7,389 research outputs found

    9th Annual Focus on Creative Inquiry Poster Forum Program

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    The 2014 Focus on Creative Inquiry Poster Forum displays a selection of the projects accomplished by Clemson University students in their Creative Inquiry teams. What is Creative Inquiry? It is small-group learning for all students, in all disciplines. It is the imaginative combination of engaged learning and undergraduate research – and it is unique to Clemson University. In Creative inquiry, small teams of undergraduate students work with faculty mentors to take on problems that spring from their own curiosity, a professor’s challenge, or the pressing needs of the world around them. Students take ownership of their projects. They ask questions, they take risks, and they get answers

    Production and distribution research center

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    Issued as Annual report, Project no. E-24-62

    Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

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    Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

    Multilayer optimisation for day-ahead energy planning in microgrids

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    In the search for low carbon, reliable and affordable ways to provide electricity, an increased attention is going to the microgrid, a small-scale power system that uses a combination of energy generation and storage devices to serve local customers. The most promising feature of the microgrid is its flexibility to act as a standalone source of electricity for remote communities, and to be connected to the main power system, selling and purchasing power as required. Additionally, a microgrid can be considered as a coordinated system approach for incorporating intermittent renewable sources of energy. Microgrid customers can have power from their batteries or distributed generators, they can buy it from the utility grid, or they can reduce their consumption.When designing a new optimal planning tool for a microgrid, a major challenge (and opportunity) is to decide on what units to operate in order to meet the demand. The question is what mix will provide the performance needed at the lowest cost, or with the lowest possible emissions. Unfortunately, both objectives are often contradictory. Generally, low costs mean high emissions, and vice versa. A microgrid system operator may care more about achieving lower costs rather than lower emissions. Given the preferences, the operator needs to decide how to configure and operate the microgrid while satisfying all technical requirements, such as voltage stability and power balance. In order to control and manage the microgrid units in real-time while fully exploiting the benefit of long-term prediction, an off-line optimisation approach imposes itself to devise the online microgrid management. In this PhD thesis, an efficient multilayer control approach is developed which obtains a day-ahead unit commitment method to provide an economically and environmentally viable unit commitment (UC) that is physically feasible in terms of voltage violations. With the multilayer control approach, the future operational states of the controllable units within the microgrid are determined ahead of time. The proposed concept follows the idea of a day-ahead coordination including the unit commitment problem (scheduling layer), an off-line power flow calculation (executive layer) and a security check with feedback control (adjustment layer). Since the complete multilayer control concept works on a day-ahead time scale, the model can be considered as an off-line optimisation approach. The power reference set points provided by the multilayer control approach can, in turn, be used for an online microgrid implementation to achieve real-time system state updates

    pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

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    pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

    Load Balancing Algorithms for Parallel Spatial Join on HPC Platforms

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    Geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of geospatial computations and analytics on large scale datasets, parallel processing is necessary. To exploit fine-grained parallel processing on large scale compute clusters, partitioning of skewed datasets in a load-balanced way is challenging. The workload in spatial join is data dependent and highly irregular. Moreover, wide variation in the size and density of geometries from one region of the map to another, further exacerbates the load imbalance. This dissertation focuses on spatial join operation used in Geographic Information Systems (GIS) and spatial databases, where the inputs are two layers of geospatial data, and the output is a combination of the two layers according to join predicate.This dissertation introduces a novel spatial data partitioning algorithm geared towards load balancing the parallel spatial join processing. Unlike existing partitioning techniques, the proposed partitioning algorithm divides the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. This workload partitioning algorithm has been evaluated on a high-performance computing system using real-world datasets. An intermediate output-sensitive duplication avoidance technique is proposed that decreases the external memory space requirement for storing spatial join candidates across the partitions. GPU acceleration is used to further reduce the spatial partitioning runtime. For dynamic load balancing in spatial join, a novel framework for fine-grained work stealing is presented. This framework is efficient and NUMA-aware. Performance improvements are demonstrated on shared and distributed memory architectures using threads and message passing. Experimental results show effective mitigation of data skew. The framework supports a variety of spatial join predicates and spatial overlay using partitioned and un-partitioned datasets

    Tabu Search: A Comparative Study

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    Water Integration for Squamscott Exeter (WISE): Preliminary Integrated Plan, Final Technical Report

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    This document introduces the goals, background and primary elements of an Integrated Plan for the Lower Exeter and Squamscott River in the Great Bay estuary in southern New Hampshire. This Plan will support management of point (wastewater treatment plant) and nonpoint sources in the communities of Exeter, Stratham and Newfields. The Plan also identifies and quantifies the advantages of the use of green infrastructure as a critical tool for nitrogen management and describes how collaboration between those communities could form the basis for an integrated plan. The Plan will help communities meet new wastewater and proposed stormwater permit requirements. Critical next steps are need before this Plan will fulfill the 2018 Nitrogen Control Plan requirements for Exeter and proposed draft MS4 requirements for both Stratham and Exeter. These next steps include conducting a financial capability assessment, development of an implementation schedule and development of a detailed implementation plan. The collaborative process used to develop this Plan was designed to provide decision makers at the local, state and federal levels with the knowledge they need to trust the Plan’s findings and recommendations, and to enable discussions between stakeholders to continue the collaborative process. This Plan includes the following information to guide local response to new federal permit requirements for treating and discharging stormwater and wastewater: Sources of annual pollutant load quantified by type and community; Assessment and evaluation of different treatment control strategies for each type of pollutant load; Assessment and evaluation of nutrient control strategies designed to reduce specific types of pollutants; Evaluation of a range of point source controls at the wastewater treatment facility based on regulatory requirements; Costs associated with a range of potential control strategies to achieve reduction of nitrogen and other pollutants of concern; and A preliminary implementation schedule with milestones for target load reductions using specific practices for specific land uses at points in time; Recommendations on how to implement a tracking and accounting program to document implementation; Design tools such as BMP performance curves for crediting the use of structural practices to support nitrogen accounting requirements; and Next Steps for how to complete this Plan
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