Planar Inlet Design and Analysis Process (PINDAP)

Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) offer significant advantages in operations requiring both vertical lift and efficient forward flight. However, VTOL aircraft design is challenge in terms of stability, control, and aerodynamic efficiency across flight regimes. Aerodynamic analysis is conducted to determine the variations of pressure distributions, lift and drag forces and center-of-pressure (CP) movement all of which are important towards design optimization and control modeling. This study focuses on aerodynamic analysis of a small VTOL fixed-wing UAV without using any additional actuators or complex tilting mechanisms. The designed UAV only uses the regular control surfaces such as Aileron, Rudder and Elevator of the fixed wing aircraft for maneuverability. Computational fluid dynamics (CFD) has become a essential tool in aerodynamic design and analysis. Studies of flyingwing VTOL UAVs revealed that CFD is effective in the prediction of stability and flow separation which is critical in transition controllability [1]. The structural and CFD studies were also coupled which showed that aeroelastic effects at high lift and inertia can be critical to performance and safety [2]. CFD has also been useful in wing and fuselage optimization to minimize drag and preserve lift as well as in the prediction of aerodynamic coefficients by use of turbulence models to provide reliable control inputs [3]. In addition to CFD, low-order methods like XFLR5 have been used to facilitate initial design through evaluation of centerof-gravity balance and optimization of wing geometry under structural constraints [4] [5]. On this basis, th present work implements CFD and XFLR5 to examine the aerodynamic forces, pressure field, and CP-CG interaction in a fixed-wing UAV-VTOL.

A summary of the computational aerodynamic performance tools and analyses for the development of the Surfing Aircraft Vortices for Energy (SAVE) formation flight concept is presented. Aerodynamic analysis is accomplished using several, multi-fidelity prediction methods, including a vortex lattice method (VLM) approach, a panel method to model body effects, and a high-fidelity hybrid computational fluid dynamics (CFD) method that couples a Reynolds Averaged Navier-Stokes (RANS) flow solver for the near-body simulation with a quasi-3D RANS approach for the evolution of wakes over long longitudinal distances. Computational analysis is used to develop a comprehensive aerodynamic database for flight simulation and control law design, to develop a drag benefit “map”, which is used to determine the optimum location of the trailing airplane relative to the leading airplane wake, and to compare with flight-derived performance data to validate the use of CFD for the SAVE program. Finally, an extension of the hybrid CFD method and initial results for a 3-airplane echelon formation to highlight the multiple wake interaction effects and complex flow physics is presented.

Aerodynamics analysis has become a mindset for high performance vehicles. This is because it provides valuable insight on a vehicle motion during different phases. There are vast varieties of software available like ANSYS Workbench, Star CCM+, Autodesk Simulation, SimFlow, FeatFlow, Autodesk Inventor etc. Amongst these softwares, Star CCM+ and ANSYS Workbench are the most widely used. Normally, it is observed that considerable users are confused in choosing the right software for CFD simulation because of a large variety of commercially available softwares. The present study provides comparative results to users of Computational Fluid Dynamics (CFD) with the driven case study. In the present case, authors chose the most commonly used CFD softwares, the ANSYS Workbench Fluent and the Star CCM+. Polyhedral meshing was applied on computer aided model of a car in both of these softwares. It has been found that coefficients of drag and lift achieved by aerodynamic analysis of a car are in a small marginal approximation between both ANSYS Workbench Fluent and Star CCM+ softwares. In the case of Star CCM+, CD was around 0.261 and CL was 0.07; however, in the case of ANSYS Workbench Fluent approximations were found to be 0.271 for CD and 0.05 for CL.

The use of turbulence models leads to uncertainties in the computed results because the models are not universal. Therefore, it is essential to validate the CFD program by experimental data. A computational fluid dynamics (CFD) program with the Re-Normalization Group (RNG) k-ϵ model was used for prediction. Validation was conducted by comparing the flow patterns, vertical profiles of temperature, concentration, velocity, and turbulence intensity between measurement and computation for an individual office, a cubicle office, and a quarter of a classroom. The computed air temperature and velocity agree well with the measured data. The agreement is less satisfactory between the computed and measured temperatures in the lower part of the office. The CFD model used therefore should be able to predict the indoor environments characterized by strong pressure and buoyancy driven flows.

