Engineering Commons^™

A Novel Physics-Assisted Genetic Algorithm For Decoupling Capacitor Optimization, Li Jiang, Ling Zhang, Shurun Tan, Da Li, Chulsoon Hwang, Jun Fan, Er Ping Li

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes a new physics-assisted genetic algorithm (PAGA) for decoupling capacitor (decap) optimization in power distribution networks (PDNs), which is a highly efficient approach to minimizing the number of decaps within an enormous search space. In the proposed PAGA method, the priority of the decap ports is first determined based on their physical loop inductances. Then, an initial solution is quickly obtained by placing decaps sequentially on the port with the highest priority. Subsequently, a GA with prior physical knowledge is developed to find better decap solutions progressively. A port removal scheme that eliminates the low-priority ports and a …

Dig Limits Optimization Using Binary Integer Linear Programming Method In Open Pit Mines, Hussam Naif Altalhi

Masters Theses

"Dig limits optimization is the process for classifying different materials (e.g., ore, stockpile material, and waste) into appropriately sized contiguous zones for open pit mining. The efficient determination of dig-limits is crucial for profitable and sustainable resource extraction in mining. Previous research has focused on defining dig-limits manually or using optimization approaches, but these methods are limited to only handling two material destinations (ore and waste). Thus, there is a need for operations research methods that consider the selectivity of mining equipment and can optimize dig-limits for metal mining operations with more than two material destinations. Consequently, the objective of …

Unmanned-Aircraft-System-Assisted Early Wildfire Detection With Air Quality Sensors †, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot …

Decoupling Optimization For Complex Pdn Structures Using Deep Reinforcement Learning, Ling Zhang, Li Jiang, Jack Juang, Zhiping Yang, Er Ping Li, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Presents a New Optimization Method for Complex Power Distribution Networks (PDNs) with Irregular Shapes and Multilayer Structures using Deep Reinforcement Learning (DRL), Which Has Not Been Considered Before. a Fast Boundary Integration Method is Applied to Compute the Impedance Matrix of a PDN Structure. Subsequently, a New DRL Algorithm based on Proximal Policy Optimization (PPO) is Proposed to Optimize the Decoupling Capacitor (Decap) Placement by Minimizing the Number of Decaps While Satisfying the Desired Target Impedance. in the Proposed Approach, the PDN Structure Information is Encoded into Matrices and Serves as the Input of the DRL Algorithm, Which …

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student's interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

Multiple Uav-Lidar Placement Optimization Under Road Priority And Resolution Requirements, Zachary Osterwisch, Omar Rinchi, Ahmad Alsharoa, Hakim Ghazzai, Yehia Massoud

Electrical and Computer Engineering Faculty Research & Creative Works

An unmanned aerial vehicle (UAV) integrated with the remote sensing technology of light detection and ranging (LiDAR) can provide accurate and real-time road traffic information. In this paper, we propose to equip UAVs with LiDAR sensors for Intelligent Transportation Systems (ITS) applications. The goal is to find the optimal 3D placement of multiple UAV-LiDAR (ULiDs) for a given road segmentation. We formulate an optimization problem to find the optimal placement such that the road coverage efficiency is maximized. The optimization problem is constrained by notable ULiD specifications such as field-of-view (FoV), point-cloud density, geographic information system (GIS) location, and road …

Training Set Optimization In An Artificial Neural Network Constructed For High Bandwidth Interconnects Design, Bo Pu, Heegon Kim, Xiao Ding Cai, Bidyut Sen, Chunchun Sui, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

In this article, a novel training set optimization method in an artificial neural network (ANN) constructed for high bandwidth interconnects design is proposed based on rigorous probability analysis. In general, the accuracy of an ANN is enhanced by increasing training set size. However, generating large training sets is inevitably time-consuming and resource-demanding, and sometimes even impossible due to limited prototypes or measurement scenarios. Especially, when the number of channels in required design are huge such as graphics double data rate (GDDR) memory and high bandwidth memory (HBM). Therefore, optimizing the training set selection process is crucial to minimizing the training …

Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay, Yongliang Yang, Yongping Pan, Cheng Zhong Xu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton–Jacobi–Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms …

A Methodical Approach For Pcb Pdn Decoupling Minimizing Overdesign With Genetic Algorithm Optimization, F. De Paulis, Y. Ding, M. Cocchini, Chulsoon Hwang, S. Connor, M. Doyle, S. Scearce, W. D. Becker, Albert E. Ruehli, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

An optimization routine is applied for the decoupling capacitor placement on Power Distribution Networks to identify the limit beyond which the placement of additional decaps is no longer effective, thus leading to wasting layout area and components, and to a cost increase. A specific test example from a real design is used together with the required target impedance and frequency band of interest for the PDN design. The effectiveness of the decap placement while selecting different layers of the stack-up, and while moving the upper limit of the PDN design band is analyzed. Such analysis leads to helpful insights based …

Particle Swarm Optimization For Critical Experiment Design, Cole Michael Kostelac

Masters Theses

“Critical experiments are used by nuclear data evaluators and criticality safety engineers to validate nuclear data and computational methods. Many of these experiments are designed to maximize the sensitivity to a certain nuclide-reaction pair in an energy range of interest. Traditionally, a parameter sweep is conducted over a set of experimental variables to find a configuration that is critical and maximally sensitive. As additional variables are added, the total number of configurations increases exponentially and quickly becomes prohibitively computationally expensive to calculate, especially using Monte Carlo methods.

This work presents the development of a particle swarm optimization algorithm to design …

Using Computational Methods To Optimize High Heat Flux Component Thermal Performance In Magnetic Confinement Fusion Reactor Research, Monica Gehrig

Doctoral Dissertations

"Heat transfer enhancement by means of internally modified geometries in tubes and channels is an important mechanism to improve the survivability of components in extreme high-heat flux environments. Various features such as ribs and fins are studied using computational fluid dynamics in both uniform and one-sided heating in tubes and rectangular channels respectively to determine the most effective geometries across a variety of different flow and heating conditions. This work examines heat transfer enhancement and rib geometry optimization to support experimental research for nuclear fusion applications. The project begins by designing and analyzing test sections supporting a helium flow loop …

Optimal Bidding Strategy For Physical Market Participants With Virtual Bidding Capability In Day-Ahead Electricity Markets, Hossein Mehdipourpicha, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Virtual bidding provides a mechanism for financial players to participate in wholesale day-ahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets, can also take advantage of virtual bidding but in a different way, which is to further amplify the value of their physical assets. Therefore, this work proposes a model for such physical MPs to maximize the profits. This model employs a bi-level optimization approach, where the upper-level subproblem maximizes the total profit from both physical generations and …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

Time-Dependent Reliability Methodologies With Saddlepoint Approximation, Zhangli Hu

Doctoral Dissertations

"Engineers always encounter time-dependent uncertainties that ubiquitously exist, such as the random deterioration of material properties and time-variant loads. Therefore the reliability of engineering systems becomes time-dependent. It is crucial to predict the time-dependent reliability in the design stage, given possible catastrophic consequences of a failure. Although extensive research has been conducted on reliability analysis, estimating the reliability accurately and efficiently is still challenging. The objective of this work is to develop accurate and efficient reliability methodologies for engineering design. The basic idea is the integration of traditional reliability methods with saddlepoint approximation (SPA), which can accurately approximate the tail …

Techno-Economic Optimization And Environmental Life Cycle Assessment Of Microgrids Using Genetic Algorithm And Artificial Neural Networks, Prashant Nagapurkar

Doctoral Dissertations

"This dissertation focuses primarily on techno-economic optimization and environmental life cycle assessment (LCA) of sustainable energy generation technologies. This work is divided into five papers. The first paper discusses the techno-economic optimization and environmental life cycle assessment of microgrids located in the USA using genetic algorithm. In this paper, a methodology was developed that assessed the techno-economic and environmental performance of a small scale microgrid located in US cities of Tucson, Lubbock and Dickinson. Providing uninterrupted power the microgrid was composed of seven components -- solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H₂ tanks. …

