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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 739 records · Page 41

Using the Chandra Project to Communicate with Underdeveloped Constituencies

We have instituted a program whose goal is to better communicate astronomy among students selected from an economically underprivileged area of Huntsville, Alabama. To achieve this goal, six local teachers were trained as astronomy teacher resource agents. These teachers were teamed with six scientists; the teams presented materials for 29 teachers, mostly from the Southeastern United States Topics included the electromagnetic spectrum, X-ray optics, gratings and spectroscopy, supernovae (and remnants), black holes, and the tools and techniques to analyze Chandra X-ray data. Beginning in September our teacher-scientist teams, in collaboration with the Huntsville Housting Authority, will present programs to specially selected students at the rate of one or two per month, until April 2004. These young people will be mentored and encouraged to develop a research project. Students, which complete a project by April 2, will be paid for their work.

Adams, M. L.↗

A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

feedstock↗

Measure Guideline: Energy-Efficient Window Performance and Selection

This document provides guidelines for the selection of energy-efficient windows in new and existing residential construction in all US climate zones. It includes information on window products, their attributes and performance. It provides cost/benefit information on window energy savings as well as information on non-energy benefits such as thermal comfort and reduced HVAC demands. The document also provides information on energy impacts of design decisions such as window orientation, total glazing area and shading devices and conditions. Information on resources for proper window installation is included as well. This document is for builders, homeowners, designers and anyone making decisions about selecting energy efficient window. It is intended to complement other Building America information and efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Generic and Multi-Functional Electromagnetic Transient Model for Grid-Following Inverter: Preprint

This article presents a generic and multi-functional electromagnetic transient (EMT) dynamic model of grid following (GFL) inverter-based resource (IBR) using the PSCAD TM software platform. The features of the developed model includes the flexibility in selecting various types and combinations of DC sources covering PV modules, battery modules, ideal DC source module, as well as flexibility in selecting either switched or averaged model of inverter. This model also covers exhaustive lists of controller logic covering open-loop/closed-loop PQ dispatch control, DC voltage and AC terminal voltage control along with the conventional current control designed in dq-domain, aB- domain and positive-negative sequence domain. Moreover, this model is equipped with the flexibility in selecting various types of current limiting schemes that includes saturation-based as well as latching-based current limiter, anti-windup protection. Moreover, the EMT model is agnostic to the MVA rating and is suitable for interfacing transmission systems by being complaint with the IEEE Std. 2800. The generality in the power circuits and the multi-functional options in operation and control of the developed EMT model makes it suitable for both academia and industry to study various power system aspects not limited to but such as fault behavior of GFL IBR and impacts on protection system, transient stability of a system interfaced with large number of GFL IBRs etc.

grid following inverter↗

Research on Selective Oxidation of Carbon and Aluminum with Introduction of CO{sub 2} in RH Refining of Low-Carbon Steel Process

The injection of CO{sub 2} during the Rheinstahl–Heraeus (RH) refining process as a lifting gas is a new attempt for the steel industry, and can promote refining effect and realize the utilization of CO{sub 2} as a resource. In the present study, the thermodynamic equilibrium of the RH refining process through CO{sub 2} injection instead of Ar was calculated using FactSage software. A selective oxidation sequence of [C] and [Al] with CO{sub 2} was studied and analyzed under the RH refining temperature and vacuum degree. In addition, the oxidation zones of carbon and aluminum were both defined. Industrial trials were preliminarily conducted to verify the above theory. The results show that CO{sub 2} has the potential to be used to refine low-carbon steel in RH, but its refining effect is affected by the [Al] content in steel. By reducing the additive amount of aluminum alloy in the ladle furnace and replenishing the aluminum during the late stage of the RH refining process, CO{sub 2} injection can achieve a lower temperature drop of molten steel and a better refining effect than Ar during the RH degassing process.

