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DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗

NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗

NASA NTRS2016

Unmanned Aerial Systems Traffic Management (UTM): Safely Enabling UAS Operations in Low-Altitude Airspace

Currently, there is no established infrastructure to enable and safely manage the widespread use of low-altitude airspace and UAS flight operations. Given this, and understanding that the FAA faces a mandate to modernize the present air traffic management system through computer automation and significantly reduce the number of air traffic controllers by FY 2020, the FAA maintains that a comprehensive, yet fully automated UAS traffic management (UTM) system for low-altitude airspace is needed. The concept of UTM is to begin by leveraging concepts from the system of roads, lanes, stop signs, rules and lights that govern vehicles on the ground today. Building on its legacy of work in air traffic management (ATM), NASA is working with industry to develop prototype technologies for a UAS Traffic Management (UTM) system that would evolve airspace integration procedures for enabling safe, efficient low-altitude flight operations that autonomously manage UAS operating in an approved low-altitude airspace environment. UTM is a cloud-based system that will autonomously manage all traffic at low altitudes to include UASs being operated beyond visual line of sight of an operator. UTM would thus enable safe and efficient flight operations by providing fully integrated traffic management services such as airspace design, corridors, dynamic geofencing, severe weather and wind avoidance, congestion management, terrain avoidance, route planning re-routing, separation management, sequencing spacing, and contingency management. UTM removes the need for human operators to continuously monitor aircraft operating in approved areas. NASA envisions concepts for two types of UTM systems. The first would be a small portable system, which could be moved between geographical areas in support of operations such as precision agriculture and public safety. The second would be a Persistent system, which would support low-altitude operations in an approved area by providing continuous automated coverage. Both would require persistent communication, navigation, and surveillance (CNS) coverage to track, ensure, and monitor conformance. UTM is creating an airspace management tool that allows the ATM system to accommodate the number of UAS that will operate in the low altitude airspace. The analogy is just because we have a car, whether its autonomous or someone is driving, does not diminish the need for a road or road signs or rules of the road.

traffic management↗

DOE OSTI2023

Automated Electric Vehicle Fleet Operations for On-Demand Service: Challenges and Opportunities

Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Liquid organic hydrogen carriers for long-duration energy storage

Liquid organic hydrogen carrier (LOHC) systems are an excellent alternative to pressurized gas and liquid hydrogen storage technologies due to their high volumetric storage capacities and straightforward adaptation to existing infrastructure. Here, we investigate various molecular and heterogenized Ru, Mn, and Fe catalysts for the reversible (de)hydrogenation of polyols. Mn catalysts, Mn-MACHO(Ph) (1) and Mn-MACHO(iPr)BH 4 (2-BH 4 ) were found to maintain catalytic activity for hydrogen production comparable to the Ru analogs, with greater than 98% conversion of 1,4 butanediol and quantitative hydrogen production. Furthermore, to assess the viability of utilizing heterogenized molecular catalysts for 1,4 butanediol (de)hydrogenation, molecular catalysts, Mn-MACHO(Ph), Ru-MACHO(Ph), and Ru-9, were polymerized to form Mn-MACHO-Poly, Ru-MACHO-Poly, and Ru-9-Poly respectively. These catalysts were then used to assess (de)hydrogenation of polyols, ethylene glycol and 1,4 butanediol. These studies reveal that Ru-MACHO-Poly is an efficient dehydrogenation catalyst with 99% conversion and a hydrogen percent yield of 96%. In addition, Ru-MACHO-Poly is a competent hydrogenation catalyst with approximately 98% conversion back to 1,4 butanediol based on quantitative NMR measurements. Overall, the data suggest these molecular and heterogenized catalysts have potential for practical use in polyalcohol-based LOHC systems.

