Aerodynamic Database Research Articles

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Fast predicting the trajectory of chip-like particles by integrating a discrete element method model with a computational fluid dynamics-based aerodynamic database

The accumulation of ice within aircraft engines poses a significant safety concern, necessitating effective and accessible methods to predict ice particle shedding trajectories. This study develops a novel method by integrating the discrete element method with a computational fluid dynamics (CFD)-based aerodynamic database, aiming to accurately predict the trajectories of chip-like ice particles under various conditions. The accuracy of the CFD-based aerodynamic database is validated through a quantitative comparison with experimental data, and the predicted trajectories align well with the experimental trajectories under varied conditions following a database-independence analysis. The results indicate that aerodynamic coefficients are independent of both the relative velocity and the scaling factor (k) for chip-like particles. Moreover, the initial angle of attack significantly influences the translational and rotational dynamics of chip-like particles. Furthermore, the chip-like ice particles released closer to the engine inlet exhibit a more uniform distribution of landing points, whereas those released at longer distances from the engine inlet tend to converge toward the central area of the engine. The methodology developed in this paper is expected to be a promising tool for fast predicting the trajectories of chip-like particles, thereby enhancing engine protection against ice impacts and improving overall operational safety.

A machine learning-augmented aerodynamic database of rectangular cylinders

Rectangular cylinders submerged in a fluid encounter intricate aerodynamic forces, and the forces significantly influence the stability and safety of structures possessing a rectangular cross section. Although aerodynamic characteristics of these cylinders have been extensively studied, a comprehensive database cataloging these characteristics remains absent. This study conducted a large number of wind tunnel pressure testings to establish an aerodynamic database for rectangular cylinders with 2470 distinct configurations, including turbulent intensities ranging from 1% to 20%, side ratios ranging from 0.6 to 5, and wind attack angles ranging from 0° to 90°. The accuracy of the database was validated by data from the literature and wind tunnel force measurement experiments. More importantly, machine learning models were developed and have substantially expanded the experimental data, resulting in a comprehensive, continuous aerodynamic database for rectangular cylinders. By evaluating the model performance and verifying its generalization capability, the accuracy of the machine learning-augmented database is proved. This database is anticipated to serve as a critical reference for academic research and a practical reference for engineering applications.

Computational Techniques to Generate Space Launch System Aerodynamic Databases

This document describes the reasoning and trade studies used to evaluate tools for constructing the aerodynamic lineload databases for the liftoff and transition phases of flight for the Space Launch System. Three computational fluid dynamics codes (USM3D, FUN3D, and Kestrel) were investigated with various turbulence models as well as detached eddy simulation variants for the launch vehicle in free air and in proximity to the tower. Decisions were made mostly based on results from brief developmental studies performed in response to specific, unforeseen challenges that were encountered in the analysis of a given configuration. The need to develop databases in a timely manner, as well as to accurately capture the expected leeward-wake flowfield characteristics, led to the selection of the Kestrel flow solver with its delayed detached eddy simulation method, the Spalart–Allmaras turbulence model, and the adaptive mesh refinement capability in the off-body Cartesian grid region.

Expert's experience-informed hierarchical kriging method for aerodynamic data modeling

Data-driven models, such as kriging, have gained popularity in aerospace engineering due to their capability of predicting multidimensional, nonlinear aerodynamic characteristics of an aircraft. However, they are still suffering from the problem associated with poor extrapolation capability and physical interpretability, which in turn has great impacts on aerodynamic performance and flight safety. To address this problem, this article proposes to incorporate an empirical aerodynamic model obtained from expert's understanding of aerodynamics in the fitting process of a kriging model. First, empirical aerodynamic models based on expert's understanding and experience are derived. Second, the regression term of a kriging model is replaced by the expert's experience-informed model so that the global trend can be consistent with physical laws and provides extra knowledge in the subregion(s) without training data, especially for the extrapolation region(s). Finally, the expert's experience-informed hierarchical kriging (EEI-HK) model is built in a sequential way. The proposed method is validated with analytical test examples and demonstrated by aerodynamic data modeling of an AGARD-B missile and an FDL-5A hypersonic flight vehicle. Results show that, compared with ordinary and universal kriging models, the proposed EEI-HK model can dramatically improve the prediction accuracy in the extrapolation domain and slightly enhance the interpolation accuracy, with assistance from physical information provided by an empirical model. In consequence, it can be a promising approach for aerodynamic data extrapolation and saving the cost of establishing an aerodynamic database.

