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NASA NTRS2019

Design Considerations and Development Status for Atmospheric Powered Descent of High-Mass Payloads at Mars

Retropropulsion, initiated at supersonic conditions, is the only presently viable approach to the descent and landing of human-scale payloads on Mars, providing both deceleration and control authority for precision landing. Atmospheric powered descent challenges traditional processes and dependencies in system design. Changes to a design by one discipline have a greater potential to affect overall vehicle performance than in the case of powered descent with no atmosphere. The interdependence is due to the sensitivity of propulsive-aerodynamic interference effects to small changes in configuration or operational environment. The fidelity of Mars exploration architectures has increased in recent years, and the conditions and designs with the lowest aerosciences uncertainties are no longer as flight-relevant. At this time, there is no human-rated vehicle relying on retropropulsion for descent and landing in an atmospheric environment. Continued development of powered descent for atmospheric environments is enabled by progressive balanced investments in ground testing, predictive computational capabilities, and flight testing and demonstration.

EDL↗

NASA NTRSDate not supplied

A Multiphysics Study to Improve Specific Energy of Primary Batteries for Low Temperature Operation for Deep Space Missions

Several lander missions on the outer planets such as Europa, Enceladus, and Titan require electrical power to operate scientific and communication equipment. The traditional power generation methods, such as a photovoltaic array, are not feasible as their efficiency drops significantly at these vast distances. The novel radioisotope power systems are not practical today based on current lander designs and the effectiveness of these systems. To perform in situ science on distant planets, a high specific energy battery (>700 Wh/kg) needs to operate for about 480 hours under cold temperatures (-40C or 0C) [1]. While a primary battery such as Li-CFx can provide high specific energy at room temperature, its specific capacity decreases significantly at low temperatures. One of the causes for this drop is low ion and electrical conductivity, and slower reaction kinetics. Slower transport and facile kinetics lead to an increase in the battery’s resistance and higher voltage drops during the cell operation, thus reducing specific capacity. Both the transport and kinetics show a strong dependence on temperature. Thus, a small temperature rise can lead to an increase in the reaction rate and ion conductivity; since the temperature, cell resistance, and specific capacity are interdependent. A conventional battery model accounts for ohmic, thermodynamic, and, electrochemical, and chemical decomposition heating. The ohmic heating can be controlled by designing a resistive microstructure and varying the ratios of the active materials [2]. The kinetics can be improved by increasing the surface area, reducing the particle size, or adding a catalyst. These parameters are often optimized to achieve high specific energy at room temperatures. A similar optimization study is not available at low temperatures and for a primary (high specific energy) battery. For this presentation, we will explore the effect of geometrical, microstructural, and material properties on optimal specific capacity at low temperatures through multiphysics simulations. The ion transport resistance depends on the porosity and the tortuosity of an electrode and the separator.

M. Mehta↗

NASA NTRSDate not supplied

Enabling Advanced Air Mobility Operations through Appropriate Trust in Human-Autonomy Teaming: Foundational Research Approaches and Applications

Emerging Advanced Air Mobility(AAM)operations will be enabled by increasingly autonomous systems, requiring technologies to take on more responsibilities and fundamentally altering traditional human-automation interaction paradigms. The growing reliance on higher levels of automation will necessitate research to identify capabilities and principles that facilitate humans and machines working and thinking better together, i.e., human-autonomy teaming (HAT). Trust is an inherent requirement in effective teams because when members work interdependently, those agents (human, automation) must be willing to accept a level of risk to rely upon each other to reach goals and contribute to team tasks. This work provides an initial approach to enabling AAM operations through appropriate trust within HAT. The main contributions of this approach resides in connecting the construct of trust to mental models. Using the outlined mental model approach, we propose novel HAT strategies, such as Adaptive Trust Calibration, and preview planned research activities derived from this approach. Additionally, we propose several practical applications that can currently be employed by AAM development communities.

