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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 667 records · Page 37

The NASA Solar Cruiser Mission - Solar Sail Propulsion Enabling Heliophysics Missions

Solar Cruiser is a Small Satellite Technology Demonstration Mission (TDM) to mature solar sail propulsion technology to enable near-term, high-priority breakthrough science missions as defined in the Solar and Space Physics Decadal Survey. Solar sails have the potential to provide high ΔV for many types of missions. Solar sails are large, mirror-like structures made of a lightweight material that reflects sunlight to propel the spacecraft. The continuous solar photon pressure provides thrust with no need for the heavy, expendable propellants used by conventional chemical and electric propulsion systems. Solar Cruiser will demonstrate a “sailcraft” platform with pointing control and attitude stability comparable to traditional platforms, upon which a new class of Heliophysics missions may fly. It will show sailcraft operation (acceleration, navigation, station keeping, heliocentric plane change) scalability of sail technologies such as the boom, membrane, and deployer to enable more demanding missions, such as high inclination solar imaging. Solar Cruiser will launch as a secondary payload with NASA’s Interstellar Mapping and Acceleration Probe (IMAP) in early 2025. The sailcraft will separate from the launch vehicle on a near-L1 trajectory (Sun-Earth Lagrangian Point 1; sunward of L1 along the Sun-Earth Line) and complete its primary mission in 11 months or less.

Solar Sail↗

Enabling Simulation Interoperability between International Standards in the Space Domain

Today, the design and development of space systems are conducted cooperatively by historical space agencies in this area such as NASA, ESA, Roscosmos, and JAXA together with their industrial partners. The space system lifecycle is characterized by high costs, uncertain conditions, and dangerous scenarios. To mitigate these issues, space agencies rely heavily on Modelling and Simulation as a key technology to support the analysis, design, and operation of space systems. To support large-scale distributed simulations, the scientific community has developed several standards to support the reuse and interoperability of simulation models such as IEEE 1516 High-Level Architecture (HLA), Real-time Platform Reference Federation Object Model (RPR FOM), Simulation Model Portability (SMP), and the novel Space Reference Federation Object Model (SpaceFOM). While the SpaceFOM standard has been specifically conceptualized for handling space systems, the other ones are more general-purpose and can be used to design and simulate generic complex systems. As a consequence, there is a lake of rules and guidelines to enable interoperability among these standards. The paper presents solutions and experiences for enabling interoperability and transferability of HLA, RPR FOM, and SMP simulation models with SpaceFOM.

Simulation↗

Enabling Supersonic Over-Land Flight Using Computational Modeling

NASA's QueSST mission is interested in exploring the potential of commercial, over-land supersonic flight, which has been banned since 1973 due to the noise intensity of the sonic booms that supersonic vehicles create. NASA has partnered with Lockheed Martin to design and construct an experimental aircraft, the X-59, that will shape the sonic boom to achieve a perceived loudness on the ground that is lower than 75dB. In this talk, we present new developments in the Launch Ascent and Vehicle Aerodynamics (LAVA) CFD solver framework which are tailored to the analysis of supersonic vehicles, like the X-59, and sonic boom propagation. Utilizing a high-order space marching method, adjoint-based grid adaptation, and robust database capabilities have enabled significant cost savings over traditional CFD methods for the same level of accuracy in perceived boom loudness. Example cases demonstrating the toolset are presented, along with comparisons to experimental wind tunnel data. These developments will enable LAVA to quickly and accurately provide simulation data to the project during X-59's first and subsequent flights in 2024 and beyond.

CST↗

Solid-state Architecture Batteries for Enhanced Rechargeability and Safety (SABERS) Beyond Li-Ion: Technology to Enable Sustainable Electric Aviation

All-electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The primary barrier to overcome is developing an energy storage system capable of meeting the rigorous aerospace safety and performance criteria. The performance metrics for eVTOL vehicles are at least two times greater than those of electric ground vehicles. Furthermore, inherently non-flammable batteries are essential for safe operation of commercial electric aerovehicles. The SABERS concept proposes a battery that meets the key performance criteria through development of a solid-state architecture battery utilizing high-capacity sulfur-selenium cathode and lithium metal anode. The combination of sulfur and selenium offers a balanced energy-to-power density ratio, which can be tailored to the specific application by altering the stoichiometric ratios of sulfur to selenium. This hybrid cathode will be developed by implementing NASA patented holey graphene technology as a highly conductive, ultra-lightweight electrode scaffold. A solid-state electrolyte will be used as a safe, non-flammable replacement to the highly flammable liquid organic electrolytes currently used in SOA lithium-ion batteries. This solid-state lithium-sulfur/selenium cell will be designed into a serial stacking configuration to enable dense packaging of the battery cells. The serial stacking configuration is termed a bipolar stack, which has the advantages of reducing overall cell weight, simplifying the interfaced connections for the cell, and minimizing the cooling requirements. Lastly, optimization of battery components will occur through a robust and rigorous combination of various computational modeling techniques covering multiple length scales. The expected result will be a fully solid-state battery with operational temperatures up to 150 °C which provides the required energy density, discharge rates, and inherent safety to meet the strict aerospace mission performance criteria. This presentation will demonstrate a feasible path for solid-state cells that possess a specific energy of greater than 400 Wh/kg to enable electric aircraft.

