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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Accelerated Materials Design for Molten Salt Technologies Using Innovative High-Throughput Methods

The focus of the project is on building an innovative accelerated materials design platform for molten salt technologies using novel high-throughput methods coupled to data analytics. The main objectives is to predict a FeCrMnNi alloy compositional space with better corrosion resistance than stainless steel 316 and identify new molten salt corrosion mechanisms. The project demonstrates the feasibility to use high-throughput methods coupled to data analytics to accelerate alloy design for extreme environments applications. Using a trained and tested machine learning (ML) model, 2000 FeCrMnNi alloy corrosion rate in molten chloride salts were predicted and a compositional field with corrosion rate lower than 316 stainless steel was identified. The ML model interpretability unveiled multiple features of importance in the model prediction. Some features were expected to be of relative significance, such as work function, surface energy and alloy electronegativity, and the ML model interpretability analysis confirmed those. On the other hand, the most important feature is the diffusion coefficient of Ni in the bulk alloy which indicates that a surface diffusion mechanism plays an important role in the overall corrosion mechanism in molten salts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of In-Situ AM Process Monitoring Techniques and Potential for Detecting Process Anomalies and Undesirable Microstructures

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing rapid qualification of new materials for fabrication of nuclear relevant components using advanced manufacturing techniques. Particular interest is placed on code-qualifying stainless steel (SS) 316H processed by laser powder bed fusion (LPBF) additive manufacturing. A paradigm that incorporates data from in-situ sensing during the printing, ex-situ characterization, and advanced artificial intelligence–based models was established under the Transformation Challenge Reactor (TCR) program to develop a pedigree for each fabricated component that could be tracked from the feedstock to the component’s release for application. Under the TCR program, the Peregrine software was developed as a tool for incorporating the vast amounts of in-situ and ex-situ characterization data collected; all data stored on a rapidly growing digital platform. The digital platform allows for users to link site-specific process anomalies to the macro- and microstructure. The platform will eventually be able to predict component performance, which will be crucial to qualifying materials and components in risk-averse industries such as those supporting and building nuclear reactors. Current in-situ process monitoring techniques that are already integrated with software like Peregrine are advantageous for identifying process anomalies including powder spatter, component edge swelling, recoating-build interactions, and so on. However, additional data are required to fully predict the resulting microstructures needed for identifying relationships to component performance. The rapid cooling rates observed in LPBF are some of the highest of any bulk manufacturing process, resulting in heterogenous microstructures and typically causing anisotropy in mechanical properties. Moreover, evolved residual thermal stresses are high, which can cause severe defects such as delamination or cracking. Therefore, other in-situ monitoring methods are warranted for exploration to measure and map the thermal history, and potentially the stress state, of each build. This report summarizes different in-situ monitoring strategies proposed for LPBF with a focus on the more developed sensor systems. Novel capabilities for measuring melt pool temperatures are also addressed to better inform modeling efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A KBase Case Study on Genome-wide Transcriptomics and Plant Primary Metabolism in Sorghum

A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by allowing them to upload private or public data into the KBase Narrative Interface, empowering them to analyze this data using a rich, extensible array of computational and data-analytics tools, and allows them to securely share scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes that are differentially expressed during drought-stress in Sorghum bicolor, which is an important sustainable production crop plant. We then show how to use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1994

