DOCSLIB.ORG

Machine Learning Paradigms for Building Energy Performance

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Machine Learning Paradigms for Building Energy Performance Machine Learning Paradigms for Building Energy Performance Simulations by Arfa Nawal Aijazi Bachelor of Science in Materials Science and Engineering, Massachusetts Institute of Technology (2013) Submitted to the Department of Architecture in partial fulfillment of the requirements for the degree of Master of Science in Building Technology at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2017 ○c Massachusetts Institute of Technology 2017. All rights reserved. Author................................................................ Department of Architecture May 25, 2017 Certified by . Leon R. Glicksman Professor of Building Technology and Mechanical Engineering Thesis Supervisor Accepted by........................................................... Sheila Kennedy Professor of Architecture Chair, Department Committee on Graduate Students Machine Learning Paradigms for Building Energy Performance Simulations by Arfa Nawal Aijazi Submitted to the Department of Architecture on May 25, 2017, in partial fulfillment of the requirements for the degree of Master of Science in Building Technology Abstract This research seeks to overcome a technical limitation of building energy performance simulations, the computation time, by using surrogate modeling, a class of supervised machine learning techniques where the output is a performance metric. Though early machine learning methods were introduced decades ago, the convergence of computation power, more data collection, and maturation of methods has led to an explosion in the types of problems machine learning can be applied to. A comparison of several common surrogate modeling techniques found that para- metric radial basis functions and Kriging are highly accurate regression techniques for predicting building energy consumption. For a single climate, these regression techniques can predict the total energy consumption to within 2% of a detailed en- ergy simulation, but in a fraction of a second, about five orders of magnitude faster. Integrating a Kriging surrogate model with multi-objective optimization, allowed for finding retrofit recommendations in Lisbon that are cost effective and canreduce the present-day energy consumption of an existing apartment by up to 20%. Simi- larly, integrating surrogate model with multi-objective optimization can find retrofit options in Boston that can reduce the present-day energy consumption and unmet hours in the future. Combined this body of works strives to add value to existing building energy performance simulation tools as more than just an exercise for code compliance but as a real design tool that can guide decision making. Thesis Supervisor: Leon R. Glicksman Title: Professor of Building Technology and Mechanical Engineering 2 Acknowledgments I begin in the name of God, for to Him belongs all praise and thanks. Sir Isaac Newton famously observed that "if I have seen further, it is by standing on the shoulders of giants" and this certainly holds true for me. I am tremendously grateful for the support of the many individuals who made this thesis possible. First, I would like to thank my academic and thesis advisor, Professor Leon Glicksman for his mentorship over the past two years. His skepticism of simulations and espousal of back-of-the-envelope calculations have made me a better engineer and researcher. In addition to our discussions on research, I am grateful for our talks on politics, religion, period dramas, and anything else under the sun. I would also like to thank Professor Caitlin Mueller for helping me draw inspiration from computational science techniques across disciplines, which greatly enriched my academic experience and research. I also thank the other faculty in the Building Technology Group, Professors John Fernandez, Les Norford, John Ochsendorf, and Christoph Reinhart for their feedback and encouragement during the Building Technology Seminar and other meetings. I deeply appreciate the other students currently or previously affiliated with the Building Technology group for their feedback on my research, assistance with soft- ware tools, and making the lab a friendly space to work. Notably these include: Alpha Arsano, David Blum, Nathan Brown, Carlos Cerezo Davila, Noor Khouri, Shreshth Nagpal, and Cody Rose. A special thanks to our administrative assistant, Kathleen Ross for expertly handling logistical details and gathering the group for social events. Together, the faculty, staff, and students of the Building Technology group foster a learning and research environment that explores critical topics for the future of the built environ- 3 ment and natural resources and it has been an honor to be a part of it. An additional thanks to staff and administrators in the Department of Architec- ture for their support. I am grateful to CRON, especially Philip Thompson, for quickly resolving problems with computer