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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Design and Development of an Aircraft Electric Powertrain Test Stand

Recent advancements in battery technology and power electronics are enabling novel approaches to powered flight that rely on fully electric or hybrid-electric powertrain systems. To mitigate the risk surrounding such unproven systems, it is necessary to characterize their performance on a component-wide and system-wide basis. In this paper, we discuss the design and development of an electric powertrain test stand that can collect high-fidelity data of motor, controller, and battery performance, thereby allowing us to determine system performance, efficiencies, and thermal characteristics.

test stand↗
NASA NTRSDate not supplied

Model Predictive Control Strategies for Turbine Electrified Energy Management

The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, Turbine Electrified Energy Management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM’s application beyond traditional proportional-integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC and distributed MPC approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage and transient excursion integral. Results reveal the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

Model Predictive Control↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRSDate not supplied

Model Predictive Control Strategies for Turbine Electrified Energy Management

The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, Turbine Electrified Energy Management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM’s application beyond traditional proportional integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC and distributed MPC approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage and transient excursion integral. Results reveal the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

Model Predictive Control↗
NASA NTRS2023

Model Predictive Control Strategies for Turbine Electrified Energy Management

The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, Turbine Electrified Energy Management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM’s application beyond traditional proportional-integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC and distributed MPC approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage and transient excursion integral. Results reveal the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

Model Predictive Control↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRSDate not supplied

SUSAN Power/Propulsion System Emulation Test Predictions

The development of all-electric and hybrid-electric propulsion systems for transport aircraft presents an opportunity for new designs that can reduce fuel consumption and emissions from commercial aviation while enabling safer and more reliable aircraft. The SUbsonic Single Aft eNgine (SUSAN) is a conceptual design for a single-aisle transport aircraft with a series/parallel partial-hybrid propulsion system that is being developed by NASA as a reference design for single-aisle transport with a high degree of electrification. The SUSAN aircraft incorporates multiple tightly coupled power, propulsion, and flight control systems. This coupling leads to challenges in the aircraft control design process, requiring a hierarchical and more coordinated control architecture. This presentation will summarize the plans for a Hardware-in-the-Loop (HIL) test performed in the Hybrid Propulsion Emulation Rig (HyPER) facility at NASA Glenn Research Center (GRC). During this test, a real-time reference model of the full-scale SUSAN powertrain and controllers will be run alongside a sub-scale electro-mechanical system representing a portion of the electrified components in the SUSAN hybrid powertrain. This test will allow the performance of the SUSAN controllers to be evaluated using real powertrain components and allows side-by-side comparison between the real and modeled subsystems. This presentation will summarize the full-scale system model, steps towards integration of representative hardware for future control testing, and predicted results.

Jonah J. Sachs-Wetstone↗
NASA NTRSDate not supplied

System Health Management for a Series/Parallel Partial Hybrid Powertrain with Distributed Electric Propulsion

Electrified aircraft powertrains contain multiple interacting subsystems, making them much more complex than traditional aircraft propulsion systems in terms of integration and control. Electrification enables aircraft to have distributed thrust-producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. A NASA concept aircraft, the SUbsonic Single Aft eNgine (SUSAN) Electrofan, is such a vehicle. SUSAN is a series/parallel partial hybrid-electric single-aisle transport aircraft that takes advantage of 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. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed and implemented. This paper describes the development of some of these algorithms for system health management applied to the powertrain of the SUSAN concept aircraft.

Electrified Aircraft Propulsion↗
NASA NTRSDate not supplied

Adaptive Control and Scaling Approach for the Emulation of Dynamic Sub-scale Torque Loads

Previous research by the authors proposed a control and scaling approach for emulating dynamic sub-scale torque loads. This approach produced an emulation controller that successfully regulated the dynamic behavior of a sub-scale electro-mechanical system intended to be a dynamical representation of the sub-scale turboelectric powertrain of a single-aisle commercial aircraft. The sub-scale system provides an environment without turbomachinery or rotors for the initial testing of electrified aircraft propulsion (EAP) control algorithms as they would be applied to a full-scale EAP system. The sub-scale turbomachinery/rotor torque loads were produced by electric machines (EMs) driven by the control and scaling approach, a full-scale turboelectric powertrain model, and an advanced EAP control algorithm. Although successfully tested, this approach produces an emulation controller that does not guarantee asymptotic stability. Modifying the original control law and integrating adaptive control techniques into the emulation controller allows the designer to guarantee asymptotic stability. This paper introduces the idea behind the emulation controller modifications, derives the controller, proves asymptotic stability, describes the implementation of the controller on a sub-scale electro-mechanical system intended to represent a parallel hybrid-electric turbofan engine, and describes the testing of a full-scale advanced EAP control algorithm. The turbofan engine and emulation controller performance are compared to results obtained using the previous, non-adaptive control and scaling approach.

