Browsing by Subject "Genetic Algorithm"
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Item A Computational-based Approach for the Design of Trip Steels(2013-08-06) Li, Sheng-YenThe purpose of this work is to optimize the chemical composition as well as the heat treatment for improving the mechanical performance of the TRIP steel by employing the theoretical models. TRIP steel consists of the microstructure with ferrite, bainite, retained austenite and minor martensite. Austenite contributes directly to the TRIP effect as its transformation to martensite under the external stress. In order to stabilize austenite against the martensitic transformation through the heat treatment, the two-step heat treatment is broadly applied to enrich the carbon and stabilize the austenite. During the first step of the heat treatment, intercritical annealing (IA), a dual phase structure (ferrite+austenite) is achieved. The austenite can be initially stabilized because of the low carbon solubility of ferrite. The bainite isothermal treatment (BIT) leads to the further carbon enrichment of IA-austenite by the formation of carbon-free ferrite. Comparing to the experiments, the thermodynamic and kinetic models are the lower and upper bounds of the carbon content of retained austenite. The mechanical properties are predicted using the swift model based on the predicted microstructure. In this work, a theoretical approach is coupled to a Genetic Algorithm-based optimization procedure to design (1) the heat treated temperatures to maximize the volume fraction of retained austenite in a Fe-0.32C-1.42Mn-1.56Si alloy and the chemical composition of (2) Fe-C-Mn-Si and (3) Fe-C-Mn-Si-Al-Cr-Ni alloy. The results recommend the optimum conditions of chemical composition and the heat treatment for maximizing the TRIP effect. Comparing to the experimental results, this designing strategy can be utilized to explore the potential materials of the novel alloys.Item A Genetic Algorithm Approach for Technology Characterization(2012-10-19) Galvan, EdgarIt is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. Several researchers have investigated approaches for modeling the capabilities of a technology with the aim of supporting the design process. In these works, the information about the physical form is typically abstracted away. However, the efficient generation of an accurate model of technical capabilities remains a challenge. Pareto frontier based methods are often used but yield results that are of limited use for subsequent decision making and analysis. Models based on parameterized Pareto frontiers?termed Technology Characterization Models (TCMs)?are much more reusable and composable. However, there exists no efficient technique for modeling the parameterized Pareto frontier. The contribution of this thesis is a new algorithm for modeling the parameterized Pareto frontier to be used as a model of the characteristics of a technology. The novelty of the algorithm lies in a new concept termed predicted dominance. The proposed algorithm uses fundamental concepts from multi-objective optimization and machine learning to generate a model of the technology frontier.Item A Hierarchical History Matching Method and its Applications(2012-02-14) Yin, JichaoModern reservoir management typically involves simulations of geological models to predict future recovery estimates, providing the economic assessment of different field development strategies. Integrating reservoir data is a vital step in developing reliable reservoir performance models. Currently, most effective strategies for traditional manual history matching commonly follow a structured approach with a sequence of adjustments from global to regional parameters, followed by local changes in model properties. In contrast, many of the recent automatic history matching methods utilize parameter sensitivities or gradients to directly update the fine-scale reservoir properties, often ignoring geological inconsistency. Therefore, there is need for combining elements of all of these scales in a seamless manner. We present a hierarchical streamline-assisted history matching, with a framework of global-local updates. A probabilistic approach, consisting of design of experiments, response surface methodology and the genetic algorithm, is used to understand the uncertainty in the large-scale static and dynamic parameters. This global update step is followed by a streamline-based model calibration for high resolution reservoir heterogeneity. This local update step assimilates dynamic production data. We apply the genetic global calibration to unconventional shale gas reservoir specifically we include stimulated reservoir volume as a constraint term in the data integration to improve history matching and reduce prediction uncertainty. We introduce a novel approach for efficiently computing well drainage volumes for shale gas wells with multistage fractures and fracture clusters, and we will filter stochastic shale gas reservoir models by comparing the computed drainage volume with the measured SRV within specified confidence limits. Finally, we demonstrate the value of integrating downhole temperature measurements as coarse-scale constraint during streamline-based history matching of dynamic production data. We first derive coarse-scale permeability trends in the reservoir from temperature data. The coarse information are then downscaled into fine scale permeability by sequential Gaussian simulation with block