Optimal attitude planning along torque equilibrium points using aerodynamic database for deorbiting large space debris

Currently, considerable attention is being directed toward active debris removal (ADR) using small, cost-effective satellites. From high altitude to re-entry, ADR missions use thrusters that consume significant amounts of fuel. The volume of the propulsion system in these satellites can be quite large, and it is difficult for a small satellite to accommodate a large-scale propulsion system. Thus, active utilization of atmospheric drag is being considered for small satellites with low capability. However, the principal challenge for small satellites to remove large debris is to deal with the large disturbance torque caused by strong atmospheric drag on the debris. This paper proposes attitude guidance around an equilibrium (balance) point in attitude dynamics acted on by gravity gradient and aerodynamic torques. The desired attitude is set to optimize two conflicting objectives: maximizing the projection area to generate large aerodynamic force and minimizing the disturbance torque. In this paper, we develop an aerodynamic database (ADDB) by rigorously calculating the aerodynamic torque for each altitude and solar array paddle (SAP) rotation based on an aerodynamic characteristic model of free molecular flow. Using the ADDB, the SAP angle and orbit altitude effects on the torque equilibrium attitude (TEA) are clarified. Then, the mean disturbance torque map at each orbit altitude is introduced to obtain the candidate TEA paths. The effectiveness of the proposed paths for debris descent is demonstrated through numerical simulations.

Uncertainty Quantification of Artemis I Space Launch System Integrated Aerodynamics Databases

Accurate prediction of integrated aerodynamic forces and moments is a necessary part of aerospace vehicle development. This accuracy can be quantified in the form of an uncertainty model, which makes the prediction more useful within an integrated vehicle design effort. Aerodynamic force and moment databases were constructed for the Artemis I mission of the Space Launch System vehicle. These databases reconcile data from multiple sources to yield unified predictions of how NASA’s most advanced launch vehicle interacts with Earth’s atmosphere as it ascends into orbit. This paper outlines how the uncertainty quantification was performed for these databases to ensure comprehensive and tractable uncertainty source coverage.

Non‐uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases

AbstractThe ability to non‐uniformly weight the input space is desirable for many applications, and has been explored for space‐filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, we apply non‐uniform sampling methods for the construction of aerodynamic databases. This paper combines non‐uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed‐form solution to a non‐uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop.

Low Observable Uncrewed Aerial Vehicle Wind Tunnel Model Design, Manufacturing, and Aerodynamic Characterization

Developing wind tunnel models is time consuming, labor intensive, and expensive. Rapid prototyping for wind tunnel tests is an effective, faster, and cheaper method to obtain aerodynamic performance results while considerably reducing acquisition time and cost for the models. Generally, the rapid prototyping models suffer from insufficient stiffness or strength to withstand the loads generated during a wind tunnel test. In the present study, a rapid prototype model reinforced with metallic inserts was produced to experimentally investigate the aerodynamic characteristics of an uncrewed aerial vehicle with various wingtip deflections. The fused deposition modeling process was used to make the outer mold, whereas the metallic parts were produced using laser cutting and the computer numerical control machining process. Then, the model was evaluated both experimentally and numerically. The test campaign presented in this work was conducted in the de Havilland low-speed wind tunnel facility at the University of Glasgow. For better characterization of flow patterns dominated by leading edge vortices, numerical simulations were run using OpenFOAM 8.0 and validated with experimental data. The experimental data obtained from the hybrid rapid-prototyped model agreed well with the numerical results. This demonstrates the efficacy of hybrid rapid-prototyped models in providing reliable results for initial baseline aerodynamic database development within a short period and at a reduced cost for wind tunnel tests.

