aerospace Technology. NASA's Crosscutting Processes are Manage Strategically, Provide Aerospace Products and Capabilities, Generate Knowledge and Communicate Knowledge. The implementation of NASA programs, science, and technology research occurs primarily at our Centers. NASA consists of a Headquarters, nine Centers, and the Jet Propulsion Laboratory, as well as several ancillary installations and offices in the United States and abroad. The nine Centers are as follows: (1) Ames Research Center, (2) Dryden Flight Research Center (DFRC), (3) Glenn Research Center (GRC), (4) Goddard Space Flight Center (GSFC), (5) Johnson Space Center, (6) Kennedy Space Center (KSC), (7) Langley Research Center (LaRC), (8) Marshall Space Flight Center (MSFC), and (9) Stennis Space Center (SSC).

Source record↗

DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1966

Symposium on Technology Status and Trends

The objective of the National Aeronautics and Space Administration's Technology Utilization Program is to identify, document, and transfer to the national economy the technical advances resulting from our space and research programs. These innovations, when properly applied in industry and business, hold great promise for improving our everyday life and contributing to the prosperity of the nation. We believe that the knowledge, progress, and advances associated with aerospace technology contain major potential for industrial applications. The content and method of presenting symposium papers by Marshall and selected contractor personnel have been designed to transfer an awareness of at least some new technology to attendees in nonaerospace fields.

Conference↗

DOE OSTI2018

Iodine in Metal–Organic Frameworks at High Pressure

Capture of highly volatile radioactive iodine is a promising application of metal–organic frameworks (MOFs), thanks to their high porosity with flexible chemical architecture. Specifically, strong charge-transfer binding of iodine to the framework enables efficient and selective iodine uptake as well as its long-term storage. As such, precise knowledge of the electronic structure of iodine is essential for a detailed modeling of the iodine sorption process, which will allow for rational design of iodophilic MOFs in the future. Here we probe the electronic structure of iodine in MOFs at variable iodine···framework interaction by Raman and optical absorption spectroscopy at high pressure (P). The electronic structure of iodine in the straight channels of SBMOF-1 (Ca- sdb , sdb = 4,4'-sulfonyldibenzoate) is modified irreversibly at P > 3.4 GPa by charge transfer, marking a polymerization of iodine molecules into a 1D polyiodide chain. In contrast, iodine in the sinusoidal channels of SBMOF-3 (Cd- sdb ) retains its molecular (I 2 ) character up to at least 8.4 GPa. Such divergent high-pressure behavior of iodine in the MOFs with similar port size and chemistry illustrates adaptations of the electronic structure of iodine to channel topology and strength of the iodine···framework interaction, which can be used to tailor iodine-immobilizing MOFs.

36 MATERIALS SCIENCE↗

NASA NTRS1993

TROPIX: A solar electric propulsion flight experiment

The Transfer Orbit Plasma Interaction Experiment (TROPIX) is a proposed scientific experiment and flight demonstration of a solar electric propulsion vehicle. Its mission goals are to significantly increase our knowledge of Earth's magnetosphere and its associated plasma environment and to demonstrate an operational solar electric upper stage (SEUS) for small launch vehicles. The scientific investigations and flight demonstration technology experiments are uniquely interrelated because of the spacecraft's interaction with the surrounding environment. The data obtained will complement previous studies of the Earth's magnetosphere and space plasma environment by supplying the knowledge necessary to attain the strategic objectives of the NASA Office of Space Science. This first operational use of a primary ion propulsion vehicle, designed to withstand the harsh environments from low Earth orbit to geosynchronous Earth orbit, may lead to the development of a new class of electric propulsion upper stages or space-based transfer vehicles and may improve future spacecraft design and safety.