Multicomponent Gas Diffusion and an Appropriate Momentum Boundary Condition

Multicomponent gas diffusion is reviewed with particular emphasis on gas flows near solid boundaries-the so-called Kramers-Kistemaker effect. The aim is to derive an appropriate momentum boundary condition which governs many gaseous species diffusing together. The many species' generalization of the traditional single gas condition, either as slip or stick (no-slip), is not obvious, particularly for technologically important cases of lower gas pressures and very dissimilar molecular weight gases. No convincing theoretical case exists for why two gases should interact with solid boundaries equally but in opposite flow directions, such that the total gas flow exactly vanishes. ln this way, the multicomponent no-slip boundary requires careful treatment The approaches discussed here generally adopt a microscopic model for gas-solid contact. The method has the advantage that the mathematics remain tractable and hence experimentally testable. Two new proposals are put forward, the first building in some molecular collision physics, the second drawing on a detailed view of surface diffusion which does not unphysically extrapolate bulk gas properties to govern the adsorbed molecules. The outcome is a better accounting of previously anomalous experiments. Models predict novel slip conditions appearing even for the case of equal molecular weight components. These approaches become particularly significant in view of a conceptual contradiction found to arise in previous derivations of the appropriate boundary conditions. The analogous case of three gases, one of which is uniformly distributed and hence non-diffusing, presents a further refinement which gives unexpected flow reversals near solid boundaries. This case is investigated alone and for aggregating gas species near their condensation point. In addition to predicting new physics, this investigation carries practical implications for controlling vapor diffusion in the growth of crystals used in medical diagnosis (e.g. mercuric iodide) and semiconductors.

Noever, David A.↗
DOE OSTI2025

Building a Collaborative Relationship between Contractor and Oversight at SRPPF

The success of Department of Energy (DOE) projects is dependent on a functional, healthy, and collaborative relationship between the contractor and the federal oversight. A healthy relationship marked by a collaborative mindset between these two groups, can facilitate better operational safety, operational efficiency, and regulatory compliance. This paper will explore aspects of such a relationship, the roles and responsibilities of the contractor and federal oversight, the present and historical relationships in the DOE complex, and best practices considered by the contractor and federal oversight used at the Savannah River Plutonium Processing Facility (SRPPF) at the Savannah River Site (SRS). Due to the current design phase of the SRPPF, the contractor and federal oversight have a unique opportunity to begin establishing a strong relationship early in the project’s life. As a result of this dynamic, the two groups have already demonstrated their ability to successfully respond to ongoing design issues. In general, a strong criticality safety program is beginning to mature for the SRPPF alongside a developing criticality safety federal oversight group. This paper will discuss the various factors that have brought about this collaborative relationship. Lastly, a case study will be incorporated into this paper demonstrating the ability of the SRPPF contractor and federal oversight to overcome challenges relating to the SRPPF project. This case study may guide groups around the DOE Complex to create better, more functional relationships between the two parties. The case study will provide a detailed description of the issue and parties involved, highlight the factors that allowed the group to overcome the challenge via effective collaboration, and the outcome of the event. Increasing collaboration may benefit the complex, as a whole, in order to meet the mission demands of the DOE.

collaboration↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Active Aerodynamic Load Control for Wind Turbines

The goal of this project was to develop and demonstrate an advanced dielectric barrier discharge (DBD) plasma actuator technology. We set out to demonstrate the efficacy and impact of the new actuator technology as a key component of an active load control system for wind turbines. A DBD plasma actuator consists of a thin layer of dielectric material separating a pair of offset electrodes. One of those electrodes is embedded between the dielectric layer and a non-conductive substrate, while the other is exposed to air. When driven by an appropriate high voltage waveform, the device ionizes the air near the surface of the dielectric and adjacent to the exposed electrode. Collisions between the ions in the plasma and neutral air molecules result in a wall-jet – a region of induced air velocity that can be used to modify the flow around a lifting body. Use of the device near the trailing edge of a wind turbine blade, designed in such a way as to amplify the effects of the flow-modifying device, can result in large changes to the global forces experienced by the blade. Because of the fast response time of the device, it can allow the turbine to react in real time to changes in the wind associated with turbulence, wind shear, gusts, and the like, when paired with appropriate sensors and control algorithms. The goal of this project was to increase the capacity of the device to induce velocity on its surface at the levels required by large, utility-scale wind turbines. The first technical goal was to increase the induced velocity from the current industry-best of about 4 m/s to 10 m/s by modifying the electrical waveform used to drive the device and by introducing a semi-conductive surface coating to control electrostatic charge build-up on the surface. The second goal was to use the device to modify the lift on a representative airfoil in a wind tunnel, demonstrating a reduction in lift coefficient of 0.2 or better. The third goal was to produce an actuator-induced velocity of 20 m/s. Alongside our partners at the University of Texas at Dallas, we also applied modern, advanced design methods to optimize the impact of the technology on the design of wind turbines. We also had planned to install a segmented, active load control system on a test turbine to demonstrate the ability to reduce unsteady aerodynamic forces on the turbine associated with changes in the wind. The highest induced velocity achieved during this research was 11 m/s. However, practical design constraints limited the change in lift coefficient to about 0.12 for a representative airfoil in the wind tunnel at a Reynolds number of 400,000. The primary conclusion is that the plasma actuator control authority remains insufficient for practical purposes when extrapolated to Reynolds numbers over 1 million. The potential impact of the active lift control concept was evaluated through detailed simulations for three wind turbine sizes: a 3.4MW onshore turbine, a 10MW offshore turbine, and a 15MW offshore turbine. A feedback control system was designed for each turbine within two scenarios: one where the active lift control is used as a retrofit capability on the baseline design, and the other where the designers were permitted to “upscale” the turbines in order increase annual energy production. The levelized cost of energy was then evaluated for the range of turbine sizes and design configuration. It was found that the LCOE reduction associated with active lift control fell in the range of 0.7% to 7.2%, with the highest reduction associated with upscaling the 3.4MW turbine.