36 MATERIALS SCIENCE↗

Investigation using data from ERTS to develop and implement utilization of living marine resources

The author has identified the following significant results. A feasibility study to demonstrate the potential of satellites for providing fisheries significant information was conducted in the Mississippi Sound and adjacent offshore waters. Attempts were made to relate satellite acquired imagery to selected oceanographic parameters and then to relate these parameters to aircraft remotely sensed distribution patterns of resident surface schooling fishes. Initial results suggest that this approach is valid and that the satellite acquired imagery may have important fisheries resource assessment implications.

Stevenson, W. H.↗

An Indicator-based Approach to Sustainable Management of Natural Resources (Chapter 12)

Assessing the sustainability of natural resource management choices for agricultural and forest lands requires quantification of potential changes to a set of environmental and socioeconomic indicators selected to characterize reference scenarios relative to projected future scenarios. Correctly framing the questions with local stakeholders is a critical first step in the sustainability assessment, and the questions that can be addressed are often limited by data availability. Selecting and prioritizing indicators with stakeholders to address their needs and concerns improves the likelihood of investment in monitoring and evaluation of those indicators over time. Computational techniques for analyzing interactions between the selected indicators are inherently affected by the scales and formats of the assembled indicator datasets. Data analytics have the potential to improve understanding of the potential synergies and tradeoffs involved with meeting multiple environmental and socioeconomic goals simultaneously, but timely and appropriate indicator datasets are not always available—even in this new era of “big data.” Continued improvements in data science and data analytics are needed to broaden understanding and acceptance of problems and to provide valuable information for natural resource management. Advances in these areas will enable society to design future landscapes that meet multiple objectives, including the provisioning of agricultural and forest resources along with a variety of ecosystem services (e.g., clean water and healthy soils).

Parish, Esther↗

Techno-Economic Analysis of Integration of Low-Temperature Geothermal Resources for Coal-Fired Power Plants

Presented here are the results of a techno-economic (TEA) study of the potential for coupling low-grade geothermal resources to boost the electrical output from coal-fired power plants. This study includes identification of candidate 500 MW subcritical coal-fired power plants in the continental United States, followed by down-selection and characterization of the North Valmy generating station, a Nevada coal-fired plant. Based on site and plant characteristics, ASPEN Plus models were designed to evaluate options to integrate geothermal resources directly into existing processes at North Valmy. Energy outputs and capital costing are presented for numerous hybrid strategies, including integration with Organic Rankine Cycles (ORCs), which currently represent the primary technology for baseload geothermal power generation.

20 FOSSIL-FUELED POWER PLANTS↗

A Lunar Surface System Supportability Technology Development Roadmap

This paper discusses the establishment of a Supportability Technology Development Roadmap as a guide for developing capabilities intended to allow NASA's Constellation program to enable a supportable, sustainable and affordable exploration of the Moon and Mars. Presented is a discussion of "supportability", in terms of space facility maintenance, repair and related logistics and a comparison of how lunar outpost supportability differs from the International Space Station. Supportability lessons learned from NASA and Department of Defense experience and their impact on a future lunar outpost is discussed. A supportability concept for future missions to the Moon and Mars that involves a transition from a highly logistics dependent to a logistically independent operation is discussed. Lunar outpost supportability capability needs are summarized and a supportability technology development strategy is established. The resulting Lunar Surface Systems Supportability Strategy defines general criteria that will be used to select technologies that will enable future flight crews to act effectively to respond to problems and exploit opportunities in a environment of extreme resource scarcity and isolation. This strategy also introduces the concept of exploiting flight hardware as a supportability resource. The technology roadmap involves development of three mutually supporting technology categories, Diagnostics Test & Verification, Maintenance & Repair, and Scavenging & Recycling. The technology roadmap establishes two distinct technology types, "Embedded" and "Process" technologies, with different implementation and thus different criteria and development approaches. The supportability technology roadmap addresses the technology readiness level, and estimated development schedule for technology groups that includes down-selection decision gates that correlate with the lunar program milestones. The resulting supportability technology roadmap is intended to develop a set of technologies with widest possible capability and utility with a minimum impact on crew time and training and remain within the time and cost constraints of the Constellation program

