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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Influences of Varied Electrical Discharge Machining Operations on Surface Conditions of Several Nickel-Based Superalloys

Nickel-based superalloys designed for use in gas turbine engines blades are difficult and expensive to properly machine without undue surface damage, due in part to their superior mechanical properties at high temperatures. Numerically controlled electrical discharge machining (EDM) has progressed to commonly be used as a machining process for initial sectioning, detailed roughing, and even some finishing operations. The objective of this study was to examine the effect of EDM on resulting surface conditions of superalloy 718 after slicing using typical rough, semi-finish, and finish condition sets with a typical wire EDM machine. Surface roughness, residual stress, and microstructures were compared for these conditions. The varied EDM conditions influenced surface roughness as intended. The residual stresses and surface roughness directly corresponding to the EDM steps were determined. While roughness decreased when progressing from rough to finish EDM conditions, tensile residual stresses unexpectedly increased. Brief assessments after EDM of two higher performance powder metallurgy disk superalloys ME209 and LSHR indicated this inverse relationship of roughness versus tensile residual stress was also present for these superalloys, where tensile residual stresses were significantly enhanced.

superalloys↗
NASA NTRS1985

Setup Aid for Electrical-Discharge Machining

Interlock assures that workpiece is correctly assembled in machining fixture. A Plunger in a Hollow Shaft actuates a switch, allowing a power supply to produce current for electrical-discharge machining. Plunger operates only when necessary parts are position.

Lines, G.↗
NASA NTRS1980

Effect of hole geometry and Electric-Discharge Machining (EDM) on airflow rates through small diameter holes in turbine blade material

The effects of two design parameters, electrode diameter and hole angle, and two machine parameters, electrode current and current-on time, on air flow rates through small-diameter (0.257 to 0.462 mm) electric-discharge-machined holes were measured. The holes were machined individually in rows of 14 each through 1.6 mm thick IN-100 strips. The data showed linear increase in air flow rate with increases in electrode cross sectional area and current-on time and little change with changes in hole angle and electrode current. The average flow-rate deviation (from the mean flow rate for a given row) decreased linearly with electrode diameter and increased with hole angle. Burn time and finished hole diameter were also measured.

Hippensteele, S. A.↗
NASA NTRSDate not supplied

Electrical Discharge Machining of Additively Manufactured Superalloys Containing Oxide Dispersions

Superalloys can be strengthened by oxide dispersions to potentially improve their mechanical properties at high temperatures. Additive manufacturing has opened new paths for introducing oxide particles into superalloys with increased processing flexibility, to potentially provide oxide dispersion strengthening. Numerically controlled electrical discharge machining (EDM) has often been used as a machining process for initial sectioning, detailed roughing, and even finishing operations of conventional superalloys. The objective of this study was to examine how additively manufactured superalloys containing oxide dispersions respond when sectioned using EDM. Consistent sections of several superalloys, two containing an oxide dispersion, were sliced using a typical EDM machine with standard brass wire and a typical roughing condition set. Roughness, microstructure, and residual stress at the EDM surfaces were compared for the varied superalloys.

superalloys↗
NASA NTRS2018

Turbine Electrified Energy Management (TEEM) For Enabling More Efficient Engine Designs

NASA is investing in Electrified Aircraft Propulsion (EAP) research as part of an effort to assist industry in meeting the future needs of a global aviation market. The integration of electric machines into traditional turbine-based propulsion provides opportunities to change system architectures effecting radical improvements in propulsive efficiency. However, less consideration has been afforded to the utilization of these electrical machines to improve the thermal efficiency and performance of the gas turbine engine. Noting this deficit, a novel operability concept is proposed and is referred to as Turbine Electrified Energy Management (TEEM). The concept is a transient control technology that supplements the main fuel control for the suppression of the natural off-design dynamics associated with changes in engine operating state. Here the electric machines, used as engine actuators during the transient, add or extract torque from the engine shafts to maintain the speed-flow characteristics of steady-state design operation. This greatly reduces the need to maintain transient stall margin stack in the compressors, among other potential benefits. This paper demonstrates the feasibility of the concept in dynamic simulation using a Numerical Propulsion System Simulation (NPSS) engine model of a NASA hybrid electric propulsion concept known as the Parallel Hybrid Electric Turbofan (hFan).

