Search NASA⌕ Search

SEARCH · Search NASA

Results for “ARPAE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

Enabling Technology - Reducing Greenhouse Gas Emissions and Energy Demands in the Meat Production Industry via Scaling Advanced 3D Culture Bioreactors

The project “Enabling Technology - Reducing Greenhouse Gas Emissions and Energy Demands in the Meat Production Industry via Scaling Advanced 3D Culture Bioreactors” focuses on developing transformational and disruptive food technologies, specifically in the area of alternative protein and meat production techniques. The impetus for developing alternative protein technologies include reducing intensive greenhouse gas emissions and energy/land/water usages associated with conventional meat production (animal agriculture). Additional rationales include providing alternatives to livestock rearing as a means of reducing antimicrobial resistance (most antibiotic and antimicrobial use occurs in food-producing animals), as well as the transmission of zoonotic diseases. The goal of this ARPA-E project was to research and develop novel and effective technologies to produce cell-cultured meat (also named in vitro meat, cultivated meat). Cell-cultured meat is an alternative protein or meat technology that involves directly growing muscle and fat tissues (which comprise meat) without using the full animal. This offers benefits in terms of animal welfare (elimination of animal slaughter), while also being projected to be more resource efficient in terms of metrics such as land use and greenhouse gas emissions, especially when compared to beef and lamb. Cell cultured meat additionally offers greater control over the cultivated cells, enabling more precise control over nutrition and taste. For example, cultured cells could be tuned to produce antioxidants such as carotenoids to improve human health. However, to date there are no scalable methods of producing cell-cultured meat cheaply at a large scale.

59 BASIC BIOLOGICAL SCIENCES↗

gollnlp and magos (Final Scientific/Technical Report)

In 2018, the Advanced Research Project Agency – Energy (ARPA-E) launched the Grid Optimization Competition (GOC), a series of competitive challenges intended to accelerate innovation in decision support software used to schedule power grid operations, making them as efficient as possible, while respecting operational constraints of power equipment and operational security. This report covers the participation of teams led by the Lawrence Livermore National Laboratory (LLNL) in Challenge 1 and Challenge 2 of the competition.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Absolute Neutron Rate Measurement and Non-Thermal/Thermonuclear Fusion Differentiation

The goal of fusion energy is to produce significantly more energy from fusion reactions than is input into the device. One of the products of fusion reactions is neutrons, which, due to their lack of charge, provide a unique view into the parameters of the device. Lawrence Livermore National Laboratory (LLNL) in collaboration with the University of California, Berkeley (UCB) have designed, assembled, and fielded a robust and portable neutron detection system known as PANDA (Portable and Adaptable Neutron Diagnostics for ARPA-E). This detector suite consists of three LaBr activation detectors that are calibrated to give a total neutron yield on shot, and twenty-four scintillators coupled to photo-multiplier tubes (SPMT). The SPMTs can be configured to attain spatial, temporal and/or energy information from fusion neutrons. The system was designed to be portable and is compartmentalized so that individual components can be used at different fusion facilities. During the duration of this work, part of the system was installed at the FuZE facility, a part of Zap Energy. Another component was installed that the CESZAR facility at the University of California, San Diego (UCSD) to support experiments by Magneto-Inertial Fusion Technologies, Inc. (MIFTI). The diagnostics were successful at both locations and the LLNL/UCB team supported the data analysis by creating and running analysis scripts and Monte-Carlo calculations. At Zap Energy the diagnostics demonstrated that the fusion from the FuZE device is thermonuclear in nature, a result that resulted in an invited talk at the American Physical Society Division of Plasma Physics and an invited paper. Additionally, temporal and spatial data was taken using the SPMTs to understand the duration and length of fusion production. At UCSD the neutron yield from the diagnostics was used to show improvements to fusion yields on their gas puff Z-pinch when using a gas shell surrounding the fuel. The success in this diagnostic has led to continued work at both Zap Energy and MIFTI, as well as follow on funding and interest at other fusion energy companies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

New Environmental-Thermal Barrier Coatings for Ultrahigh Temperature Alloys

Our ARPA-E funded project, New Environmental-Thermal Barrier Coatings for Ultrahigh Temperature Alloys, has made significant strides in advancing the understanding of design and fabrication of novel thermal barriers coatings. Through rigorous research and innovative approaches, our team has explored new frontiers, pushing the boundaries of knowledge in this critical field.

