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NASA NTRS1994

Regional climates in the GISS general circulation model: Surface air temperature

One of the more viable research techniques into global climate change for the purpose of understanding the consequent environmental impacts is based on the use of general circulation models (GCMs). However, GCMs are currently unable to reliably predict the regional climate change resulting from global warming, and it is at the regional scale that predictions are required for understanding human and environmental responses. Regional climates in the extratropics are in large part governed by the synoptic-scale circulation and the feasibility of using this interscale relationship is explored to provide a way of moving to grid cell and sub-grid cell scales in the model. The relationships between the daily circulation systems and surface air temperature for points across the continental United States are first developed in a quantitative form using a multivariate index based on principal components analysis (PCA) of the surface circulation. These relationships are then validated by predicting daily temperature using observed circulation and comparing the predicted values with the observed temperatures. The relationships predict surface temperature accurately over the major portion of the country in winter, and for half the country in summer. These relationships are then applied to the surface synoptic circulation of the Goddard Institute for Space Studies (GISS) GCM control run, and a set of surface grid cell temperatures are generated. These temperatures, based on the larger-scale validated circulation, may now be used with greater confidence at the regional scale. The generated temperatures are compared to those of the model and show that the model has regional errors of up to 10 C in individual grid cells.

Hewitson, Bruce↗

DOE OSTI2022

Smart Contract Architectures and Templates for Blockchain-based Energy Markets (V.1.0)

Within the field of Transactive Energy Systems (TES), there is an active need for tools that can support and accelerate the development of these new grid solutions. Among the many tools available, blockchain stands out as a viable instrument that can help researchers develop decentralized, autonomous, and tamper-resistant grid applications. In this work, we explore the use of smart contracts (SCs), a subset of blockchain technology, and analyze their applicability to facilitating the implementation of TES solutions. In particular, we focus on presenting areas of opportunity and potential drawbacks, along with use cases that can benefit from this technology building upon previous research developed by Pacific Northwest National Laboratory and other research organizations. This work builds upon the fundamentals of TES and smart contract technology to develop a series of software templates that can be used by industry to build TES-oriented grid solutions. These templates are intended to be platform agnostic and take into consideration the unique properties of SCs and distributed ledger storage mechanisms to ensure actual code implementations remain aware of the limitations of the technology. The proposed templates have the potential to enable software architects to mix and match components to satisfy their application requirements, thereby reducing the number of resources required to implement blockchain-based solutions. These templates are divided into two main components—data and behavioral models. The data models are intended to help software engineers represent the underlying grid objects along with their properties in a ledger-based storage system. The behavioral models are used to describe the processes and actions that actors within a system must perform to achieve a given outcome such as registering an asset, placing a bid, and performing bid clearances. These two components are documented in a Unified Modeling Language (UML) format and are intended for use in SC-based implementations, with special behavioral considerations to account for the asynchronous properties of the underlying ledger and the typical execution model of smart contracts. Finally, future research ideas and potential extensions to this work are discussed. In particular, known limitations and potential improvements of the developed product are identified and expected to be addressed in future revisions of the template model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2018

Generating Exploration Mission-3 Trajectories to a 9:2 NRHO using Machine Learning

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA's Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can bean abstract task and relies on an orbit analyst to structure a mission based on an established mission design methodology associated to the performance of Orion and NASA's Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions.

Guzman, Esteban↗

NASA NTRS1994

Flight experience with lightweight, low-power miniaturized instrumentation systems

Engineers at the NASA Dryden Flight Research Facility (NASA-Dryden) have conducted two flight research programs with lightweight, low-power miniaturized instrumentation systems built around commercial data loggers. One program quantified the performance of a radio-controlled model airplane. The other program was a laminar boundary-layer transition experiment on a manned sailplane. The purpose of this article is to report NASA-Dryden personnel's flight experience with the miniaturized instrumentation systems used on these two programs. This article will describe the data loggers, the sensors, and the hardware and software developed to complete the systems. It also describes how the systems were used and covers the challenges encountered to make them work. Examples of raw data and derived results will be shown as well. For some flight research applications where miniaturized instrumentation is a requirement, the authors conclude that commercially available data loggers and sensors are viable alternatives. In fact, the data loggers and sensors make it possible to gather research-quality data in a timely and cost-effective manner.