A Constitutive Model For Entangled Polydisperse Linear Flexible Polymers With Entanglement Dynamics And A Configuration Dependent Friction Coefficient. Part I: Model Derivation, David W. Mead, Saman Monjezi, Joontaek Park

Chemical and Biochemical Engineering Faculty Research & Creative Works

A new polydisperse "toy" constitutive model is derived and developed from fundamental principles and ideas governing the nonlinear rheology of linear flexible polymers [Mead et al., J. Rheol. 59, 335-363 (2015)]. Specifically, the new model is comprised of four fundamental pieces. First, the model contains a simple differential description of the entanglement dynamics of discrete entanglement pairs. Second, the model contains a differential description of the ij entanglement pair orientation tensor dynamics. Third, following a similar development by Mead and Mishler [J. Non-Newtonian Fluid Mech. 197, 61-79 and 80-90 (2013).], a diluted stretch tube is constructed to describe the relative …

Loading Time Flexibility In Cross-Docking Systems, Dincer Konur, Mihalis M. Golias

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this study, we investigate truck-to-door assignment problem for loading outgoing trucks in a cross-docking system with flexible handling times. Specifically, a truck's loading time depends on the number of workers assigned to the outbound door, where the truck is being loaded. An optimization problem is formulated to jointly determine the number of workers and the trucks to be loaded at each door. The resulting problem is a nonlinear integer programming model. Due to the complexity of this model, two evolutionary heuristic methods are proposed for solution. First heuristic method is based on truck assignments while the second heuristic is …

Determination Of Significant Parameters That Drive Fracture Optimization In The Glauconite Formation, Southern Chile, Ghassan Saleh Mahdi Alqatrani

Masters Theses

"The Glauconite Formation in the Magallanes Basin of Southern Chile is a clay- and silica-rich formation with low permeability. As with many of the unconventional resources, the Glauconite Formation requires a hydraulic fracturing operation to enhance the productivity of the wells in this area.

Data and pertinent information of fracturing, completion, and reservoir quality parameters along with post-fracture production data were collected to initiate a database of nearly 70 wells, to be used to develop a better understanding of the fracturing behavior, optimize the well stimulation, and overcome the major barriers in the hydraulic fracturing of the Glauconite Formation. The …

Approximation For Single-Channel Multi-Server Queues And Queuing Networks With Generally Distributed Inter-Arrival And Service Times, Carlos Roberto Chaves

Doctoral Dissertations

"This dissertation is divided into two papers. The first paper is related to developing a closed-form approximation for single-channel multiple-server queues with generally distributed inter-arrival and service times, which are often found in numerous settings, e.g., airports and manufacturing systems. Unfortunately, exact models for such systems require distributions for the underlying random variables. Further, data for fitting distributions is sometimes not available, and one only has access to means and variances of the underlying input random variables. Under heavy traffic, excellent approximations already exist for this purpose. In the first paper, a new approximation method for medium traffic is presented. …

Aspen Simulation Of Oil Shale And Biomass Process, Anand Alembath

Masters Theses

"This thesis focuses on design and analysis of two major chemical processes using computer simulation which performs a steady state computation. The objective is to design processes using Aspen simulation to establish optimum operating conditions by performing various simulation runs which are challenging to execute at lab scale.