Eric T Chancey↗

NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗

NASA NTRSDate not supplied

Keplerian Analysis for Versatile Evaluation of Arbitrary Trajectories

Designing interplanetary missions is an iterative process, with a tight coupling between mission analysis and vehicle design. The ability to simultaneously process both analyses early on can provide significant benefits in both the concept formulation and in overall mission feasibility. To implement such a simultaneous analysis process, there is a need to model interplanetary trajectories rapidly while maintaining an acceptable level of fidelity. Many simple patched conics tools make too many assumptions to satisfactorily address all relevant constraints and objectives, while full-fidelity trajectory design tools often require more time and expertise than is available in early conceptual studies. Hence, some form of middle-ground modeling tool would provide value. This paper introduces the development of such a tool using Python. This tool, Keplerian Analysis for Versatile Evaluation of Arbitrary Trajectories (KAVEAT), implements a patched conics method that enforces continuity of state (time, mass, position, velocity) through all transitions between various spheres of influence on the trajectory. The trajectory is constructed in an object-oriented manner, dynamically assembling unique maneuvers such as departure burns, heliocentric transfers (ballistic or with electric propulsion), and planetary flybys (powered or unpowered). Each maneuver has its own specific set of basic inputs needed to define it. The tool then takes these defined maneuvers, strings them together creating all the interdependencies required, and solves for the ideal trajectory using a gradient-based optimizer.

Katherine T McBrayer↗

NASA NTRS1993

Ionospheric Imaging From a Low Earth Orbiter Tracking GPS

Tomographic imaging of the ionosphere is examined using singular value decomposition analysis. The interdependency of the obtainable resolution, the accuracy of the solution and the noise data is explained. A simulation is illustrated where a true 2-D ionospheric structure is generated, and a tomographic inversion of the structure is carried out. Inclusion of data taken in an occultation geometry reveals the strength that is added by these data. The effect of the ionosphere on a GPS signal as viewed by a user in space is examined. Under somewhat strong solar conditions, the absolute bending of the signal is of the order of 0.01 degrees for the L1 signal; the phase advance can be as large as 90 meters.

ionosphere↗

NASA NTRS2011

HyspIRI Level-2 Thermal Infrared (TIR) land surface temperature and emissivity algorithm theoretical basis document.

This document outlines the theory and methodology for generating the HyspIRI TIR Level-2 land surface temperature and emissivity (LST&E) products. The LST&E products are derived from the surface radiance product that is calculated by atmospherically correcting the at-sensor radiance, making them interdependent products. LST&E products are essential for a wide range of Earth system studies.

Hook, Simon J.↗

NASA NTRSDate not supplied

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts.1,2 The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners.1 However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen M Middleton↗

NASA NTRS2019

ESD Reviews: Climate Feedbacks in the Earth System and Prospects for Their Evaluation

Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).

Climate feedbacks↗

NASA NTRS2021

Sensitivity of Wide Bandwidth Radiometer for Remote Sensing of Ocean Salinity

Passive microwave remote sensing of sea surface salinity from space is currently being done with measurements in the 27 MHz wide spectral window at 1.413 GHz (L-band) protected for passive use. Modern L-band instruments, such as the radiometers on SMOS and Aquarius, have demonstrated the feasibility of monitoring surface salinity from space, and they have also demonstrated the need for better accuracy, especially in cold water. Proposals to improve accuracy have largely involved adding measurement at more frequencies. For example, adding lower frequencies to improve the sensitivity to salinity in cold water and adding higher frequencies to enable simultaneous retrieval of sea surface temperature which is needed in the retrieval of salinity. These strategies involve trade-offs, some obvious such as the effects of interference from anthropogenic sources of radio frequency radiation (RFI) when operating outside the protected band and the loss of spatial resolution at lower frequencies. But, some are more subtle and arise because of the dependence of the retrieval of salinity on other parameters of the ocean surface, in particular, water temperature and roughness (wind speed). The effect of these interdependencies on the potential accuracy of salinity remote sensing in the frequency range 0.3 – 3.0 GHz is examined here to gain insight into the potential for future wide bandwidth instruments for remote sensing of salinity and the optimization of their design. There is benefit including the low frequencies, especially for cold water, but a danger of increased error including frequencies above 1.5 - 2.0 GHz depending on temperature.