kriging, and updated by local-scale streamline-based history matching. he power and utility of our approaches have been demonstrated using both synthetic and field examples.Item Adaptive Control of Third Harmonic Generation via Genetic Algorithm(2010-10-12) Hua, XiaGenetic algorithm is often used to find the global optimum in a multi-dimensional search problem. Inspired by the natural evolution process, this algorithm employs three reproduction strategies -- cloning, crossover and mutation -- combined with selection, to improve the population as the evolution progresses from generation to generation. Femtosecond laser pulse tailoring, with the use of a pulse shaper, has become an important technology which enables applications in femtochemistry, micromachining and surgery, nonlinear microscopy, and telecommunications. Since a particular pulse shape corresponds to a point in a highly-dimensional parameter space, genetic algorithm is a popular technique for optimal pulse shape control in femtosecond laser experiments. We use genetic algorithm to optimize third harmonic generation (THG), and investigate various pulse shaper options. We test our setup by running the experiment with varied initial conditions and study factors that affect convergence of the algorithm to the optimal pulse shape. Our next step is to use the same setup to control coherent anti-Stocks Raman scattering. The results show that the THG signal has been enhanced.Item Adequacy Assessment in Power Systems Using Genetic Algorithm and Dynamic Programming(2011-02-22) Zhao, DongboIn power system reliability analysis, state space pruning has been investigated to improve the efficiency of the conventional Monte Carlo Simulation (MCS). New algorithms have been proposed to prune the state space so as to make the Monte Carlo Simulation sample a residual state space with a higher density of failure states. This thesis presents a modified Genetic Algorithm (GA) as the state space pruning tool, with higher efficiency and a controllable stopping criterion as well as better parameter selection. This method is tested using the IEEE Reliability Test System (RTS 79 and MRTS), and is compared with the original GA-MCS method. The modified GA shows better efficiency than the previous methods, and it is easier to have its parameters selected. This thesis also presents a Dynamic Programming (DP) algorithm as an alternative state space pruning tool. This method is also tested with the IEEE Reliability Test System and it shows much better efficiency than using Monte Carlo Simulation alone.Item Application of Fast Marching Method in Shale Gas Reservoir Model Calibration(2013-07-26) Yang, ChangdongUnconventional reservoirs are typically characterized by very low permeabilities, and thus, the pressure depletion from a producing well may not propagate far from the well during the life of a development. Currently, two approaches are widely utilized to perform unconventional reservoir analysis: analytical techniques, including the decline curve analysis and the pressure/rate transient analysis, and numerical simulation. The numerical simulation can rigorously account for complex well geometry and reservoir heterogeneity but also is time consuming. In this thesis, we propose and apply an efficient technique, fast marching method (FMM), to analyze the shale gas reservoirs. Our proposed approach stands midway between analytic techniques and numerical simulation. In contrast to analytical techniques, it takes into account complex well geometry and reservoir heterogeneity, and it is less time consuming compared to numerical simulation. The fast marching method can efficiently provide us with the solution of the pressure front propagation equation, which can be expressed as an Eikonal equation. Our approach is based on the generalization of the concept of depth of investigation. Its application to unconventional reservoirs can provide the understanding necessary to describe and optimize the interaction between complex multi-stage fractured wells, reservoir heterogeneity, drainage volumes, pressure depletion, and well rates. The proposed method allows rapid approximation of reservoir simulation results without resorting to detailed flow simulation, and also provides the time-evolution of the well drainage volume for visualization. Calibration of reservoir models to match historical dynamic data is necessary to increase confidence in simulation models and also minimize risks in decision making. In this thesis, we propose an integrated workflow: applying the genetic algorithm (GA) to calibrate the model parameters, and utilizing the fast marching based approach for forward simulation. This workflow takes advantages of both the derivative free characteristics of GA and the speed of FMM. In addition, we also provide a novel approach to incorporate the micro-seismic events (if available) into our history matching workflow so as to further constrain and better calibrate our models.Item Automated and Optimized Project Scheduling Using BIM(2014-04-04) Faghihi, VahidConstruction project scheduling is one of the most important tools for project managers in the Architecture, Engineering, and Construction (AEC) industry. The Construction schedules allow project managers to track and manage the time, cost, and quality (i.e. Project Management Triangle) of projects. Developing project schedules is almost always troublesome, since it is heavily dependent on project planners? knowledge of work