hardware and software. I thank the MIT Department of Architecture and the MIT Portugal Program for funding my graduate education at MIT. I am indebted to my research collaborators in Portugal as part of the SusCity project for their support remotely throughout my research and for their hospital- ity during my trips to Lisbon in January 2016 and 2017. Notably these include: Joana Fernandes of ADENE, Laura Aelenei and Ana Ferreira of LNEG, Ri- cardo Gomez and Claudia Sousa Monteiro of IST, and Professor Guilherme Carrilho da Graca and Ana Filipa Silva of FCUL. A special thank you to Dr. Erwan Monier at the MIT Center for Global Change Science and the Joint Program on the Science and Policy of Climate Change for guiding me through the current scientific discourse related to modeling climate change. I am grateful to my mentors at Simpson Gumpertz and Heger Inc, where I worked in the years between my bachelors and masters. They’ve instilled in me an aspiration for my research to be relevant to industry for it to have the greatest impact. Personally, I owe everything to my family. I can’t thank my parents enough for their boundless love and support of all my ventures and my siblings, Osman, Assad, and Safiya for their encouragement. 4 Contents 1 Background 16 1.1 Introduction . 16 1.2 Motivation . 17 1.2.1 Global Building Energy Consumption . 17 1.2.2 Climate Change and Global Warming . 18 1.2.3 Barriers to Building Performance Simulations . 19 1.3 Context . 21 1.3.1 Overview of Building Energy Performance Simulation . 21 1.3.2 Overview of Machine Learning . 25 1.3.3 Machine Learning and Building Performance . 26 1.4 Research Objectives . 27 2 Surrogate Modeling Techniques 28 2.1 Introduction . 28 2.1.1 Previous Work . 30 2.1.2 Research Objectives . 31 2.2 Methodology . 31 2.2.1 Building a Surrogate Model . 31 5 2.2.2 Quantifying Error and Uncertainty . 32 2.2.3 Regression Techniques . 34 2.2.4 Climatic Model Input Parameters . 36 2.2.5 Geometric Model Input Parameters . 41 2.2.6 Natural Ventilation Model Input Parameters . 48 2.2.7 Summary . 51 2.3 Results and Discussion . 51 2.3.1 Climatic Model Input Parameters . 54 2.3.2 Geometric Model Input Parameters . 66 2.3.3 Natural Ventilation Model Input Parameters . 70 2.4 Conclusions . 70 3 Optimization of Building Retrofits 71 3.1 Introduction . 71 3.1.1 Design Optimization . 72 3.1.2 The SusCity Project . 76 3.1.3 Scope of Work . 79 3.2 Methodology . 79 3.2.1 Surrogate Model Development . 79 3.2.2 Retrofit Costs Model . 85 3.2.3 Multi-objective Optimization . 87 3.2.4 Return on Investment . 88 3.2.5 Model Error . 89 3.3 Results and Discussion . 89 3.3.1 Existing Buildings Energy Consumption . 89 3.3.2 Multi-Objective Optimization . 91 6 3.3.3 Return on Investment . 99 3.3.4 Model Error . 100 3.4 Conclusions . 102 4 Modeling Climate Change Impacts 104 4.1 Introduction . 104 4.2 Methodology . 105 4.2.1 Modeling Climate Change . 105 4.2.2 Building Energy Case Study . 107 4.2.3 Building Envelope Retrofit Optimization . 109 4.3 Results and Discussion . 111 4.3.1 Future Weather . 111 4.3.2 Building Energy Case Study . 112 4.3.3 Building Envelope Retrofit Optimization . 117 4.4 Conclusions . 128 5 Conclusions 130 5.1 Key Contributions . 130 5.2 Overall Discussion . 132 5.3 Future Work . 133 5.4 Concluding Remarks . 133 7 List of Figures 2-1 Schematic represetnation of surrogate modeling concept in one dimen- sion. Graph reproduced from [27]. 29 2-2 NREL midrise apartment commercial reference building energy model visualized in SketchUp with Euclid plugin. 37 2-3 Weather file locations in ASHRAE CZ 4A-7A used in stratified sample of heating degree days. 39 2-4 Screenshot of IDF Editor for EnergyPlus showing an example of build- ing geometry definition in a text-based environment. 42 2-5 Screenshot in Rhino and Grasshopper with the Archsim plugins show- ing an example of building geometry definition in a visual environment. 43 2-6 Examples of three rectiliniear floor plates generated using Grasshopper. 44 2-7 Methodology for generating a randomized floor plate in Grasshopper. 45 2-8 Examples of three randomized floor plates generated using Grasshopper. 45 2-9 Example of thermal zoning and identification of orientation of an ex- terior face for a randomized form. 46 2-10 Visualization of building energy model with geometric model input parameters in Rhino. 47 8 2-11 Plot of Pearson correlation coefficient for transformed and untrans- formed design variables to simulation outputs for climatic model inputs. 53 2-12 Plot comparing computation time of detailed simulation versus surro- gate model parameter estimation and prediction. 55 2-13 Plots of predicted versus simulated energy consumption for surrogate model using Kriging regression on a test data set. 57 2-14 Plots of percent difference between surrogate model prediction and simulated output over the design space for multi-climate and single climate surrogate models. 58 2-15 Plots of mean absolute value of percent difference between surrogate model prediction and detailed simulation by simulation output.