Advances in Space Launch System Booster Separation Computational Fluid Dynamics

The Space Launch System (SLS) employs two Space Shuttle–derived solid rocket boosters, which separate from the SLS core while still experiencing appreciable aerodynamic loads. Creating an aerodynamic database for this phase of flight can be challenging due to the large number of independent variables needed to fully constrain the problem and the complex flow induced by exhaust plumes of the booster separation motors and core main engines impinging on other parts of the vehicle. This paper details recent efforts in generating aerodynamic data used to create databases for the SLS during the booster separation event using viscous computational fluid dynamics (CFD) simulations obtained using NASA’s FUN3D solver. Particular challenges faced when modeling a complex problem, such as booster separation, are presented. Reductions in interpolation error estimates were observed through the introduction of a physics-based covariance approach to building the CFD run matrix, eliminating infeasible booster location permutations that could potentially skew final response surfaces. Interpolation error control is shown using test cases outside of the main database. Code-to-code comparisons between the FUN3D and OVERFLOW solvers are also presented to further verify the results.

Assessment of the effectiveness of stall delay and tip loss corrections for the simulation of small propeller performance with Virtual Blade Model

The powertrain efficiency of most small unmanned air vehicles (SUAVs) depend on the aerodynamic performance of multiple propellers, which must be modelled before its optimisation. To predict propeller performance while capturing interaction effects not accountable for by momentum theories, the Virtual Blade Model (VBM) can be used at much lower costs than conventional Computational Fluid Dynamics (CFD). However, the accuracy of VBM depends on aerofoil polar data that often misrepresent rotational and three-dimensional effects that influence small propellers. In an attempt to expand the capabilities of VBM, it is a common practice to introduce correction models for aerofoil coefficients in its formulation, but the actual effectiveness of such approach for small propeller analysis remains unassessed. In this paper, VBM in OpenFOAM was extended with stall delay and tip loss models, while aerodynamic databases at low Reynolds numbers were built with XFOIL and extrapolated to the full angle of attack range. The accuracy of three VBM versions was validated against wind tunnel measurements of two off-the-shelf small propellers. For the lower-pitch propeller, thrust and power at low advance ratios and efficiency at high advance ratios were significantly improved by the stall delay model. But for the higher-pitch propeller, thrust and power were overpredicted with extended versions of VBM under most operating conditions, which is attributable to an excessive shift of effective angles of attack to the post-stall region by the stall delay model and to uncertainties in extrapolated polar data. The results suggest that without reliable procedures for obtaining polar data, correction models should be implemented in VBM only for the simulation of low-pitch propellers operating at low advance ratios, given their avoidance of the effective angle of attack range where XFOIL and extrapolation methods are mainly expected to fail in predicting lift behaviour.

Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition

Investigating flow separation regions on the surfaces of three-dimensional bluff bodies in turbulent flows is important because these regions can induce significant aerodynamic loads. Separation bubbles can generate extreme pressures, making the roof components of low-rise buildings vulnerable. In this study, proper orthogonal decomposition (POD) was applied to wind-induced roof pressures to elucidate the physical significance of the dominant modes. Based on the interpretation of the first mode from the POD, the mean reattachment length of the roof separation bubbles on a low-rise building model in turbulent flow was determined. The mean reattachment length derived from the POD was then compared with the length obtained from an aerodynamic database. For the centerline of the roof, the mean reattachment length based on the POD aligned well with that from the aerodynamic database, showing a difference of less than 5%. This study highlights the efficacy of POD as a powerful tool for estimating the reattachment length of separation bubbles on bluff bodies.

Extended Hierarchical Kriging Method for Aerodynamic Model Generation Incorporating Multiple Low-Fidelity Datasets