Oeftering, Richard C.↗

Robust Network Interdiction Considering Multiple Defense Resources

We extend the network interdiction problem in order to consider multiple options for defending arcs in a network. We apply our model to select an optimal set of countermeasures for nuclear material smuggling. In order account for the joint impact of multiple countermeasures, we consider multiple components of defense: situational awareness, detection, and interdiction. We develop a bi-level programming model, where the adversary's goal is to maximize damage to the system while the defender seeks to minimize damage by selecting countermeasure options under a restricted bud- get. Furthermore, since effectiveness of countermeasures is often difficult to assess with high accuracy, we use robust optimization to allow for parametric uncertainty. We test our method on a synthetic example of the U.S. supply chain network that includes rail, air, and maritime transportation between both domestic and international locations. For this test example, we find that increasing the budget for countermeasures has the greatest impact within the first 1-2% of the cost to implement all countermeasures and that the uncertainty tolerance has a greater impact on expected damage than the countermeasure budget does. Thus, when strategically selecting countermeasures it is important to precisely understand the effectiveness of each option and the adversary's probability of subverting them.

97 MATHEMATICS AND COMPUTING↗

A Lunar Surface System Supportability Technology Development Roadmap

This paper discusses the establishment of a Supportability Technology Development Roadmap as a guide for developing capabilities intended to allow NASA s Constellation program to enable a supportable, sustainable and affordable exploration of the Moon and Mars. Presented is a discussion of supportability, in terms of space facility maintenance, repair and related logistics and a comparison of how lunar outpost supportability differs from the International Space Station. Supportability lessons learned from NASA and Department of Defense experience and their impact on a future lunar outpost is discussed. A supportability concept for future missions to the Moon and Mars that involves a transition from a highly logistics dependent to a logistically independent operation is discussed. Lunar outpost supportability capability needs are summarized and a supportability technology development strategy is established. The resulting Lunar Surface Systems Supportability Strategy defines general criteria that will be used to select technologies that will enable future flight crews to act effectively to respond to problems and exploit opportunities in an environment of extreme resource scarcity and isolation. This strategy also introduces the concept of exploiting flight hardware as a supportability resource. The technology roadmap involves development of three mutually supporting technology categories, Diagnostics Test and Verification, Maintenance and Repair, and Scavenging and Recycling. The technology roadmap establishes two distinct technology types, "Embedded" and "Process" technologies, with different implementation and thus different criteria and development approaches. The supportability technology roadmap addresses the technology readiness level, and estimated development schedule for technology groups that includes down-selection decision gates that correlate with the lunar program milestones. The resulting supportability technology roadmap is intended to develop a set of technologies with widest possible capability and utility with a minimum impact on crew time and training and remain within the time and cost constraints of the Constellation program.

Oeftering, Richard C.↗

Above‐T g Annealing Benefits in Nanoparticle‐Stabilized Carbon Molecular Sieve Membrane Pyrolysis for Improved Gas Separation

Nanoparticles can suppress asymmetric precursor support collapse during pyrolysis to create carbon molecular sieve (CMS) membranes. This advance allows elimination of standard sol-gel support stabilization steps. Here we report a simple but surprisingly important thermal soaking step at 400 °C in the pyrolysis process to obtain high performance CMS membranes. The composite CMS membranes show CO 2 /CH 4 (50 : 50) mixed gas feed with an attractive CO 2 /CH 4 selectivity of 134.2 and CO 2 permeance of 71 GPU at 35 °C. Furthermore, a H 2 /CH 4 selectivity of 663 with H 2 permeance of 240 GPU was achieved for promising green energy resource-H 2 separation processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

