Engineering Systems Division
Engineering Systems Division
Leow W.L.,Engineering Systems Division |
Leow W.L.,Massachusetts Institute of Technology |
Larson R.C.,Engineering Systems Division |
Larson R.C.,Massachusetts Institute of Technology |
Kirtley J.L.,Massachusetts Institute of Technology
Energy and Buildings | Year: 2013
In this paper, we outline a framework and the algorithms that will enable a home controller to accomplish occupancy-moderated multi-zone, multi-inhabitant space-conditioning under a demand-driven pricing scheme for electricity. We investigated the effects that influencing factors such as the number of zones and inhabitants, randomness of occupancy patterns and thermal mass of the residence can have on the efficacy of occupancy-moderated zonal space-conditioning (OZS) under different scenarios. Simulations driven by real data and highly realistic proxy data reveal that OZS can achieve improvements averaging 23% over the case without under certain settings. Our investigations also revealed that a house with heavier thermal mass stands to gain as much as 15% from OZS with pre-conditioning than a lighter one. We further offer a few pointers on reducing the cost of space-conditioning using OZS under a demand-driven pricing scheme to the average homeowner based on our findings.© 2013 Elsevier B.V. All rights reserved.
Rader A.A.,Mission Integrator |
Ross A.M.,Engineering Systems Division |
Fitzgerald M.E.,Engineering Systems Division
AIAA SPACE 2014 Conference and Exposition | Year: 2014
The value of a system depends heavily on the future contexts it will encounter. For complex space systems with multi-year design and deployment phases, it is useful to design a system so that it delivers value to stakeholders over a wide range of future contexts. Epoch-Era Analysis, a computational scenario planning approach, decomposes the lifecycle of a system into sequential epochs that each have fixed contexts and value expectations. This paper applies Multi-Epoch Analysis (a subset of Epoch-Era Analysis) along with Multi-Attribute Tradespace Exploration (MATE) to the design of a satellite constellation, with the aim of maximizing value across a range of end-user subscription and geographic distribution contexts. The system level tradespace is assembled using a bottom-up iterative approach based on expert knowledge, and accounts for performance attributes metrics such as revisit times, data latencies, observation times, and data downlink volumes. Competing designs consisting of alternative orbital, ground station location, and deployment configurations are evaluated in terms of their fuzzy Normalized Pareto Trace (fNPT) across epochs. The resulting staged deployment strategy delivers robust value based on stakeholder preference across a wide range of future contexts.
News Article | October 23, 2015
Mention the economy of the United Arab Emirates to people from outside the region, and the first word that may come to their minds is “oil.” But today, the UAE is striving to change that initial response to “innovation.” As part of a longstanding effort to diversify its economy, the UAE is today focusing on initiatives in a variety of other areas — for instance, finding ways to quench the Gulf nation’s growing thirst for fresh water and develop alternative energy sources. Several MIT faculty members are contributing to those efforts through collaborative long-distance research initiatives with their counterparts at Masdar Institute, a graduate-level technology university in the UAE’s capital city, Abu Dhabi. “This is one of MIT’s largest, longest-standing, and most important international initiatives,” says Duane Boning, a professor of electrical engineering and computer science and director of the Cambridge-based MIT and Masdar Institute Cooperative Program, which helped the UAE launch the university in 2007 and has supported its research and academic programs ever since. Currently, MIT and Masdar Institute researchers are collaborating on nine flagship research projects focusing on clean and renewable energy, water purification, and next-generation critical infrastructure “smart” technologies. These three-year efforts, currently at various stages of completion, have been beneficial to both institutions, says Charles Cooney, the Robert T. Haslam Professor in Chemical Engineering and senior MIT faculty member on the Cooperative Program’s steering committee. The collaborations have fostered a cross-disciplinary community of researchers at the two institutions and served as valuable teaching tools, says Cooney, who has been involved with the collaborative program since its inception. “It’s an opportunity to show students and postdocs how to do interdisciplinary collaborative research,” Cooney says. “It’s a powerful model.” In 2014, the two schools launched an additional initiative, the Masdar Institute and MIT Innovation Program (MMIP), which competitively awards one-year “ignition” grants to joint MIT-Masdar Institute research teams. “They’re specifically intended to link research at MIT and Masdar Institute with the potential for commercialization,” Cooney says. The first four MMIP projects include a low-cost water-monitoring device for sensing blooms