Microwave Remote Sensing↗

NASA NTRSDate not supplied

Human Factors and Behavioral Performance Exploration Measures Harmonized Across HERA, NEK, and ISS: Teams Risk

BACKGROUNDThe Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture risks. HFBP-EM were collected during Human Exploration Research Analogs (HERA) campaigns 4 (C4) and 5 (C5), and during SIRIUS 17 and 19 missions in the Russian Ground Based Experiment Complex, NEK, to document the feasibility, flexibility, and acceptability of these measures in analogsof the spaceflight environment. A subset of the HFBP-EM suite was collected during spaceflight as part HRP’s Standard Measures in Spaceflight Project. Whenever possible, the HFBP-EM protocol and measures are kept the same across studies, however, differences across research settings (e.g., experimental manipulations, mission scenarios, mission length) and implementation of the measures require the data are harmonized to ensure comparable views across missions. The purpose of our project is to develop a harmonized database of HFBP-EM data from different settings, and to summarize the trajectory of behavioral health and performance within and between research settings. In this presentation, we will summarize the harmonized dataset and the trajectory of measures related to the Team Risk, including team performance, team cohesion, team processes, and psychological safety, over time and between and within settings.METHODSWe followed best practices for data harmonization. Characteristics of each research setting were assessed for harmonization potential, and we deemed NEK-SIRIUS 17 as inappropriate due to study aims, length, and data quality. Common variables of interest were identified. Study characteristics and key variables with which datafiles could be merged were defined as “meta-data.” HFBP-EM data from all settings were processed under a common format. We then created a harmonized team-level database designed to facilitate analyses that address HRP research gaps related to HRP’s Team risk. In this database, team cohesion, processes, performance, psychological safety, and group living were operationalized as the team mean of the crew responses for each data collection (e.g., on mission day 7). Data collected on the International Space Station (ISS) included a subset of scale items administered to participants. Data collected from ISS team members within +/- 7 days of the first data collection and every following 30 +/-4 days were aggregated to the team level. We generated figures that display the mean and variability of team constructs across research settings. We also generated plots of changes in team constructs with mission day and with percentage of the mission completed. Where possible, we compared data from spaceflight analogs with data from astronauts aboard the ISS. RESULTS AND DISCUSSIONThe team-level harmonized database was structured such that each row represents data for a team on a specific mission day. Team constructs (e.g., team cohesion) were included as columns (i.e., wide format) with repeated measures across mission days given in rows (i.e., long format). The database included data from 18 crews: five, 4-person American crews in HERA C4, four 4-person American crews in HERA C5, one 6-person multinational crew in NEK-SIRIUS 19, and eight, 2–11-person multinational crews aboard the ISS. Results provide insights into mission and campaign differences in team functioning and performance. For example, between campaign differences were observed for team processes—the interdependent team actions that orchestrate taskwork in pursuit of the team’s goals [1]. Greater between-team variability on team processes was detected during HERA C4 than during C5, and a downward trend was observed over the duration of C4, but not C5 or SIRIUS 19. This may be due to the campaign-level differences such as the sleep deprivation implemented during C4. Responses on the subset of team process items administered on the ISS indicate between-crew variability more like those observed in C4 than C5 and NEK, with some, but not all ISS crews demonstrating a downward trend over time. Additional findings will be presented. REFERENCESMarks, M. A., et al (2001) Academy of Management Review, 26(3), 356-376.

S T Bell↗

NASA NTRS2022

Systemic Risk

Systemic risk is the name given to impacts that spread within and across systems and sectors (e.g. ecosystems, health, infrastructure and the food sector) via the movements of people, goods, capital and information within and across boundaries (e.g. regions, countries and continents). The spread of these impacts can lead to potentially existential consequences and system collapse across a range of time horizons. Globalization contributes to systemic risk affecting people worldwide. The impacts of climate change or COVID-19 show how the challenges of addressing systemic risk go beyond conventional risk management and governance. Critical system interdependencies, amplified by underlying vulnerabilities, highlight that there is a growing need to better understand cascading impacts, systemic risks and the possible political (governance) and societal responses. This includes improving our understanding of the root causes of systemic risk, both biophysical and socio-economic, and related information needs. Addressing contemporary challenges in terms of systemic risk requires integrating different systems perspectives and fostering system thinking, while implementing key intergovernmental agendas, such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals.

systemic risk↗

NASA NTRS2021

Thermoelectric System Economics - The Apex: New Paradigms in Manufacturing and Interface Performance Relationships Driving System Cost Optimizations