Load more

Recommended publications
  • The Emergence of Numerical Weather Prediction: Richardson’S Dream Peter Lynch Frontmatter More Information
    Cambridge University Press 978-1-107-41483-9 - The Emergence of Numerical Weather Prediction: Richardson’s Dream Peter Lynch Frontmatter More information THE EMERGENCE OF NUMERICAL WEATHER PREDICTION Richardson’s Dream In the early twentieth century, Lewis Fry Richardson dreamt that scientific weather prediction would one day become a practical reality. The method of computing changes in the state of the atmosphere that he mapped out in great detail is essen- tially the method used today. Before his ideas could bear fruit several advances were needed: better understanding of the dynamics of the atmosphere; stable com- putational algorithms to integrate the equations of motion; regular observations of the free atmosphere; and powerful automatic computer equipment. By 1950, advances on all these fronts were sufficient to permit the first computer weather forecast to be made. Over the ensuing 50 years progress in numerical weather prediction has been dramatic, allowing Richardson’s dream to become a reality. Weather prediction and climate modelling have now reached a high level of sophistication. This book tells the story of Richardson’s trial forecast, and the fulfilment of his dream of practical weather forecasting and climate modelling. It has a complete reconstruction of Richardson’s forecast, and analyses in detail the causes of the fail- ure of this forecast. It also includes a description of current practice, with particular emphasis on the work of the European Centre for Medium-Range Weather Fore- casts. This book will appeal to everyone involved in numerical weather forecasting, from researchers and graduate students to professionals. Peter Lynch is Met Eireann´ Professor of Meteorology at University College Dublin (UCD) and Director of the UCD Meteorology and Climate Centre.
    [Show full text]
  • The Role of Nuclear Energy in Mitigating Greenhouse Warming
    LA-UR-97-4380 Title: The Role of Nuclear Energy in Mitigating Greenhouse Warming Author(s): R. A. Krakowski Submitted to: http://lib-www.lanl.gov/la-pubs/00412585.pdf Los Alamos NATIONAL LABORATORY Los Alamos NATIONAL LABORATORY Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by the University of California for the U.S. Department of Energy under contract W-7405-ENG-36. By acceptance of this article, the publisher recognizes that the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or to allow others to do so, for U.S. Government purposes. The Los Alamos National Laboratory requests that the publisher identify this article as work performed under the auspices of the U.S. Department of Energy. Los Alamos National Laboratory strongly supports academic freedom and a researcher’s right to publish; therefore, the Laboratory as an institution does not endorse the viewpoint of a publication or guarantee its technical correctness. Form No. 836 R5 ST 2629 10/91 THE ROLE OF NUCLEAR ENERGY IN MITIGATING GREENHOUSE WARMING R. A. Krakowski Systems Engineering and Integration Group Technology and Safety Assessment Division Los Alamos National Laboratory Los Alamos, New Mexico 87545 ABSTRACT A behavioral, top-down, forced-equilibrium market model of long-term (~2100) global energy-economics interactions* has been modified with a “bottom-up” nuclear energy model and used to construct consistent scenarios describing future impacts
    [Show full text]
  • Computer Simulation Tools to Enhance Undergraduate Power Systems Edu- Cation
    Paper ID #9640 Computer Simulation Tools to Enhance Undergraduate Power Systems Edu- cation Dr. Matthew Turner, Purdue University (Statewide Technology) Matthew Turner is an Assistant Professor of Electrical and Computer Engineering Technology at Purdue University in New Albany, IN. Previously with the Conn Center for Renewable Energy Research at the University of Louisville, his research interests include power distribution system modelling, best practices for power systems education, and electric energy and public policy. Dr. Chris Foreman, Purdue University, West Lafayette Chris Foreman (Ph.D. Computer Science and Engineering, University of Louisville, 2008) is a Senior Member of IEEE, the Power and Energy Society, and holds both B.S. (1990) and M.Eng. (1996) degrees in Electrical Engineering, also from the University of Louisville. He is an Assistant Professor in the Department of Electrical and Computer Engineering Technology at Purdue University. He teaches and performs research in renewable energy systems, smart power grids, industrial control systems, and cyber- security. He has over 15 years of power industry experience with companies such as Westinghouse Process Control Division (now Emerson Process Management), Cinergy (now Duke Energy), and Alcoa Inc. Dr. Rajeswari Sundararajan, Purdue University, West Lafayette c American Society for Engineering Education, 2014 Computer Simulation Tools to Enhance Undergraduate Power Systems Education Abstract This paper presents a review of software simulation tools relevant for use in undergraduate electrical power systems education. A study of the software packages is presented with respect to their utility in teaching according to the Cognitive Domain Hierarchy of Bloom's Taxonomy. 1. Introduction In recent years a variety of factors have combined to place increasing pressure on the electric power industry; including increasing electrical energy demand, aging infrastructure, energy independence and security goals, and increasingly stringent environmental regulation.