Multi-fidelity surrogate modeling (MFSM) methods are gaining recognition for their effectiveness in addressing simulation-based design challenges. Prior approaches have typically relied on recursive techniques, combining a limited number of high-fidelity (HF) samples with multiple low-fidelity (LF) datasets structured in hierarchical levels to generate a precise HF approximation model. However, challenges arise when dealing with non-level LF datasets, where the fidelity levels of LF models are indistinguishable across the design space. In such cases, conventional methods employing recursive frameworks may lead to inefficient LF dataset utilization and substantial computational costs. To address these challenges, this work proposes the extended hierarchical Kriging (EHK) method, designed to simultaneously incorporate multiple non-level LF datasets for improved HF model construction, regardless of minor differences in fidelity levels. This method leverages a unique Bayesian-based MFSM framework, simultaneously combining non-level LF models using scaling factors to construct a global trend model. During model processing, unknown scaling factors are implicitly estimated through hyperparameter optimization, resulting in minimal computational costs during model processing, regardless of the number of LF datasets integrated, while maintaining the necessary accuracy in the resulting HF model. The advantages of the proposed EHK method are validated against state-of-the-art MFSM methods through various analytical examples and an engineering case study involving the construction of an aerodynamic database for the KP-2 eVTOL aircraft under various flying conditions. The results demonstrated the superiority of the proposed method in terms of computational cost and accuracy when generating aerodynamic models from the given multi-fidelity datasets.

An efficient and accurate DSRFG method via nonuniform energy spectra discretization

An appropriate set-up of inflow boundary conditions is crucial for accurate prediction of wind effects on the target structures using Large Eddy Simulation (LES). The discretizing and synthesizing random flow generation (DSRFG) method is a commonly used method which however requires considerable computational cost. This paper attempts to improve the efficiency and accuracy of the DSRFG method by modifying the way of discretizing the energy spectra. Since the DSRFG method is originated from the Kraichnan’s method, the grid effects on both methods are analyzed theoretically and examined numerically. Research results show that the Kraichnan’s method is sensitive to the grid effects while the improved DSRFG method is slightly affected. Additionally, the improved DSRFG method is applied to simulate the wind effects on a low-rise building by LES and the results agree well with the NIST aerodynamic database.

Model based aircraft design and optimization: a case study with cessna 172N aircraft

Abstract This study aims to create an interdisciplinary, multi-level design approach for fixed wing aircrafts. A case study is performed using Cessna 172N aircraft characteristics that is based on a mathematical model having six degrees of freedom (DOF). A Model Based Aircraft Design Software (MAD) is developed using a mathematical dynamic model in Python environment for use in the optimization phase of the Cessna 172N aircraft. The MAD environment is composed of analysis tools that communicate with each other by an automated process chain system. The model includes aerodynamics, engine, mass, control, atmosphere, and landing gear submodules. In addition, the model has trim and 6 DOF simulation analysis capabilities which provide a diversity of analyses that can be considered in early design phases. In order to get trimming and simulation capability, MAD software does not need any precalculated aerodynamic database. The force and moment calculations are performed instantly via DATCOM and AVL solvers for each iteration of simulation or trim. Thanks to this method, alternative aircraft geometries can be compared at the first phase of the design and optimization processes without creating an overall database. All methodologies of model submodules, performance, stability and control calculations, design, and optimization applications are implemented in the MAD environment.

Aerodynamic Modeling and System Identification from Flight Data

System identification is fundamental to modeling of any observed dynamic process. This overview presentation brings out why and what system identification is as applied to flight vehicles. A historical background is briefly provided, tracing the developments to the 18th Century and early flight experiments in 1920s. Transition from classical to modern methods is brought out, highlighting the unified Quad-M approach to flight vehicle system identification that has evolved over the last three decades. Various aspects of kinematic consistency checking of recorded flight data and typical problems encountered are covered in detail. This is followed by several advanced examples pertaining to aerodynamic databases, covering (1) modeling at extreme flight conditions of large sideslip angles and stall hysteresis, (2) proof-of-match for critical flight phases, and (3) Phoenix RLV demonstrator. At the end, re-emerging aspects of recursive parameter estimation are briefly presented. The paper is concluded by indicating the possible future directions of work.