<div class="htmlview paragraph">Continued advances in the various elements that comprise the field of computational fluid dynamics (CFD) are promoting a radically different approach to the aerodynamic design and analysis of aerospace vehicles and systems. The elements of CFD generally include numerical algorithm development, transition and turbulence modeling, surface modeling and grid generation, scientific visualization, and validation methodologies. This paper discusses the research progress and prospects for the future in each of these key elements within the National Aeronautics and Space Administration's (NASA's) CFD and Experimental Validation Program. The applicability of computational methods for the purposes of understanding complex flow phenomena, exploring new aerodynamic concepts, and providing vehicle design input is also addressed.</div>

In order to develop high efficiency fans and blowers the design methods are being improved continuously. The same is valid for the modern CFD (Computational Fluid Dynamics) programs, which are used in the design process in order to validate the designs. However, at the end of the development process measurements at test rigs are needed in order to verify the final design. CFD is substituting to some extend EFD (Experimental Fluid Dynamics), but still EFD is the final way to evaluate a design. Usually the universities and the industry have some test rig where measurements are done. These test rigs in general are unique and built according to a corresponding standard. However, these standards, as for example the German DIN 24 163 [1] or the European DIN EN ISO 5801 [2] prescribe only the main proportions of such test rigs while several important features are not described in detail. In particular the aerodynamic theory behind the standard is very often omitted. For example, the measurement of the pressure characteristic of a fan is performed at a pressure tab at the test chamber wall and not at the fan itself. How to assure that the pressure measured at the wall tab corresponds to the fan pressure? In this work the relevant theory behind the design of test rigs was worked out in detail for the relevant test rig features where the standards do not explain the fluid mechanical aspects. On this basis pressure and suction side test rigs were designed and completely simulated with a commercial CFD program, Ansys CFX. The goal was to develop compact test rigs according to the European DIN EN ISO 5801 [2] standard. It is shown in this work how the size of the test chamber influences the measuring results. Furthermore an in detail CFD study of a series of flow measurement devices, as inlet and Venturi nozzles, was performed. In such a way it was possible to show the influence of the dimensions of these devices on the accuracy of the measurements. Finally two test rigs were built, one for suction side and the other one for pressure side measurements. Compared to other test rigs in this category in use in the industry in Germany it was possible to reduce the size of these test rigs by a factor of about two complying with the measurement uncertainty of the DIN EN ISO 5801 standard.

해양에 기름이 유출되면 방제작업의 초기단계에서 기름의 이동 및 확산을 차단하기 위하여 오일 붐을 사용하는 것이 일반적이다. 그러나 오일 붐은 유속, 기름의 밀도와 점성, 수심, 기름의 양, 그리고 붐 흘수의 길이 등과 같은 여러 인자들에 의해 그 포획능력이 영향을 받는다. 본 연구에서는 이들 누유인자가 오일 붐의 포획과정에 어떠한 영향을 미치는지를 체계적으로 파악하기 위하여 컴퓨터 모델링을 수행하였다. 모델링을 위하여 CFD(computational fluid dynamics) 프로그램 중에서 가장 널리 알려진 Fluent를 이용하였으며 기존의 실험자료와 비교함으로써 모델링 결과를 검증하였다. 본 연구의 결과는 방제작업 책임자와 오일 붐 설계자에게 유용한 참고자료가 될 것으로 기대된다. When oil is spilled at sea, the oil boom is commonly used to tackle the movement and spreading of oil in an early stage of oil spill combat. But the retaining capability of oil boom is affected by various factors, such as water velocity, viscosity and density of oil, water depth, oil volume and the length of boom draft. In this study, a computer modelling was peformed to investigate how these factors influence the oil retaining process. The Fluent, most popular one of many CFD(computational fluid dynamics) programs is chosen for modelling and modelling results were verified using the empirical data. It is expected that results of this study will be useful data for oil boom designer and oil spill response commander.