08 HYDROGEN↗

NASA NTRS2000

A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

Kerczewski, Robert J.↗

DOE OSTI2021

The rise of electric vehicles—2020 status and future expectations

Electric vehicles (EVs) are experiencing a rise in popularity over the past few years as the technology has matured and costs have declined, and support for clean transportation has promoted awareness, increased charging opportunities, and facilitated EV adoption. Suitably, a vast body of literature has been produced exploring various facets of EVs and their role in transportation and energy systems. This paper provides a timely and comprehensive review of scientific studies looking at various aspects of EVs, including: (a) an overview of the status of the light-duty-EV market and current projections for future adoption; (b) insights on market opportunities beyond light-duty EVs; (c) a review of cost and performance evolution for batteries, power electronics, and electric machines that are key components of EV success; (d) charging-infrastructure status with a focus on modeling and studies that are used to project charging-infrastructure requirements and the economics of public charging; (e) an overview of the impact of EV charging on power systems at multiple scales, ranging from bulk power systems to distribution networks; (f) insights into life-cycle cost and emissions studies focusing on EVs; and (g) future expectations and synergies between EVs and other emerging trends and technologies. The goal of this paper is to provide readers with a snapshot of the current state of the art and help navigate this vast literature by comparing studies critically and comprehensively and synthesizing general insights. This detailed review paints a positive picture for the future of EVs for on-road transportation, and the authors remain hopeful that remaining technology, regulatory, societal, behavioral, and business-model barriers can be addressed over time to support a transition toward cleaner, more efficient, and affordable transportation solutions for all.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol

With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management.

Stackelberg game↗

DOE OSTI2026

Enabling kilometer-scale E3SM land model simulation over North America: A new integrated framework solution

This study introduces a novel framework designed to enhance the performance, scalability, and portability of the kilometer-scale E3SM Land Model (km-ELM) within the E3SM modeling infrastructure. By seamlessly integrating cutting-edge data tools, we address existing challenges such as slow performance, limited scalability, and difficulties in software integration in current data-driven ELM simulation over large geographic areas. Our innovative approach leverages the KiloCraft data toolkit to generate unified inputs for simulations ranging from a single-cite case, to a 72,083-cell regional case to a continental configuration encompassing 21.6 million land grid cells at a 1 km × 1 km resolution. We conduct extensive strong- and weak-scaling experiments on three state-of-the-art supercomputers, utilizing up to 100,800 CPU cores across 2400 compute nodes to evaluate end-to-end metrics including wall-clock time, simulation-years-per-day (SYPD), initialization costs, and I/O throughput. Our results reveal the land (LND) component’s efficient scaling, demonstrating near-ideal weak scaling and strong-scaling parallel efficiencies reaching up to 87% at 50,400 cores. We confirm portability and reproducibility through bitwise-equivalent outputs across different machines using identical inputs over supported machines. Notably, at extreme scales, we identify I/O as a critical bottleneck and that leads to effective solution with the SCORPIO/ADIOS stack. Collectively, these findings validate the deployment of km-ELM at a continental scale with high parallel efficiency and provide essential guidance on configuration, decomposition, and I/O settings for optimized kilometer-scale land simulations in E3SM. This work emphasizes the innovative design and practical solutions that enhance the operational capabilities of km-ELM, focusing on software performance and scalability while leaving detailed scientific evaluations of simulated land processes for future investigations.

E3SM land model (ELM), km-ELM, scalability, perfor↗

DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward

The ever-increasing volumes of scientific data combined with sophisticated techniques for extracting information from them have led to the increasing popularity of ensemble workflows which are a collection of runs of individual workflows. A traditional approach followed by scientists to run ensembles is to rely on simple scripts to execute different runs and manage resources. This approach is not scalable and is error-prone, thereby motivating the development of workflow management systems that specialize in executing ensembles on HPC clusters. However, when the size of both the ensemble and the target system reach extreme scales, existing workflow management systems face new challenges that hamper their efficient execution. In this paper, we describe our experience scaling an ensemble workflow from the computational biology domain from the early design stages to the execution at extreme scale on Summit, a leadership class supercomputer at the Oak Ridge National Laboratory. We discuss challenges that arise when scaling ensembles to several million runs on thousands of HPC nodes. We identify challenges with composition of the ensemble itself, its execution at large scale, post-processing of the generated data, and scalability of the file system. Based on the experience acquired, we develop a generic vision of the capabilities and abstractions to add to existing workflow management systems to enable the execution of ensemble workflows at extreme scales. We believe that the understanding of these fundamental challenges will help application teams along with workflow system developers with designing the next generation of infrastructure for composing and executing extreme-scale ensemble workflows.

