High-performance Computing and Data-driven Modeling of Aircraft Contrails

飞机尾迹的高性能计算和数据驱动建模

• 批准号: 1854815
• 负责人: Georgeta-Elisab Marai
• 金额: $ 44.64万
• 依托单位: University of Illinois at Chicago
• 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: 2019-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854815&HistoricalAwards=false
• 关键词: performance; Computing; Data; driven; Modeling

This project aims to model the early-phase formation of condensation trails ("contrails"), which are ice clouds generated by water exhaust from aircraft engines. Although the contrails initially appear to be linear, they can spread under favorable atmospheric conditions and form cirrus clouds that can persist for hours and eventually become almost indistinguishable with natural cirrus. Contrails and contrail-cirrus are indeed the most uncertain aviation contributions to the Earth-radiation budget (that is, the balance of energy entering, reflected, absorbed, and emitted by the Earth). Modeling this complex multi-physics problem is challenging because the different physical processes interact at different time and spatial scales. This project addresses this challenge using high-resolution numerical simulations that rely extensively on high-performance computing and advanced visualization techniques to help identify, capture and model contrail features. The primary focus of the modeling is on the early phase of contrail evolution where the presence of large-scale motions generated by the aircraft wake vortices and the small-scale perturbations induced by the jet and wake turbulence interact. The contrail parameterization results will support the integration of emissions into global atmospheric models. The sensitivity of contrail properties to the initial particle emissions may suggest potential mitigation strategies. Underrepresented minority students from the University of Illinois at Chicago, a Hispanic Serving Institution and a Minority Serving Institution, will be engaged in the computational research.The specific goals of the research are: (1) to carry out the first fully three-dimensional spatial large-eddy simulations (LES) of contrail formation that include the full aircraft geometry and to develop an accurate data-set of contrail evolution in the jet and vortex regime; (2) to identify the three-dimensional contrail features and fit parameters by journaling the simulation workflow using advanced visualization techniques; (3) to reduce the large dimensionality of the generated data-set and provide a general and accurate model of contrail structure at the end of the jet regime using Artificial Neural Networks based on high-fidelity data training; and (4) to reconstruct contrail global properties over the full time evolution using statistically inspired methods such as Polynomial-Chaos expansions for parameterization into global models. The techniques developed through this work will enable contrail parameterizations that are consistent with the physical assumptions and the conservation equations used in global atmospheric models. These techniques will further handle large uncertainties in the microphysical and optical/radiative properties of ice particles. The techniques will also handle large variability in atmospheric conditions that determine the background and boundary conditions for LES of contrails. A visual analysis framework will enable detection and analysis of flow features, as well as multi-scale and multi-run analysis of multiple simulations and parameters.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.

该项目旨在模拟凝结尾迹（“轨迹”）的早期形成，这是由飞机发动机排出的水产生的冰云。虽然尾迹最初看起来是线性的，但它们可以在有利的大气条件下扩散，形成卷云，这些卷云可以持续数小时，最终变得与自然卷云几乎无法区分。凝结尾迹和凝结尾迹卷云确实是航空对地球辐射收支（即进入地球、被地球反射、吸收和发射的能量的平衡）最不确定的贡献。模拟这个复杂的多物理场问题是具有挑战性的，因为不同的物理过程在不同的时间和空间尺度上相互作用。该项目使用高分辨率数值模拟来解决这一挑战，该模拟广泛依赖于高性能计算和先进的可视化技术，以帮助识别，捕获和建模轨迹特征。建模的主要重点是轨迹演变的早期阶段，在此阶段，飞机尾流涡流产生的大尺度运动和由射流和尾流湍流引起的小尺度扰动相互作用。凝结尾迹参数化结果将有助于将排放量纳入全球大气模型。轨迹特性对初始粒子排放的敏感性可能提示潜在的减缓策略。来自伊利诺伊大学芝加哥分校、西班牙裔服务机构和少数族裔服务机构的少数族裔学生将参与计算研究。研究的具体目标是：（1）对尾迹形成进行首次全三维空间大涡模拟（LES），包括飞机的全部几何形状，并开发准确的数据-（2）通过使用先进的可视化技术记录模拟工作流程，识别三维轨迹特征并拟合参数;（3）降低生成数据的大维度-使用基于高保真数据训练的人工神经网络，设置并提供射流状态结束时的轨迹结构的通用且精确的模型;以及（4）使用统计启发的方法（如多项式混沌展开）重建轨迹在整个时间演化过程中的全局特性，以参数化为全局模型。通过这项工作开发的技术将使轨迹参数化与全球大气模型中使用的物理假设和守恒方程相一致。这些技术将进一步处理冰粒子的微物理和光学/辐射特性的大的不确定性。这些技术还将处理大气条件的巨大变化，这些条件决定了凝结尾迹大涡模拟的背景和边界条件。可视化分析框架将能够检测和分析流动特征，以及对多个模拟和参数进行多尺度和多运行分析。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审查标准进行评估，被认为值得支持。

项目成果

Numerical Experiments of Subsonic Jet Flow Simulations Using RANS with OpenFOAM

使用 RANS 和 OpenFOAM 进行亚音速射流模拟的数值实验

• DOI: 10.4236/ojfd.2022.122011
• 发表时间: 2022
• 期刊: Open Journal of Fluid Dynamics
• 作者: Zadeh, Negar Naghash;Paoli, Roberto
• 通讯作者: Paoli, Roberto

DASS Good: Explainable Data Mining of Spatial Cohort Data

• DOI: 10.1111/cgf.14830
• 发表时间: 2023-06-01
• 期刊: COMPUTER GRAPHICS FORUM
• 影响因子: 2.5
• 作者: Wentzel，A.;Floricel，C.;Marai，G. E.
• 通讯作者: Marai，G. E.

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

• DOI: 10.1109/tvcg.2023.3326939
• 发表时间: 2024-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Floricel，Carla;Wentzel，Andrew;Marai，G. Elisabeta
• 通讯作者: Marai，G. Elisabeta

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.

• DOI: 10.1109/tvcg.2021.3114810
• 发表时间: 2022-01
• 期刊: IEEE transactions on visualization and computer graphics
• 影响因子: 5.2
• 作者: Floricel C;Nipu N;Biggs M;Wentzel A;Canahuate G;Van Dijk L;Mohamed A;Fuller CD;Marai GE
• 通讯作者: Marai GE

Parameter Analysis and Contrail Detection of Aircraft Engine Simulations

飞机发动机仿真的参数分析和轨迹检测

• DOI: 10.1109/ldav53230.2021.00016
• 发表时间: 2021
• 期刊: 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV
• 作者: Nipu, Nafiul;Floricel, Carla;Naghashzadeh, Negar;Paoli, Roberto;Marai, G. Elisabeta
• 通讯作者: Marai, G. Elisabeta