Modeling and Analysis of Liquid Sloshing and Its Effects on Vehicle Stability

液体晃动对公路、铁路、海洋和空间运输系统的动力学和稳定性有重要影响。然而，由于缺乏必要的计算算法和高保真模型来发展知识和更好地理解晃动对车辆动力学和稳定性的影响，危险材料的运输导致了致命的、昂贵的和破坏环境的事故。液体与卡车、飞机、铁路车辆、导弹、火箭、海上结构和海洋应用等上的容器和/或罐车相互作用时，会产生巨大的力。现有常用的简单液体晃动方法不适合复杂的运动场景。虽然使用计算算法来预测详细车辆模型的响应是必要的，但目前还没有一种高效、准确的基于连续体的液体晃动方法来量化在弯道过弯、快速变道以及加速和制动场景中可能很重要的晃动效应。该奖项支持基础研究，以开发第一代液体晃动计算算法，用于建模复杂的运动场景，制定安全指南和准确的事故重建。特别是，通过更好地了解晃动对公路、铁路、海洋和空间运输系统的影响，可以避免或大大减少造成重大经济损失、环境污染、生命损失和财产损失的危险物质事故。该项目还将导致新知识、新方法和新工具的发展，这些新知识、新方法和新工具可用于伊利诺伊大学芝加哥分校的高级本科生和研究生教育。伊利诺伊大学芝加哥分校被指定为少数民族服务机构。利用高效的基于连续体的液体晃动模型，捕捉液体的分布惯性和粘度，可以克服现有液体晃动模型的局限性。为此，将开发新的液体晃动模型，并将其与多体系统（MBS）算法集成，从而允许开发和求解包含重要细节的车辆模型的微分/代数方程。为了实现液体晃动/MBS算法的高效集成，避免现有基于欧拉的流体公式与基于拉格朗日的MBS算法集成的困难，将引入新的全拉格朗日非增量液体晃动求解程序。将开发三种不同的液体晃动方法；采用有限元（FE）绝对节点坐标公式（ANCF）建立高保真MBS模型，采用浮动参考框架（FFR）公式建立低阶MBS模型，采用光滑粒子流体动力学（SPH）方法建立不同的模型来评估液体湍流对车辆动力学的影响。拉格朗日方法将允许在复杂的车辆运动场景中准确地捕捉流体惯性的影响。

Liquid sloshing has a significant impact on the dynamics and stability of highway, rail, marine, and space transportation systems. Nonetheless, because of the lack of computational algorithms and high fidelity models necessary to develop the knowledge and better understanding of the sloshing effect on vehicle dynamics and stability, transportation of hazardous materials has resulted in deadly, costly, and environmentally damaging accidents. Liquid can produce significant forces as the results of the interaction with containers and/or tank cars mounted on trucks, aircraft, railroad vehicles, missiles, rockets, offshore structures, and marine applications among many others. Existing simple liquid sloshing approaches commonly used are not designed for complex motion scenarios. While the use of computational algorithms is necessary to predict the response of detailed vehicle models, there is no in existence today an efficient and accurate continuum-based liquid sloshing approach to quantify the sloshing effect which can be significant during curve negotiations, rapid lane changes, and acceleration and braking scenarios. This award supports fundamental research to develop the first generation of liquid sloshing computational algorithms for modeling complex motion scenarios, developing safety guidelines, and accurate accident reconstructions. In particular, hazardous material accidents, which result in significant economic loss, environmental contamination, loss of lives, and property damage, can be avoided or significantly reduced through better understanding of the effect of sloshing on highway, rail, marine, and space transportation systems. This project will also lead to the development of new knowledge, approaches, and tools that can be used in the education of senior undergraduate and graduate students at University of Illinois at Chicago which is designated as a minority serving institution. The use of efficient continuum-based liquid sloshing models that capture the liquid distributed inertia and viscosity can overcome the limitations of existing liquid sloshing models. To this end, new liquid sloshing models will be developed and integrated with multibody system (MBS) algorithms that allow for developing and solving the differential/algebraic equations of vehicle models that include significant details. In order to achieve efficient integration of liquid sloshing/MBS algorithms and avoid the difficulties of integrating existing Eulerian-based fluid formulations with the Lagrangian-based MBS algorithms, new total-Lagrangian non-incremental liquid sloshing solution procedures will be introduced. Three different liquid sloshing approaches will be developed; the finite element (FE) absolute nodal coordinate formulation (ANCF) will be used to develop high fidelity MBS models, the floating frame of reference (FFR) formulation will be used to develop low-order MBS models, and the smoothed particle hydrodynamics (SPH) method will be used to develop a different model to assess the effect of the liquid turbulence on vehicle dynamics. The Lagrangian approaches will allow for accurately capturing the effect of the fluid inertia in complex vehicle motion scenarios.