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Reconciling wind tunnel data with real performance: influence of freestream turbulence intensity and large-scale unsteadiness

Reconciling wind tunnel data with real performance: influence of freestream turbulence intensity and large-scale unsteadiness
风洞数据与实际性能的协调:自由流湍流强度和大规模不定常的影响
批准号:
2889141
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
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英文摘要
Our current inability to reconcile test data with actual performance of land, sea, and air vehicles, especially under the unsteady conditions which they often encounter in the real world represents a major barrier in improving designs for the energy efficiency of large platforms. Many forms of transportation vehicles face unsteady or turbulent scenarios and without the understanding of what effects this has on the vehicle, there is no way of confidently improving the vehicles design and its efficiency. Prior to the development of CFD tools, model test data pertaining to vehicle performance and their flow fields was almost always acquired in wind-tunnels (or water-tunnels) with various technical methods used to acquire physical measurements. Typically, these flow facilities incorporated carefully designed flow conditioning devices to ensure that the vehicle model was subjected to steady uniform flow conditions with a purposely low free-stream turbulence (FST) intensity. These FST levels can then be deliberately increased using passive or active grids or a combination of the two.The role that FST, defined by both intensity and length scales (e.g. integral, Taylor, Kolmogorov), plays on boundary layer flows, particularly transition-to-turbulence, is still not fully understood. For example, even in the simplest case of flow over a smooth flat plate, the role that the FST intensity and integral length scale directly plays on boundary layer transitions has only recently been characterised. Beyond this canonical case, the role of FST and, indeed inflow unsteadiness in general on applied aero- and hydrodynamic applications requires further investigation. With the rapid growth and development of additive manufacturing, the research community now has at its disposal the ability to rapidly create prototypes and therefore rapidly test different flow conditioning devices with various intricate geometries far being simple screens and grids. This can create conditions, whether steady or unsteady, within a wind tunnel at lower cost and more rapidly than with conventional fabrication technologies.This project leverages pre-existing physical infrastructure in addition to new equipment designed for the specifics of the problems researched. The project will facilitate the design, build and test of a wind-tunnel flow conditioning device, specifically to elevate the turbulence intensity in addition to being able to induce a level of shear flow. This will lead to 'extreme' testing of various bluff-body vehicles on test models representative of land, sea and air vehicles. This data will then serve as computational fluid dynamics (CFD) validation cases and will allow insight gains leading to improved empirical models and improved platform designs. The initial premise of this project is to use a simplistic bluff body model such as the Ahmed body to conduct high-fidelity flow and performance measurements on. Once this testing has been conducted the project will move onto more complex geometries.
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Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: