A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Using Inverse Fire Modeling With Multiple Input Signals to Obtain Heat Release Rates in Compartment Fire Scenarios(2014) Price, Michael David; Marshall, Andre; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A set of multi-room compartment fire experiments were conducted to obtain measurements of hot gas layer temperature and depth. These measurements were used as an input to an inverse fire model that coupled a genetic algorithm with a zone fire model to calculate a unique solution to the original fire size and door opening used in the experiments. The objective of this research was to calculate simultaneously the real-time fire size and fire door opening of the experiment using a combination of hot gas layer temperature and hot gas layer height measurements from a multi-room compartment in concert with an inverse fire model. This research focused on increasing the robustness of an inverse fire model (IFM) with respect to physical accuracy and multi-variable calculations. The IFM successfully identified a unique solution and calculated fire size within 10-40% of experimental values.Item Single and Multiresponse Adaptive Design of Experiments with Application to Design Optimization of Novel Heat Exchangers(2009) Aute, Vikrant Chandramohan; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Engineering design optimization often involves complex computer simulations. Optimization with such simulation models can be time consuming and sometimes computationally intractable. In order to reduce the computational burden, the use of approximation-assisted optimization is proposed in the literature. Approximation involves two phases, first is the Design of Experiments (DOE) phase, in which sample points in the input space are chosen. These sample points are then used in a second phase to develop a simplified model termed as a metamodel, which is computationally efficient and can reasonably represent the behavior of the simulation response. The DOE phase is very crucial to the success of approximation assisted optimization. This dissertation proposes a new adaptive method for single and multiresponse DOE for approximation along with an approximation-based framework for multilevel performance evaluation and design optimization of air-cooled heat exchangers. The dissertation is divided into three research thrusts. The first thrust presents a new adaptive DOE method for single response deterministic computer simulations, also called SFCVT. For SFCVT, the problem of adaptive DOE is posed as a bi-objective optimization problem. The two objectives in this problem, i.e., a cross validation error criterion and a space-filling criterion, are chosen based on the notion that the DOE method has to make a tradeoff between allocating new sample points in regions that are multi-modal and have sensitive response versus allocating sample points in regions that are sparsely sampled. In the second research thrust, a new approach for multiresponse adaptive DOE is developed (i.e., MSFCVT). Here the approach from the first thrust is extended with the notion that the tradeoff should also consider all responses. SFCVT is compared with three other methods from the literature (i.e., maximum entropy design, maximin scaled distance, and accumulative error). It was found that the SFCVT method leads to better performing metamodels for majority of the test problems. The MSFCVT method is also compared with two adaptive DOE methods from the literature and is shown to yield better metamodels, resulting in fewer function calls. In the third research thrust, an approximation-based framework is developed for the performance evaluation and design optimization of novel heat exchangers. There are two parts to this research thrust. First, is a new multi-level performance evaluation method for air-cooled heat exchangers in which conventional 3D Computational Fluid Dynamics (CFD) simulation is replaced with a 2D CFD simulation coupled with an e-NTU based heat exchanger model. In the second part, the methods developed in research thrusts 1 and 2 are used for design optimization of heat exchangers. The optimal solutions from the methods in this thrust have 44% less volume and utilize 61% less material when compared to the current state of the art microchannel heat exchangers. Compared to 3D CFD, the overall computational savings is greater than 95%.Item Inverse fire modeling to estimate the heat release rate of compartment fires(2007-07-31) Neviackas, Andrew William; Trouve, Arnaud; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The objective of this research is to develop a new paradigm in fire-fighting techniques and demonstrate the feasibility of using fire imaging technology (e.g., thermal imaging cameras to monitor smoke conditions from a burning building) combined with fire modeling software for real-time fire analysis to assist firefighter operations. This project focuses on the development of a prototype inverse fire modeling (IFM) algorithm. The IFM uses: MATLAB as the programming language; BRI2002 as the zone model; and a genetic algorithm for optimization. The IFM is tested as a stand-alone component in which the camera-based observations of smoke layer properties are replaced by data on the upper layer temperature (TUL) coming from a reference BRI simulation with a certain heat release rate (HRRref). The objective of the IFM algorithm is then to provide an estimate of HRRref from the sole knowledge of TUL. The performance of the IFM algorithm has been studied in a series of tests of gradually increasing complexity.Item Real-Time Terminal Area Trajectory Planning for Runway Independent Aircraft(2006-01-24) Xue, Min; Atkins, Ella M; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The increasing demand for commercial air transportation results in delays due to traffic queues that form bottlenecks along final approach and departure corridors. In urban areas, it is often infeasible to build new runways, and regardless of automation upgrades traffic must remain separated to avoid the wakes of previous aircraft. Vertical or short takeoff and landing aircraft as Runway Independent Aircraft (RIA) can increase passenger throughput at major urban airports via the use of vertiports or stub runways. The concept of simultaneous non-interfering (SNI) operations has been proposed to reduce traffic delays by creating approach and departure corridors that do not intersect existing fixed-wing routes. However, SNI trajectories open new routes that may overfly noise-sensitive areas, and RIA may generate more noise than traditional jet aircraft, particularly on approach. In this dissertation, we develop efficient SNI noise abatement procedures applicable to RIA. First, we introduce a methodology based on modified approximated cell-decomposition and Dijkstra's search algorithm to optimize longitudinal plane (2-D) RIA trajectories over a cost function that minimizes noise, time, and fuel use. Then, we extend the trajectory optimization model to 3-D with a k-ary tree as the discrete search space. We incorporate geography information system (GIS) data, specifically population, into our objective function, and focus on a practical case study: the design of SNI RIA approach procedures to Baltimore-Washington International airport. Because solutions were represented as trim state sequences, we incorporated smooth transition between segments to enable more realistic cost estimates. Due to the significant computational complexity, we investigated alternative more efficient optimization techniques applicable to our nonlinear, non-convex, heavily constrained, and discontinuous objective function. Comparing genetic algorithm (GA) and adaptive simulated annealing (ASA) with our original Dijkstra's algorithm, ASA is identified as the most efficient algorithm for terminal area trajectory optimization. The effects of design parameter discretization are analyzed, with results indicating a SNI procedure with 3-4 segments effectively balances simplicity with cost minimization. Finally, pilot control commands were implemented and generated via optimization-base inverse simulation to validate execution of the optimal approach trajectories.Item Bus Network Scheduling with Genetic Algorithms and Simulation(2005-05-02) Park, Seong Jae; Schonfeld, Paul M; Chang, Gang-Len; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis investigates the costs associated with a bus scheduling problem in an urban transit network for both deterministic and stochastic arrival processes and proposes computerized models for each. A simple genetic algorithm (SGA) with some problem-specific genetic operators is developed for the deterministic arrival process and a simulation-based genetic algorithm (SBGA) is developed for the stochastic arrival process. The new models are applied to an artificial bus network to test their efficiency. Several sensitivity analyses and a goodness test are conducted for each arrival process. The results show that the SGA model can find the optimized solution very quickly when it uses problem-specific operators such as the coordinated headway generator, coordinated headway crossover and coordinated headway mutation. They also show that the SBGA model can find a good solution even though it uses general genetic operators.