36 MATERIALS SCIENCE↗

Sandia Wind Energy Program - FY23 Accomplishments

This report summarizes Fiscal Year 2023 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), Advanced Manufacturing Office (AMO), the Sandia Laboratory Directed Research and Development (LDRD) program, and private industry. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world. Sandia’s Wind Energy Program is primarily built around core capabilities as expressed in the strategic plan thrust areas, with 29 staff members in the Wind Energy Design and Experimentation department and the Wind Energy Computational Sciences department leading and supporting R&D at the time of this report. Staff from other departments at Sandia support the program by leveraging Sandia’s unique capabilities in other disciplines.

17 WIND ENERGY↗

Sandia Wind Energy Program: FY22 Accomplishments

This report summarizes Fiscal Year 2022 accomplishments from Sandia National Laboratories Wind Energy Program. The portfolio consists of funding provided by the DOE EERE Wind Energy Technologies Office (WETO), Advanced Research Projects Agency-Energy (ARPA-E), Advanced Manufacturing Office (AMO), and the Sandia Laboratory Directed Research and Development (LDRD) program. These accomplishments were made possible through capabilities investments by WETO, internal Sandia investment, and partnerships between Sandia and other national laboratories, universities, and research institutions around the world. Sandia’s Wind Energy Program is primarily built around core capabilities as expressed in the strategic plan thrust areas, with 29 staff members in the Wind Energy Design and Experimentation department and the Wind Energy Computational Sciences department leading and supporting R&D at the time of this report. Staff from other departments at Sandia support the program by leveraging Sandia’s unique capabilities in other disciplines.

17 WIND ENERGY↗

Operation and Dynamic Control of Hybrid Power Systems

Poster describing the planned startup process of a solid oxide gas turbine hybrid power system to be constructed at a 20 kW scale. The work is part of the ARPA-E project award to NETL under the INTEGRATE program.

Harun, Nor Farida↗

DigiFloat (Final Scientific/Technical Report)

This purpose of this report is to document the development of a floating offshore wind platform Digital Twin for the ARPA-E-funded DigiFloat project led by Principle Power, Inc. In Offshore Wind, Digital Twins are aimed at reducing farm-wide OPEX expenditures through reduced-basis inspection, assessing life extension methods, and obtaining better knowledge of the deployed assets for future product optimization. There is a high level of technical effectiveness and economic feasibility for the technology investigated, as it uses commercial off-the-shelf sensors, and a commercial software package. A substantial portion of the project goals are improvement of existing technology for use with floating wind turbines, and new application of an existing technology to a rapidly-growing sector. A few sensors applied to a small number of units bring benefit to the entire fleet of floating wind turbines in a given farm, and in other future wind farms. The benefits to the public of this project are further optimization and maturation of floating wind technology, to bring it to the U.S. market faster and cheaper.

17 WIND ENERGY↗

Nested Pebble Bed Blanket (NesPeB)