17 WIND ENERGY↗
DOE OSTI2024

Development of a Two-Dimensional CASTLE Transmission-Line Model for all Levels of Saturn

This report describes a two-dimensional model of Saturn based on the CASTLE transmission line code. Building on previous modeling efforts, 2D circuit models based on the “chain-link fence” geometry are constructed for pre-ReCap Saturn and post-ReCap Saturn. The 2D model results are in better agreement with data from Shot 4550 measurements of load currents and doses then the previous 1D model. Lower doses (9%) predicted by the new model can be compensated by increasing the load A-K gap.

43 PARTICLE ACCELERATORS↗
NASA NTRS2004

Refining the W1 and SE1 Facilities

The Engine Research Building (ERB) houses more than 60 test rigs that study all aspects of engine development. By working with Mary Gibson in the SE1 and W1A Turbine Facilities, I became aware of her responsibilities and better acquainted with the inner workings of the ERB. The SE1 Supersonic/Subsonic Wind Tunnel Facility contains 2 small wind tunnels. The first tunnel uses an atmospheric inlet, while the second uses treated 40-psig air. Both of the tunnels are capable of subsonic and supersonic operation. An auxiliary air supply and exhaust piping providing both test sections with suction, blowing, and crossfire capabilities. The current configuration of SE1 consists of a curved diffuser that studies the blockage along the endwalls. The W1A Low Speed Compressor Facility provides insight for the complex flow phenomena within its 4-stage axial compressor, sand the data obtained from W 1A is used to develop advanced models for fluid dynamic assessment. W1A is based off of a low speed research compressor developed by GE in the 1950's. This compressor has a removable casing treatment under rotor 1, which allows for various tip treatment studies. The increased size and low speed allows instrumentation to be located in the compressor s complex flow paths. Air enters the facility through a filtered roof vent, conditioned for temperature and turbulence, and then passed through the compressor W1A is described as a dynamic facility with many projects taking place simultaneously. This current environment makes it challenging to follow the various affairs that are taking place within the area. During my first 4 weeks at the NASA Glenn Research Center, I have assisted Mary Gibson in multiple tasks such as facility documents, record keeping, maintenance and upgrades. The facility has lube systems for its gearbox and compressor. These systems are critical in the successful operation of the facility. I was assigned the task of creating a facility estimate list, which included the filters and strainers required for the compressor. For my remaining time spent here, we expect to complete a facility parts listing and a virtual project summary so that W1A and SE1 will become ergonomic facilities that will make it easier for people to observe the capabilities and history of the area and the employees that operate. Bolstering our efforts in achieving these goal are the online technical tutorials, software such as Microsoft Excel. Macromedia Flash MX Macromedia Dreamweaver MX, Photoshop 6.0 and the assistance of several NASA employees.