How to estimate soil organic carbon stocks of agricultural fields? perspectives using ex-ante evaluation

Estimating soil organic carbon (SOC) stocks of agricultural fields has a range of important applications from development of sustainable management practices to monitoring carbon stocks. There are many estimation strategies with the potential for more reliable estimates of SOC stock and more efficient use of soil sampling and analysis resources, especially by leveraging readily available auxiliary information such as remote sensing. However, concrete guidance for strategy selection is lacking. This study narrows this gap with a comparison of strategies for estimating deep SOC stock (0–60 cm) in a prototypical field. Using high density SOC stock measurements and simulation, we built on past studies by 1) ex-ante evaluating a large number of strategy options, 2) using a Bayesian approach to quantify the uncertainty of the comparison, and 3) considering multiple Bayesian models to assess sensitivity to this modeling choice. We found that, using readily available auxiliary information, both balanced and stratified sampling offer substantial improvements over simple random sampling. The auxiliary information most important for this improvement is a Sentinel-2 SOC index = blue / (green × red), followed by the topographic wetness index. We found that these results are robust to the choice of mapping method, but that there is uncertainty in the magnitude of improvement. Here, we recommend future studies implement this Bayesian approach for simulated ex-ante evaluation of SOC stock estimation strategies across more fields to investigate the generalizability of these findings.

54 ENVIRONMENTAL SCIENCES↗

Carbon fibers derived from liquefied and fractionated poplar lignins: The effect of molecular weight

Lignin recovered from poplar, a key woody biomass resource proposed for lignocellulosic refineries, was investigated for conversion into carbon fibers. Aqueous solutions of ethanol at selected temperatures and compositions, where the requisite solvent and liquefied-lignin phases form, were used to purify and fractionate hybrid poplar (HP) lignin using the Aqueous Lignin Purification with Hot Agents (ALPHA) process. Sugars (<0.1%) and ash (0.01 ± 0.01%) content of the highest molecular weight fraction (Mw = 52 kDa) approached the limits of detection. This 52 kDa fraction could be dry spun with excellent stability and was subsequently converted into carbon fibers having strength (1.1 ± 0.2 GPa) and modulus (78 ± 8 GPa) essentially double those previously achieved with neat poplar and other hardwood lignins. Of equal importance, stabilization times were decreased by a factor of five. Such stabilization and strength/modulus improvements are essential for reducing the overall manufacturing cost of carbon fibers being proposed for cost-sensitive applications.

09 BIOMASS FUELS↗

Data-Driven Approaches for Bead Geometry Prediction Via Melt Pool Monitoring

In the realm of additive manufacturing, the selection of process parameters to avoid over and under deposition entails a time-consuming and resource-intensive trial-and-error approach. Given the distinct characteristics of each part geometry, there is a pressing need for advancing real-time process monitoring and control to ensure consistent and reliable part dimensional accuracy. Here, this research shows that support vector regression (SVR) and convolutional neural network (CNN) models offer a promising solution for real-time process control due to the models’ abilities to recognize complex, non-linear patterns with high accuracy. A novel experiment was designed to compare the performance of SVR and CNN models to indirectly detect bead height from a coaxial image of a melt pool from a single-layer, single bead build. The study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively.

36 MATERIALS SCIENCE↗

Developing County-level Water Footprints of Biofuel Produced from Switchgrass and Miscanthus x Giganteus in the United States

Perennial grass has been proposed as a potential candidate for producing cellulosic biofuel because of its promising productivity and benefits to water quality, and because it is a non-food feedstock. While extensive research focuses on selecting and developing species and conversion technologies, the impact of grass-based biofuel production on water resources remains less clear. As feedstock growth requires water and the type of water consumed may vary considerably from region to region, water use must be characterized with spatial resolution and on a fuel production basis. This report summarizes a study that assesses the impact of biofuel production on water resource use and water quality at county, state, and regional scales by developing a water footprint of biofuel produced from switchgrass and Miscanthus × giganteus via biochemical conversion.