Urban Air Mobility (UAM) Vehicles↗

The Role of Trust and Usability in Enabling Spaceflight Crew Autonomy

Future long duration exploration missions will require an increased use of onboard automated systems as spaceflight crews venture further than before and have longer communications delays with ground support. The design of these systems must support appropriate crew trust and have sufficient usability to enable spaceflight crew autonomy or risk being misused while crews wait to communicate with ground support. We evaluated trust & usability in our self-scheduling tool, Playbook, for crew mission timelines. Data was collected in a controlled lab experiment with 31 participants. Participants in the study conducted two tasks: scheduling, where participants were responsible for scheduling a majority of a day's operational tasks, and rescheduling, where participants were provided a schedule and asked to reschedule higher priority activities. We found a significant correlation between system trust and usability, irrespective of self-scheduling tasks. We conclude that system usability may play a bigger role in how trust is learned while conducting novel crew autonomy tasks such as self-scheduling. Future research should investigate the role of usability to encourage appropriate trust in onboard automated crew systems and enable crew autonomy.

crew autonomy↗

Overview of the Research Aircraft for eVTOL Enabling techNologies (RAVEN) Activity

The Research Aircraft for eVTOL Enabling techNologies (RAVEN) activity is a collaboration between Georgia Tech and NASA to design and develop a 1,000 lb gross weight class eVTOL research aircraft. The vision for RAVEN is that the aircraft will serve as a “flying laboratory” for enduring research and technology development applications across the realm of eVTOL technologies. A major goal of RAVEN is to disseminate the aircraft design geometry and data from flight tests for the benefit of the broader aeronautics community. Initial research applications will include flight dynamics, controls, acoustics, and automation/autonomy. The aircraft is based on the airframe of a fixed-wing experimental homebuilt airplane that will be modified to incorporate a distributed propulsion system, battery system, fly-by-wire flight control system, and avionics to enable remotely piloted operation. The aircraft is being designed to use commercial off-the-shelf components to the maximum extent practicable to save costs and to accelerate the development schedule without compromising the goal of publishing design geometry and test data. The RAVEN activity is also focused on workforce development by training the next generation of aerospace engineers in eVTOL technologies.

eVTOL↗

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA’s Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.

Kenneth R. Lyons↗

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA's Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.

Kenneth R Lyons↗

Rocket Lab Venus - Enabling Low-Cost Interplanetary Missions

Rocket Lab’s mission to Venus, launching in January 2025, aims to demonstrate that small launch vehicles, such as Electron, and high-energy small spacecraft, such as Photon, can enable a new paradigm of regular, low-cost interplanetary missions. The primary science mission involves a small ~20 kg direct entry probe that will sample the Venusian cloud layers with an autofluorescing nephelomoeter. The entry probe thermal protection system (TPS) is even more critical than usual due to the selected entry trajectory and the lack of suitable heritage TPS materials. NASA’s newly developed 3D Woven Carbon Phenolic, a derivative of the Heatshield for Extreme Entry Environment Technology (HEEET) material, is an enabling technology for this mission, and it will comprise the probe forebody heat shield. The work that led to the selection of 3D Woven Carbon Phenolic, the TPS cost-reduction approaches taken, and the future possibilities of such an approach to probe development are presented.