Exploring the impact of vertically separated flows on wind loads of multi-level structures

The complex dynamics of vertically separated flows pose a significant challenge when it comes to assessing the wind loads on multi-level structures, demanding a nuanced understanding of the intricate interplay between atmospheric conditions and architectural designs. Previous studies and wind loading standards provide insufficient guidance for designing wind pressures on multi-level buildings. The behavior of wind around perpendicularly attached surfaces is not quite similar to that of individual flat roofs or walls. When a body is composed of several surfaces with right or oblique angles, the separated flow from surfaces and their interactions will cause complex flow patterns around each surface. A wind tunnel experimental study was carried out on bluff bodies with attached flat plates and other adjacent bluff bodies with different heights to examine the wind-induced pressures on such complex shapes. Mean and peak pressure coefficients were measured to determine the flow interaction patterns and location of localized peak pressures. The results were compared to the Tokyo Polytechnic University Aerodynamic Database of isolated low-rise buildings without eaves. The research findings indicated that there was a noteworthy disparity between the minimum and maximum values and locations of peak pressures on both the wall and roof surfaces of the models used in this study, as compared to the results obtained by the Tokyo Polytechnic University. Moreover, the study conceivably pointed to the difference between the peak negative and positive pressure coefficient locations with the ASCE 7-22 wind loading zones. The peak suction zones were affected by the combined flows at perpendicular faces, and as a result, different wind load zones were obtained dissimilar to those introduced by ASCE 7-22. Wind loading standards may need to be modified to account for the wind pressures on complex building structures with an emphasis on the location of the peak negative pressure zones.

Grid Method Based Store Separation Suite Using Paranam Mesh Free Solver

A store separation suite based on Point Prediction methodology was developed at CSIR-National Aerospace Laboratories (NAL) using PARANAM (Parallel NAL MCIR) mesh free CFD solver along with 6-DOF code and dynamic point cloud pre-processor [7,8]. This store separation suite is well validated and tested and is being used widely for various strategic store separation projects at CSIR-NAL [7,8]. However, in some scenarios point prediction based method is difficult to employ owing to either large number of trajectory studies or time and resource requirements. In such scenarios a quick but sufficiently accurate method is needed. The Grid method which is an approximate method based on aerodynamic database has been used to develop a store separation suite to achieve the same. In this work we have exploited mesh free nature of PARANAM solver to generate the aerodynamic database required for Grid method efficiently. The suite has been applied to standard Eglin test case for carrying out validation. Further we have demonstrated the utility of such a tool in parametric studies for assessing sensitivity.

An analysis tool for boundary layer and correlation-based transition onset assessment on generic geometries

AbstractToday’s CFD simulations tools and the related computational resources provide engineers an enormously high-fidelity design tool to generate an enormous aerodynamic and aerothermal database, for both the laminar and turbulent state, in a short time span. However, transitional flow simulation is still in its infancy that one still needs to rely on practical engineering correlations composed of typical boundary-layer parameters. This work describes the methodology to extract necessary parameters and profile information of the local boundary layer from any vast simulation dataset in an automatic fashion, allowing to apply a huge variety of best-practise correlations for transition prediction relying on local quantities on any type of 3D geometric body. The validation on a simple test case of a flat plate and application to a representative and complex use cases with the geometry of the HEXAFLY-INT hypersonic glider demonstrate the potential of this engineering methodology.

Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Multi-fidelity Data Fusion (MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion (AMDF) framework is proposed to produce a composite surrogate model which can efficiently model multi-fidelity data featuring non-uniform structures. Firstly, the design space of the input data with non-uniform data structures is decomposed into subdomains containing simplified structures. Secondly, different MDF frameworks and a rule-based selection process are adopted to construct multiple local models for the subdomain data. On the other hand, the Enhanced Local Fidelity Modeling (ELFM) method is proposed to combine the generated local models into a unique and continuous global model. Finally, the resulting model inherits the features of local models and approximates a complete database for the whole design space. The validation of the proposed framework is performed to demonstrate its approximation capabilities in (A) four multi-dimensional analytical problems and (B) a practical engineering case study of constructing an F16C fighter aircraft’s aerodynamic database. Accuracy comparisons of the generated models using the proposed AMDF framework and conventional MDF approaches using a single global modeling algorithm are performed to reveal the adaptability of the proposed approach for fusing multi-fidelity data featuring non-uniform structures. Indeed, the results indicated that the proposed framework outperforms the state-of-the-art MDF approach in the cases of non-uniform data.