    [Show full text]
  • 1- Validating Simulation Models: a General Framework and Four
    -1- Validating Simulation Models: AGeneral Framework and Four Applied Examples Robert E. Marks Australian Graduate School of Management Australian School of Business University of NewSouth Wales SydneyNSW 2052 Australia [email protected] June 18, 2007 ABSTRACT:This paper provides a framework for discussing the empirical validation of simulation models of market phenomena, in particular of agent-based computational economics models. Such validation is difficult, perhaps because of their complexity; moreover, simulations can prove existence, but not in general necessity.The paper highlights the Energy Modeling Forum’sbenchmarking studies as an exemplar for simulators. A market of competing coffee brands is used to discuss the purposes and practices of simulation, including explanation. The paper discusses measures of complexity,and derivesthe functional complexity of an implementation of Schelling’s segregation model. Finally,the paper discusses howcourts might be convinced to trust simulation results, especially in merger policy. Acknowledgments: We thank Rich Burton, Yaneer Bar-Yam, Ian Wilkinson, Shayne Gary, Peter McBurney, Hill Huntington, Leigh Tesfatsion, the participants at the Fourth UCLA LakeArrowhead Conference on Human ComplexSystems, and twoanonymous referees for their comments and assistance. JEL codes: C63, C15 Ke ywords: simulation; validation; agent-based computational economics; antitrust; functional complexity; Schelling; sufficiency; Daubert criteria -2- 1. Introduction The apparent past reluctance of some in the discipline to accept computer simulation models of economic phenomena might stem from their lack of confidence in the behaviour and results exhibited by such models. Even if there are other reasons, better validation of such models would reduce anyskepticism about their results and their usefulness. Leombruni et al.
    [Show full text]
  • Interim Report 11-1-10
    New York State Climate Action Council Interim Report 11-1-10 Additional Material Full Descriptions of Adaptation Recommendations This document provides additional information for each of the Adaptation Recommendations, including the following: • Potential implementation mechanisms • Related efforts • Research and information needs In some cases, sections are expanded from what is provided in the Climate Action Plan Interim Report. New York State Climate Action Council Interim Report 11-1-10 Agriculture Vision Statement Develop and adopt strategies and technological advances that recognize agriculture as a critical climate and resource dependent New York State industry that is inextricably linked to Earth’s carbon and nitrogen cycles, and ensure that in 2050, the agricultural sector is not only viable, but thriving in a carbon-constrained economy, and is continually adapting to a changing climate. Background Agriculture is a significant component of the New York economy; it includes large wholesale grower-shippers selling products nationally and internationally, a substantial dairy industry, and thousands of small farm operations selling direct retail and providing communities throughout the state with local, fresh produce. Farmers will be on the front lines of coping with climate change, but the direct impacts on crops, livestock, and pests, and the costs of farmer adaptation will have cascading effects beyond the farm gate and throughout the New York economy. While climate change will create unprecedented challenges, there are likely to be new opportunities as well, such as developing markets for new crop options that may come with a longer growing season and warmer temperatures. Taking advantage of any opportunities and minimizing the adverse consequences of climate change will require new decision tools for strategic adaptation.