Thermoelectric energy recovery systems require cost-optimized designs with high thermal and thermoelectric (TE) performance to surmount common commercialization barriers controlling the acceptance of thermoelectric generator (TEG) systems. Recent work has addressed this requirement with new analytic tools and paradigms that allow integrated TEG cost-performance analysis and optimization. New work herein has identified the existence of optimized TE element design (i.e., TE element length) for minimizing TEG cost and provides design guidelines associated with critical thermal and electrical contact resistance effects and TE manufacturing sensitivities. Once optimum hot-side heat flux criteria are satisfied, this work highlights the tradeoffs between interface resistance effects and manufacturing cost effects in determining optimum-cost TE element lengths, and then quantifies the increase in optimum-cost TE lengths as manufacturing costs become more sensitive to TE element lengths. Optimized TE element design criteria and key non-dimensional design parameters (i.e., [𝐶′′′LTE/𝐶′′], [TE/con], [con/(TE,aveLTE)]) driving the design are presented and explained showing the interdependencies between manufacturing cost parameters and thermal and electrical interface impacts on integrated TE cost-performance analysis and design. Comprehensive TEG system cost-performance relationships clearly demonstrate the two parameters that most greatly impact the TEG system cost are heat exchanger costs ($/(W/K)) and TE hot side heat flux. Reducing heat exhanger cost from $1/(W/K) to $0.5/(W/K) can decrease TEG system cost by $2/W to $4/W depending on TE hot side heat fluxes from 4-10 W/cm2. The impact of TE hot side heat flux on TEG system cost is larger; reducing TE system cost by factors of 2-4 as TE hot side heat flux increases from 4 W/cm2 to high levels around 18 W/cm2. The levels of thermal contact resistance and electrical contact resistance required to even approach the critical TEG system cost level of $1/W are characterized by con = 1.0 x 10-10 -m2 and r = 0.1, respectively. This not only lowers TEG costs, but it also makes the TEG system costs less sensitive to TE manufacturing dependences on TE element length. New design paradigms and relationships create holistic, integrated TE performance-cost models that enhance understanding of crucial interrelationships between component costs, TE design parameters and material properties, heat exchanger design parameters, interfacial heat flux, and now thermal and electrical interface effects in minimizing TEG system costs.

Hendricks, Terry J.↗

NASA NTRS2022

Directed Acyclic Graph Guidance Documentation

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks to astronaut health and performance that occur before, during and after spaceflight. The Human System Risk Board (HSRB), a Health and Medical Technical Authority (HMTA) Board at NASA Johnson Space Center, is the entity responsible for identifying, assessing, analyzing, and monitoring the official understanding of the risk or risk posture for each of the Human System Risks and determining – based on evaluation of the available evidence – when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce the risk that astronaut crews face during spaceflight. Historically, research, development and operations relevant to one risk have been conducted in isolation from other risks; these individual risk ‘silos’ enabled initial characterization of each specific risk. In spaceflight however, the impact of exposure to risk for astronaut crews is cumulative, and not independent of exposures or other risks, as all the adverse effects of the spaceflight environment begin at launch, continue throughout the duration of the mission and in some cases across the lifetime of the crews. In January of 2020, the HSRB at NASA embarked on a pilot project designed to assess the potential value of causal diagramming as a tool to facilitate understanding these cumulative and interdependent effects as applied within Human System Risk management. This process uses directed acyclic graphs as a means of formalizing a shared mental model of the causal flow of risk among Risk Board stakeholders. Initially this model was to improve communication among those stakeholders, but the potential value exceeds communication alone. Formalization of the process for creating these causal diagrams will enable the creation of a composite risk network that is vetted by members of the NASA community and configuration managed. The causal diagrams are formulated as directed acyclic graphs (DAGs) to function as a type of knowledge graph for reference for the board and its stakeholders. This document outlines the pilot process, the standardized approaches, and guidance for risk custodian teams when creating and updating DAGs as a part of the NASA Human System Risk Management process.