Military Freefall (MFF), which includes both HALO (High Altitude Low Opening) and HAHO (High Altitude High Opening High Opening) operations, is among the most versatile techniques used by Army Airborne Special Forces. A spike in MC-4 main pilot parachute hesitation incidents in training led to a series of wind and water tunnel experiments. This work in turn suggested that digital simulation capability, specific to the aerodynamics and dynamics of MFF, would be valuable. This paper describes the initial development of the Free Fall Analysis and Simulation Tool (FAST) intended to meet this need. The core of FAST is a Computational Fluid Dynamics (CFD) program that implements the Cartesian grid immersed boundary method to simplify grid generation for complex and potentially non-rigid body shapes. Particular emphasis is being devoted to turbulence modeling for the complex and energetic wake. FAST includes a “digital manikin” that simplifies the generation of equipped body shapes in arbitrary poses for input to the CFD program. The digital manikin will also estimate the mass properties of the parachutist plus equipment. Capability will be implemented to combine the aerodynamic predictions from CFD with the mass properties to analyze free fall stability and control using linearized dynamics and nonlinear simulation.

Commercially available Computational Fluid Dynamics (CFD) software have been applied in indoor environmental design in recent years, but the prediction accuracy depends on an understanding of fluid dynamics fundamentals, in setting appropriate boundary and numerical conditions. This study aims to provide practical modelling information related to prediction accuracy and problematic areas in CFD applications in air conditioning and ventilation, through a series of benchmark tests and reported the results. Six commercial CFD codes were evaluated while two benchmark test cases were performed on isothermal/non-isothermal flow in 2D and 3D room models. The influence of mesh design, and turbulence models showed that using a standard k-ε model on a coarse mesh could provide sufficiently accurate results for practical purposes, by reducing the relaxation coefficient. Evaluation using different CFD programs on a non-isothermal room airflow showed different performances in predicting temperature distributions. The OpenFOAM code showed the closest matching results between three codes tests.

In order to predict the velocity and attitude of a sailing yacht travelling in a given wind speed and wind angle, the hydrodynamic problem and the aerodynamic problem need most of the time to be decoupled. Two matrices are built to characterize the hydrodynamic and the aerodynamic behavior separately. Then a Velocity Prediction Program (VPP) interpolates the matrices and finds the equilibrium between the forces acting on the hull and appendages on one side, and the forces acting on the sails on the other side. This gives the velocity and attitude of the yacht depending on the wind speed and wind angle. Two main approaches are currently used to build the hydrodynamic matrix. The first method is to build a reduced or a virtual model with the proper hull shape and test it in a towing tank or a Computational Fluid Dynamics (CFD) program. This approach can lead to a very precise estimation of the matrix for a given hull shape, but it is time consuming and gives no indication on other possible hull shapes. The second method is to build and test various hull shapes and use this database to build analytical formulas describing the evolution of the hydrodynamic forces depending on the speed and attitude, but also on the hull shape, via several “shape parameters”. During the early stage of design, numerous hulls are to be evaluated, and it is very valuable to understand the influence of the design parameters on hydrodynamic efficiency of the hull. Therefore, the second method should be much more efficient at this stage of the design process. The most used regressions have been provided by J.A. Keuning et al., based on the Delft Systematic Yacht Hull Series, [1], [2]. This work began in 1971; the sailing yacht hull shapes have changed a lot since then. The aim of the present work is to enhance these regressions, by using new shapes in the database and by adding new “shape parameters” to describe the hulls. A powerful loop driven by the commercial software ModeFrontier has been developed in order to build a database by means of CFD. Systematic morphing of the hull shapes, parallel computing, automatic meshing and automatic post-treatment will provide a large database in a relatively short time. The aim of the ongoing work is to improve the accuracy and sensitivity of the prediction of yacht hull performance during the early stages of the design process. The study will focus on flat water, steady predictions. The following results concern exclusively bare hulls, the interaction between the hull and its appendages will be treated separately.

Natural fliers glide and minimize wing articulation to conserve energy for endured and long-range flights. Elucidating the underlying physiology of such a capability could potentially address numerous challenging problems in flight engineering. This study investigates the aerodynamic characteristics of an insect species called desert locust (Schistocerca gregaria) with extraordinary gliding skills at low Reynolds numbers. Here, locust tandem wings are subjected to a computational fluid dynamics (CFD) simulation using two-dimensional and three-dimensional (3-D) Navier–Stokes equations, revealing fore–hindwing interactions and the influence of their corrugations on aerodynamic performance. Furthermore, the obtained CFD results are mathematically parameterized using the PARSEC method and optimized based on a novel fusion of genetic algorithms and Nash game theory to achieve Nash equilibrium. It was concluded that the lift–drag (gliding) ratio of the optimized profiles were improved by at least 77% and 150% compared to the original wing and the published literature, respectively. Ultimately, the profiles are integrated and analyzed using 3-D CFD simulations that demonstrated a 14% performance improvement, validating the proposed wing models for further fabrication and rapid prototyping presented in a future study.

