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A HIGH-RISK HIGH-REWARD APPROACH TO PUBLIC-PRIVATE COLLABORATIVE RESEARCH IN PREDICTIVE MODELING CONTROL OF COMPLEX SYSTEMS (*)

机译:高风险的高奖励方法,可用于复杂系统预测建模和控制中的公私合作研究(*)

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In this paper, we propose an approach to public-private collaborative research in predictive modeling and control of complex engineered systems. Society depends intimately on complex systems. The behavior of a simple system can be modeled and the model can be validated by experimental observations, if the behavior of each component and its interface with other components are known and well-defined. In contrast, a complex system cannot be modeled accurately enough to effectively predict and control the behaviors of the overall system. One example of an engineered complex system network (CSN) is the electricity power grid, which encompasses power generation, transmission, distribution, and consumption, as one giant system that includes electric generators, transformers, substation switchyards, transmission lines, consumer devices, and a multitude of new evolving components. The electricity power grid depends on other complex systems, e.g., climate systems that govern wind current for wind turbines, river water levels for thermoelectric cooling, and economic systems for service demand, pricing, revenue collection, and for business capital supply. Operational robustness, reliability, and efficiency of CSN's are in the interest of all the subsystem owners, end users, and the public welfare of the nation. Conundrum? Who is responsible for the overall CSN's operational robustness, reliability and efficiency, when so many parts of the system reside in so many different hands with the ultimate beneficiaries of the systems being the general public? Which entities are responsible for funding critical high-risk research, whose ultimate benefits do not reside with any one subset of stakeholders? These questions characterize the challenge of sourcing R&D funds that can be focused on modeling, understanding, and management of CSNs in general. To address such needs for innovative collaborative research, Congress established the Technology Innovation Program (TIP) at the National Institute of Standards and Technology (NIST) as part of the 2007 America COMPETES Act. Its purpose is to "assist United States businesses and institutions of higher education or other organizations, such as national laboratories and nonprofit research institutions, to support, promote, and accelerate innovation in the United States through high-risk, high-reward research in areas of critical national need." Ongoing efforts by TIP to identify and qualify societal challenges in the critical national need area of Complex System Networks are introduced.
机译:在本文中,我们提出了一种在预测建模和复杂工程系统控制中的公私协作研究方法。社会依赖于复杂的系统。如果每个组件的行为及其与其他组件的接口是已知的并且定义,可以通过实验观察进行建模的简单系统的行为,并且可以通过实验观察验证。相比之下,无法准确地建模复杂的系统,以有效地预测和控制整个系统的行为。工程化复杂系统网络(CSN)的一个示例是电力电网,包括发电,传输,分配和消耗,作为包括发电机,变压器,变电切换器,传输线,消费设备和消费设备和的一个巨大系统多种新的不断发展的组件。电力电网取决于其他复杂系统,例如,气候系统,控制风力涡轮机的风电流,用于热电冷却的河水水平,以及服务需求,定价,收入和商业资本供应的经济系统。 CSN的运营鲁棒性,可靠性和效率符合所有子系统所有者,最终用户和国家公共福利的兴趣。难题?谁负责整体CSN的运营稳健性,可靠性和效率,当系统的许多部分居住在许多不同的手中,与普通的系统的最终受益者?哪些实体负责资助关键的高风险研究,其最终福利不会与利益相关者的任何一个子集一起居住?这些问题的特征是采购研发资金的挑战,这些资金可以集中在CSN的建模,理解和管理方面。为解决创新合作研究的需求,国会在2007年美国竞争法案的一部分,建立了国家标准与技术研究所(NIST)的技术创新计划(TIP)。其目的是“协助美国的美国企业和高等教育机构或其他组织,例如国家实验室和非营利组织研究机构,通过在地区的高风险,高奖励研究中支持,促进和加速美国的创新批评国家需求。“介绍了提出努力在复杂的系统网络中批评国家需求领域识别和符合社会挑战的努力。

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