Recent advances in magnetic confinement fusion technology have attracted billions of dollars of investments in startups from venture capitals and corporations, resulting in the development of devices aiming to demonstrate net energy gain in a self-heated burning plasma, such as SPARC (under construction) and others. However, future fusion power plants must operate in regimes that will require technologies far beyond current experience. According to a National Academies of Science, Engineering, and Medicine report, to have nuclear fusion power plants contributing in a timely manner to the planned reduction of atmospheric carbon dioxide, a pilot plant should be built by 2035, and it should demonstrate fusion power production and the performance of the tritium fuel system (requiring a high enough tritium breeding) by 2040. A recognized key technology gap by [26] is the fusion first wall and blanket since no current blanket concept is considered satisfactory or has been built and proven. The first wall and blanket in magnetic fusion reactors form a vital and complex system, as it must satisfy different functions such as power extraction, tritium breeding, plasma containment, radiation shielding, and safety. The list of design requirements is even longer: high enough tritium production for fusion self-sufficiency, low material activation, decay heat and shutdown dose rates, high thermal efficiency, high-capacity factor, high magnets-divertor-vacuum vessel-first wall life, low corrosion, low cost, and intrinsically safe (requiring minimal licensing). Despite fifty-plus years of research, the first wall and blanket concepts proposed suffer from fundamental technical problems and immaturity (TRL=2-3) that jeopardize the timely delivery of a commercial fusion power plant. A fusion first-wall blanket has never been built nor tested, and a "winning", practical functioning design requires enough engineering margins (high enough tritium breeding considering the uncertainty, etc.), manufacturing simplicity, ease of continuous operation, maintenance, and low cost. A new, groundbreaking blanket concept called "Nested Pebble Bed Blanket" (NesPeB) was developed at ORNL under the successful ARPA-E GAMOW FERMI project (patent application allowed by the USPTO). The NesPeB blanket concept addresses current blanket concepts' shortcomings and technical immaturity, paving the way for accelerated delivery of fusion power plants. NesPeB is based on nested pebbles, which are binary-sized lithium-ceramic pebbles enclosed in "Beryllide" perforated and coated spherical shells, which are also binary-sized, stacked on top of each other, forming a "bed" and cooled by Nitrogen gas also "sweeping" the Helium and Tritium generated by the neutron irradiation of Lithium; the vacuum vessel plasma facing material is Molybdenum-96 and -97 with the first wall cooled by Helium while the divertor armor is made of Tungsten. The simulations of the NesPeB blanket using Fusion Reactors Models Integrator (FERMI) are encouraging as they estimate a tritium breeding ratio (TBR) greater than 1.2 using natural Lithium, acceptable pressure drop, and excellent heat transfer properties. Furthermore, the NesPeB blanket is not limited by magneto-hydro-dynamics (MHD) effects, is designed for online refueling, relies on existing tritium extraction technologies, has a simple construction, and limits the corrosion and chemical reactivity problems. NesPeB has the potential to be transformational and disruptive since it can solve all the main, challenging technical problems of fusion device blankets and accelerate a pilot plant delivery for 10 or more years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Dual Mode Intermediate Temperature Fuel Cell: Liquid Fuels and Electricity - CRADA 351 (Abstract)

FuelCell Energy, Inc. (FCE) in collaboration with Pacific Northwest National Laboratory (PNNL), The University of Connecticut (UCONN), The Energy and Environmental Research Center at The University of North Dakota (EERC-UND), and Massachusetts Institute of Technology (MIT), has entered into a co-operative agreement with the U.S. Department of Energy Advanced Research Project Agency – Energy (ARPA-E), agreement number DE-AR0000503, to develop an electrochemical device for conversion of methane gas to a usable liquid product, such as methanol or formaldehyde. The objective of this CRADA is to develop the anode electro-catalyst for the aforementioned electrochemical cell during Q1-Q8. PNNL will utilize thermodynamic modeling, batch-mode reactor screening and button-scale electrochemical testing to develop this anode."

09 BIOMASS FUELS↗

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗

MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.

36 MATERIALS SCIENCE↗

Fast and robust strategies for large-scale mixed-integer SCOPF

This project develops scalable, computationally efficient algorithms to solve realistic large-scale power system optimization problems, including systems with more than 8,000 buses, as part of a larger series of competitions run by ARPA-E. These problems are critical because the secure and reliable operation of the power grid is becoming increasingly challenging, especially under conditions of increased uncertainty and variability. The economic feasibility of our methods is high, given that they are purely software-based solutions designed to operate power grids more efficiently. The technical effectiveness balances heuristics and approximations to provide a trade-off between speed and accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Capability in Theory, Modeling, and Validation for a Range of Innovative Fusion Concepts using High-Fidelity Moment-Kinetic Models