packages, on-the-job-experience, planning capability, and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a three-dimensional (3D) representation of a project (a.k.a. Building Information Model or BIM) has recently drawn the attention of the construction industry. In this dissertation, the author demonstrates how to develop and extend the usage of the Genetic Algorithm (GA) not only to generate construction schedules, but to optimize the outcome for different objectives (i.e. cost, time, and job-site movements). The basis for the GA calculations is the embedded data available in BIM of the project that should be provided as an input to the algorithm. By reading through the geometry information in the 3D model and receiving more specific information about the project and its resources from the user, the algorithm generates different construction schedules. The output Pareto Frontier graphs, 4D animations, and schedule wellness scores will help the user to find the most suitable construction schedule for the given project.Item Base Isolation of a Chilean Masonry House: A Comparative Study(2010-01-16) Husfeld, Rachel L.The objective of this study is to reduce the interstory drifts, floor accelerations, and shear forces experienced by masonry houses subject to seismic excitation. Ambient vibration testing was performed on a case study structure in Maip?, Chile, to identify characteristics of the system. Upon creating a multiple degree-of-freedom (MDOF) model of the structure, the effect of implementing several base isolation techniques is assessed. The isolation techniques analyzed include the use of friction pendulum systems (FPS), high-damping rubber bearings (HDRB), two hybrid systems involving HDRB and shape memory alloys (SMA), and precast-prestressed pile (PPP) isolators. The dynamic behavior of each device is numerically modeled using analytical formulations and experimental data through the means of fuzzy inference systems (FIS) and S-functions. A multiobjective genetic algorithm is utilized to optimize the parameters of the FPS and the PPP isolation systems, while a trial-and-error method is employed to optimize characteristic parameters of the other devices. Two cases are studied: one case involves using eight devices in each isolation system and optimizing the parameters of each device, resulting in different isolated periods for each system, while the other case involves using the number of devices and device parameters that result in a 1.0 sec fundamental period of vibration for each baseisolated structure. For both cases, the optimized devices are simulated in the numerical model of the case study structure, which is subjected to a suite of earthquake records. Numerical results for the devices studied indicate significant reductions in responses of the base-isolated structures in comparison with their counterparts in the fixed-base structure. Metrics monitored include base shear, structural shear, interstory drift, and floor acceleration. In particular, the PPP isolation system in the first case reduces the peak base shear, RMS floor acceleration, peak structural shear, peak interstory drift, and peak floor acceleration by at least 88, 87, 95, 95, and 94%, respectively, for all of the Chilean earthquakes considered. The PPP isolation system in the second case (yielding a 1.0 sec period) and the FPS isolation systems in both cases also significantly reduce the response of the base-isolated structure from that of the fixed-base structure.Item Modified Niched Pareto Multi-objective Genetic Algorithm for Construction Scheduling Optimization(2012-10-19) Kim, KyungkiThis research proposes a Genetic Algorithm based decision support model that provides decision makers with a quantitative basis for multi-criteria decision making related to construction scheduling. In an attempt to overcome the drawbacks of similar efforts, the proposed multi-objective optimization model provides insight into construction scheduling problems. In order to generate optimal solutions in terms of the three important criteria which are project duration, cost, and variation in resource use, a new data structure is proposed to define a solution to the problem and a general Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate optimization procedure. The main features of the proposed Multi-Objective Genetic Algorithm (MOGA) are: A fitness sharing technique that maintains diversity of solutions. A non-dominated sorting method that assigns ranks to each individual solution in the population is beneficial to the tournament selection process. An external archive to prevent loss of optimal or near optimal solutions due to the random effect of genetic operators. A space normalization method to avoid scaling deficiencies. The developed optimization model was applied to two case studies. The results indicate that a wider range of solutions can be obtained by employing the new approach when compared to previous models. Greater area in the decision space is considered and tradeoffs between all the objectives are found. In addition, various resource use options are found and visualized. Most importantly, the creation of a simultaneous optimization model provides better insight into what is obtainable by each option. A limitation of this research is that schedules are created under the assumption of unlimited resource availability. Schedules created with this assumption in real world situations are often infeasible given that resources are commonly constrained and not readily