09 BIOMASS FUELS↗

Play Fairway Analysis: Structurally Controlled Geothermal Systems in the Eastern Great Basin Extensional Regime, Utah

A research team with membership from the University of Utah/Energy & Geoscience Institute, the University of Utah/Dept. of Geology & Geophysics, and the Utah Geological Survey, undertook a play fairway analysis (PFA) for geothermal resources in the Eastern Great Basin (EGB) extensional tectonic regime of western Utah. This is a high-priority region for geothermal exploration because active Basin and Range (B&R) extension with volcanism having a N-S strike is superimposed upon pre-existing E-W belts of plutonic rocks and large-scale structural lineaments. Cumulative heat flow along the N-S strike of the state totals approximately 5 GWt above background stable interior. Three electricity producing power plants currently exist with substantial potential for increase. Succinctly, our PFA approach aims to resolve potential sources of heat and permeability in the region, which are the two principal criteria for establishing a geothermal resource. An initial Phase 1 was carried out using only existing geoscientific data in the area. Criteria selected for focusing heat potential include direct heat flow measurements in boreholes, magnetotelluric (MT) low resistivity anomalies, fluid/gas geochemistry, and proximity to recent volcanic eruptions. Permeability is established through geological structures (fault density, critically stressed areas, seismicity, gravity), and MT low resistivity anomalies. In Phase II of this PFA project, additional geological, geophysical and geochemical data were acquired and analysis carried out primarily over promising composite common risk (CCR) areas initially identified in Phase I in order to focus prospectivity and prepare for drilling recommendations. These prospects are near the Twin Peaks rhyolite field, high heat flow areas north of the producing Cove Fort system, and geophysical structure beneath the Crater Knoll area off the northeast flank of the Mineral Mountains. Additional data included MT site fill-in, structural mapping and analysis using high-resolution imagery, gravity and on-ground mapping, Nodal 3C passive seismic collection, and passive 3He surveying. Heat source and permeability potential are again expressed in terms of their individual common risk segment (CRS) maps, with a color scheme using green for most favorable (low risk) and red for least favorable (high risk). Diverse data types are united through the technique of probability kriging, which establishes prospectivity thresholds for each data type and then computes probability of exceeding that threshold over the PFA area. Modified CRS and CCRS maps are compared to those of Phase I to highlight tighter prospectivity focus. In doing so, the prospectivity threshold for heat was increased significantly to narrow the targeting. In the final Phase III of this project where a recommended deep thermal gradient hole was sited, additional geophysical, geochemical and geological field collection and analysis was carried out to refine drill hole targeting. This includes prospect-scale MT, gravity, structure, passive seismic deployment (Cove Fort area), and detailed 3He isotope profiling. It was the recommendation of the DOE Technical Monitoring Team (TMT) that one or more holes be sited in the north-ern Cove Fort area where legacy TG gradient holes showed high cumulative heat flow. These were to be of moderate depth, 2000-3000 feet, to reach the geothermal fluid table expected to start in excess of 1000 feet depth. The drilling organization stipulated by the DOE/GTO was that of the USGS Research Drilling Program (RDP) centered in Las Vegas, NV. A detailed well plan, appended to this report, was developed principally by Dr. Ben Barker consulting to University of Utah, Dr. Steve Pye on the DOE TMT, Mr. Steven Crawford of the USGS-RDP, and the project PI Phil Wannamaker. However, temperature and possible H 2 S at the systems lead to cancellation of the drilling last-minute as this appeared outside the experience base of the USGS-RDP. We hope to have the opportunity to revisit the test drilling and expand the Play Fairway Analysis of this region at some point in the future.

15 GEOTHERMAL ENERGY↗

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale that has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify highvalue data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies.

58 GEOSCIENCES↗