Hickman, J. Mark↗

NASA NTRS2017

The Effects of Long-Duration Spaceflight on Training Retention and Transfer

Training our crew members for long duration, exploration-class missions will have to maximize long-term retention and transfer of the trained skills. The expected duration of the missions, our inability to predict all the possible tasks the crew will be called upon to perform, and the low training-to-mission time ratio require that the training be maximally effective such that the skills acquired during training will be retained and will be transferrable across a wide range of specific tasks that are different from the particular tasks used during training. However, to be able to design training that can achieve these ambitious goals, we must first understand the ways in which long-duration spaceflight affects training retention and transfer. Current theories of training retention and transfer are largely based on experimental studies conducted at university laboratories using undergraduate students as participants. Furthermore, all such studies have been conducted on Earth. We do not know how well the results of these studies predict the performance of crew members. More specifically, we do not know how well the results of these studies predict the performance of crew members in space and especially during long-duration missions. To address this gap in our knowledge, the current on-going study seeks to test the null hypothesis that performance of university undergraduate students on Earth on training retention and transfer tests do in fact predict accurately the performance of crew members during long-duration spaceflights. To test this hypothesis, the study employs a single 16-month long experimental protocol with 3 different participant groups: undergraduate university students, crew members on the ground, and crew members in space. Results from this study will be presented upon its completion. This poster presents results of study trials of the two tasks used in this study: a data entry task and a mapping task. By researching established training principles, by examining future needs, and by using current practices in spaceflight training as test beds, this research project is mitigating program risks and generating templates and requirements to meet future training needs.

ISS↗

NASA NTRS2018

The Effects of Long-Duration Spaceflight on Training Retention and Transfer

training↗

DOE OSTI2022

Kingdom-Wide Analysis of Fungal Protein-Coding and tRNA Genes Reveals Conserved Patterns of Adaptive Evolution

Protein-coding genes evolved codon usage bias due to the combined but uneven effects of adaptive and nonadaptive influences. Studies in model fungi agree on codon usage bias as an adaptation for fine-tuning gene expression levels; however, such knowledge is lacking for most other fungi. Our comparative genomics analysis of over 450 species supports codon usage and transfer RNAs (tRNAs) as coadapted for translation speed and this is most likely a realization of convergent evolution. Rather than drift, phylogenetic reconstruction inferred adaptive radiation as the best explanation for the variation of interspecific codon usage bias. Although the phylogenetic signals for individual codon and tRNAs frequencies are lower than expected by genetic drift, we found remarkable conservation of highly expressed genes being codon optimized for translation by the most abundant tRNAs, especially by inosine-modified tRNAs. As an application, we present a sequence-to-expression neural network that uses codons to reliably predict highly expressed transcripts. The kingdom Fungi, with over a million species, includes many key players in various ecosystems and good targets for biotechnology. Collectively, our results have implications for better understanding the evolutionary success of fungi, as well as informing the biosynthetic manipulation of fungal genes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Constraining spectroscopic factors near the r -process path using combined measurements: Kr 86 ( d , p ) 87 Kr

Background: Current nuclear structure models indicate that the shell structure near the valley of stability, with well-established shell closures at N=50, for example, changes in very neutron-rich nuclei far from stability. Single-particle properties of nuclei away from stability can be probed in single-neutron (d,p) transfer reactions with beams of rare isotopes. The interpretation of these data requires reaction theories with various effective interactions. Often, approximations made to the final bound-state potential introduce a large uncertainty in the extracted single-particle properties, in particular the spectroscopic factor. Purpose: Mitigate this uncertainty using a combined measurement method to constrain the shape of the bound-state potential and to reliably extract the spectroscopic factor. Methods: The 2 H( 86 Kr,p) 87 Kr reaction was measured at 33 MeV/u at the National Superconducting Cyclotron Laboratory (NSCL) as a test of the combined method. The reaction protons were detected with the Oak Ridge Rutgers University Barrel Array (ORRUBA) of position sensitive silicon strip detectors, the first implementation of ORRUBA coupled to the S800 spectrograph with fast beams at NSCL. Results: These measurements at 33 MeV/u are combined with previous studies of the 86 Kr(d,p) reaction at 5.5 MeV/u to demonstrate a successful case of the combined method to constrain the shape of the single-particle potential and deduce asymptotic normalization coefficients and spectroscopic factors. In particular, the single-particle asymptotic normalization coefficient for the ground state of 87 Kr was constrained to b d5/2 =6.46$^{+1.12}_{–0.57}$ fm –1/2 , and therefore the deduced spectroscopic factor is S=0.44$^{+0.09}_{–0.13}$ with uncertainties dominated by experimental statistics. Conclusions: By combining measurements at two very different beam energies, single-particle asymptotic normalization coefficients, at least for low angular momentum transfers, can be constrained. Thus, spectroscopic factors can be deduced with uncertainties dominated by experimental uncertainties, rather than limited knowledge of bound-state potential parameters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Hiperclust