Mehta, Kshitij↗

DOE OSTI2026

Scalable and Secure Power Outage Data Reporting: A Hexagonal Geospatial Approach

Power outages disrupt critical infrastructure and cause billions of dollars in economic losses annually in the United States. Accurate and granular outage reporting is vital for effective restoration and mitigation. This paper examines the integration of the Hexagonal Hierarchical Geospatial Indexing System (H3) to enhance power outage reporting, leveraging its uniform grid structure, scalable resolutions, and support for privacy-preserving analysis. Using high-resolution LandScan Global population data and K-anonymization techniques, this work achieves a balance between data granularity and privacy. Results show that lower privacy thresholds (e.g., K-anonymity = 2) enable higher resolution, while stricter thresholds (e.g., >15 people per hex) reduce granularity, potentially affecting localized responses. State-and county-level resolution case studies demonstrate H3’s adaptability and the trade-offs between precision and privacy. The proposed H3-based framework offers a scalable and efficient solution for geospatial data integration within the energy sector, such as outage data, aiding utilities and regulators in improving resilience and response efforts, particularly in disaster-prone regions.

Ahmad, Nasir [ORNL] (ORCID:0000000150677368)↗

DOE OSTI2020

Addressing Human and Organizational Factors in Nuclear Industry Modernization: An Operationally Focused Approach to Process and Methodology

Utility owners and operators of commercial nuclear power plants in the United States (U.S.) are and will be modernizing their nuclear power plants by performing a digital transformation involving design of an integrated set of systems that together enable a technology centric operating plant. The Plant Modernization Pathway of the U.S. Department of Energy Light Water Reactor Sustainability Program has a strategic action plan that lays the groundwork for a digital transformation of the nuclear industry. The model for this transformation is an advanced concept of operations, with an end point vision, “To achieve the maximum aggregate benefit enabled by this digital transformation.” To achieve this, the digital infrastructure for a nuclear plant must be designed as an integrated set of systems that together enable a technology centric operating model. The digital transformation process obviously needs to involve technology considerations and systems engineering, but it also needs to include human and organizational expertise. Thus, human and organizational factors, including sociotechnical systems methods and techniques (e.g., Cognitive Systems Engineering, Systems Theoretic Accident Modeling and Processes, human systems integration, and Macroergonomics) need to be considered for digital transformation projects in order to effectively integrate human and organizational expertise efforts into the new work system that results from nuclear power plant digital modernization. That is, the work system is the basic unit of sociotechnical systems analysis and contains three components: personnel, technical, and organization and management. These components should be jointly optimized with respect to the interdependence of systems performance criteria of effectiveness, efficiency and safety. Joint optimization can be achieved through the application of three human and organization functions: knowledge representation, knowledge elicitation, and cross-functional integration. This report provides a strategic framework for effective integration of human and organizational expertise within nuclear power plant digital modernization efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2008

Alternate Fuels for Use in Commercial Aircraft

The engine and aircraft Research and Development (R&D) communities have been investigating alternative fueling in near-term, midterm, and far-term aircraft. A drop in jet fuel replacement, consisting of a kerosene (Jet-A) and synthetic fuel blend, will be possible for use in existing and near-term aircraft. Future midterm aircraft may use a biojet and synthetic fuel blend in ultra-efficient airplane designs. Future far-term engines and aircraft in 50-plus years may be specifically designed to use a low- or zero-carbon fuel. Synthetic jet fuels from coal, natural gas, or other hydrocarbon feedstocks are very similar in performance to conventional jet fuel, yet the additional CO2 produced during the manufacturing needs to be permanently sequestered. Biojet fuels need to be developed specifically for jet aircraft without displacing food production. Envisioned as midterm aircraft fuel, if the performance and cost liabilities can be overcome, biofuel blends with synthetic jet or Jet-A fuels have near-term potential in terms of global climatic concerns. Long-term solutions address dramatic emissions reductions through use of alternate aircraft fuels such as liquid hydrogen or liquid methane. Either of these new aircraft fuels will require an enormous change in infrastructure and thus engine and airplane design. Life-cycle environmental questions need to be addressed.