adaptive↗
NASA NTRS2024

Revolutionary Vertical Lift Technology Lift + Cruise Concept Vehicle Powertrain Dynamic Model

The Lift + Cruise (L+C) is one of several Revolutionary Vertical Lift Technology (RVLT) concept aircraft identified by NASA to investigate Urban Air Mobility (UAM) requirements. This six-passenger vehicle has a conventional tube-and-wing configuration, but with eight lift rotors (four on each wing) and a pusher rotor on the tail. This arrangement allows it to take off and land vertically as well as fly like an airplane. This report presents a novel dynamic model of the L+C powertrain built using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) and the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), both of which are NASA-developed toolboxes, and together are capable of modeling hybrid-electric powertrain architectures. This research effort models the complex behavior of the powertrain at the mechanical timescale (15 to 20 ms). The model consists of a turboshaft engine, generator, direct current bus, energy storage management system, gearboxes, and electric propulsor systems coupled together to create a dynamic model that mimics the operation of the multidomain powertrain. Two control algorithms, designed to equalize the power consumed to the power produced, are compared to assess their practicality: (1) independent proportional-integral (PI) control and (2) linear quadratic integral regulator (LQIR) control. Simulation results highlight the key features present in the model and demonstrate its operation under the two control schemes.

revolutionary vertical lift technology↗
NASA NTRS2024

System Health Management for a Series/Parallel Partial Hybrid Powertrain with Distributed Electric Propulsion

Electrified aircraft powertrains contain multiple interacting subsystems, making them much more complex than traditional aircraft propulsion systems in terms of integration and control. Electrification enables aircraft to have distributed thrust-producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. A NASA concept aircraft, the SUbsonic Single Aft eNgine (SUSAN) Electrofan, is such a vehicle. SUSAN is a series/parallel partial hybrid-electric single-aisle transport aircraft that takes advantage of 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. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed and implemented. This paper describes the development of some of these algorithms for system health management applied to the powertrain of the SUSAN concept aircraft.

Electrified Aircraft Propulsion↗
NASA NTRSDate not supplied

Aviary: Open-Source Software Tool for Optimizing Next-Generation Aircraft Designs

Existing aircraft design tools are limited in their ability to provide meaningful trends for hybrid-electric & all-electric aircraft. Aviary is an open-source software tool that modernizes legacy design tools and enables multidisciplinary analysis and optimization of unconventional vehicles like X-66A. Aviary has the potential to revolutionize aircraft analysis by making aircraft conceptual designs more efficient.

Jennifer Gratz↗
NASA NTRSDate not supplied

Initial Motor Winding Insulation Lifetime Experimental Results for Electric Aircraft Applications

Electric and hybrid-electric aircraft require high-performance and reliable electric motors to meet aviation performance and safety requirements. One of the most common failure mechanisms in electric motors is winding insulation breakdown. In this paper, initial set of aging experiments are carried out on twisted pairs and motorette specimens. The twisted pairs are aged with multiple steady-temperature thermal aging cycles. The motorette specimens are thermally cycled by applying current to the windings. Partial discharge inception voltage is tracked and reported throughout the aging process. The end goal of this work is to develop methods and models for assessing the lifetime of motor winding insulation in electric and hybrid electric aircraft applications.

Thomas Tallerico↗
NASA NTRSDate not supplied

SUbsonic Single Aft eNgine (SUSAN) Power/Propulsion System Hardware-in-the-Loop Test Results

Electrified Aircraft Propulsion (EAP) technology offers a promising path forward for reducing greenhouse gas emissions and other negative environmental effects from the commercial aviation sector. EAP systems can reduce fuel burn and improve performance over state-of the-art designs, however the increased complexity and highly coupled nature of these systems present challenges that require new control approaches. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a concept aircraft developed by NASA as a reference design for a commercial transport aircraft with a highly integrated hybrid-electric powertrain. This paper summarizes the results of a Hardware-in-the-Loop (HIL) test of a control architecture developed for the SUSAN power/propulsion system (PPS). The test was performed in the Hybrid Propulsion Emulation Rig (HyPER) facility at the NASA Glenn Research Center (GRC) and involved a real-time reference model of the SUSAN PPS and control system running with a sub-scale electro-mechanical system replacing one of the PPS subsystems. A side-by-side comparison of the simulated and real systems in the HIL test results shows that the control architecture functions well in both the simulation and the real-time HIL environment. In both cases the controller is able to simultaneously deliver the required thrust response and balance power levels between the electrical and turbomachinery subsystems.

