Institute for Systems Research Technical Reportshttp://hdl.handle.net/1903/43762017-06-14T22:09:52Z2017-06-14T22:09:52ZOptimal replacement strategy for residential solar panels using monte carlo simulations and nonlinear optimization methodsPoissant, Andrewhttp://hdl.handle.net/1903/192292017-06-07T02:50:10Z2017-05-01T00:00:00ZOptimal replacement strategy for residential solar panels using monte carlo simulations and nonlinear optimization methods
Poissant, Andrew
The purpose of this analysis is to determine the optimal replacement strategy for a residential
photovoltaic (PV) array. Specifically, the optimal year and number of solar modules that should
be replaced on a residential solar panel system. This analysis aims at saving the stakeholder,
a homeowner with a residential PV array, money. A Monte Carlo simulation and nonlinear
mixed-integer programming are the analytic techniques used in determining the replacement
strategy. Localized cost of electricity (LCOE) is the objective function in these analyses. Modular, environmental, and market factors are all variables that can affect the LCOE. University
of Maryland’s LEAFHouse was the basis of these analyses because it is a house equipped with
an aging PV array and readily accessible data. Based on the findings in this report, it was
determined that 0 ± 0 solar modules should be replaced after 1.42 ± 0.32 years with a reference
year of initial installation being 2007. While the analysis results were not expected, they were
proven to be reasonable based on cost trends for solar panels and the calculated monetary value of the power production lost from the PV array.
2017-05-01T00:00:00ZOn The Number of Unlabeled Bipartite GraphsAtmaca, AbdullahOruc, Yavuz Ahttp://hdl.handle.net/1903/191862017-04-11T02:40:24Z2016-01-01T00:00:00ZOn The Number of Unlabeled Bipartite Graphs
Atmaca, Abdullah; Oruc, Yavuz A
Let $I$ and $O$ denote two sets of vertices, where $I\cap O =\Phi$, $|I| = n$, $|O| = r$, and $B_u(n,r)$ denote the set of unlabeled graphs whose edges connect vertices in $I$ and $O$. It is shown that the following two-sided equality holds.
$\displaystyle \frac{\binom{r+2^{n}-1}{r}}{n!} \le |B_u(n,r)| \le 2\frac{\binom{r+2^{n}-1}{r}}{n!} $
This paper describes a result that has been obtained in joint work with Abdullah Atmaca of Bilkent University, Ankara, Turkey
2016-01-01T00:00:00ZA Framework for Design Theory and Methodology ResearchHerrmann, Jeffreyhttp://hdl.handle.net/1903/174542016-04-12T02:30:30Z2016-04-01T00:00:00ZA Framework for Design Theory and Methodology Research
Herrmann, Jeffrey
The scholarly study of design continues to develop new knowledge through a variety of approaches. Some researchers examine how designers work, and many develop new methods to help designers do design tasks. Studying design is complex for many reasons. There are many domains in which design occurs, including all of the disciplines of engineering, architecture, and other fields. More significantly, humans design, and human behavior can be difficult to understand. Designers sometimes work alone and sometimes in a group or team. Designers experience design work in multiple ways. Design researchers have been exploring many different aspects of design and experimenting with many different approaches and generating a variety of different design theories. The focus on exploration, however, has meant that there has been less emphasis on exploiting previous research and creating an organized body of knowledge. Building a unified body of knowledge is a long-term challenge. This paper describes a proposed framework for design theory and methodology research. This framework, which is based on ideas from education research, does not specify specific topics or methodologies. Instead, it describes six different research types: (1) Foundational Research, (2) Early-Stage or Exploratory Research, (3) Design and Development Research, (4) Efficacy Research, (5) Effectiveness Research, and (6) Scale-up Research. Illustrating these types are examples based on a table design example. The paper explains how these six research types are related to each other and how, collectively, they serve to generate valid knowledge about design. The research types follow a logical sequence in which researchers develop basic knowledge, create design methods, and test design methods. Although the framework numbers the research types following this natural progression, it does not insist that researchers do or should work by rigidly following this sequence. These research types actually form a cycle of research that iterates through three “phases”: description, explanation, and testing. In this cycle, researchers observe and describe a phenomenon, develop theories to explain the phenomenon and its interactions and effects, and test that theory against the phenomenon, and then, based on the results, refine their descriptions, revise their theories, and conduct more testing. Over time, the description of the phenomenon is improved (e.g., made more precise or more general), better explanations (theories) are found, and additional testing further demonstrates their correctness (or indicates their limitations). The proposed framework can show how different research studies are related to each other because they are the same research type or they fit into the progress of a design theory or the development of a design method. Thus, the proposed framework, while not a theory of design, can help researchers respond to the challenges of coordinating the different types of research needed to create useful design theories and build a unified body of knowledge. Future work is needed to analyze, test, and refine this framework so that it becomes truly useful to the design research community.
2016-04-01T00:00:00ZPhysically Constrained Design Space Modeling for 3D CPUsSerafy, CalebSrivastava, AnkurYeung, Donaldhttp://hdl.handle.net/1903/171802016-03-29T02:45:44Z2015-12-01T00:00:00ZPhysically Constrained Design Space Modeling for 3D CPUs
Serafy, Caleb; Srivastava, Ankur; Yeung, Donald
Design space exploration (DSE) is becoming increasingly complex as the number of tunable design parameters increases in cutting edge CPU designs. The advent of 3D integration compounds the problem by expanding the architectural design space, causing intricate links between memory and logic behavior and increasing the interdependence between physical and architectural design. Exhaustive simulation of an architectural design space has become computationally infeasible, and previous work has proposed fast DSE methodologies using modeling or pseudo-simulation.
Modeling techniques can be used to predict design space properties by regression fitting. However in the past such techniques have only been applied to optimization metrics such as performance or energy efficiency while physical constraints have been ignored. We propose a technique to apply spline modeling on a 3D CPU design space to predict optimization metrics and physical design properties (e.g. power, area and temperature). We use these models to identify optimal 3D CPU architectures subject to physical constraints while drastically reducing simulation time compared to exhaustive simulation. We show that our technique is able to identify design points within 0.5% of the global optimal while simulating less than 5% of the design space.
2015-12-01T00:00:00Z