Lyle Campbell↗

Rocket Lab Venus - Enabling Low-Cost Interplanetary Missions

Rocket Lab’s mission to Venus [1], launching in January 2025, aims to demonstrate that small launch vehicles, such as Electron, and high-energy small spacecraft, such as Photon, can enable a new paradigm of regular, low-cost interplanetary missions. The Electron launch vehicle will deliver the Photon spacecraft carrying the entry probe into low earth orbit, at which point Photon will take over and put both itself and the attached probe on an Earth-Venus transfer trajectory, with assistance from a Lunar flyby. The primary science objective of the mission is to sample the Venusian cloud layers between atitudes of 45 and 60 km above the surface with an autofluorescing nephelometer. The ~1kg scientific instrument will be carried by a small ~17 kg direct entry probe which will separate from the Photon spacecraft approximately 30 minutes prior to atmospheric entry. The probe outer mould line is a scaled down version of the Deep Space 2 probes [2]. NASA’s newly developed 3D Woven Carbon Phenolic, a derivative of the Heatshield for Extreme Entry Environment Technology (HEEET) material [3], is an enabling technology for this mission, and it will comprise the probe forebody heat shield.The probe will communicate directly back to the Earth during its descent via a small on-board radio frequency (RF) antenna. The hyperbolic Venus encounter will see the probe enter the Venusia atmosphere at approximately 11 km/s with an entry flight path angle (EFPA) of about 10°. This relatively low entry angle was selected to increase the duration of the descent phase, reduce the peak deceleration, and reduce the sensitivity of the nominal entry environments to navigational errors. For reference, the Pioneer Venus probes entered at EFPAs varying from about 25° to 70°, resulting in peak decelerations between around 220 and 450 g’s and peak stagnation point heat fluxes ranging from about 40 to 75 MW/m2 [4]. In comparison, the Rocket Lab Venus probe will experience a lower preak deceleration of around 60 g’s and a lower peak stagnation point heat flux of about 15 MW/m2, at the cost of a greater total heat load and correspondingly higher thermal protection system (TPS) mass fraction. This paper recounts the development history of the TPS for the Rocket Lab Venus probe, with a focus on the unique requirements of the mission. In particular, the effect on the TPS design of the mission’s tight budget, short development timeline, slim mass and RF link margins, and mild but long duration entry heating. Rocket Lab’s initial investigations into a ‘hybrid’ TPS [5] based on a scaled version of the Pioneer Venus full-density carbon phenolic forebody shield, and the abandonment of this approach in favour of NASA’s 3D Woven Carbon Phenolic is presented. Additionally, the leveraging of previous testing under the HEEET project at higher conditions and the associated TPS margining strategy that was adopted to avoid the need for extensive mission-specific arc jet testing is discussed. Finally, this paper concludes with a brief consideration of the feasibility of a future low-cost aerocapture technology demonstrator mission based on the 2025 Rocket Lab Venus mission and entry probe.

Lyle Campbell↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

Progressively Enabling Earth Independent Medical Operations (EIMO)

This panel presents the findings from a series of Technical Interchange Meetings (TIMs) hosted by the Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program. The topics for the TIMs were derived from a 2-day conference of senior leaders and subject matters experts that collectively outlined a multi-faceted strategy designed to optimize crew health and performance through an increasingly autonomous medical approach. The first abstract in this panel outlines the scope of issues related to data collection, usage, transmission and computing capacity to facilitate EIMO. The second presentation provides an overview of the challenges in developing curricula and advanced training tools to baseline knowledge, skills and abilities (KSA), verify clinical competency and assure retention during prolonged durations inherent in exploration-class missions. An overview of the complicated medical supply and resource chain necessary to facilitate EIMO is provided in the third presentation of this panel. The final presentation in this EIMO panel surveys the breadth and depth of demands on cognitive load expected to be experienced by crew on an exploration mission and proposes strategies to mitigate the prospect of cognitive overload through methods to shift task load from the crew to multi-modal artificial intelligence based medical support systems. Taken together, these presentations summarize the challenges to be expected and potential solution spaces to be explored and developed to progressively enable increasing autonomous medical operations to support crewed missions beyond low earth orbit. Through EIMO focused pre-mission planning, integrated data architecture design, innovative training development and AI-assisted task load management, the gradual transition of medical care and decision making from terrestrial to space-based assets enabling support of astronaut health and performance and reducing overall mission risk is achievable.

Jay Lemery↗

Progressively Enabling Earth Independent Medical Operations (EIMO)

This panel presents the findings from a series of Technical Interchange Meetings (TIMs) hosted by the Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program. The topics for the TIMs were derived from a 2-day conference of senior leaders and subject matters experts that collectively outlined a multi-faceted strategy designed to optimize crew health and performance through an increasingly autonomous medical approach. The first abstract in this panel outlines the scope of issues related to data collection, usage, transmission and computing capacity to facilitate EIMO. The second presentation provides an overview of the challenges in developing curricula and advanced training tools to baseline knowledge, skills and abilities (KSA), verify clinical competency and assure retention during prolonged durations inherent in exploration-class missions. An overview of the complicated medical supply and resource chain necessary to facilitate EIMO is provided in the third presentation of this panel. The final presentation in this EIMO panel surveys the breadth and depth of demands on cognitive load expected to be experienced by crew on an exploration mission and proposes strategies to mitigate the prospect of cognitive overload through methods to shift task load from the crew to multi-modal artificial intelligence based medical support systems. Taken together, these presentations summarize the challenges to be expected and potential solution spaces to be explored and developed to progressively enable increasing autonomous medical operations to support crewed missions beyond low earth orbit. Through EIMO focused pre-mission planning, integrated data architecture design, innovative training development and AI-assisted task load management, the gradual transition of medical care and decision making from terrestrial to space-based assets enabling support of astronaut health and performance and reducing overall mission risk is achievable.