A computational modeling capability is created and available to the fusion community to understand and design lower-cost and innovative fusion concepts. The approach uses high- fidelity kinetic, moment-kinetic, and moment models and includes sophisticated plasma- boundary interactions. A majority of fusion-relevant simulations are performed with magnetohydrodynamic models and hybrid particle-in-cell codes, with limited-fidelity electron and kinetic physics. However, in fusion configurations like Z-pinches, field-reversed- configurations, plasma jet magneto-inertial fusion, spinning mirrors, and others, kinetic effects (both electron and ions) are critical to understand the physics and design scaling into the highly kinetic regime of a burning fusion plasma. Furthermore, as present fusion machines move towards a burning plasma regime, liquid-metal blankets are needed to handle first-wall heat- flux, reduce erosion, and eventually for energy conversion and fuel breeding. The work performed under this ARPA-E BETHE Capability Team advances the state-of-the-art in modeling and understanding plasma dynamics in fusion devices and its coupling with liquid-metal dynamics. These are critical areas of research for fusion energy to become realizable. To address these complex problems, we have leveraged and extended computational capabilities through the code, Gkeyll (developed jointly with Princeton Plasma Physics Laboratory and academic partners), for kinetic and moment modeling of fusion plasmas. The Concept Teams supported by this Capability Team include the Wisconsin High-field Axisymmetric Mirror (WHAM), Centrifugal Mirror Experiment (CFME), Plasma-Jet Magneto- Inertial Fusion (PJMIF), and solid and liquid wall plasma-material interaction studies relevant to a number of fusion concepts including Zap Energy’s Z-pinch. This software is open-source and available to the fusion community as a high-fidelity tool for the design of lower-cost fusion experiments. 3D gyrokinetic simulations of WHAM are now possible for long enough time scales to understand the evolution of interchange instabilities. 3D multi-fluid simulations of CMFE at higher Mach numbers are now possible for detailed design iterations with the goal of stability. The state-of-the-art in understanding shock formation and shock mitigation regimes in merging liners for PJMIF have been furthered by our kinetic simulations. Our novel models and frameworks studying plasma-material interaction by incorporating wall emission for various solid wall materials of relevance to pulsed and steady fusion concepts have advanced the state-of-the-art in our understanding of particle fluxes, heat fluxes, and other quantities at cathodes and anodes. The results from this work may explain discrepancies between experimental and theoretical predictions of achieved current densities in pulsed concepts such as Z-pinches. Another significant contribution of this Capability Team is the development and deployment of a novel experimental platform, LEX (Liquid Electrode eXperiment), at Virginia Tech to understand liquid metal free-surface response to electromagnetic pulses. The novel experiments along with model validation quantified the effect of different materials and sizes of liquid metal droplets on the radiative power balance of fusion plasmas for pulsed concepts. Furthermore, these experiments provided mitigation strategies for violent liquid metal response for high current pulses as would be expected in fusion regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Zero-Power Wireless Infrared Digitizing Sensors for Large Scale Energy-Smart Farm

This project, funded by ARPA-E and led by Northeastern University, developed zeropower infrared digitizing sensors to optimize irrigation and enhance crop yields. Traditional water stress detection methods are costly and require frequent maintenance, limiting their effectiveness. Our research identified shortwave infrared (SWIR) transmittance as the most reliable indicator of plant water stress and developed plasmonically enhanced micromechanical photoswitches (PMPs) that operate with minimal power. The sensors offer low-cost, large-scale deployment, auto-calibration across different crops, and a 10-year battery life, significantly reducing maintenance costs. The system achieved 4x greater accuracy than conventional soil moisture sensors while ensuring economic feasibility. By enabling precision irrigation, this technology conserves water, enhances crop productivity, and lowers operational costs, making it a scalable solution for sustainable agriculture and global food security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

GO Competition Challenge 1 Benchmark Approach and Validation Support

At the outset ARPA-e’s grid optimization competition sought to test a hypothesis that modern nonlinear optimization methods had reached a level of performance and reliability necessary to optimize energy grids using nonlinear power flow equations. The results demonstrated by this project and the other participants in the grid optimization competition have demonstrated beyond any shadow of doubt that this hypothesis is true. This result suggest that the mathematical optimization technology now exists to produce a revolution in power grid optimization software, which would improve the efficiency of energy grid operations to the broad benefit of society.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Reinforced AEM Separators Based on Triblock Copolymers for Electrode-decoupled RFBs

Washington University in St. Louis (WUSTL), in collaboration with The University of Texas at San Antonio (UTSA) and Giner Inc. developed highly selective anion exchange membranes (AEMs) and a novel electrode-decoupled redox flow battery (RFB) for grid scale energy storage as part of the ARPA-E IONICS program (with connections to the DAYS program in the later part of the project). RFBs exhibit the crucial characteristic of system-level decoupled scaling of energy and power which makes them cost effective for the multi-GWh scales envisioned for grid-scale energy storage solutions. This has led to extensive (and deserved) research interest and attention. This project aimed to enhance the design space available for redox-flow batteries (RFBs) by enabling the long-term separation of disparate cationic (elemental) actives using a highly selective membrane separator, while concurrently permitting the transport of anions to balance charge. The approach proposed was to design and develop a highly selective anion-exchange membrane (AEM), which would in turn permit the design and development of electrode-decoupled RFBs. Pairs of (different element) cationic species with redox reactions exhibiting a large difference in their standard electrode potentials were identified to develop high voltage, high power RFBs while disrupting the existing paradigm of using a single element which can ionize to more than two soluble oxidation states (e.g.: Vanadium).

25 ENERGY STORAGE↗

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