Jonah J Sachs-Wetstone↗
NASA NTRSDate not supplied

A Call To Action To Engage The Community To Meet The Challenges That Must Be Tackled To Make Electrified Aircraft Propulsion Real

Technology risk reduction is essential, as it is necessary to demonstrate the potential of Electrified Aircraft Propulsion (EAP). However, more is needed for implementation. The industry is leading EAP by developing a diverse community of novel vehicles from short-haul, small, urban-focused electric vertical takeoff and landing (eVTOL) to regional air mobility (RAM) and hybrid-electric, single-aisle transport category airplanes. There are a variety of novel EAP technologies for each of these novel vehicles. And the industry is not only looking at novel technology to advance the state of the art. Instead, the industry is looking to certify these novel aircraft through their regulatory authorities, such as the US Federal Aviation Administration (FAA), the European Union Aviation Safety Authority (EASA), Transport Canada Civil Aviation (TCCA), and Brazil’s Agência Nacional de Aviação Civil (National Civil Aviation Agency, ANAC), as well as other regulatory authorities. The NASA Electrified Powertrain Flight Demonstration (EPFD) project has partnered with two industry partners to advance integrated MW-class powertrain system technology demonstration that includes an assessment of their regulatory and standards gaps in their technology. The EPFD has conducted a generic regulatory gap analysis of hybrid electric engines that aligns with the industry partners’ efforts. The EPFD regulations and standards team is integrated into the industry standards community. The international industry standards community is wrestling with critical key challenges to certification. While some certification elements are proprietary, several technology elements cut across company propriety in aircraft engines (US 14 CFR Part 33 and EASA CS-E, regulations that only reflect reciprocating and turbine engines). The approach that several of these regulatory authorities have taken is to collaborate to address their challenges. The Certification Management Team (CMT) consists of the EASA, FAA, TCCA, and ANAC, and they have begun to address common questions, such as the Loss of Power Control (LOPC) for electric engines. They have reached out to the standards community to seek answers. The industry standards development organizations (SDO) have also looked ahead to address current regulations and standards gaps. The ASTM has built key committees in its ASTM F44 General Aviation Committee and F39 Aircraft Systems Committee. The SAE has established the E-40 Electric Propulsion and AE-10 High Voltage committees.

Standards↗
NASA NTRS2026

A Review of Superconducting Electric Machines with On-Board Cryocoolers

This paper reviews the evolution and emerging direction of superconducting electric machines that employ onboard cryocoolers integrated directly into the rotor, eliminating the need for cryogenic fluid coupling and, in some cases, rotary seals. Traditional low-temperature superconducting (LTS) machines relied on external cryogenic systems and liquid helium transfer couplers, which introduced excessive complexity, poor reliability, and significant parasitic energy losses. The advent of high-temperature superconductors (HTS) has enabled compact, closed-cycle cryocoolers that support self-contained, fluid-free refrigeration architectures suitable for rotating applications. This paper examines the key technological challenges associated with on-board cryocooler integration and reviews three representative efforts by KAIST, NASA, and Hinetics, each illustrating distinct strategies and milestones toward practical implementation. KAIST demonstrated early proof-of-concept for rotating machines with on-board cryocoolers, NASA developed a shaft-integrated Stirling-type pulse tube cryocooler for a 1.4 MW hybrid-electric motor, and Hinetics achieved full-scale validation of a self-contained HTS rotor incorporating a commercial Stirling cryocooler and spoke-suspension torque tube. Collectively, these achievements confirm the technical feasibility of on-board cryogenic refrigeration and highlight steady progress toward compact and efficient superconducting rotating systems across various applications. Embedding cryocoolers directly within the rotor enables practical, efficient, and commercially viable superconducting propulsion technologies.

Cryogenics↗
NASA NTRS2014

NASA Green Propulsion Technologies Pushing Aviation to New Heights

Center Director Free is providing the Keynote at the Disruptive Propulsion Conference, sponsored by Cranfield University, Cranfield, Bedfordshire, England in November. Director Free will be presenting a PowerPoint presentation titled, NASA Green Propulsion Technologies Pushing Aviation to New Heights at both the conference and a meeting at the Royal Aeronautical Society.

Subsonic aeronautics research↗
NASA NTRS2018

A Generalized Power System Architecture Sizing and Analysis Framework

The aeronautics industry has been challenged to increase efficiency, reduce noise and emissions, and decrease dependency on carbon-based fuels. To address these needs, NASA has identified and begun to pursue electrified aircraft as a possible solution. The power system for an electric aircraft can exist in many different forms, however; at the early design stage the engineer(s) must identify whether a hybrid- or turbo- electric solution may be best, whether the power transmission system is to be AC or DC, and to ultimately answer the question: does the electric solution provide a net system benefit compared to the fully mechanical solution? This paper describes a generalized power system architecture sizing and analysis framework to provide a mechanism to answering these questions, along with an example based on the STARC-ABL Architecture.

Turbo-Electric↗