Culley, Dennis E.↗
NASA NTRS2024

Design of an In-Slot Cooled Air-Core Flux-Focusing Permanent Magnet Synchronous Machine for Electric Aircraft Applications

High performance and reliable permanent magnet synchronous machines are potentially a key technology for enabling reduced emissions in future generations of sustainable aircraft. In this paper, a novel air-core flux focusing permanent magnet synchronous machine with carbon fiber shafting for aviation applications is presented. A low-fidelity analytical sizing tool is detailed for the motor topology. The sizing tool is used to explore design trades and create an example 100 kW flux-focusing motor for eVTOL applications. Higher fidelity finite element analysis was used to validate the predicted performance of the motor. The selected design is able to achieve roughly 97 percent efficiency and 6 kW/kg at a continuous operating power of 100 kW.

Jonathan Gutknecht↗
NASA NTRSDate not supplied

NASA Glenn Electric Propulsion Work

NASA Glenn Electrified Aircraft Propulsion (EAP) Presentation Outline: High-Voltage, High-Power EAP - Standards and Regulations - Safety and Certification - High Power Density, High-Efficiency Electric Machines and Drives - Thermal Management - Fault Management - Superconducting Electric Machines - Materials Research - Modeling Tools - Advanced Controls

Electric Propulsion↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗
NASA NTRSDate not supplied

High Efficiency Megawatt Motor Stator Thermal Performance

Enabling single aisle electric aircraft propulsion requires power dense, megawatt scale, high efficiency electric machines. To that end NASA has been developing the High Efficiency Megawatt Motor (HEMM) 1.46 MW (16kW/kg), 98% efficient electric machine. The success of this effort is highly dependent on the stator’s thermal (cooling) design. The design to date has been based upon computation fluid dynamic and finite element analysis models that have been validated with testing of stator sub sections that replicate expected thermal conditions. However, the test to validate the model can only be so accurate without fully representing the full geometry and components of the stator. A full HEMM stator has been fabricated, potted, and installed in a housing complete with vacuum tube. This apparatus was used to validate the full thermal environment of HEMM and act as a final validation of the stator design before fully fabricating the machine. This paper discusses the modeling and test results from this penultimate HEMM stator build.

electric machine↗
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

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 NTRSDate not supplied

Selecting an Encapsulant for an Aerospace Superconducting Machine

Achieving larger benefits from electrified propulsion in single aisle aircraft (the largest commercial aircraft market segment) necessitates that a significant fraction of their 20 MW or greater propulsion system be electrified. These aircraft would require up to 10-20 of today’s state-of-the art machines, which have been demonstrated up to 2.5 MW for aerospace use, and could benefit further from 5 MW+ designs. However, when conventionally cooled, high power density machines still produce a significant amount of waste heat, translating into heavy thermal management and detracting from system benefits. Highly efficient fully superconducting electric machines (or those with a cryogenically cooled, high purity copper or aluminum stator) are a possible solution but have multiple barriers to overcome. One such barrier is the durability of superconducting windings. Superconductors are typically brittle, and encapsulants used to hold windings in place are a significant source of stress due to mismatches in coefficient of thermally induced contraction between ambient and operational temperatures (20 to 77 K). Lack of compliance between the materials at operational temperatures also adds significant stress. These encapsulants also form a thermal barrier that can be detrimental to cooling the superconductors. This work describes an initial study of commercially available encapsulants as it relates to superconducting machine applications, along with initial modeling of thermal stress in superconducting windings for a superconducting or cryogenically cooled stator in a 5 MW electric machine.

