Direct And Evolutionary Approaches For Optimal Receiver Function Inversion

Receiver functions are time series obtained by deconvolving vertical component seismograms from radial component seismograms. Receiver functions represent the impulse response of the earth structure beneath a seismic station. Generally, receiver functions consist of a number of seismic phases related to discontinuities in the crust and upper mantle. The relative arrival times of these phases are correlated with the locations of discontinuities as well as the media of seismic wave propagation. The Moho (Mohorovicic discontinuity) is a major interface or discontinuity that separates the crust and the mantle. In this research, automatic techniques to determine the depth of the Moho from the earth’s surface (the crustal thickness H) and the ratio of crustal seismic P-wave velocity (Vp) to S-wave velocity (Vs) (ï«= Vp/Vs) were developed. In this dissertation, an optimization problem of inverting receiver functions has been developed to determine crustal parameters and the three associated weights using evolutionary and direct optimization techniques

Analysis of Model Predictive Intersection Control for Autonomous Vehicles

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios

Modeling And Predictive Control Of High Performance Buildings With Distributed Energy Generation And Thermal Storage

Building-integrated photovoltaic-thermal (BIPV/T) systems replace conventional building cladding with solar technology that generates electricity and heat. For example, unglazed transpired solar collectors, known as UTCs, can be integrated with open-loop photovoltaic thermal (PV/T) systems to preheat ventilation air and/or to feed hot air into an air source heat pump, thus satisfying a significant part of the building’s heating and/or hot water requirements while also generating electricity. In the present study a model for a BIPV/T system with a two-stage prototype UTC integrated with PV panels has been developed and used to create a new component in TRNSYS. An open plan office space at Purdue’s Living Lab is used as a test-bed to explore system integration approaches with building HVAC systems and thermal storage mechanisms. The BIPV/T system is coupled with the building through a thermal storage tank, which serves as the heat source, and is connected to the air-to-water heat pump, for the radiant floor heating. The building ventilation system is coupled with the air outlet of the BIPV/T system. A detailed building energy model is developed in TRNSYS, which is used to evaluate the annual performance with the results showing significant energy savings. The objective is to develop models that can be implemented within a predictive control framework for the optimal set-point trajectory of the thermal storage tank. In the MPC formulation, the cost function is the integral of the electric energy consumption over the prediction horizon (48 hrs) subject to thermal comfort and equipment constraints. The study also investigates the impacts of the uncertainty in weather forecast (solar radiation) on MPC performance robustness for the integrated solar system. In our methodology, the TRNSYS model is used as a true representation of the building to identify the parameters of a 3rd order linear time invariant state-space model. The sum of squares minimization was used to identify model parameters that minimize the root-mean-squared error (RMSE) of time series predictions for the three state variables (floor surface temperature of the room; room air temperature; building envelope interior surface temperature) between the reduced order and the TRNSYS model. Known inputs to the system include the ambient temperature, solar radiation (absorbed by the envelope or transmitted through the south-facing glazed façade), internal heat gains (occupancy schedule, equipment, mechanical ventilation and infiltration) and the tank set point temperature. A pattern search optimization algorithm has been used over the training data space to identify the parameter values. Parameter bounds were set to constrain the solution space to physically plausible values. The training and calibration data sets includes 2351 (from Jan 4 to Feb 22, 7 weeks) and 1823 (from Feb 23 to Mar 30) data points, respectively. The simplified 3rd order model shows satisfactory performance with the RMSE for the three state variables within 0.5 °C. Model predictive control relevant identification methods such as 4SID (black-box identification) are also considered and the results will be compared with those using grey-box techniques

Shape optimization for tumor location

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Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table

Lung cancer model calibration using linearly constrained optimization

When modeling cancer evolution to simulate possible future prevention strategies, it is of great importance to calibrate the model to make sure that it reproduces properly the present situation. This leads to a constrained optimization problem where the objective function has not an analytic expression and is expensive to evaluate. The goal of this work is to study this kind of optimization problems and calibrate an existing lung cancer model to make it reproduce real data. Two different approaches are being considered. One using a derivative-free optimization method to solve the problem. The other will take advantage of the particularities of the lung cancer model to transform the problem into a constrained differentiable optimization problem