LOW POWER MINIATURIZED INSTRUM↗

DOE OSTI2025

Geologic hydrogen as an emerging fuel: experimental insights, thermodynamics, kinetics, and reactive transport modeling

Geologic hydrogen (GeoH 2 ) is emerging as a viable clean energy source. It is largely produced through serpentinization, a geological process in which ultramafic rocks react with water under suitable temperature and pressure. Here, this review synthesizes the current understanding of H 2 generation by serpentinization, with an emphasis on reaction mechanisms, kinetics, and thermodynamics, as well as on modeling flow and transport of reacting fluids in geological formations. We describe the role of mineral assemblages, such as olivine and pyroxene, fluid-rock interactions, and catalytic surfaces, in influencing GeoH 2 yield and reaction rates. By integrating models of reaction kinetics, subsurface reactive flow and transport, and the serpentinization process, and by accounting for the thermodynamic state of the system, this review aims to guide future GeoH 2 research and to evaluate the potential of natural hydrogen as a sustainable clean energy source.

Moradi, Rasoul [Univ. of Southern California, Los ↗

DOE OSTI2023

Evaluation of Nanaerobic Digestion as a Mechanism to Explain Surplus Methane Production in Animal Rumina and Engineered Digesters

Nanaerobes are a newly described class of microorganisms that use a unique cytochrome bd oxidase to achieve nanaerobic respiration at <2 μM dissolved oxygen (~1% of atmospheric oxygen) but are not viable above this value due to the lack of other terminal oxidases. Although sharing an overlapping ecological niche with methanogenic archaea, the role of nanaerobes in methanogenic systems has not been studied so far. To explore their occurrence and significance, we re-analyzed published meta-omic datasets from animal rumina and waste-to-energy digesters, including conventional anaerobic digesters and anaerobic digesters with ultra-low oxygenation. Results show that animal rumina share broad similarities in the microbial community and system performance with oxygenated digesters, rather than with conventional anaerobic digesters, implying that trace levels of oxygen drive the efficient digestion in ruminants. The rumen system serves as an ideal model for the newly named nanaerobic digestion, as it relies on the synergistic co-occurrence of nanaerobes and methanogens for methane yield enhancement. The most abundant ruminal bacterial family Prevotellaceae contains many nanaerobes, which perform not only anaerobic fermentation but also nanaerobic respiration using cytochrome bd oxidase. These nanaerobes generally accompany hydrogenotrophic methanogens to constitute a thermodynamically and physiologically consistent framework for efficient methane generation. Our findings provide new insights into ruminal methane emissions and strategies to enhance methane generation from biomass.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Validation of Fault Displacements from Dynamic Rupture Simulations against the Observations from the 1992 Landers Earthquake

ABSTRACT Coseismic fault displacements in large earthquakes have caused significant damage to structures and lifelines on and near fault lines. Coseismic displacements represent a real threat, especially to distributed infrastructure systems. For infrastructure systems that can not avoid active faults, engineering displacement demands are defined using probabilistic fault-displacement hazard analyses (PFDHA). However, PFDHA models are sparse and poorly constrained partly due to the scarcity of detailed fault-displacement observations. Advancements in dynamic rupture simulation methods make them an attractive approach to address this important issue. Because fault displacements can be simulated for various geologic conditions as constrained by current knowledge about earthquake processes, they can be used to supplement the observation datasets. In addition to providing on-fault displacements, when used with appropriate constitutive models for the bulk medium, they can capture off-fault distributed inelastic deformations as well. For viable extrapolation, simulations must first be validated against data. In this article, we summarize the calibration and validation of the dynamic rupture model against the observations of the well-documented 1992 Landers earthquake. We defined a preferred model that reproduces several first-order fault-displacement metrics such as the on-fault partition of the total displacement, the mean fault-zone width, and the location of the peak displacement. Simulated ground motions consistent with the observations ensure that all physics important to modeling have been properly parameterized. For the extrapolation, we generated a suite of dynamic rupture models to quantify expected fault-displacement metrics, their intercorrelations, and magnitude dependencies, which are in part supported by the Landers and other recent earthquakes. Our validation and extrapolation exercise paves the way for using dynamic rupture modeling to quantitatively address fault-displacement hazard on a broader scale. The results are promising and are expected to be useful to inform PFDHA model development.