    [Show full text]
  • Forecasting Natural Gas: a Literature Survey
    International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2018, 8(3), 216-249. Forecasting Natural Gas: A Literature Survey Jean Gaston Tamba1*, Salomé Ndjakomo Essiane2, Emmanuel Flavian Sapnken3, Francis Djanna Koffi4, Jean Luc Nsouandélé5, Bozidar Soldo6, Donatien Njomo7 1Department of Thermal and Energy Engineering, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 2Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 3Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 4Department of Thermal and Energy Engineering, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 5Department of Renewable Energy, Higher Institute of the Sahel, University of Maroua, PO Box 46, Maroua, Cameroon, 6HEP-Plin Ltd., Cara Hadrijana 7, HR-31000 Osijek, Croatia, 7Environmental Energy Technologies Laboratory, University of Yaoundé I, PO Box 812, Yaoundé, Cameroon. *Email: [email protected] ABSTRACT This work presents a state-of-the-art survey of published papers that forecast natural gas production, consumption or demand, prices and income elasticity, market volatility and hike in prices. New models and techniques that have recently been applied in the field of natural gas forecasting have discussed with highlights on various methodologies, their specifics, data type, data size, data source, results and conclusions. Moreover, we undertook the difficult task of classifying existing models that have been applied in this field by giving their performance for instance. Our objective is to provide a synthesis of published papers in the field of natural gas forecasting, insights on modeling issues to achieve usable results, and the future research directions.
    [Show full text]
  • Introduction to Bioinformatics
    Mesleki İngilizce - Technical English II • Notes: Prof. Dr. Nizamettin AYDIN – In the slides, • texts enclosed by curly parenthesis, {…}, are examples. • texts enclosed by square parenthesis, […], are [email protected] explanations related to examples. http://www.yildiz.edu.tr/~naydin 1 2 Computer Simulation Computer Simulation • Learning Objectives • Keywords – to acquire basic knowledge about simulation kinds and – mathematical model history • an abstract model that uses mathematical language to – to understand difference between simulation and describe a system modelling – to understand the power of simulation and its influence – stochastic process on modern science • a process with an indeterminate or random element as • Sub-areas covered opposed to a deterministic process that has no random element – Computer simulation – Discrete – Computer graphics – Mathematics • not supporting or requiring the notion of continuity – discrete objects are countable sets such as integers 3 4 Computer Simulation Computer Simulation • Keywords • Reading text – Computer Generated Imagery (CGI) • an application of the field of computer graphics (or, more • Pre-reading questions specifically, 3D computer graphics) to special effects in films, television programs, commercials and simulation – How can we solve problems in cases where – differential equation physical models are too complex or expensive to • a mathematical equation for an unknown function of one or build? several variables that relates the values of the function itself and of its derivatives
    [Show full text]
  • Computer Simulation of Building Energy Consumption and Building Energy Efficiency
    The 2nd International Conference on Computer Application and System Modeling (2012) Computer Simulation of Building Energy Consumption and Building Energy Efficiency Zhao Lei Song Jingying Architectural Engineering Architectural Engineering Xinxiang University Xinxiang University Abstract—Computer simulation of building energy begins. Different architectural style, different materials, consumption is one of important assisted tools in the field of different building equipment systems can be combined into energy efficiency, architects can easily design process at any many programs, from the many programs to select the most stage of the design of energy-saving evaluation by computer energy-efficient solutions, energy consumption must be simulation, or test can predict the future or existing Building estimated for each program, which is the computer building energy consumption, diagnostic analysis of building thermal energy simulation technology. process, so as to optimize the building design to minimize Computer simulation of building energy consumption is energy consumption to provide an accurate basis. one of important assisted tools in the field of energy efficiency. architects can easily design process at any stage Keywords-uilding energy efficiency, Building energy simulation computer technology, Architectural design of the design of energy-saving evaluation by computer simulation, or test can predict the future or existing buildings energy consumption, diagnostic analysis of building thermal I. INTRODUCTION process, so as to optimize the building design, to minimize All manuscripts must be in English. These guidelines energy consumption to provide an accurate basis. Just type in include complete descriptions of the fonts, spacing, and the program model of the architect, you can complete in related information for producing your proceedings design software, thermal performance, natural light, artificial manuscripts.