Chambers, Rodney D.↗
DOE OSTI2023

Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis (BSA) project uses systems thinking and analysis to assess how techno economics, research and development, deployment strategies, policy, and market conditions affect the potential development trajectories of the developing bioenergy industry. This project informs researchers, decision makers, and industry by identifying opportunities for and constraints to industrial development and quantifying important industry metrics (e.g., energy, economic, environmental) towards a sustainable domestic bioenergy system. One of the tools used in this project, the Bioenergy Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. Analysis products from this effort enable the development of the bioenergy industry by (1) encouraging policy-makers to explore multiple levers simulating outside impacts on biofuels production, identifying policy actions; (2) improving industry's understanding of growth potential under different market conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with analysis tools and analyses that can be adapted to meet research and teaching objectives, thus connecting students with careers that build the industry.

bioenergy↗
DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A New State of the Art: Zero Energy Modular Multifamily Construction

In 2020, the U.S. Department of Energy's Building Technologies Office (BTO) launched the Advanced Building Construction (ABC) Initiative. This initiative aimed to integrate energy efficiency into high-production construction practices. One focus area of the initiative is off-site construction, an approach that may achieve scalable, efficient, and high-performing construction buildings through process standardization. This report presents the findings and recommendations from our ABC Initiative project: A New State of the Art: Zero-Energy Modular Multifamily Construction System. The objectives of the BTO-funded project are to: 1) Achieve energy performance 50% better than the 2018 International Energy Conservation Code (IECC). 2) Achieve this level of energy performance at no additional cost VEIC worked with Louisiana State University (LSU), National Renewal Energy Lab (NREL), [the project team], and industry partners at the Mod Coach, KBS, and Solar Home Factory to identify and analyze multifamily business designs and modular construction practices. These designs and processes hold the promise of achieving the goals set out by the DOE.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Infrastructure first principles for the Anthropocene

Abstract There appears to be a growing decoupling between the conditions that infrastructures were designed for and today’s rapidly changing environments. Infrastructures today are largely predicated on the technologies, goals, and governance structures from a century ago. While infrastructures continue to deliver untold value, there is growing evidence that these critical, basic, and lifeline systems appear ill-equipped to confront the volatility, uncertainty, accelerating conditions, and complexity that define them and their changing environments. Innovative and disruptive first principles are needed to guide infrastructures in the Anthropocene. Drawing from emerging infrastructure research and disciplines that appear better able to confront disruption and change, a novel set of first principles are identified: (1) Plan for complex conditions and surprise; (2) Recouple with agility and flexibility; (3) Govern for exploration and instability; (4) Build consensus as control decentralizes; (5) Restructure to engage with porous boundaries; and, (6) Cyberthreat planning is now mission critical. These principles should guide infrastructure planning recognizing the changing nature and increasingly obsolete boundaries that have defined engineered systems in the modern era.

Chester, Mikhail (ORCID:0000000293542102)↗
DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗
NASA NTRS1977

Industrial Productivity

NASTRAN is an offshoot of the computer-design technique used in construction of airplanes and spacecraft. [n this technique engineers create a mathematical model of the aeronautical or space vehicle and "fly" it on the ground by means of computer simulation. The technique enables them to study performance and structural behavior of a number of different designs before settling on the final configuration and proceeding with construction. From this base of aerospace experience, NASA-Goddard developed the NASTRAN general purpose computer program, which offers an exceptionally wide range of analytic capability with regard to structures. NASTRAN has been applied to autos, trucks, railroad cars, ships, nuclear power reactors, steam turbines, bridges, and office buildings. NASA-Langley provides program maintenance services regarded as vital by many NASTRAN users. NASTRAN is essentially a predictive tool. It takes an electronic look at a computerire$.dedgn and reports how the structure will react under a great many different conditions. It can, for example, note areas where high stress levels will occur-potential failure points that need strengthening. Conversely, it can identify over-designed areas where weight and material might be saved safely. NASTRAN can tell how pipes stand up under strong fluid flow, how metals are affected by high temperatures, how a building will fare in an earthquake or how powerful winds will cause a bridge to oscillate. NASTRAN analysis is quick and inexpensive. It minimizes trial-and-error in the design process and makes possible better, safe, lighter structures affording large-scale savings in development time and materials. Some examples of the broad utility NASTRAN is finding among industrial firms are shown on these pages.