Geochemistry & Geophysics↗

DOE OSTI2024

Developing and Evaluating a Smart Curtailment Strategy Integrated with a Wind Turbine Manufacturer Platform

The Renewable Energy Wildlife Institute lead a team of scientists, wind developers, and turbine manufacturers in a study to develop and test a “smart curtailment” system intended to help reduce bat collisions with wind turbines. The Vestas Bat Protection System (VBPS) is a newly developed software module within the Supervisory Control and Data Acquisition (SCADA) system of Vestas turbines. The VBPS combines data from commercially available environmental sensors and the turbine’s built-in sensors with the Vestas SCADA system. VBPS is designed to receive environmental data from sensors on the turbine such as temperature, wind speed, wind direction, time of day, and time of year, relays that information to the SCADA system to determine whether to execute turbine curtailments at any given time. The goals of this study were to 1) develop a bat fatality risk model based on bat activity data and environmental data collected in year 1, and to 2) evaluate the VBPS, using the bat fatality risk model to implement curtailment, in comparison to “blanket curtailment” (turbines curtailed when wind speed is below 5.0 meters per second (m/s)) and “control” (normally operating, feathered below 3.0 m/s) turbines in year 2. The field study took place at a wind energy facility in Iowa during the fall bat migration seasons (July – October) in 2021 and 2022. For VBPS to succeed as a viable strategy for the minimization of bat fatalities, it should meet or exceed the performance of blanket curtailment. Specifically, the VBPS should meet the following performance targets to demonstrate whether it an effective, practical risk reduction measure: (1) Turbines operating VBPS should have equal or fewer bat fatalities compared to turbines operating with blanket curtailment, and significantly fewer bat fatalities compared to control turbines; and (2) Turbines operating VBPS should have greater power production compared to turbines operating with blanket curtailment. The study was completed in accordance with the Statement of Project Objectives and within the terms of the Budget Justification. This Final Report describes the progress, challenges, and outcomes of the study.

17 WIND ENERGY↗

DOE OSTI2022

Persistent, extensive channelized drainage modeled beneath Thwaites Glacier, West Antarctica

Abstract. Subglacial hydrology is a leading control on basal friction and the dynamics of glaciers and ice sheets. At low discharge, subglacial water flows through high-pressure, sheet-like systems that lead to low effective pressures. However, at high discharge, subglacial water melts the overlying ice into localized channels that efficiently remove water from the bed, thereby increasing effective pressure and basal friction. Recent observations suggest channelized subglacial flow exists beneath Thwaites Glacier, yet it remains unclear if stable channelization is feasible in West Antarctica, where surface melting is nonexistent and water at the bed is limited. Here, we use the MPAS-Albany Land Ice model to run a suite of over 130 subglacial hydrology simulations of Thwaites Glacier across a wide range of physical parameter choices to assess the likelihood of channelization. We then narrow our range of viable simulations by comparing modeled water thicknesses to previously observed radar specularity content, which indicates flat, spatially extensive water bodies at the bed. In all of our data-compatible simulations, stable channels reliably form within 100–200 km of the grounding line and reach individual discharge rates of 35–110 m3 s−1 at the ice–ocean boundary. While only one to two channels typically form across the 200 km width of the glacier in our simulations, their high efficiency drains water across the entire lateral extent of the glacier. We posit the large catchment size of Thwaites Glacier, its funnel-like geometry, and high basal melt rates together accumulate enough water to form stable channels. No simulations resembled observed specularity content when channelization was disabled. Our results suggest channelized subglacial hydrology has two consequences for Thwaites Glacier dynamics: (i) amplifying submarine melting of the terminus and ice shelf while (ii) simultaneously raising effective pressure within 100 km of the grounding line and increasing basal friction. The distribution of effective pressure implied from our modeling differs from parameterizations typically used in large-scale ice sheet models, suggesting the development of more process-based parameterizations may be necessary.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Assessment of Models of Galactic Thermal Dust Emission Using COBE/FIRAS and COBE/DIRBE Observations

