CAREER: High-End Computing in Environmental Engineering With Application to Subsurface Characterization

职业：环境工程中的高端计算及其在地下表征中的应用

• 批准号: 0238623
• 负责人: Gnanamanikam Mahinthakumar
• 金额: $ 40万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0238623&HistoricalAwards=false
• 关键词: CAREER; End; Computing; Environmental; Engineering

0238623 Mahinthakumar Accurate characterization of the subsurface is an important element in the development of reliable and efficient groundwater management practices. Accurate and reliable estimation of hydraulic conductivity distribution, contaminant distribution, and/or contaminant source release history is necessary for problems such as estimating groundwater yields, design of efficient cleanup strategies, and identifying responsible parties in a contamination incident. This requires solution of an inverse problem because direct measurement of detailed subsurface properties is not feasible. Inverse problems are difficult to solve and are computationally demanding. This multidisciplinary CAREER proposal will investigate novel computational strategies for the efficient solution of large-scale inverse problems in subsurface characterization.A major focus of this career proposal is to investigate the use of hybrid genetic algorithm - local search (GA-LS) approaches and parallel computing for subsurface characterization inverse problems by developing a flexible prototype test environment.The proposed development will target parallel supercomputers as well as emerging computing environments such as the computational grid. Several new ideas will be explored in this proposal to improve the efficiency and flexibility of the approach. The computational approach will be rigorously tested and validated using a number of subsurface characterization problems including case studies, published field results, and application to field problems in North Carolina through collaboration with North CarolinaDepartment of Environment and Natural Resources (NCDENR). The testing and validation activities will lead to a greater understanding of using hybrid GA-LS algorithms for a wide range of subsurface characterization problems and may lead to further improvements of the approaches.Currently there are no concerted efforts in bringing computational science content into environmental engineering education. The educational activities will focus on bridging the gap between environmental engineering and computational science by integrating several components of the proposed research into existing and new courses. Research activities in inverse modeling, subsurface characterization, GA-LS algorithms, and parallel computing will be incorporated into graduate and undergraduate courses. One of these courses, an entry-level graduate course, will be targeted for the NCSU Distance Education Program. An interactive training module will be developed to introduce different inverse modeling approach to students and practitioners. Easy access to this module will be provided via the web for educators and practitioners throughout the nation. Introduction of computational science education to minority environmental engineering students will be pursued through collaboration with North Carolina A&T State University. These educational activities will impact computational science education among environmental engineering students and practitioners. The collaborations with NCDENR will result in the transfer of knowledge to practitioners and policy makers in North Carolina. These activities are expected to foster long-term relationships between the PI and NCDENR in research and educational activities beyond the proposal period. In addition to publications and professional presentations, a conscious effort will be made to quickly disseminate teaching materials, research methodologies and findings, and software through a project web page.

0238623 马欣塔库马尔 地下水的准确特性是制定可靠和有效的地下水管理做法的一个重要因素。准确和可靠的水力传导率分布，污染物分布，和/或污染源释放历史的估计是必要的问题，如估计地下水产量，有效的清除策略的设计，并确定在污染事件的责任方。这需要解决一个反问题，因为直接测量详细的地下属性是不可行的。反问题很难解决，并且计算量很大。这个多学科的职业计划将研究新的计算策略，用于有效解决地下特征的大规模反问题。这个职业计划的一个主要重点是研究混合遗传算法-局部搜索（GA-LS）的使用。方法和并行计算地下表征反问题，通过开发一个灵活的原型测试环境。拟议的发展将目标并行超级计算机以及新兴的计算环境，如计算网格。 本提案将探讨若干新的想法，以提高这一办法的效率和灵活性。计算方法将严格测试和验证，使用一些地下表征问题，包括案例研究，发表的现场结果，并通过与北卡罗莱纳州环境和自然资源部（NCDENR）合作，在北卡罗来纳州的现场问题的应用。测试和验证活动将导致更好地理解使用混合GA-LS算法的广泛的地下表征问题，并可能导致进一步改进的approaches.Currently有没有协同努力，将计算科学的内容纳入环境工程教育。教育活动将侧重于弥合环境工程和计算科学之间的差距，将拟议研究的几个组成部分整合到现有的和新的课程。在反求建模，地下表征，GA-LS算法和并行计算的研究活动将被纳入研究生和本科课程。这些课程之一，入门级的研究生课程，将针对NCSU远程教育计划。将开发一个交互式培训模块，向学生和从业人员介绍不同的反建模方法。全国各地的教育工作者和从业人员将通过网络方便地进入这一模块。将通过与北卡罗来纳州AT州立大学的合作，向少数民族环境工程专业的学生介绍计算科学教育。这些教育活动将影响环境工程专业学生和从业人员的计算科学教育。通过与NCDENR的合作，将向北卡罗来纳州的从业人员和决策者传授知识。预计这些活动将促进PI和NCDENR在研究和教育活动中的长期关系，超过提案期。除了出版物和专业介绍外，还将有意识地努力通过项目网页迅速传播教学材料、研究方法和研究结果以及软件。