of potentially toxic algae; a wastewater filtration and treatment system; a high-efficiency membrane-based approach to desalination; and an energy-efficient transmitter for wireless communication. The MMIP grants are sponsored by the Cooperative Program and administered by MIT’s Deshpande Center for Technological Innovation in cooperation with the Institute Center for Innovation and Entrepreneurship at Masdar Institute. Investigators from the nine flagship research projects and the four current MMIP projects shared updates at the recent Masdar Institute and MIT Research and Innovation Conference in Abu Dhabi. More than 200 people — including 32 MIT faculty, staff, and students, the largest such MIT delegation ever to visit Masdar Institute — attended the one-day event. The turnout “is a very, very good indication of the commitment that both institutions have to working hand-in-hand and promoting the interests of the UAE and Abu Dhabi,” Masdar Institute President Fred Moavenzadeh said in his opening remarks. (Moavenzadeh, the James Mason Crafts Professor in MIT’s Engineering Systems Division and in the Department of Civil and Environmental Engineering, has been on leave to head Masdar Institute since 2010.) The UAE, created in 1971 by the union of seven emirates, or principalities, has historically relied on an economy dominated by the oil industry — and hasn’t traditionally emphasized research and development. But this is changing rapidly. In recent years, the UAE’s leaders have charted a course for a post-oil world with a knowledge-based economy that emphasizes innovation and global leadership in sustainable energy. (Those efforts also reflect the vision of the UAE’s founding leader, the late H.E. Sheikh Zayed bin Sultan Al Nahyan, who, during his 33-year tenure as UAE president, often cited education, economic diversification, and environmental protection as priorities for the nation’s future.) Last year, the UAE declared 2015 “the Year of Innovation” and launched a strategic initiative to make the small country one of the world’s most innovative nations by 2021 — an ambitious goal for a 43-year-old nation with a population of less than 10 million. Masdar Institute, with its graduate-level programs and tight focus on science and technology — and, of course, its MIT affiliation — is a critical component in the UAE’s innovation strategy. “We are here to develop human capital,” Moavenzadeh says. “We are here to develop an R&D infrastructure.” But he and other Masdar Institute officials emphasize that the relationship is mutually beneficial, with the UAE’s unique environment and available capital creating plenty of research opportunities for MIT faculty. Most of the joint research projects focus on solving some of the region’s — and the world’s — most pressing problems, especially meeting demand for fresh water and finding new sources of clean and renewable energy. Following are snapshots of just two such projects. Desalinating water: The UAE’s consumption of fresh water far exceeds what is naturally available in its hot desert climate. As a coastal nation, the UAE relies heavily on desalination to meet its water needs, removing salt and minerals from seawater to render it potable. But traditional thermal desalination processes are costly and have a big carbon footprint, says John Lienhard, the Abdul Latif Jameel Professor of Water and Food in MIT’s Department of Mechanical Engineering. Together with Hassan Arafat, an associate professor of chemical engineering at Masdar Institute, Lienhard has been developing an advanced, highly efficient membrane-based distillation system. The two investigators believe the system — which also has potential applications for wastewater treatment — will lead to far more efficient desalination at lower cost and with reduced environmental impact. Harnessing solar energy: One of Masdar Institute’s most distinctive landmarks is its 66-foot Beam Down Tower, which investigators are using for research on concentrated solar power. The tower’s 33 heliostats, or mirrors, are designed to follow the sun’s path throughout the day, focusing light downward to achieve intense heat that, in turn, can boil water to create steam, and ultimately generate electricity using an electric turbine. The problem: The sun isn’t available 24 hours a day, and the region’s natural dustiness and occasional sandstorms sometimes disrupt the solar energy collection process. So MIT and Masdar Institute researchers are experimenting with thermal energy storage (TES) — essentially, harvesting sun-generated heat to draw on when sunlight isn’t available. “We want to store the heat for later use” — for example, during cloudy periods, or at night, says Nicolas Calvet, an assistant professor of mechanical and materials engineering at Masdar Institute who’s working with Alexander Slocum, the Neil and Jane Pappalardo Professor of Mechanical Engineering at MIT. The advantages of effective TES: higher efficiency, lower cost, and an energy supply that’s unaffected by the weather or time of day. Overall, the ongoing research projects are “giving us a glimpse of the future, of where we’re heading together,” Boning says. “Fortunately, the future looks really bright.”