Accurate modeling of the spectrum of thermal dust emission at millimeter wavelengths is important for improving the accuracy of foreground subtraction for cosmic microwave background (CMB) measurements, for improving the accuracy with which the contributions of different foreground emission components can be determined, and for improving our understanding of dust composition and dust physics. We fit four models of dust emission to high Galactic latitude COBE/FIRAS and COBE/DIRBE observations from 3 mm to 100m and compare the quality of the fits. We consider the two-level systems (TLS) model because it provides a physically motivated explanation for the observed long wavelength flattening of the dust spectrum and the anti-correlation between emissivity index and dust temperature. We consider the model of Finkbeiner et al. because it has been widely used for CMB studies, and the generalized version of this model that was recently applied to Planck data by Meisner and Finkbeiner. For comparison we have also fit a phenomenological model consisting of the sum of two-graybody components. We find that the two-graybody model gives the best fit and the FDS model gives a significantly poorer fit than the othermodels. The Meisner and Finkbeiner model and the TLS model remain viable for use in Galactic foreground subtraction, but the FIRAS data do not have a sufficient signal-to-noise ratio to provide a strong test of the predicted spectrum at millimeter wavelengths.

Odegard, N.↗

DOE OSTI2024

Scalable and compact magnetocaloric heat pump technology

Magnetocaloric heat pumping (MCHP) promises to be more efficient than traditional vapor compression while also eliminating the deleterious effects of gaseous refrigerants. While MCHP devices have shown the temperature spans and efficiencies needed for different heating and cooling applications, they struggle to become commercially viable due to their large size and mass, and resultant high cost. This paper evaluates a baseline MCHP device and explores methods to boost its system power density (SPD). The key components of the baseline system are the gadolinium packed-particle bed active magnetic regenerator (AMR) and a magnetic source composed of permanent magnets and high permeability magnetic steel. To enhance the SPD, the paper evaluates maximizing the AMR volume, opting for first-order magnetocaloric materials, optimizing the magnet and AMR geometry, and reducing the size of magnets and magnetic steel parts. At larger thermal powers, increasing the AMR diameter and the number of magnetic poles were evaluated. Using finite element models, solid models, and estimates of magnetocaloric material performance, thermal powers ranging from 37 W to 44 kW at a nominal 10 K temperature span were projected, and SPD was estimated to improve from 6 W/kg to 81 W/kg. Neglecting end effects, an upper limit of 114 W/g is estimated. Compared to SPD of off-the-shelf compressors with similar environment temperatures, MCHP power density using gadolinium is competitive up to roughly 200 W of cooling power. This is extended to 1 kW when using LaFeSi alloys and up to 3 kW in the limiting case. In conclusion, these results indicate that the performance and mass of MCHP can match that of compressors, which is a critical step toward cost-competitive magnetocaloric technology.

42 ENGINEERING↗

DOE OSTI2025

Characterizing the Potential for Sustainable Azelaic Acid Production from High-Oleic Vegetable Oil Using Two-Step Oxidative Cleavage

