MDC: A High-Performance Problem-Solving Environment for Optimization and Control of Chemical and Biological Processes

MDC：用于优化和控制化学和生物过程的高性能问题解决环境

• 批准号: 9527151
• 负责人: Linda Petzold
• 金额: $ 165万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-10-01 至 1998-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9527151&HistoricalAwards=false
• 关键词: MDC; Performance; Problem; Solving; Environment

The primary goal of this research is the development of high-performance problem solving environment (PSE) for the optimization and control of chemical and biological processes, with initial emphasis on bioengineering applications. The optimization and control of such processes requires the repetitive solution of time-dependent partial equations (PDEs) in two or three spatial dimensions. The requirements of this problem, which must be solved interactively, can only be met by the use of massively parallel computers. Such a comprehensive and powerful PSE does not currently exist, and its development presents significant computational and computer science challenges. The initial application is the design and optimization of a small diameter bioartificial artery. Over 600,000 surgical procedures for blood vessel replacement are conducted in the U.S. annually, many involving synthetic polymer substitutes for small diameter blood vessels that fail due to lack of biocompatability, which is not an issue for bioartificial arteries. A PDE model based on continuum mechanical theory that describes the distribution of cells, fibers, and stresses in a tissue will be used to simulate the evolving compaction of a bioartificial artery. Optimization of the ultimate properties of the bioartificial artery is sought, in particular the ability to withstand pulsatile arterial pressure. PDE systems of a similar mathematical structure commonly arise as models for chemical processes with a need for their control and optimization. The project includes collaboration with and input from scientists and engineers at several industrial and government laboratories with applications in processing. To optimize or control a process described by PDEs, the original time interval is divided into subintervals in a multiple- shooting type approach. The PDEs on each subinterval are discretized in space via parallel adaptive finite el ement methods to give a large system of algebraic equations (DAEs). Parameters or control variables are then optimized, subject to state and control variable and continuity constraints (equality and inequality) using large-scale nonlinear programming techniques. Extensive knowledge and research experience in adaptive methods for PDEs, DAEs, large-scale optimization, parallel computing, computing environments, and chemical and biological processes, are required to successfully develop such a PSE. The reaerch team members cover this broad range of expertise, and have a successful record of collaboration with each other.

这项研究的主要目标是开发用于优化和控制化学和生物过程的高性能问题解决环境（PSE），最初的重点是生物工程应用。 此类过程的优化和控制需要在两个或三个空间维度上重复求解瞬态偏方程 (PDE)。 这个问题必须以交互方式解决，只有使用大规模并行计算机才能满足。 目前还不存在如此全面且强大的 PSE，其开发提出了重大的计算和计算机科学挑战。 最初的应用是小直径生物人工动脉的设计和优化。 美国每年进行超过 600,000 例血管置换手术，其中许多涉及小直径血管的合成聚合物替代品，这些血管由于缺乏生物相容性而失败，这对于生物人工动脉来说不是问题。 基于连续力学理论的偏微分方程模型描述了组织中细胞、纤维和应力的分布，将用于模拟生物人工动脉不断变化的压缩过程。 寻求生物人工动脉最终特性的优化，特别是承受脉动动脉压的能力。 具有类似数学结构的偏微分方程系统通常作为需要控制和优化的化学过程模型而出现。 该项目包括与多个工业和政府实验室的科学家和工程师合作并提供加工应用的投入。 为了优化或控制偏微分方程描述的过程，原始时间间隔被分成多重射击类型的子间隔。 每个子区间上的偏微分方程通过并行自适应有限元方法在空间中离散化，以给出一个大型代数方程组 (DAE)。 然后使用大规模非线性编程技术，根据状态和控制变量以及连续性约束（等式和不等式）对参数或控制变量进行优化。 成功开发这样的 PSE 需要在 PDE、DAE、大规模优化、并行计算、计算环境以及化学和生物过程的自适应方法方面拥有丰富的知识和研究经验。 研究团队成员涵盖广泛的专业知识，并拥有成功的相互合作记录。