encapsulant↗
NASA NTRSDate not supplied

Turbine Electrified Energy Management with Model Predictive Control

Affordability, sustainability, and efficiency are primary motivators driving the future of NASA aeronautics research. These factors are realized, in part, through the development and implementation of new technologies and strategies enabling efficient, affordable, and safe hybrid-electric aircraft. Research supporting electrified aircraft propulsion control systems exemplifies such new methodologies, offering varied opportunities to integrate electric machines with gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures seek to introduce energy storage and exploit electrical power system components to assist gas-based system components. Capitalizing on the electric machines in hybridized engines, Turbine Electrified Energy Management (TEEM) is a control approach that enhances transient operability to improve overall propulsion and vehicle efficiency by injecting or extracting power from engine shafts. Traditionally implemented with proportional-integral (PI) control, this study expands the application of TEEM by presenting model predictive control (MPC) schemes to execute the TEEM concept. Via cost function design and constraint selection, the transient operability goals for TEEM are considered in the controller designs. The proposed MPCs are simulated on a nonlinear turbofan engine model at two environmental conditions, with comparisons drawn to a baseline PI. Performance is evaluated using compressor maps and two TEEM-specific metrics: transient stack usage and transient excursion integral. Simulation results reveal the developed schemes perform comparably to the benchmark controller and can be implemented in two distinct configurations. Potential modifications for future investigations include cost function measures that optimize energy use, additional performance effectiveness measures, and battery storage capabilities.

Elyse D. Hill↗
NASA NTRSDate not supplied

Select Variables Affecting Thermal System Design of a Liquid-Cooled Stator

NASA Glenn Researchers have endeavored to create a high-power-density, efficient electric motor called the High Efficiency Megawatt Motor (HEMM) with a goal of exceeding 98% efficiency and 1.46 MW of power. These aggressive goals were set because increasing the power density and efficiency of electric machines (motors and generators) is integral to bringing electrified aircraft (EA) to commercial realization. Resistive losses in the stator windings are by far the largest (34%) contributor to this design’s total energy losses. Previous testing of a HEMM-like stator test article (statorette) showed good agreement between measured performance and model predictions. Therefore, a foundation was provided to test other engineering parameters that are encountered when designing an electric machine. Of particular interest are the effects of changes to the thickness of the epoxy from the potting process, changes in the fluid volume/flow path around the end windings, and changes in the viscosity of the cooling fluid on the overall thermal performance of the unit. The impact of those parameters as revealed through a third-generation build, test, and finite element model analysis of a statorette test section is the focus of this paper.

EAP↗
NASA NTRSDate not supplied

Failure Behavior and Control-Based Mitigation for a Parallel Hybrid Propulsion System

NASA is pursuing research to advance Electrified Aircraft Propulsion (EAP) technologies that address fuel burn and emission reduction goals. EAP brings the potential for improved performance over the state of the art. However, for these systems to be practical and certifiable, they need to possess adequate robustness to adverse conditions including a variety of system failures that are not applicable to conventional turbofans today. Numerous EAP concepts interface gas turbine engines with an electrical power system that includes electric machines and sometimes electrical energy storage. The expansion of the powertrain increases the probability of encountering a failure and introduces new failure modes. Failures within the electrical power system may also impact the gas turbine engine(s) to which the electrical powertrain is coupled. This effort investigates failures originating in the electrical power system and their impact on the parallel hybrid propulsion system. Reversionary control strategies are also demonstrated to reduce the impact of the failures. Failure mitigation strategies were devised and employed in simulation. Various failure scenarios were simulated including those occurring during steady state operation, transients, and takeoff and landing scenarios. The timing of the failure and delay in failure identification and activation of mitigation strategies are noteworthy variables in the study. While the system remained stable throughout all failure scenarios, delays in failure identification could result in undesirable conditions such as increased operating temperatures and reduced stall margin. The results demonstrate successful mitigation of failures through reversionary control modes and help to generate confidence in the robustness of the conceptual parallel hybrid propulsion system.

Failure behavior↗