Risk↗

NASA NTRSDate not supplied

Authoring, Analyzing, and Monitoring Requirements for a Lift-Plus-Cruise Aircraft

Requirements specification and analysis is widely applied to ensure the correctness of industrial systems in safety critical domains. Requirements are often initially written in natural language, which is highly ambiguous, and as a second step transformed into a language with rigorous semantics for formal analysis. [Question/problem] In this paper, we report on our experience in requirements creation and analysis, as well as run-time monitor generation using the Formal Requirement Elicitation Tool (FRET), on an industrial case study for a Lift-Plus-Cruise concept aircraft. [Principal ideas/results] We study the creation of requirements directly in the structured language of FRET without a prior definition of the same requirements in natural language. We focus on requirements describing state machines and discuss the challenges that we faced, in terms of creating requirements and generating monitors. We demonstrate how realizability, i.e., checking whether a requirements specification can be implemented, is crucial for understanding temporal interdependencies among requirements. [Contribution] Our study is the first complete attempt at using FRET to create industrial, realizable requirements and generate run-time monitors. Insight from lessons learned was materialized into new features in the FRET and JKind analysis frameworks.

Requirements engineering↗

NASA NTRS2022

Directed Acyclic Graphs: A Tool for Understanding the NASA Human Spaceflight System Risks - Human System Risk Board

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks to astronaut health and performance that occur before, during and after spaceflight. The Human System Risk Board (HSRB), a Health and Medical Technical Authority (HMTA) Board at NASA Johnson Space Center, is the entity responsible for identifying, assessing, analyzing, and monitoring the official understanding of the risk or risk posture for each of the Human System Risks and determining – based on evaluation of the available evidence – when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce the risk that astronaut crews face during spaceflight. Historically, research, development and operations relevant to one risk have been conducted in isolation from other risks; these individual risk ‘silos’ enabled initial characterization of each specific risk. In spaceflight however, the impact of exposure to risk for astronaut crews is cumulative, and not independent of exposures or other risks, as all the adverse effects of the spaceflight environment begin at launch, continue throughout the duration of the mission and in some cases across the lifetime of the crews. In January of 2020, the HSRB at NASA embarked on a pilot project designed to assess the potential value of causal diagramming as a tool to facilitate understanding of these cumulative and interdependent effects as applied within Human System Risk management. This process uses directed acyclic graphs as a means of formalizing a shared mental model of the causal flow of risk among Risk Board stakeholders. Initially this model was to improve communication among those stakeholders, but the potential value exceeds communication alone. The causal diagrams are formulated as directed acyclic graphs (DAGs) to function as a type of knowledge graph for reference for the board and its stakeholders. This document is a sister document to NASA/TM 20220006812 Directed Acyclic Graph Guidance Documentation (1). In that document, the basic guidance for creating and standardizing directed acyclic graphs as tools for cross-risk analysis is provided. This document contains the initial configuration managed DAGs that were created as a result of applying those principles. These initial versions were accepted by the HSRB in January of 2022. Each of the Human System Risks are represented by a DAG that has been reviewed by the larger Human Health and Performance community at NASA including life scientists, physical scientists, physicians, nurses, pharmacists, exercise specialists and more. These results show the starting point for Human System Risk DAGs as shared mental models and communication aids across the boundaries of the various expertise needed to understand and mitigate the human risks in spaceflight. Because they are a starting point, each of these DAGs can be expected to change over time as new or refined evidence becomes available. The process for updating these DAGs can be found in the JSC-66705 Human System Risk Management Plan (2) that is publicly available on the NASA Technical Reports Server.

Erik L. Antonsen↗

NASA NTRSDate not supplied

Control Architecture for a Concept Aircraft with a Series/Parallel Partial Hybrid Powertrain and Distributed Electric Propulsion

Traditional aircraft propulsion control approaches are insufficient for electrified aircraft powertrains due to their increased complexity compared to current systems. New control approaches are required to manage the increased interdependency and complexity of these electrified powertrains. Additionally, electrification enables aircraft to have multiple distributed thrust producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. This paper describes the control architecture for a concept vehicle with these characteristics, the SUbsonic Single Aft eNgine (SUSAN) Electrofan. SUSAN is a series/parallel partial hybrid electric single-aisle transport aircraft that leverages its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. This paper presents a summary of the SUSAN powertrain design and discusses several of the novel control approaches used to manage the complex electrified powertrain.

Control↗

NASA NTRSDate not supplied

Control Architecture for a Concept Aircraft with a Series/Parallel Partial Hybrid Powertrain and Distributed Electric Propulsion

Control↗