available. As such, a discussion is provided regarding future research as to what data structure has to be developed in order to perform such scheduling under resource constraints.Item Multiobjective Design and Optimization of Polymer Flood Performance(2013-07-22) Ekkawong, PeerapongThe multiobjective genetic algorithm can be used to optimize two conflicting objectives, oil production and polymer utility factor in polymer flood design. This approach provides a set of optimal solutions which can be considered as trade-off curve (Pareto front) to maximize oil production while preserving polymer performance. Then an optimal polymer flood design can be considered from post-optimization analysis. A 2D synthetic example, and a 3D field-scale application, accounting for geologic uncertainty, showed that beyond the optimal design, a relatively minor increase in oil production requires much more polymer injection and the polymer utility factor increases substantially.Item Novel cost allocation framework for natural gas processes: methodology and application to plan economic optimization(Texas A&M University, 2004-09-30) Jang, Won-HyoukNatural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.Item Optimized Design of Statically Equivalent Mooring and Catenary Ryser Systems(2015-03-03) Felix Gonzalez, IvanDue to size limitations of wave basins worldwide it is necessary to employ statically equivalent truncated mooring and riser systems to test floating systems to be deployed in deep and ultra-deep waters. A procedure for the optimized design of the statically equivalent truncated mooring and riser system was developed using a Genetic Algorithm, considering that the equivalent mooring/system needs to reproduce the net static forces and moments exerted by the prototype mooring/riser system on the floater in its six rigid body degrees of freedom (surge, sway, heave, roll, pitch and yaw). A fit-for-purpose program was developed to evaluate the three-dimensional static equilibrium of floating structures, considering the attached mooring and steel catenary riser systems. The static response is calculated for a set of offsets in the surge direction from the calm water equilibrium position up to a maximum user defined offset. Four study cases were considered to demonstrate the effectiveness and robustness of a Genetic Algorithm procedure developed for the optimize design of the statically equivalent mooring and riser system. The four study cases were a semisubmersible with a symmetric polyester mooring system, a semisubmersible with a symmetric steel wire mooring system, a semisubmersible with a non-symmetric polyester mooring and steel catenary riser system attached, and a spar with a non-symmetric polyester mooring and a steel catenary riser system attached. To gain insight on the distortion of the dynamic mooring forces exerted on the floater when dynamic effects are ignored in the design, a procedure to assess the mooring system inertia and damping force contributions to the floater was developed. The application of the procedure was demonstrated using two study cases corresponding to deepwater polyester and steel mooring systems.Item Structural control Architecture Optimization for 3-D Systems Using Advanced Multi-Objective Genetic Algorithms(2010-01-14) Cha, Young JinThe architectures of the control devices in active control algorithm are an important fact in civil structural buildings. Traditional research has limitations in finding the optimal architecture of control devices such as using predefined numbers or locations of sensors and dampers within the 2-and 3-dimensional (3-D) model of the structure. Previous research using single-objective optimization only provides limited data for defining the architecture of sensors and control devices. The Linear Quadratic Gaussian (LQG) control algorithm is used as the active control strategy. The American Society of Civil Engineers (ASCE) control benchmark building definition is used to develop the building system model. The proposed gene manipulation genetic algorithm (GMGA) determines the near-optimal Pareto fronts which consist of varying numbers and locations of sensors and control devices for controlling the ASCE benchmark building by considering multi-objectives such as interstory drift and minimizing the number of the control devices. The proposed GMGA reduced the central processing unit (CPU) run time and produced more optimal Pareto fronts for the 2-D and 3-D 20-story building models. Using the GMGA provided several benefits: (1) the possibility to apply any presuggested multi-objective optimization mechanism; (2) the availability to perform a objective optimization problem; (3) the adoptability of the diverse encoding provided by the GA; (4) the possibility of including the engineering judgment in generating the next generation population by using a gene creation mechanisms; and (5) the flexibility of the gene creation mechanism in applying and changing the mechanism dependent on optimization problem. The near-optimal Pareto fronts obtained offer the structural engineer a diverse choice in designing control system and installing the control devices. The locations and numbers of the dampers and sensors in each story are highly dependent on the sensor locations. By providing near-Pareto fronts of possible solutions to the engineer that also consider diverse earthquakes, the engineer can get normalized patterns of architectures of control devices and sensors about random earthquakes.