Scholarlink

<p>To address the low-Reynolds-number operating conditions of long-endurance grazing Unmanned Aerial Vehicles (UAVs) in plateau environments, this study proposes a thrust-constrained parametric design framework aimed at maximizing propeller efficiency. By varying the design thrust coefficient from 1.1 T to 1.6 T (T = 10&#xa0;N) and jointly optimizing it via a Genetic Algorithm (GA) and Computational Fluid Dynamics (CFD), the propeller configuration and speed achieving maximum efficiency under the required thrust are identified, thereby effectively decoupling propeller efficiency from rotational speed constraints. Integrated (CFD) analyses reveal a nonmonotonic efficiency–thrust relationship, with a maximum efficiency of 0.72 at 2000&#xa0;rpm under a 1.3 T configuration—achieving a 5.3% efficiency gain and a 9.1% speed reduction compared to the baseline design (2200&#xa0;rpm). Further multi-objective optimization via an adaptive‐mutation genetic algorithm refines blade chord and pitch distributions, raising efficiency to 0.747 (a cumulative 9.2% improvement) while maintaining thrust constraints. Experiments validate the CFD–GA co‐design, with thrust and torque errors below 10%. The efficiency peak at 1.3 T is due to the balance between the reduction of resistive losses due to the gain of the Reynolds effect (dominant below 1.3 T) and the increase of induced losses (dominant above 1.3 T). This methodology provides a case-specific design guideline for high‐performance propeller design in similar low-Reynolds-number, density-depleted environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Thrust-constrained parametric design of high-altitude propellers: a CFD and genetic algorithm co-optimization for low-reynolds number efficiency gains

  • 期刊论文

  • 发表日期：

    2026年3月26日

查看全文
  • Chenyang Guan,
  • Kelong Wang,
  • Chuan Wang,
  • Nanyi Li,
  • Bing Guo

摘要

To address the low-Reynolds-number operating conditions of long-endurance grazing Unmanned Aerial Vehicles (UAVs) in plateau environments, this study proposes a thrust-constrained parametric design framework aimed at maximizing propeller efficiency. By varying the design thrust coefficient from 1.1 T to 1.6 T (T = 10 N) and jointly optimizing it via a Genetic Algorithm (GA) and Computational Fluid Dynamics (CFD), the propeller configuration and speed achieving maximum efficiency under the required thrust are identified, thereby effectively decoupling propeller efficiency from rotational speed constraints. Integrated (CFD) analyses reveal a nonmonotonic efficiency–thrust relationship, with a maximum efficiency of 0.72 at 2000 rpm under a 1.3 T configuration—achieving a 5.3% efficiency gain and a 9.1% speed reduction compared to the baseline design (2200 rpm). Further multi-objective optimization via an adaptive‐mutation genetic algorithm refines blade chord and pitch distributions, raising efficiency to 0.747 (a cumulative 9.2% improvement) while maintaining thrust constraints. Experiments validate the CFD–GA co‐design, with thrust and torque errors below 10%. The efficiency peak at 1.3 T is due to the balance between the reduction of resistive losses due to the gain of the Reynolds effect (dominant below 1.3 T) and the increase of induced losses (dominant above 1.3 T). This methodology provides a case-specific design guideline for high‐performance propeller design in similar low-Reynolds-number, density-depleted environments.