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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Exploring Airfoil Table Generation using XFOIL and OVERFLOW

The rotorcraft design process is a continuously evolving field of research that incorporates a number of software programs. An accurate airfoil table is critical in the design and testing process for rotorcraft. With multiple flow solvers available and flow conditions of multirotor UAM vehicles potentially covering a wide range of Reynolds and Mach numbers, a documented approach for developing airfoil tables is needed. Using benchmark data from legacy airfoil tables and wind tunnel tests for comparison, simulations for a comprehensive test matrix could guide rotorcraft design engineers in generating their own airfoil tables using the XFOIL and OVERFLOW solvers. The motivation for this study is to investigate flow solver features to develop a best practices document for airfoil table generation. The study uses the OVERFLOW and XFOIL flow solvers, coupled with the airfoil table generator AFTGen, to analyze three airfoils for a specific Reynolds numbers flow regime and provide details on how well each flow solver performs within a specific angle of attack range, Mach number range, Reynolds number range, and in different flow conditions, such as turbulent and transitional flow. OVERFLOW analyses in AFTGen for fully turbulent and transition flow are compared with XFOIL results and experimental test data for the section lift, section drag, and pressure coefficients. XFOIL ultimately yields results that are accurate within the linear angle of attack range and below a Mach number of 0.4 but tends to overpredict lift and underpredict drag unless the flow is in the compressible regime. XFOIL cannot accurately model stall and post-stall conditions due to the nature of the solver. This is evident in nearly every case run with XFOIL, where the linear range is usually predicted acceptably and the lift coefficient is overpredicted as the stall angle of attack is approached (with the exception being the generally poor correlation with most of the SSC-A09 cases). OVERFLOW is limited at low Mach numbers, and appears to perform best at Mach numbers of 0.4 and above. The exploration of airfoil table generation using XFOIL and OVERFLOW yielded moderately successful results for the NACA 0012 airfoil, reasonably good results for the RC(4)-10 airfoil, and less accurate results for the SSC-A09 airfoil.

Airfoil Table Generation↗
NASA NTRSDate not supplied

Airfoil Table Generation Comparison Utilizing XFOIL and UNS2D Flow Solvers

The flow solvers XFOIL and UNS2D were utilized within the airfoil table generator AFTGen to generate airfoil performance tables, and these results were compared to determine accuracy at low Mach numbers that may be relevant to Urban Air Mobiltiy (UAM) applications. Airfoil tables were generated for the NACA 0012 airfoil as well as a set of three asymmetrical airfoils developed in this study from which the rotor blade CSUS 001 was comprised. To determine which solver more accurately represented real-world aerodynamics, the airfoils were experimentally tested in the CSU 2-foot by 2-foot wind tunnel. The results of XFOIL and UNS2D were compared to the experimental results. It is important to note that due to differences in the Reynolds numbers between simulations and experimental testing, the magnitude of the section lift and drag coefficient varies between the two methods. However, observations on the accuracy of XFOIL and UNS2D could still be made based on the trends of the data. For the symmetrical NACA 0012 airfoil, it was found that XFOIL and UNS2D accurately predicted the trends of the section lift and drag coefficients. However, XFOIL predicted slightly lower section drag coefficients than UNS2D for the tested angles of attack. For the asymmetrical CSUS 001 airfoils, XFOIL could not predict a steady to decreasing section lift coefficient phenomenon at negative angles of attack observed in both the UNS2D and experimental results. Similarly, XFOIL was unable to predict an increased section drag coefficient at negative angles of attack for the CSUS 001 airfoils as observed in the UNS2D and experimental results. Additionally, 3D computational fluid dynamics was utilized through RotCFD to compare the performance of a rotor blade using the CSUS 001 airfoils versus a rotor blade using the NACA 0012 airfoil.

UAM↗
NASA NTRS1991

Airfoil optimization with efficient gradient calculations

The viscous airfoil design analysis code XFOIL was extended to allow optimization using conformal mapping coefficients as design variables. The optimization technique used was the Steepest Descent method applied to a Penalty Function. The gradients of the aerodynamic variables with respect to the design variables were cheaply calculated as by-products of XFOIL's integral boundary layer Newton solver. The speed of the optimization process further increased by updating the Newton system boundary layer variables after each optimization step using the available gradient information. Two examples are presented.

Sorensen, Thomas↗
NASA NTRS2013

A Non-Cut Cell Immersed Boundary Method for Use in Icing Simulations

This paper describes a computational fluid dynamic method used for modelling changes in aircraft geometry due to icing. While an aircraft undergoes icing, the accumulated ice results in a geometric alteration of the aerodynamic surfaces. In computational simulations for icing, it is necessary that the corresponding geometric change is taken into consideration. The method used, herein, for the representation of the geometric change due to icing is a non-cut cell Immersed Boundary Method (IBM). Computational cells that are in a body fitted grid of a clean aerodynamic geometry that are inside a predicted ice formation are identified. An IBM is then used to change these cells from being active computational cells to having properties of viscous solid bodies. This method has been implemented in the NASA developed node centered, finite volume computational fluid dynamics code, FUN3D. The presented capability is tested for two-dimensional airfoils including a clean airfoil, an iced airfoil, and an airfoil in harmonic pitching motion about its quarter chord. For these simulations velocity contours, pressure distributions, coefficients of lift, coefficients of drag, and coefficients of pitching moment about the airfoil's quarter chord are computed and used for comparison against experimental results, a higher order panel method code with viscous effects, XFOIL, and the results from FUN3D's original solution process. The results of the IBM simulations show that the accuracy of the IBM compares satisfactorily with the experimental results, XFOIL results, and the results from FUN3D's original solution process.

Sarofeen, Christian M.↗
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↗