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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and automation, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Tong, Michael T.↗
NASA NTRS2019

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning of historical and existing engine data could provide insights that help drive for better engine design. This work explored the application of machine learning to engine preliminary design. Engine core-size prediction was chosen for the first study because of its relative simplicity in terms of number of input variables required (only three). Specifically, machine-learning predictive tools were developed for turbofan engine core-size prediction, using publicly available data of two hundred manufactured engines and engines that were studied previously in NASA aeronautics projects. The prediction results of these models show that, by bringing together big data, robust machine-learning algorithms and data science, a machine learning-based predictive model can be an effective tool for turbofan engine core-size prediction. The promising results of this first study paves the way for further exploration of the use of machine learning for aircraft engine preliminary design.

Core Size↗
NASA NTRS2019

High Speed Testing of a High Efficiency Concentric Magnetic Gear

This presentation presents measurements of the efficiency of NASA's 2nd magnetic gear prototype. A detailed discussion of the test rig used to make these measurements was presented, including a thorough uncertainty analysis. The reported uncertainties are 95% confidence intervals that include the effects of temperature and parasitic loads. The prototype's response was measured at output speeds between 124 rpm and 744 rpm for a controlled output torque of 10 Nm (8% of the prototype's maximum torque). After correcting for tare losses, the prototype's efficiency was found to decrease from 90.0% to 83.0% as speed increased. If the efficiency is extrapolated to a typical operating condition (85% of maximum torque) using the good assumption that energy loss is approximately independent of the transmitted torque,the expected efficiency would be 99.0% to 98.4%, which exceeds the state of the art for these speeds.

Scheidler, Justin↗
NASA NTRS2023

Regenerable Liquid Desiccants for High Efficiency Humidity Control in Microgravity

The NASA X-Hab project aims to design, manufacture, test, and prove functionality of an air humidity control subsystem to dehumidify and re-humidify air from a space cabin. The method of regulating cabin air humidity utilizes vortex phase separation, which uses an ionic liquid (IL) desiccant for air-water phase separation. This subsystem is intended to be integrated with a CO 2 removal system requiring de-humidified air to operate efficiently. The air humidity control subsystem is composed of a cold-desiccant or cold-side Vortex Phase Separator (VPS) that dehumidifies the cabin air. The dehumidified air exits the cold-side (CS) VPS chamber to flow into the CO 2 removal module, and the cold, water-laden liquid desiccant flows to a regenerative heat exchanger. From the heat exchanger the liquid desiccant continues to the heater. After heating, the desiccant flows into a hot-desiccant or hot-side (HS) VPS as the dehumidified air from CO 2 removal module enters through the HS VPS air inlet. The rehumidified air exits the HS VPS into the space cabin. One pump installed at the liquid exit of the CS VPS and one pump installed at the liquid exit of the HS VPS transport the fluid through the system. To evaluate the system’s efficiency and effectiveness, the temperature, pressure, flow rate and relative humidity are read, recorded, and analyzed at critical points along the module.

Gerardo Castro↗
NASA NTRS2024

Achieving High Efficiency in Reduced Order Modeling for Large Scale Polycrystal Plasticity Simulations

Reduced order models for the nonlinear response of heterogeneous microstructures typically require a construction (or training) stage to build the reduced order basis. In this manuscript, an efficient model construction strategy for the eigenstrain homogenization method (EHM) is presented. The proposed strategy relies on a parallel, element-by-element, conjugate gradient solver. Near linear scaling has been achieved with respect to the number of degrees of freedom used to resolve the microstructure. Linear scaling with respect to the number of pre-analyses required to construct the reduced order model (ROM) follows from the EHM formulation. Furthermore, a parallel implementation for fast evaluation of the constructed ROM has been developed using shared memory parallelization. It has been shown that for large microstructures with ≈ 10,000 grains, the total computational cost of evaluating the nonlinear response of a polycrystal could be reduced by approximately an order of magnitude using 32 cores with respect to serial ROM simulation. The present methodology has been verified using an additively manufactured polycrystalline microstructure of a nickel-based superalloy, Inconel 625. The capability of the developed framework to construct a ROM for such large microstructures, as well as the ability of the ROM to predict average and local quantities of interest has been demonstrated.

microscale↗