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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.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Evolving Energy Efficient Convolutional Neural Networks

As deep neural networks have been deployed in more and more applications over the past half decade and are finding their way into an ever increasing number of operational systems, their energy consumption becomes a concern whether running in the datacenter or on edge devices. Hyperparameter optimization and automated network design for deep learning is a quickly growing field, but much of the focus has remained only on optimizing for the performance of the machine learning task. In this work, we demonstrate that the best performing networks created through this automated network design process have radically different computational characteristics (e.g. energy usage, model size, inference time), presenting the opportunity to utilize this optimization process to make deep learning networks more energy efficient and deployable to smaller devices. Optimizing for these computational characteristics is critical as the number of applications of deep learning continues to expand.

Young, Steven↗
NASA NTRS2012

NASA's Next Generation Space Geodesy Program

Requirements for the ITRF have increased dramatically since the 1980s. The most stringent requirement comes from critical sea level monitoring programs: a global accuracy of 1.0 mm, and 0.1mm/yr stability, a factor of 10 to 20 beyond current capability. Other requirements for the ITRF coming from ice mass change, ground motion, and mass transport studies are similar. Current and future satellite missions will have ever-increasing measurement capability and will lead to increasingly sophisticated models of these and other changes in the Earth system. Ground space geodesy networks with enhanced measurement capability will be essential to meeting the ITRF requirements and properly interpreting the satellite data. These networks must be globally distributed and built for longevity, to provide the robust data necessary to generate improved models for proper interpretation of the observed geophysical signals. NASA has embarked on a Space Geodesy Program with a long-range goal to build, deploy and operate a next generation NASA Space Geodetic Network (SGN). The plan is to build integrated, multi-technique next-generation space geodetic observing systems as the core contribution to a global network designed to produce the higher quality data required to maintain the Terrestrial Reference Frame and provide information essential for fully realizing the measurement potential of the current and coming generation of Earth Observing spacecraft. Phase 1 of this project has been funded to (1) Establish and demonstrate a next-generation prototype integrated Space Geodetic Station at Goddard's Geophysical and Astronomical Observatory (GGAO), including next-generation SLR and VLBI systems along with modern GNSS and DORIS; (2) Complete ongoing Network Design Studies that describe the appropriate number and distribution of next-generation Space Geodetic Stations for an improved global network; (3) Upgrade analysis capability to handle the next-generation data; (4) Implement a modern survey system to measure inter-technique vectors for co-location; and (5) Develop an Implementation Plan to build, deploy and operate a next-generation integrated NASA SGN that will serve as NASA's contribution to the international global geodetic network. An envisioned Phase 2 (which is not currently funded) would include the replication of up to ten such stations to be deployed either as integrated units or as a complement to already in-place components provided by other organizations. This talk will give an update on the activities underway and the plans for completion.

Merkowitz, S. M.↗
NASA NTRS2012

NASA's Next Generation Space Geodesy Program

Requirements for the ITRF have increased dramatically since the 1980s. The most stringent requirement comes from critical sea level monitoring programs: a global accuracy of 1.0 mm, and 0.1mm/yr stability, a factor of 10 to 20 beyond current capability. Other requirements for the ITRF coming from ice mass change, ground motion, and mass transport studies are similar. Current and future satellite missions will have ever-increasing measurement capability and will lead to increasingly sophisticated models of these and other changes in the Earth system. Ground space geodesy networks with enhanced measurement capability will be essential to meeting the ITRF requirements and properly interpreting the satellite data. These networks must be globally distributed and built for longevity, to provide the robust data necessary to generate improved models for proper interpretation of the observed geophysical signals. NASA has embarked on a Space Geodesy Program with a long-range goal to build, deploy and operate a next generation NASA Space Geodetic Network (SGN). The plan is to build integrated, multi-technique next-generation space geodetic observing systems as the core contribution to a global network designed to produce the higher quality data required to maintain the Terrestrial Reference Frame and provide information essential for fully realizing the measurement potential of the current and coming generation of Earth Observing spacecraft. Phase 1 of this project has been funded to (1) Establish and demonstrate a next-generation prototype integrated Space Geodetic Station at Goddard s Geophysical and Astronomical Observatory (GGAO), including next-generation SLR and VLBI systems along with modern GNSS and DORIS; (2) Complete ongoing Network Design Studies that describe the appropriate number and distribution of next-generation Space Geodetic Stations for an improved global network; (3) Upgrade analysis capability to handle the next-generation data; (4) Implement a modern survey system to measure inter-technique vectors for co-location; and (5) Develop an Implementation Plan to build, deploy and operate a next-generation integrated NASA SGN that will serve as NASA s contribution to the international global geodetic network. An envisioned Phase 2 (which is not currently funded) would include the replication of up to ten such stations to be deployed either as integrated units or as a complement to already in-place components provided by other organizations. This talk will give an update on the activities underway and the plans for completion.

Pearlman, M. R.↗
DOE OSTI2019

Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression

Surface barriers are normally installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We further added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We discovered that uMLR, combined with robust decision-making, provides a simple, flexible, and high-quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.