Sun L.,Engineering Systems Division |
Webster M.,Engineering Systems Division |
McGaughey G.,University of Texas at Austin |
McDonald-Buller E.C.,University of Texas at Austin |
And 5 more authors.
Environmental Science and Technology | Year: 2012
Emission controls that provide incentives for maximizing reductions in emissions of ozone precursors on days when ozone concentrations are highest have the potential to be cost-effective ozone management strategies. Conventional prescriptive emissions controls or cap-and-trade programs consider all emissions similarly regardless of when they occur, despite the fact that contributions to ozone formation may vary. In contrast, a time-differentiated approach targets emissions reductions on forecasted high ozone days without imposition of additional costs on lower ozone days. This work examines simulations of such dynamic air quality management strategies for NOx emissions from electric generating units. Results from a model of day-specific NOx pricing applied to the Pennsylvania-New Jersey-Maryland (PJM) portion of the northeastern U.S. electrical grid demonstrate (i) that sufficient flexibility in electricity generation is available to allow power production to be switched from high to low NOx emitting facilities, (ii) that the emission price required to induce EGUs to change their strategies for power generation are competitive with other control costs, (iii) that dispatching strategies, which can change the spatial and temporal distribution of emissions, lead to ozone concentration reductions comparable to other control technologies, and (iv) that air quality forecasting is sufficiently accurate to allow EGUs to adapt their power generation strategies. © 2012 American Chemical Society.
Simchi-Levi D.,Engineering Systems Division |
Simchi-Levi D.,Massachusetts Institute of Technology |
Wei Y.,Massachusetts Institute of Technology
Operations Research | Year: 2012
The long chain has been an important concept in the design of flexible processes. This design concept, as well as other sparse designs, have been applied by the automotive and other industries as a way to increase flexibility in order to better match available capacities with variable demands. Numerous empirical studies have validated the effectiveness of these designs. However, there is little theory that explains the effectiveness of the long chain, except when the system size is large, i.e., by applying an asymptotic analysis. Our attempt in this paper is to develop a theory that explains the effectiveness of long chain designs for finite size systems. First, we uncover a fundamental property of long chains, supermodularity, that serves as an important building block in our analysis. This property is used to show that the marginal benefit, i.e., the increase in expected sales, increases as the long chain is constructed, and the largest benefit is always achieved when the chain is closed by adding the last arc to the system. Then, supermodularity is used to show that the performance of the long chain is characterized by the difference between the performances of two open chains. This characterization immediately leads to the optimality of the long chain among 2-flexibility designs. Finally, under independent and identically distributed (i.i.d.) demand, this characterization gives rise to three developments: (i) an effective algorithm to compute the performances of long chains using only matrix multiplications; (ii) a result that the gap between the fill rate of full flexibility and that of the long chain increases with system size, thus implying that the effectiveness of the long chain relative to full flexibility increases as the number of products decreases; (iii) a risk-pooling result implying that the fill rate of a long chain increases with the number of products, but this increase converges to zero exponentially fast. © 2012 INFORMS.