This software leverages transfer learning to analyze atom probe tomography (APT) data. It is trained on synthetic data and then applies this knowledge to predict the optimal number of clusters for a given APT dataset. Initially, the software used preliminary clustering to estimate the general structure of the data. Based on this, it provides suggestions for key parameters like minimum cluster size and minimum number of points. These parameters are critical for algorithms like HDBSCAN, ensuring accurate cluster formation without the need for trial-and-error testing. The software runs on High-Performance computing (HPC) systems, enabling fast, scalable analysis of large APT datasets, ultimately saving time and improving the reliability of clustering outcomes.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗

NASA NTRS1947

The Calculation of the Heat Required for Wing Thermal Ice Prevention in Specified Icing Conditions

Flight tests were made in natural icing conditions with two 8-ft-chord heated airfoils of different sections. Measurements of meteorological variables conducive to ice formation were made simultaneously with the procurement of airfoil thermal data. The extent of knowledge on the meteorology of icing, the impingement of water drops on airfoil surfaces, and the processes of heat transfer and evaporation from a wetted airfoil surface have been increased to a point where the design of heated wings on a fundamental, wet-air basis now can be undertaken with reasonable certainty.

Bergrun, Norman R.↗

NASA NTRS2003

The NASA Materials Science Research Program - It's New Strategic Goals and Plans

In 2001, the NASA created a separate science enterprise, the Office of Biological and Physical Research (OBPR), to perform strategical and fundamental research bringing together physics, chemistry, biology, and engineering to solve problems needed for future agency mission goals. The Materials Science Program is one of basic research disciplines within this new Enterprise's Division of Physical Sciences Research. The Materials Science Program participates to utilize effective use of International Space Station (ISS) experimental facilities, target new scientific and technology questions, and transfer results for Earth benefits. The program has recently pursued new investigative research in areas necessary to expand NASA knowledge base for exploration of the universe, some of which will need access to the microgravity of space. The program has a wide variety of traditional ground and flight based research related types of basic science related to materials crystallization, fundamental processing, and properties characterization in order to obtain basic understanding of various phenomena effects and relationships to the structures, processing, and properties of materials. A summary of the types and sources for this research is presented and those experiments planned for the space. Areas to help expand the science basis for NASA future missions are described. An overview of the program is given including the scope of the current and future NASA Research Announcements with emphasis on new materials science initiatives. A description of the planned flight experiments to be conducted on the International Space Station program along with the planned facility class Materials Science Research Rack (MSRR) and Microgravity Glovebox (MSG) type investigations.

Schlagheck, Ronald A.↗

NASA NTRS2015

Cryogenic Propellant Storage and Transfer Engineering Development Unit Hydrogen Tank

The Cryogenic Propellant Storage and Transfer (CPST) project has been a long-running program in the Space Technology Mission Directorate to enhance the knowledge and technology related to handling cryogenic propellants, specifically liquid hydrogen. This particular effort, the CPST engineering development unit (EDU), was a proof of manufacturability effort in support of a flight article. The EDU was built to find and overcome issues related to manufacturability and collect data to anchor the thermal models for use on the flight design.

Werkheiser, Arthur↗

DOE OSTI2018

Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder

Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a transfer learning based deep network framework for calculating virtual battery state of a given ensemble of flexible thermostatic loads, from the available end-use measurements. This proposed framework extracts first order virtual battery model parameters for the given ensemble. We illustrate the effectiveness of this novel framework on different ensembles of ACs and WHs.

Stacked Autoencoder, Convolution Neural Network, v↗