58 GEOSCIENCES↗
DOE OSTI2024

Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Uncrewed Underwater Vehicles (UUVs) are a major beneficiary of the phase change material (PCM)-based ocean thermal energy harvesting technology for their mission needs. However, this technology relies on different parameters and energy conversion steps that could be critical to the general energy generation efficiency. Sea trials showed that the design performed lower than their laboratory design specifications. This underperformance results from different factors, mainly the UUV’s trajectory, travel time, underwater ocean currents, temperature fluctuations, and biofouling on the heat exchanger due to long term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance, for mission planning purposes. Two major parameters influencing the energy harvesting system include the final pressure inside the hydraulic energy storage vessel or accumulator, and the electrical load value. Here, this work focuses on the hydraulic to electric energy conversion system. Therefore, a combination of numerical model and experimental testing is used to develop a predictive model using artificial neural network using MATLAB. After validation with experimental testing, 1000 data samples obtained from the numerical model are used to train the ANN. Compared to the experimental results, the developed ANN model can predict in less than a second the designed benchtop system’s total efficiency with less than 15 percent maximum error range. This predictive model development represents a cost-effective way for optimization and a computational energy efficient mode aboard UUVs for mission planning for deployed UUVs using PCM-based ocean thermal energy harvesting technology.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1988

Electrooptical adaptive switching network for the hypercube computer

An all-optical network design for the hyperswitch network using regular free-space interconnects between electronic processor nodes is presented. The adaptive routing model used is described, and an adaptive routing control example is presented. The design demonstrates that existing electrooptical techniques are sufficient for implementing efficient parallel architectures without the need for more complex means of implementing arbitrary interconnection schemes. The electrooptical hyperswitch network significantly improves the communication performance of the hypercube computer.

Chow, E.↗
NASA NTRS2012

NASA's Next Generation Space Geodesy Network

NASA's Space Geodesy Project (SGP) is developing a prototype core site for a next generation Space Geodetic Network (SGN). Each of the sites in this planned network co-locate current state-of-the-art stations from all four space geodetic observing systems, GNSS, SLR, VLBI, and DORIS, with the goal of achieving modern requirements for the International Terrestrial Reference Frame (ITRF). In particular, the driving ITRF requirements for this network are 1.0 mm in accuracy and 0.1 mm/yr in stability, a factor of 10-20 beyond current capabilities. Development of the prototype core site, located at NASA's Geophysical and Astronomical Observatory at the Goddard Space Flight Center, started in 2011 and will be completed by the end of 2013. In January 2012, two operational GNSS stations, GODS and GOON, were established at the prototype site within 100 m of each other. Both stations are being proposed for inclusion into the IGS network. In addition, work is underway for the inclusion of next generation SLR and VLBI stations along with a modern DORIS station. An automated survey system is being developed to measure inter-technique vectorties, and network design studies are being performed to define the appropriate number and distribution of these next generation space geodetic core sites that are required to achieve the driving ITRF requirements. We present the status of this prototype next generation space geodetic core site, results from the analysis of data from the established geodetic stations, and results from the ongoing network design studies.

Desai, S. D.↗
DOE OSTI2022

EVI-Equity

EVI-Equity (Electric Vehicle Infrastructure for Equity) is a $200k project, started around in June of 2021, with a funding from the Vehicle Technologies Office (VTO). The motivation was to create a new analytical capability that can enable us to quantify and investigate equitable access to and distribution of existing and future deployment of PEVs and EVSEs in neighborhoods, cities, states, and the nation. EVI-Equity is a bottom-up equity-focused analysis model, built upon individual (synthetic) households, aggregated by census block groups. It consists of four core components - community engagement, environmental profiling, household expenditures, and network design. Although there are some commonalities, EVI-Equity is not a vehicle choice model, charging simulation model, or transportation demand model. EVI-Equity is rather a cross-cutting analysis tool, dedicated for evaluating equitable EV adoption and EVSE deployment, encompassing and bridging a wide variety of related tools, models, and frameworks. Some of the results indicate the importance of used vehicle market for low-income households. The presentation also highlights similarities and variations as to preferred public charging locations. For example, regardless of household income, retail spots are the most preferred location for public charging, followed by curbside/street. However, the results also imply that the importance of workplace charging may vary with income - the lower the income, the less important. Environmental profiling results, with an example of ground-level ozone in Atlanta area, show that the contrast between the haves and the have nots of plug-in electric vehicles depends on location. The assessment of household expenditures illustrates the economic impact of home charging access on an individual household level - the lower the income, the greater the impact is. Lastly, Denver metro area and the state of South Dakota are used to showcase the impact of different network design of charging infrastructure, as well as alternative (vs. baseline/existing) electric vehicle adoption pattern.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
NASA NTRS1998

Neural Networks for Rapid Design and Analysis

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Sparks, Dean W., Jr.↗
NASA NTRS1993

Using satellite communications for a mobile computer network

The topics discussed include the following: patrol car automation, mobile computer network, network requirements, network design overview, MCN mobile network software, MCN hub operation, mobile satellite software, hub satellite software, the benefits of patrol car automation, the benefits of satellite mobile computing, and national law enforcement satellite.

Wyman, Douglas J.↗
NASA NTRS1995

An executable specification for the message processor in a simple combining network

While the primary function of the network in a parallel computer is to communicate data between processors, it is often useful if the network can also perform rudimentary calculations. That is, some simple processing ability in the network itself, particularly for performing parallel prefix computations, can reduce both the volume of data being communicated and the computational load on the processors proper. Unfortunately, typical implementations of such networks require a large fraction of the hardware budget, and so combining networks are viewed as being impractical. The FFP Machine has such a combining network, and various characteristics of the machine allow a good deal of simplification in the network design. Despite being simple in construction however, the network relies on many subtle details to work correctly. This paper describes an executable model of the network which will serve several purposes. It provides a complete and detailed description of the network which can substantiate its ability to support necessary functions. It provides an environment in which algorithms to be run on the network can be designed and debugged more easily than they would on physical hardware. Finally, it provides the foundation for exploring the design of the message receiving facility which connects the network to the individual processors.

Middleton, David↗