John Lemery↗

Alignment of NASA’s Human System Risks with Technological Capability Gaps to Enable Integrated Research and Technology Development Strategic Planning

BACKGROUND: Radiation, reduced gravity, distance from earth, isolation and confinement, and habitation within artificially created and controlled life support environments are hazards that present risk to human space explorers. NASA’s Human System Risk Board (HSRB) maintains a set of twenty-nine different Human System Risks with subject matter experts from across the agency providing regular updates to the estimated likelihood and consequence associated with each risk. The Human Research Program (HRP) has historically used these risk classifications as a primary basis for identifying and prioritizing human research investments aimed at characterizing and/or mitigating the respective human system risks. In many cases, technology development is required to mature and validate risk mitigation strategies, however, NASA’s primary technology development programs have not typically used Human System Risks as a basis for strategic planning. A primary function of the Environmental Control and Life Support Systems (ECLSS) – Crew Health and Performance (CHP) System Capability Leadership Team (SCLT) is to continually identify, review, and update technological capability gaps and to establish and oversee multiyear strategic roadmaps aimed at guiding NASA’s technology development priorities in these areas. These roadmaps are intended to be agency-wide and independent of any program, directorate, or other organization within NASA. DESCRIPTION: The SCLT and HRP collaborated to establish a set of Human System Capability Gaps, which establish a formal linkage between the capability gaps used to prioritize investments across much of NASA, and the Human System Risks that are primarily used to inform and prioritize human research. This set of twenty-eight gaps was developed by subject matter experts, and each gap mapped to the primary associated Human System Risks. The gaps and risk mapping were then reviewed and approved via the HSRB, thereby formally aligning the research-focused Human System Risks with the technology-focused capability gaps. DISCUSSION: This effort has enabled development of integrated roadmaps and budget coordination with research and technology development activities that are formally linked to agency-recognized capability gaps and Human System Risks. Close and ongoing coordination between the SCLT, HRP, Mars Campaign Office, and the Health & Medical Technical Authority (HMTA) is essential to ensure alignment and prioritization of CHP-related research and technology development to enable NASA’s future exploration missions.

Andrew F J Abercromby↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

Testing of Advanced Capabilities to Enable In-time Safety Management and Assurance for Future Flight Operations

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

sUAS↗

Solid-state Architecture Batteries for Enhanced Rechargeability and Safety (SABERS) Beyond Li-Ion: Technology to Enable Sustainable Electric Aviation

All-electric and hybrid electric aerovehicle concepts face numerous technical barriers prior to their introduction into the consumer marketplace in order to meet sustainable aviation goals. One of the primary barriers to overcome is developing an energy dense storage system capable of meeting the rigorous aerospace safety and performance criteria. Additionally, the electric aviation market requires that the batteries be fabricated in a sustainable manner with materials that are readily available and abundant within the U.S. to ensure there are no supply chain issues during manufacturing. The performance metrics to enable electric aviation are at least two times greater than those of electric ground vehicles. Furthermore, inherently non-flammable batteries are essential for safe operation of commercial electric aerovehicles. The SABERS concept proposes a battery that meets the key performance criteria through development of a solid-state architecture battery utilizing high-capacity sulfur-selenium cathode and lithium metal anode. The combination of sulfur and selenium offers a balanced energy-to-power density ratio, which can be tailored to the specific application by altering the stoichiometric ratios of sulfur to selenium. The hybrid cathode has been developed by implementing NASA patented holey graphene technology as a highly conductive, ultra-lightweight electrode scaffold. A solid-state electrolyte has been used as a safe, non-flammable replacement to the highly flammable liquid organic electrolytes currently used in SOA lithium-ion batteries. This solid-state lithium-sulfur/selenium cell will be designed into a serial stacking configuration to enable dense packaging of the battery cells. The serial stacking configuration is termed a bipolar stack, which has the advantages of reducing overall cell weight, simplifying the interfaced connections for the cell, and minimizing the cooling requirements. Lastly, optimization of battery components will occur through a robust and rigorous combination of various computational modeling techniques covering multiple length scales. The expected result will be a fully solid-state battery with operational temperatures up to 150 °C which provides the required energy density, discharge rates, and inherent safety to meet the strict aerospace mission performance criteria. This poster summarizes recent results from battery component optimization to scale-up production of full SABERS pouch cells.

Urban Air Mobility (UAM) Vehicles↗