Source record↗
NASA NTRS2017

Strain Monitoring of Flexible Structures

One of the biggest challenges facing NASA's deep space exploration goals is structural mass. A long duration transit vehicle on a journey to Mars, for example, requires a large internal volume for cargo, supplies and crew support. As with all space structures, a large pressure vessel is not enough. The vehicle also requires thermal, micro-meteoroid, and radiation protection, a navigation and control system, a propulsion system, and a power system, etc. As vehicles get larger, their associated systems also get larger and more complex. These vehicles require larger lift capacities and force the mission to become extremely costly. In order to build large volume habitable vehicles, with only minimal increases in launch volume and mass, NASA is developing lightweight structures. Lightweight structures are made from non-metallic materials including graphite composites and high strength fabrics and could provide similar or better structural capability than metals, but with significant launch volume and mass savings. Fabric structures specifically, have been worked by NASA off and on since its inception, but most notably in the 1990's with the TransHAB program. These TransHAB developed structures use a layered material approach to form a pressure vessel with integrated thermal and micro-meteoroid and orbital debris (MMOD) protection. The flexible fabrics allow the vessel to be packed in a small volume during launch and expand into a much larger volume once in orbit. NASA and Bigelow Aerospace recently installed the first human-rated inflatable module on the International Space Station (ISS), known as the Bigelow Expandable Activity Module (BEAM) in May of 2016. The module provides a similar internal volume to that of an Orbital ATK Cygnus cargo vehicle, but with a 77% launch volume savings. As lightweight structures are developed, testing methods are vital to understanding their behavior and validating analytical models. Common techniques can be applied to fabric materials, such as tensile testing, fatigue testing, and shear testing, but common measurement techniques cannot be used on fabric. Measuring strain in a material and during a test is a critical parameter for an engineer to monitor the structure during the test and correlate to an analytical model. The ability to measure strain in fabric structures is a challenge for NASA. Foil strain gauges, for example, are commonplace on metallic structures testing, but are extremely difficult to interface with a fabric substrate. New strain measuring techniques need to be developed for use with fabric structures. This paper investigates options for measuring strain in fabric structures for both ground testing and in-space structural health monitoring. It evaluates current commercially available options and outlines development work underway to build custom measurement solutions for NASA's fabric structures.

Litteken, Douglas A.↗
DOE OSTI2020

Using AI Simulations to Dynamically Model Multi-agent Multi-team Energy Systems

The complexity of energy systems is well known as they are complex and intricate systems. As a result, many extant studies have used many simplifications or generalizations that do not accurately reflect the nature of this complex system. In particular, most HVAC systems are modeled as a single unit, or several large units, rather than as a hierarchical composite (e.g., as a floor rather than as a collection of disparate rooms). The net result of this is that the simulations are too generic to perform meaningful analysis, machine learning, or integrated simulation. We propose using a multi-agent multi-team strategic simulations framework called SiMAMT to better define, model, simulate, and learn the HVAC environment. SiMAMT allows us to create distinct models for each type of room, hierarchically aggregate them into units (like floors, or sections), and then into larger sets (like buildings or a campus), and then perform a simulation that interacts with each sub-element individually, the teams of sub-elements collectively, and the entire set in aggregation. Further, and most importantly, we additionally model another ‘team’ within the simulation framework - the users of the systems. Again, each individual is modeled distinctly, aggregated into sub-sets, then collected into large sets. Each user, or agent, is performing on their own but with respect to the larger team goals. This provides a simulation that has a much higher model fidelity and more applicable results that match the real-world.

Franklin, D. Michael↗
DOE OSTI2023

The first field application of a low-cost MPC for grid-interactive K-12 schools: Lessons-learned and savings assessment

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. Further, the paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