Ezra O. McNichols | Engineering Papers

NASA NTRSDate not supplied

Battery Cell-to-Pack Scaling Laws for Electric Aircraft

Battery pack gravimetric energy density is one of the most important, yet often miss-estimated design parameters for sizing all-electric aircraft. Proper accounting for thermal, structural, and operational safety margins are frequently lost when extrapolating performance from the cell level to the aircraft level. This paper summarizes the relevant engineering and certification details needed to better account for the penalties associated when assembling battery packs. The relationship between the cell and pack energy density is not linear, as is often assumed. Furthermore, the relationship varies depending on pack requirements, cell chemistry, and architecture. Parametric, high-fidelity models are used to determine optimal battery pack sizes over a range of conditions to better quantify technology scaling effects.

Battery Electric Aircraft↗

NASA NTRSDate not supplied

NASA Small Engine Components Compressor Test Facility: High Efficiency Centrifugal Compressor Vaneless Diffuser and Transition Duct Configurations

The original vaned configuration of the High Efficiency Centrifugal Compressor (HECC) was developed with aggressive design targets to facilitate advancement of state-of-the-art technology in gas turbine compressors. This work documents modifications to the Small Engine Components Compressor Test Facility to support testing of the HECC vaneless diffuser and transition duct configurations, both of which are open geometries. These configurations were developed as follow-up studies to improve understanding of the differences between the predicted and actual performance of the original HECC vaned diffuser stage. Specifically, the vaneless diffuser was designed to provide the impeller with ideal exit conditions such that the impeller performance could be isolated from downstream stationary components, and the additively manufactured transition duct inlet configuration enables investigation into the effects of the inlet flow path on the stage performance. Features of note in the facility are active clearance control, bleed flow regulation, modular inlet configurations, and steady-state and fast-response instrumentation throughout the flow path. Details of the facility and instrumentation are presented as well as the baseline performance of the compressor stage. The geometry, performance, and detailed aerodynamic data have been made available to the public at https://storage.googleapis.com/heccdata/NASA-HECC-Data-Archive.zip.

centrifugal compressor↗

NASA NTRSDate not supplied

NASA Small Engine Components Compressor Test Facility: High Efficiency Centrifugal Compressor Vaneless Diffuser and Transition Duct Configurations

The original vaned configuration of the High Efficiency Centrifugal Compressor (HECC) was developed with aggressive design targets to facilitate advancement of state-of-the-art technology in gas turbine compressors. This work documents modifications to the Small Engine Components Compressor Test Facility to support testing of the HECC vaneless diffuser and transition duct configurations, both of which are open geometries. These configurations were developed as follow-up studies to improve understanding of the differences between the predicted and actual performance of the original HECC vaned diffuser stage. Specifically, the vaneless diffuser was designed to provide the impeller with ideal exit conditions such that the impeller performance could be isolated from downstream stationary components, and the additively manufactured transition duct inlet configuration enables investigation into the effects of the inlet flow path on the stage performance. Features of note in the facility are active clearance control, bleed flow regulation, modular inlet configurations, and steady-state and fast-response instrumentation throughout the flow path. Details of the facility and instrumentation are presented as well as the baseline performance of the compressor stage. The geometry, performance, and detailed aerodynamic data have been made available to the public at https://storage.googleapis.com/hecc-data/NASA-HECC-Data-Archive.zip.

centrifugal compressor↗

NASA NTRSDate not supplied

Laser-Processed Condensing Heat Exchanger for Space Applications

In sizing a condensing heat exchanger for space life support systems, there is a limit to allowable pressure drop along with a required vapor recuperation rate. This sizing is performed by using known heat transfer and pressure drop correlations in a numerical model. Dimpling the surface of an air-liquid channeled heat exchanger increases the convective heat transfer rate however creates difficulties within the numerical model as the flat plate correlations are no longer accurate. To increase the fidelity of the numerical model used for sizing the heat exchanger Computational Fluid Dynamics (CFD) with an SST k-ω turbulence model was performed on a periodic portion of the heat exchanger. Through CFD modeling a heat flux and pressure drop was measured across the heat exchanger channel, this information was used to create a localized Nusselt number that was incorporated into the numerical model along with a more accurate pressure drop due to the dimpled surface. Additionally, through modeling the heat exchanger after manufacturing and assembly in Computer Aided Drafting (CAD) software there was an increase in the measurement accuracy of geometric features which was incorporated into the numerical model for further increase in fidelity.

heat exchanger↗

NASA NTRSDate not supplied

GT2024-128885: Flow Reconstruction in a Transonic Turbine Cascade using Physics-Informed Neural Networks (PINNs)

This presentation investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. PINNs are a machine learning method trained on losses calculated from reconstructed governing equations, assigned boundary/initial conditions, and measured data. We reconstruct the 2-D flow field in a transonic turbine cascade in two ways: the traditional forward approach (without training/experimental data) and by training the PINN using experimental data. We then compare the PINN solutions to measured data. This is repeated for three different turbine blades with distinct loading characteristics. The experimental data used for training is the static pressure measurements along the suction and pressure sides of each blade. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. It's shown that the PINN can reconstruct the flow field in all cases with acceptable errors. Cases where the PINN is trained on all the data, and even half the data, resulted in the lowest errors. The exit Mach number is inferred for each case and compared to the experimentally calculated value.

Machine Learning↗