    [Show full text]
  • The Use of Computer Simulation to Establish Energy Efficiency Parameters for a Building Code of a City in Brazil
    Eighth International IBPSA Conference Eindhoven, Netherlands August 11-14, 2003 THE USE OF COMPUTER SIMULATION TO ESTABLISH ENERGY EFFICIENCY PARAMETERS FOR A BUILDING CODE OF A CITY IN BRAZIL Joyce Carlo1, Enedir Ghisi2 and Roberto Lamberts3 1. [email protected]; [email protected]; [email protected] LabEEE – Energy Efficiency in Buildings Laboratory Federal University of Santa Catarina Florianópolis, SC, 88040-970 - Brazil 1310TWh in 20 years (2000 to 2020), while India ABSTRACT would save 1659TWh in the same period and Mexico The first energy efficiency law for Brazil was 550TWh, representing reductions of 12%, 11% and presented in 2001, which required that buildings 12% in 20 years, respectively. One year before, in should have some energy efficiency regulation. 1995, Mexico approved its first energy standard, for Salvador city building code was then submitted to a commercial buildings [COMISIÓN NACIONAL study to include energy efficiency parameters related PARA EL AHORRO DE ENERGÍA, 1995a] and for to the building envelope. artificial lighting in buildings [COMISIÓN NACIONAL PARA EL AHORRO DE ENERGÍA, The energy consumption limits were associated with 1995b]. Both were based on ASHRAE Standard 90.1 the envelope parameters using simulation and using a [1999]. Recently, a DOE press release [DOE, 2002] multi-variable regression equation developed using stated that Mexico, United States and Canada are simulation. Twelve models were defined with the moving towards unifying their energy efficiency variables that influence most on energy consumption standards. This trend is already in progress in and were combined to the envelope variables Europe, where the European directives for energy resulting in the analysis of 1616 prototype buildings.
    [Show full text]
  • Fully Coupled Geomechanics and Discrete Flow Network Modeling of Hydraulic Fracturing for Geothermal Applications
    LLNL-CONF-466396 Fully Coupled Geomechanics and Discrete Flow Network Modeling of Hydraulic Fracturing for Geothermal Applications P. Fu, S.M. Johnson, Y. Hao, and C. R. Carrigan. Released on January 20, 2011 36th Annual Stanford Workshop on Geothermal Reservoir Engineering Stanford, California, January 31 - February 2, 2011 Disclaimer This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. PROCEEDINGS, Thirty-Sixth Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California, January 31 - February 2, 2011 SGP-TR-191 FULLY COUPLED GEOMECHANICS AND DISCRETE FLOW NETWORK MODELING OF HYDRAULIC FRACTURING FOR GEOTHERMAL APPLICATIONS Pengcheng Fu, Scott M. Johnson, Yue Hao, and Charles R. Carrigan Lawrence Livermore National Laboratory 7000 East Ave., L-286 Livermore, CA 94551, USA e-mail: [email protected] stimulation models capable of addressing the ABSTRACT propagation of clusters of fractures in hard rock.
    [Show full text]
  • An Overview of the Model Integration Process: from Pre-Integration Assessment to Testing
    Environmental Modelling & Software 87 (2017) 49e63 Contents lists available at ScienceDirect Environmental Modelling & Software journal homepage: w ww.elsevier.com /locate/envsoft An overview of the model integration process: From pre-integration assessment to testing a, * a b Getachew F. Belete , Alexey Voinov , Gerard F. Laniak a University of Twente, ITC, 7500 AE Enschede, Netherlands b US Environmental Protection Agency, Office of Research and Development, USA a r t i c l e i n f o a b s t r a c t Article history: Integration of models requires linking models which can be developed using different tools, method- Received 15 March 2016 ologies, and assumptions. We performed a literature review with the aim of improving our under- Received in revised form standing of model integration process, and also presenting better strategies for building integrated 30 August 2016 modeling systems. We identified five different phases to characterize integration process: pre- Accepted 30 October 2016 integration assessment, preparation of models for integration, orchestration of models during simula- tion, data interoperability, and testing. Commonly, there is little reuse of existing frameworks beyond the development teams and not much sharing of science components across frameworks. We believe this Keywords: must change to enable researchers and assessors to form complex workflows that leverage the current Integrated modeling Interoperability environmental science available. In this paper, we characterize the model integration process and Interfaces compare integration practices of different groups. We highlight key strategies, features, standards, and Wrapping practices that can be employed by developers to increase reuse and interoperability of science software Components components and systems.
    [Show full text]
  • Passive and Renewable Low Carbon Strategies for Residential Buildings in Hot Humid Climates
    University of Bath PHD Passive and renewable low carbon strategies for residential buildings in hot humid climates Al Shamsi, Yahya Award date: 2017 Awarding institution: University of Bath Link to publication Alternative formats If you require this document in an alternative format, please contact: [email protected] General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 11. Oct. 2021 University of Bath PHD Passive and renewable low carbon strategies for residential buildings in hot humid climates Al Shamsi, Yahya Award date: 2017 Awarding institution: University of Bath Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
    [Show full text]