Azelaic acid is a renewable monomer conventionally produced via the energy-intensive ozonolysis of oleic acid. Recent advancements have enabled the use of high-oleic vegetable oils (rather than tallow-derived oleic acid) and replaced ozonolysis with two-step oxidative cleavage using hydrogen and oxygen. Although this shift would improve process safety, the financial viability and environmental implications remain uncertain. In this study, we characterized the sustainability of azelaic acid production from high-oleic vegetable oil using two-step oxidative cleavage. Process design, simulation, technoeconomic analysis (TEA), and life cycle assessment (LCA) were executed under uncertainty using BioSTEAM. The modeled system produces azelaic acid at a market-competitive minimum selling price (MSP) of 8.32 [4.93−13.34] $\$kg$ −1 (median 5th−95th percentiles), below the minimum estimated market price of 9.93 $\$kg$ −1 . Further, it has the potential to approach carbon neutrality (0.0 [−5.5 to 5.6] kg of CO 2 -eq kg −1 ) under displacement allocation. Improvements to dihydroxylation (86 to 99%) and oxidative cleavage conversions (93 to 99%) would reduce MSP to $\$5.24$ kg −1 and carbon intensity to −1.90 kg of CO 2 -eq kg −1 (displacement). Additionally, increasing the feedstock triolein content (75 to 85%) lowers MSP by $\$0.82$ kg −1 . Overall, this research demonstrates the potential for financially viable production of azelaic acid from vegetable oils and the utility of agile TEA/LCA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Mass transfer coefficients, kL, and air-CO2 ingassing rates in 3.4 m2 and 1-acre raceway ponds.

The US DOE 2016 Billion Ton Update (Langholtz 2016) projected that CO2 in flue-gas from power plants or similar sources would limit U.S. algal biofuel potential to under 5 billion gallons gasoline equivalent (gge) per year, due to lack of sufficient land, water and other requirements near such flue gas sources. 2nd generation carbon capture technologies are proposed to overcome this constraint (Davis 2017), and could expand this resource potential nearly 10-fold, assuming CO2 costs near the future, 2025 DOE NETL, goal of $40/metric ton CO2. An alternative pathway is the direct uptake of air CO2 into the algal ponds through an increase in the air-CO2 transfer rate resulting from the reaction of CO2 with hydroxide ions. However, such a ‘chemical enhancement’ in mass transfer depends on a high pH in the culture, and, by extension, the ability of microalgae to maintain high rates of carbon assimilation under such conditions. Abiotic experiments characterized the air-CO2 mass-transfer rate in 1-acre and 3.4 m2 raceway ponds as a function of pH, with rates approaching 10 g C/m2-day at pH 12 in the brackish water tested, equivalent to a biomass productivity potential of near 20 g AFDW/m2-day (@ 0.47 g C/g AFDW). The mass transfer rate was found to be nearly independent of the mass-transfer coefficient, indicating that at turbulence levels achievable in typical raceway ponds, the system is reaction rate, rather than diffusion limited. In biotic trials with an unknown green microalga, biomass productivity in ponds receiving CO2 from only direct surface air-CO2 exchange averaged 5 +/- 0.5 g AFDW/m2-day in August - September central California conditions, about half-that of experimental controls fertilized with supplemental CO2. pH in the air-only treatment reached a maximum of 10.5, supporting a model predicted air-CO2 ingassing rate between 2-3 g C/m2-day, consistent with the observed biomass carbon assimilation rate. Results suggest that unique alkaliphilic strains are required if reliant on direct in-pond air-CO2 transfer for algal inorganic carbon supply, and that reaching an economically viable biomass productivity will require strains that thrive at pH 11 and above.

09 BIOMASS FUELS↗

NASA NTRS2010

Modeling Flight: The Role of Dynamically Scaled Free-Flight Models in Support of NASA's Aerospace Programs

The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific community, Leonardo da Vinci, George Cayley, and the Wright brothers are examples of early aviation pioneers who frequently used models during their scientific efforts to understand and develop flying machines. Progress in the technology associated with model testing in worldwide applications has firmly established model aircraft as a key element in new aerospace research and development programs. Models are now routinely used in many applications and roles, including aerodynamic data gathering in wind tunnel investigations for the analysis of full-scale aircraft designs, proof-of-concept demonstrators for radical aeronautical concepts, and problem-solving exercises for vehicles already in production. The most critical contributions of aerospace models are to provide confidence and risk reduction for new designs and to enhance the safety and efficiency of existing configurations.

Chambers, Joseph↗

DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

25 ENERGY STORAGE↗

DOE OSTI2025

Learning from Arctic Microgrids: Cost and Resiliency Projections for Renewable Energy Expansion with Hydrogen and Battery Storage

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security.

25 ENERGY STORAGE↗

NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