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NASA NTRS2023

Predicting Two-Dimensional Airfoil Performance Using Graph Neural Networks

Computer simulations require the use of meshes to simulate geometries. These meshes capture important geometric features of the design and can be used in machine learning modeling. This report explores the use of graph neural networks (GNNs) to learn features from two-dimensional (2D) airfoil designs represented as a set of nodes connected using edges. This type of network is common in aerospace applications: most geometries are represented as a mesh in order to perform analysis. The objective of this work is to use GNNs to predict the performance of 2D airfoils generated using the program XFOIL. The predicted performance parameters include bulk quantities such as coefficients of lift (C L ), drag (C d , C dp ), moment (C m ), and node-specific quantities such as coefficient of pressure (C p ). In this report, a spline convolutional graph-based neural network is compared with deep learning neural networks to predict both bulk and node-specific quantities. The findings indicate the GNNs are able to predict bulk quantities quite well; however, when the number of outputs is increased, the deep neural network (DNN) proves to be better in its prediction capability. Two different normalization strategies were compared in the training of both GNNs and DNNs: minmax and standard deviation. In both types of networks, standard deviation scaling proved to be the best.

machine learning↗

NASA NTRSDate not supplied

Effects of Trailing Edge Thickness and Blade Loading Distribution on the Aerodynamic Performance of Simulated CMC Turbine Blades

The aerodynamic performance of three blade sets that represent the geometric manufacturing constraints of Ceramic Matrix Composite (CMC) blades was measured experimentally in a large-scale transonic turbine blade cascade. The trailing edge thicknesses of CMC blades are anticipated to be significantly larger than those of current state-of-the-art metallic blades. The blades tested in the current study had trailing edge thicknesses of 5%, 7%, and 9% relative to the blade axial chord. The three blade sets were designed with matching throat dimensions, so the blade loading distributions were varied to retain similar overall loading levels. Data were acquired at four Reynolds numbers, covering a factor of six range. All data were acquired at the design isentropic exit Mach number of 0.74. Measurements include blade loading and five-hole probe surveys at two downstream stations. The effects of inlet turbulence intensity were also quantified. Total pressure loss data were integrated to determine overall loss levels for each of the three measured blade passages. Excellent periodicity was noted. For low inlet turbulence levels, losses were surprisingly lower for the thickest trailing edge at low Reynolds numbers, but were highest at the maximum Reynolds number. In general, losses were found to scale well with Reynolds number, although front loading was found to significantly reduce the sensitivity of loss to Reynolds number. For high inlet turbulence intensity, losses were found to scale with trailing edge thickness as expected, and the Reynolds number sensitivity was reduced for all three blade sets. Loss levels at the highest Reynolds number were comparable at low and high inlet turbulence intensity levels.

Paul W Giel↗

NASA NTRSDate not supplied

Effects of Trailing Edge Thickness and Blade Loading Distribution on the Aerodynamic Performance of Simulated CMC Turbine Blades

The aerodynamic performance of three blade sets that represent the geometric manufacturing constraints of Ceramic Matrix Composite (CMC) blades was measured experimentally in a large-scale transonic turbine blade cascade. The trailing edge thicknesses of CMC blades are anticipated to be significantly larger than those of current state-of-the-art metallic blades. The blades tested in the current study had trailing edge thicknesses of 5%, 7%, and 9% relative to the blade axial chord. The three blade sets were designed with matching throat dimensions, so the blade loading distributions were varied to retain similar overall loading levels. Data were acquired at four Reynolds numbers, covering a factor of six range. All data were acquired at the design isentropic exit Mach number of 0.74. Measurements include blade loading and five-hole probe surveys at two downstream stations. The effects of inlet turbulence intensity were also quantified. Total pressure loss data were integrated to determine overall loss levels for each of the three measured blade passages. Excellent periodicity was noted. For low inlet turbulence levels, losses were surprisingly lower for the thickest trailing edge at low Reynolds numbers, but were highest at the maximum Reynolds number. In general, losses were found to scale well with Reynolds number, although front loading was found to significantly reduce the sensitivity of loss to Reynolds number. For high inlet turbulence intensity, losses were found to scale with trailing edge thickness as expected, and the Reynolds number sensitivity was reduced for all three blade sets. Loss levels at the highest Reynolds number were comparable at low and high inlet turbulence intensity levels.