In the development of bio-inspired Micro Air Vehicles (MAVs), flapping-wing configurations derived from natural fliers such as honeybees offer unique advantages in maneuverability and stability within complex environments. However, modeling the unsteady aerodynamic behavior of flapping wings is computationally intensive. In this study, a simplified quasi-steady approach is adopted by simulating the aerodynamics of a rigid, honeybee-inspired drone at multiple static orientations using a virtual inflow rotation technique. Computational Fluid Dynamics (CFD) simulations are performed at various effective angles of attack by altering the direction of the incoming flow while keeping the wing geometry fixed. Key vortex structures, including leading-edge and wingtip vortices, are analyzed to evaluate their role in lift and thrust generation. Aerodynamic forces (lift, drag, and thrust) are quantified across multiple static orientations to gain insights into aerodynamic performance. In parallel, a basic static structural analysis of the rigid wing is conducted using the aerodynamic pressure loads obtained from the CFD simulations. The structural response under representative aerodynamic loads is evaluated to assess stress distribution and potential wing deformation. This combined aerodynamic and structural analysis provides a comprehensive understanding of wing performance and structural integrity, offering valuable data for optimizing bio-mimetic MAV design.

In this paper, the Fluid-Structure Interaction (FSI) is performed to get the aerodynamic and structural reactions of a 1.5MW 3D wind turbine blade by utilizing the commercialized ANSYS. The aerodynamic analysis is carried out using both modified Blade Element Momentum Method (BEM) and Computational Fluid Dynamics (CFD), whereas, structural modeling is done using Finite Element Method (FEM). However, for FSI, CFD method is employed to develop pressure loading which later on passed as pressure loads to ANSYS static structural module to determine stresses and deformations on the blade. The complete analysis follows the interface of CFD and FEA through one way coupling. Five operational conditions are evaluated. The calculation of total deformation and von-misses stress have been done by considering various material and geometrical parameters and are found to be within material and structural limits as per the requisite design standards.

This paper presents a comparative aerodynamic analysis of two vehicle models—a hatchback and a sedan—using computational fluid dynamics (CFD) simulations conducted with SimScale software. The primary objective is to evaluate the behavior of the external flow around the body, focusing on determining the drag (Cd) and lift (Cl) coefficients, as well as analyzing the flow patterns and recirculation zones. The vehicle geometries were generated using the artificial intelligence tool Tripo3D.AI, to investigate the feasibility of automated models for aerodynamic analysis. The study was conducted with the k – ω SST turbulence model, suitable for simulations in regions with flow separation and boundary layers. The mesh was composed of polyhedral elements with local refinements in regions of interest, automatically generated by SimScale. The boundary conditions included velocity inputs of 25 m/s, walls with no-slip and slip conditions, and a lower wall with movement equal to the flow velocity. The results indicated a drag coefficient (Cd) of 0.308 for the sedan model and 0.601 for the hatchback, representing an increase of approximately 95% in Cd for the hatchback compared to the sedan. Regarding the lift force, a value of -0.00014 was observed for the sedan and -1.97 for the hatchback, indicating a greater vertical aerodynamic effect in the hatchback. The simulations showed good numerical convergence, validating the adopted approach.

The geometry of the car's side mirror influences the aerodynamic noise. This study will use computational fluid dynamics (CFD) to investigate the effect of aerodynamic analysis on noise from the side mirror. In this study, we will design two types of side mirrors using the Honda Civic EG9 for sedans and the Range Rover Evoque for SUVs. SolidWorks software was used for geometry modelling and the analysis was completed using the ANSYS software. Three streamline velocity values (80 km/h,100km/h, 120 km/h) were chosen as references. The results of the analysis included the side mirror's drag force, acoustic contour, and velocity streamlines. Design 1, produces a maximum acoustic power level of 77.21 dB when travelling at a high speed of 120 km/h. The maximum acoustic power level for Design 2, which travels at 120 km/h is 75.71dB. In summary, Design 2's side mirror has a higher acoustic power level than Design 1's side mirror. Design 1 produces a drag force of 4.52 N at a high speed of 120 km/h while Design 2 results in a drag force of 6.36 N at a high speed of 120 km/h.