An international travel proposal to NSF requesting support of the PI to attend the 13th International Symposium on Particle Image Velocimetry

向 NSF 提交的国际旅行提案，请求 PI 支持参加第 13 届粒子图像测速国际研讨会

• 批准号: 1933176
• 负责人: Xiaofeng Liu
• 金额: $ 0.45万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933176&HistoricalAwards=false
• 关键词: international; travel; proposal; NSF; requesting

Travel funds will enable the PI, upon invitation by the symposium chair, to participate in the 13th International Symposium on Particle Image Velocimetry (ISPIV 2019, July 22-24, Munich, Germany) and deliver an oral presentation of significant scientific importance. The oral presentation and the conference paper titled ?Error propagation from the PIV-based pressure gradient to the integrated pressure by the omni-directional integration method? reports for the first time a comprehensive theoretical analysis and the corresponding numerical and experimental validation results for error propagation characteristics of the ?omni-directional integration method?, which is shown to have better performance in data accuracy in comparison with the conventional Poisson equation reconstruction approach. The travel support will help the effort in proving and establishing the advantage of the ?parallel ray omni-directional integration method? over the conventional Poisson equation approach for pressure reconstruction from noise embedded experimental data. Moreover, since there are numerous occasions in science and engineering practice that require solution of a scalar potential from a conservative vector field using Poisson equation, e.g., the reconstruction of temperature or wavefront from their corresponding gradients, the ?parallel ray omni-directional integration method? is readily applicable to those generic occasions. Thus, the new pressure reconstruction method will not only bring lasting impact to the fluids community, but also to a much larger audience in science and engineering applications as well.The goal of this project is to prove and establish the advantage of the ?parallel ray omni-directional integration method? over the conventional Poisson equation approach in pressure reconstruction from noise embedded experimental data. Characterization of the accuracy of the PIV pressure reconstruction methods is of critical importance in understanding the capabilities and limitations of these methods. Unfortunately, until the proposed oral presentation by the PI to be delivered at ISPIV 2019, there is no theoretical analysis available for the omni-directional integration method that characterizes the error propagation from the measured pressure gradient to the reconstructed pressure field. The proposed presentation and paper by the PI at ISPIV 2019 intends to fill the gap, and help settle the question about why the omni-directional method has better performance over the conventional Poisson equation based on theoretical analysis, numerical simulation and experimental data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

差旅费将使PI能够应研讨会主席的邀请参加第13届粒子图像测速国际研讨会（ISPIV 2019，7月22日至24日，慕尼黑，德国），并发表具有重要科学意义的口头报告。口头报告和会议论文的题目是？误差从PIV压力梯度向全方向积分法积分压力的传播？报告首次全面的理论分析和相应的数值和实验验证结果的误差传播特性的？全方位集成方法，与传统的泊松方程重建方法相比，该方法在数据精度方面具有更好的性能。旅行支助将有助于证明和确立联合国的优势。平行射线全方向积分法与传统的泊松方程方法相比，该方法用于从噪声嵌入的实验数据中重建压力。此外，由于在科学和工程实践中有许多场合需要使用泊松方程从保守向量场求解标量势，例如，温度或波前的重建从其相应的梯度，？平行射线全方向积分法很容易适用于这些通用场合。因此，新的压力重建方法不仅会给流体界带来持久的影响，而且在科学和工程应用中也会给更大的受众带来持久的影响。平行射线全方向积分法与传统的泊松方程方法相比，在从嵌入噪声的实验数据中重建压力方面具有更好的效果。 PIV压力重建方法的准确性表征对于理解这些方法的能力和局限性至关重要。不幸的是，直到PI在ISPIV 2019上提出的口头报告，才有全方位积分方法的理论分析，该方法描述了从测量的压力梯度到重建的压力场的误差传播。PI在ISPIV 2019上提出的演讲和论文旨在填补差距，并帮助解决为什么全方向方法比基于理论分析的传统泊松方程具有更好的性能的问题，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Error propagation from the PIV-based pressure gradient to the integrated pressure by the omnidirectional integration method

• DOI: 10.1088/1361-6501/ab6c28
• 发表时间: 2020-01
• 期刊: Measurement Science and Technology
• 影响因子: 2.4
• 作者: Xiaofeng Liu;J. Moreto