Turbine↗

NASA NTRSDate not supplied

Preliminary Study of Heat Pipe Turbine Vane Cooling in the NASA N+3 Reference Engine

This paper investigates the effects of implementing Heat Pipe Turbine Vane (HPTV) cooling in the NASA N+3 engine. The HPTV can be thought of as a heat pipe in the shape of a turbine vane, functioning as both an aerodynamic body and heat exchanger. The heat-absorbing section of the HPTV remains fixed in the vanes of the high pressure turbine (HPT), while the heat-rejecting section can be placed in any stage of the low pressure turbine (LPT) as well as the bypass stream. The optimal location of the condensing (heat-rejecting) section is shown to be in the bypass stream for both HPT stages. The thrust specific fuel consumption increased by 0.2%, which is mainly attributed to the elimination of air cooling in the second stage HPT vanes combined with the transfer of energy from the main gas path to elsewhere in the cycle. Air cooling is also eliminated in the first stage HPT vanes, but this cooling air is nonchargeable. A preliminary design is proposed and shown to demonstrate the desired performance and operates below all the heat flux limitations in the first stage HPT . The design in the second stage HPT exceeds a limiting phenomenon but is expected to be remedied by changing the working fluid.

Gas Turbine↗

NASA NTRSDate not supplied

Preliminary Study of Heat Pipe Turbine Vane Cooling in the NASA N+3 Reference Engine

This investigates the effects of implementing Heat Pipe Turbine Vane (HPTV) cooling in the NASA N+3 engine. The HPTV can be thought of as a heat pipe in the shape of a turbine vane, functioning as both an aerodynamic body and heat exchanger. The heat-absorbing section of the HPTV remains fixed in the vanes of the high pressure turbine (HPT), while the heat-rejecting section can be placed in any stage of the low pressure turbine (LPT) as well as the bypass stream. The optimal location of the condensing (heat-rejecting) section is shown to be in the bypass stream for both HPT stages. The thrust specific fuel consumption increased by 0.2%, which is mainly attributed to the elimination of air cooling in the second stage HPT vanes combined with the transfer of energy from the main gas path to elsewhere in the cycle. Air cooling is also eliminated in the first stage HPT vanes, but this cooling air is nonchargeable. A preliminary design is proposed and shown to demonstrate the desired performance and operates below all the heat flux limitations in the first stage HPT . The design in the second stage HPT exceeds a limiting phenomenon but is expected to be remedied by changing the working fluid.

Gas Turbine↗

NASA NTRSDate not supplied

Embracing the fourth Industrial Revolution - Challenges, Opportunities and Path Forward for Propulsion

The aerospace industry is at a point where components are reaching design maturity and performance improvements are incremental. Aggressive goals to achieve sustainability and the threat of climate change necessitate a new paradigm. An artificial intelligence (AI) approach that enables revolutionary changes in system architecture, mission analysis and performance metrics is needed. The growing interest and development in the field of machine learning presents an opportunity to speed up by 10X or more the discovery, analysis and development of aerospace systems using artificial intelligence. Through IDEAS (Intelligent Design and Engineering of Aerospace Systems) we are embarking on a research and development effort that addresses this opportunity. The objective of IDEAS is to enable design of systems based on requirements. We discuss possible approaches to generating data, training models and applying them to near term applications. Results of recent workshops with industry, academia and other agencies to identify challenges to adopting AI and machine learning are presented.

Machine learning↗

NASA NTRSDate not supplied

Embracing the Fourth Industrial Revolution - Challenges, Opportunities and Path Forward for Propulsion

The aerospace industry is at a point where components are reaching design maturity and performance improvements are incremental. Aggressive goals to achieve sustainability and the threat of climate change necessitate a new paradigm. An artificial intelligence (AI) approach that enables revolutionary changes in system architecture, mission analysis and performance metrics is needed. The growing interest and development in the field of machine learning presents an opportunity to speed up by 10X or more the discovery, analysis and development of aerospace systems using artificial intelligence. Through IDEAS (Intelligent Design and Engineering of Aerospace Systems) we are embarking on a research and development effort that addresses this opportunity. The objective of IDEAS is to enable design of systems based on requirements. We discuss possible approaches to generating data, training models and applying them to near term applications. Results of recent workshops with industry, academia and other agencies to identify challenges to adopting AI and machine learning are presented.

Machine learning↗

NASA NTRSDate not supplied

Power and Propulsion: Small Core Advanced Thermal Management Project Overview

This presentation is intended to provide an overview of the various efforts under the Advanced Air Transport Technology (AATT) project related to engine thermal management. Sustainable flight is the key motivator for all our efforts, and innovative thermal technologies play a crucial role in achieving this goal. The technologies described in this presentation can be applied to both traditional gas turbines and advanced cycles which utilize alternative fuels. Within our project, we utilize the capabilities of artificial intelligence (AI), machine learning (ML), and additive manufacturing. This ranges from using AI to generate heat exchanger fin topologies, to using ML for a reduction in computational cost which allows for a more thorough design exploration. Many times, the resulting topologies can only be realized through additive manufacturing techniques. Most of what’s presented is currently low TRL, but the intent is to achieve TRL 4 by the end of the project.

