The adjoint method has long been considered as the tool of choice for gradient-based optimisation in computational fluid dynamics (CFD). It is the independence of the computational cost from the number of design variables that makes it particularly attractive for problems with large design spaces. Originally developed by Lions and Pironneau in the 70’s, the adjoint method has evolved towards a standard tool within the development processes of the aeronautical industries. Its uptake in the automotive industry, however, lags behind. The first systematic applications of adjoint methods in automotive CFD have interestingly not taken place in the classical shape design arena, but in a relatively young discipline of sensitivity-based optimisation: fluid dynamic topology optimisation. While being an established concept in structure mechanics for decades already, its transfer to fluid dynamics took place just ten years ago. We demonstrate that specifically for ducted flow applications, like airducts for cabin ventilation or engine intake ports, it constitutes a very powerful tool and has matured over the last years to a level that allows its systematic usage for various automotive applications. To drive adjoint-based shape optimisation to the same degree of maturity and robustness for car applications is the subject of ongoing research collaborations between academia and the car industry. Achievements and challenges encountered during these efforts are presented.

/pdf/adjoint-methods-for-car-aerodynamics-u51l3ddzfs.pdf

Adjoint methods for car aerodynamics

A passive flow control on a generic car model was experimentally studied. This control consists of a deflector placed on the upper edge of the model rear window. The study was carried out in a wind tunnel at Reynolds numbers based on the model height of 3.1 × 105 and 7.7 × 105. The flow was investigated via standard and stereoscopic particle image velocimetry, Kiel pressure probes and surface flow visualization. The aerodynamic drag was measured using an external balance and calculated using a wake survey method. Drag reductions up to 9% were obtained depending on the deflector angle. The deflector increases the separated region on the rear window. The results show that when this separated region is wide enough, it disrupts the development of the counter-rotating longitudinal vortices appearing on the lateral edges of the rear window. The current study suggests that flow control on such geometries should consider all the flow structures that contribute to the model wake flow.

Bluff-body drag reduction using a deflector

Coupling active and passive techniques to control the flow past the square back Ahmed body

The lasting high fuel cost has recently inspired resurgence in drag reduction research for vehicles, which calls for a thorough understanding of the vehicle wake. The simplified Ahmed vehicle model is characterized by controllable flow separation, thus especially suitable for this purpose. In spite of a considerable number of previous investigations, our knowledge of flow around this model remains incomplete. This work aims to revisit turbulent flow structure behind this model. Two rear slant angles, i.e., α = 25o and 35o, of the model were examined, representing two distinct flow regimes. The Reynolds number was 5.26 × 104 based on the model height (H) and incident flow velocity. Using particle image velocimetry (PIV), flow was measured with and without a gap (g/H = 0.174) between the vehicle underside and ground in three orthogonal planes, viz. the x–z, x–y and y–z planes, where x, y, and z are the coordinates along longitudinal, transverse, and spanwise directions, respectively. The flow at g/H = 0 serves as an important reference for the understanding of the highly complicated vehicle wake (g/H ≠ 0). While reconfirming the well-documented major characteristics of the mean flow structure, both instantaneous and time-averaged PIV data unveil a number of important features of the flow structure, which have not been previously reported. As such, considerably modified flow structure models are proposed for both regimes. The time-averaged velocities, second moments of fluctuating velocities, and vorticity components are presented and discussed, along with their dependence on g/H in the two distinct flow regimes.

Turbulent near wake of an Ahmed vehicle model

To speed up gradient estimation in a slope-seeking controller two different modifications are proposed in this study. In a first approach, the gradient estimation is based on a locally identified b...

/pdf/extensions-of-adaptive-slope-seeking-for-active-flow-control-10gxqftmum.pdf

Extensions of adaptive slope-seeking for active flow control

Experimental and numerical investigations were carried out aiming at the reduction of the total aerodynamic drag of a generic car model by means of active separation control. For two different configurations separate control approaches were tested, taking into account the differences in the wake topology of the models. The targeted excitation of the respective dominant structures in the wake region leads to their effective attenuation. The experiments as well as the numerical simulations showed that a weakening of a spanwise vortex in the separated flow over the slant is strongly coupled with the occurrence of stronger streamwise vortices along the slant edges and vice versa.

Active Drag Control for a Generic Car Model

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

D. Sperber

Author Tools

Chat about Author

Papers

Active Drag Control for a Generic Car Model