Applied & Computational MathematicsChallenges for the Design and Control of Dynamic Energy Systems

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The Energy Independence and Security Act of 2007 (EISA) was passed with the goal 'to move the United States toward greater energy independence and security.' Energy security and independence cannot be achieved unless the United States addresses the issue of energy consumption in the building sector and significantly reduces energy consumption in buildings. Commercial and residential buildings account for approximately 40% of the U.S. energy consumption and emit 50% of CO{sub 2} emissions in the U.S. which is more than twice the total energy consumption of the entire U.S. automobile and light truck fleet. A 50%-80% improvement in building energy ... continued below

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Brown, D L; Burns, J A; Collis, S; Grosh, J; Jacobson, C A; Johansen, H et al. March 10, 2011.

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The Energy Independence and Security Act of 2007 (EISA) was passed with the goal 'to move the United States toward greater energy independence and security.' Energy security and independence cannot be achieved unless the United States addresses the issue of energy consumption in the building sector and significantly reduces energy consumption in buildings. Commercial and residential buildings account for approximately 40% of the U.S. energy consumption and emit 50% of CO{sub 2} emissions in the U.S. which is more than twice the total energy consumption of the entire U.S. automobile and light truck fleet. A 50%-80% improvement in building energy efficiency in both new construction and in retrofitting existing buildings could significantly reduce U.S. energy consumption and mitigate climate change. Reaching these aggressive building efficiency goals will not happen without significant Federal investments in areas of computational and mathematical sciences. Applied and computational mathematics are required to enable the development of algorithms and tools to design, control and optimize energy efficient buildings. The challenge has been issued by the U.S. Secretary of Energy, Dr. Steven Chu (emphasis added): 'We need to do more transformational research at DOE including computer design tools for commercial and residential buildings that enable reductions in energy consumption of up to 80 percent with investments that will pay for themselves in less than 10 years.' On July 8-9, 2010 a team of technical experts from industry, government and academia were assembled in Arlington, Virginia to identify the challenges associated with developing and deploying newcomputational methodologies and tools thatwill address building energy efficiency. These experts concluded that investments in fundamental applied and computational mathematics will be required to build enabling technology that can be used to realize the target of 80% reductions in energy consumption. In addition the finding was that there are tools and technologies that can be assembled and deployed in the short term - the next 3-5 years - that can be used to significantly reduce the cost and time effective delivery of moderate energy savings in the U.S. building stock. Simulation tools, which are a core strength of current DOE computational research programs, provide only a part of the answer by providing a basis for simulation enabled design. New investments will be required within a broad dynamics and control research agenda which must focus on dynamics, control, optimization and simulation of multi-scale energy systems during design and operation. U.S. investments in high performance and high productivity computing (HP2C) should be leveraged and coupled with advances in dynamics and control to impact both the existing building stock through retrofits and also new construction. The essential R&D areas requiring investment are: (1) Characterizing the Dynamics of Multi-scale Energy Systems; (2) Control and Optimization Methodologies of Multi-scale Energy Systems Under Uncertainty; and (3) Multiscale Modeling and Simulation Enabled Design and Operation. The concept of using design and control specific computational tools is a new idea for the building industry. The potential payoffs in terms of accelerated design cycle times, performance optimization and optimal supervisory control to obtain and maintain energy savings are huge. Recent advances in computational power, computer science, and mathematical algorithms offer the foundations to address the control problems presented by the complex dynamics of whole building systems. The key areas for focus and associated metrics with targets for establishing competitiveness in energy efficient building design and operation are: (1) Scalability - Current methodology and tools can provide design guidance for very low energy buildings in weeks to months; what is needed is hours to days. A 50X improvement is needed. (2) Installation and commissioning - Current methodology and tools can target a three month window for commissioning of building subsystems; what is needed is one week. A 10X improvement is needed. (3) Quality - Current design tools can achieve 30% accuracy; what is needed to make design decisions is 5% with quantification of uncertainty. A 5X improvement is needed. These challenges cannot be overcome by raw computational power alone and require the development of new algorithms. Here algorithms mean much more than simulating the building physics but need to be inclusive of a much better understanding of the building and the control systems associated with the building and to capture the entire set of dynamics. The algorithmsmust represent computationally new mathematical approaches to modeling, simulation, optimization and control of large multi-scale dynamic systems and bringing these elements to bear on industry in simulation enabled design approaches.

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PDF-file: 26 pages; size: 1 Mbytes

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  • Report No.: LLNL-TR-474351
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/1029751 | External Link
  • Office of Scientific & Technical Information Report Number: 1029751
  • Archival Resource Key: ark:/67531/metadc831341

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  • March 10, 2011

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

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  • Nov. 22, 2016, 7:30 p.m.

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Brown, D L; Burns, J A; Collis, S; Grosh, J; Jacobson, C A; Johansen, H et al. Applied & Computational MathematicsChallenges for the Design and Control of Dynamic Energy Systems, report, March 10, 2011; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc831341/: accessed August 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.