Propulsion↗

NASA NTRSDate not supplied

Flow Reconstruction in A Transonic Turbine Cascade Using Physics-Informed Neural Networks (PINNS)

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. The 2-D flow field in a transonic turbine cascade is reconstructed in three ways: the traditional forward approach (PINN not trained on experimental data), by training the PINN using discrete sets of experimentally measured pressure at midspan, and in the inverse sense where no inlet or outlet pressure boundary conditions are applied. Comparisons between the PINN solutions to measured data are made. This is repeated for three different turbine blades with distinct loading characteristics. Good agreement is shown between a CFD calculation of the CMC7 blade, and the PINN model trained with all data. 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. The forward problem results deviate the most from experimental data but show promise. Solutions from the assisted training cases show that the PINN can reconstruct the flow field with acceptable accuracy when trained on measurements along the entire blade. In the inverse case, it is shown that to simultaneously achieve acceptable errors for inlet Mach number and outlet isentropic Mach number, the PINN must be trained on the static pressure data along the entire blade.

Machine Learning↗

NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗

NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗

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↗

NASA NTRSDate not supplied

Turbo-Design: Open-Source Radial Equilibrium Turbomachinery Solver: Part I - Turbines

Advances in 3D Geometrical Designs and Cooling have played a significant role in improving the efficiency of turbomachinery. However, these advancements must be effectively translated back to the modeler. Machine learning can facilitate this transition. Specifically, machine learning–based loss models can bridge the gap between 3D and 1D designs, enabling modelers not only to predict velocity triangles but also to extract additional geometric features. Currently, the design tools used at NASA have not been updated to support such integration—until now. TurboDesign is an open-source, Python-based framework that replaces TD2 (LEW-11029-1) and AXOD2 (LEW-16323-1), both of which are radial equilibrium solvers for axial turbines. The goal of this update is to enable the integration of machine learning loss models into radial equilibrium equations. Additionally, TurboDesign is designed to support radial machines. This paper presents the governing equations, the assumptions underlying the code, the integration of legacy loss models, an example of machine learning model integration, and a validation comparison with CFD. All code, tutorials, and documentation are available at: https://www.github.com/nasa/turbo-design

Radial Equilibrium↗

NASA NTRSDate not supplied

A GPU-based Approach for Turbomachinery Application

Accurate modeling of coolant airflows, which form protective films over turbine blades, is essential for designing fuel-efficient and environmentally sustainable gas turbine engines. Excessive coolant reduces thermal efficiency, while insufficient coolant leads to blade overheating and causes damage. Therefore, precise prediction of flow field interactions with cooling air is critical for optimizing turbine performance. This study numerically investigates the cooling effectiveness of purge and film cooling flows within a high-pressure turbine (HPT) rotor using Large Eddy Simulation (LES). The study utilizes NASA Glenn Research Center’s Glenn-HT solver. The simulation models ethe conditions of the Penn State University START rotating rig. A high-fidelity structured mesh comprising up to 800 million cells is employed to resolve high-Reynolds number flow (Re ≈ 350,000) and to capture intricate secondary flow structures, including tip leakage and purge-induced vortices. Film cooling effectiveness computations are highly sensitive to boundary conditions at the cooling holes and to grid resolution. Even with well-resolved grids and included plena, strong mixing challenges traditional eddy viscosity models. To address this, a simplified configuration is simulated: a truncated row of shaped holes on the suction side near the leading edge and a row on the pressure side, both fed from internal plena while the purge slot and tip clearance are also modeled. Two isothermal LES cases are conducted at two distinct wall temperatures, which yield the adiabatic wall temperature and the heat transfer coefficient. The definition and means of computation of the effectiveness is discussed in this paper. The simulations reveal detailed three-dimensional unsteady flow features, including coherent vortical structures and secondary flows originating from the purge cavity. Film cooling effectiveness and Nusselt number distributions are presented for both the blade surface and tip, highlighting regions of elevated heat transfer and complex thermal behavior. These findings underscore the importance of high-resolution LES and realistic boundary conditions in capturing the dynamics of purge and film cooling, offering valuable insights for improving turbine blade design and thermal management strategies.

Gas Turbine↗