Daggett, David L.↗

NASA NTRS1993

NASA Propulsion Engineering Research Center, volume 1

Over the past year, the Propulsion Engineering Research Center at The Pennsylvania State University continued its progress toward meeting the goals of NASA's University Space Engineering Research Centers (USERC) program. The USERC program was initiated in 1988 by the Office of Aeronautics and Space Technology to provide an invigorating force to drive technology advancements in the U.S. space industry. The Propulsion Center's role in this effort is to provide a fundamental basis from which the technology advances in propulsion can be derived. To fulfill this role, an integrated program was developed that focuses research efforts on key technical areas, provides students with a broad education in traditional propulsion-related science and engineering disciplines, and provides minority and other under-represented students with opportunities to take their first step toward professional careers in propulsion engineering. The program is made efficient by incorporating government propulsion laboratories and the U.S. propulsion industry into the program through extensive interactions and research involvement. The Center is comprised of faculty, professional staff, and graduate and undergraduate students working on a broad spectrum of research issues related to propulsion. The Center's research focus encompasses both current and advanced propulsion concepts for space transportation, with a research emphasis on liquid propellant rocket engines. The liquid rocket engine research includes programs in combustion and turbomachinery. Other space transportation modes that are being addressed include anti-matter, electric, nuclear, and solid propellant propulsion. Outside funding supports a significant fraction of Center research, with the major portion of the basic USERC grant being used for graduate student support and recruitment. The remainder of the USERC funds are used to support programs to increase minority student enrollment in engineering, to maintain Center infrastructure, and to develop research capability in key new areas. Significant research programs in propulsion systems for air and land transportation complement the space propulsion focus. The primary mission of the Center is student education. The student program emphasizes formal class work and research in classical engineering and science disciplines with applications to propulsion.

Source record↗

NASA NTRS2019

A Control Strategy for Turbine Electrified Energy Management

Hybrid electric propulsion architectures provide the infrastructure to enable additional benefits to the propulsion system that are otherwise unrealizable with the sole use of the current, state-of-the-art, gas-driven, turbine engines. The presence of electric machines (EMs) coupled to the shaft(s) of the turbine engine provide the ability to actively alter the operation of the engine to the benefit of the propulsion system and the aircraft it propels. This is the goal of the Turbine Electrified Energy Management (TEEM) concept, which at its broadest level addresses the management of energy across the electrified propulsion system. Prior work has demonstrated the use of this concept to alter steady-state operation and improve transient operability of a hybrid-electric propulsion system. The main benefits previously illustrated include the elimination of stability bleeds and expansion of the turbomachinery design space in order to enable more efficient designs. This paper focuses on the development of control strategies to implement the TEEM concept, and it explores several possible architecture variants for applying this concept. Comparison studies are conducted between a purely gas-driven turbofan (baseline engine configuration) and TEEM augmented variants of the baseline engine. The variants are distinguished by the shaft(s) that possess an EM. The configurations consider EMs on both shafts, an EM on the high pressure spool (HPS) only, and an EM on the low pressure spool (LPS) only. These configurations are referred to as the dual-spool configuration, the HPS configuration, and LPS configuration, respectively. The studies expose several options in configuring and controlling the system, including the use of a single EM coupled to a single shaft of a two-spool engine to positively impact the operability of both shafts. The studies also demonstrate the use of independently designed controllers for the electric machine(s) that allow for a decoupled control design process.

Electrified↗

NASA NTRS2019

A Control Strategy for Turbine Electrified Energy Management

Energy management↗

NASA NTRSDate not supplied

Some NASA Perspectives on H2

This presentation provides the NASA perspective for government and industry technology developers world-wide who are exploring the potential for hydrogen (H2) in aviation applications including electric aircraft, airport vehicles, and airport related equipment. NASA’s H2 interest has been related to vehicle-level technology development and integration only, but NASA is keenly aware of the necessity of work focused on solving the challenges of H2 airport infrastructure. Some historical perspectives are provided on NASA efforts in H2 fuel cell development for space and aeronautics applications. NASA’s current research portfolio includes some investigation of H2 energy storage and hydrocarbon conversion for fuel cells on electric aircraft. There is no current emphasis on the other H2-related challenges. Over the years NASA has conducted research into “Quiet Green Transport” with a H2 fuel cell powered aircraft, an Emissionless Aircraft Study with Non Flow Through – Regenerative Fuel Cell (NFT-RFC) technology which captures the water, and an H2 fuel cell powered Unmanned Aerial Vehicle (UAV) study. NASA has flown a long endurance UAV using a RFC in the Low Emissions Alternative Power (LEAP) program. Recently, NASA conducted feasibility studies on hybrid SOFC with onboard hydrocarbon fuel reformation for aircraft primary propulsive power and secondary power under the Fostering Ultra-Efficient, Low-Emitting Aviation Power (FUELEAP) program. NASA currently has a multiyear program with the Center for Cryogenic High-Efficiency Electrical Technologies for Aircraft (CHEETA) to explore the benefits of using cryogenic H2 on aircraft for fuel cell propulsion and to provide superconductivity in electrical power management on the aircraft.

Power↗