Increasing energy needs and decline of global oil prices has shifted our focus on commercially developing unconventional and renewable resources. Commercialization of any process relies on developing a process model that identifies different process parameters by performing a steady state mass and heat balance. Aspen simulation is considered an effective process modeling …

Applications Of Simulation And Optimization Techniques In Optimizing Room And Pillar Mining Systems, Angelina Konadu Anani

Doctoral Dissertations

"The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) …

The Evaluation Of Sequential Optimization And Reliability Analysis, Guannan Liu

Masters Theses

"Sequential Optimization and Reliability Assessment (SORA) has been used for more than one decade for reliability-based design (RBD), but comprehensive theoretical studies on its performance have not been conducted. Further investigations on its performance are still needed. The objective of this thesis is to evaluate the performance of SORA for various testing problems. The performance of SORA evaluated in this thesis includes (1) accuracy, (2) efficiency, and (3) convergence behavior or robustness with numerical testing problems. SORA is evaluated with comparison with other major RBD methodologies. The testing problems are in different scales (numbers of design variables, random variables, and …

A Computational Intelligence Approach To System-Of-Systems Architecting Incorporating Multi-Objective Optimization, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions …

Numerical And Field Data Analysis Of Drill Stem Vibration, Mohammed Fayez Al Dushaishi

Doctoral Dissertations

"Drill stem vibration is a major cause of premature failure of drill stem components and drilling inefficiency. In severe cases, drill stem vibration may lead to wellbore instability that could lead to increased operational cost. Drill stem vibrations are affected by design decisions and the drilling environment. Examples are; bottom hole assembly configurations, selection of operational parameters, and frequent changes in lithology. Vibration modeling, analysis of vibration data, and specialized vibration reduction tools are methods in use to prevent and mitigate severe vibrations.

A drill stem vibration model was created using nonlinear strain formulation which couples the axial, lateral and …

A Static Voltage Security Region For Centralized Wind Power Integration-Part Ii: Applications, Tao Ding, Qinglai Guo, Rui Bo, Hongbin Sun, Boming Zhang, Tianen Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In Part I of this work, a static voltage security region was introduced to guarantee the safety of wind farm reactive power outputs under both base conditions and N-1 contingency. In this paper, a mathematical representation of the approximate N-1 security region has further studied to provide better coordination among wind farms and help prevent cascading tripping following a single wind farm trip. Besides, the influence of active power on the security region is studied. The proposed methods are demonstrated for N-1 contingency cases in a nine-bus system. The simulations verify that the N-1 security region is a small subset …

Probabilistic Engineering Analysis And Design Under Time-Dependent Uncertainty, Zhen Hu

Doctoral Dissertations

"Time-dependent uncertainties, such as time-variant stochastic loadings and random deterioration of material properties, are inherent in engineering applications. Not considering these uncertainties in the design process may result in catastrophic failures after the designed products are put into operation. Although significant progress has been made in probabilistic engineering design, quantifying and mitigating the effects of time-dependent uncertainty is still challenging. This dissertation aims to help build high reliability into products under time-dependent uncertainty by addressing two research issues. The first one is to efficiently and accurately predict the time-dependent reliability while the second one is to effectively design the time-dependent …

Optimization In Microgrid Design And Energy Management, Tu Anh Nguyen

Doctoral Dissertations

"The dissertation is composed of three papers, which cover microgrid systems performance characterization, optimal sizing for energy storage system and stochastic optimization of microgrid operation. In the first paper, a complete Photovoltaic-Vanadium Redox Battery (VRB) microgrid is characterized holistically. The analysis is based on a prototype system installation deployed at Fort Leonard Wood, Missouri, USA. In the second paper, the optimal sizing of power and energy ratings for a VRB system in isolated and grid-connected microgrids is proposed. An analytical method is developed to solve the problem based on a per-day cost model in which the operating cost is obtained …

An Analysis Of Burn Cut Pull Optimization Through Varying Relief Hole Depths, Michael Robert Allen

Masters Theses

"In underground blasting, the pull of the initial cut is the limiting factor for the success of the rest of the round. By improving the pull of the first cut, a critical step is made towards improving the entire round. This project attempted to optimize a burn cut's effective pull by varying the depths of the relief holes in the burn, and then analyzing the results. In testing, relief holes were drilled to depths both shorter and longer than that of the cut's charged holes. The overall objective was to consistently achieve greater pull than in a standard burn, using …

Advanced Design Optimization Of Cold-Formed Steel Portal Frame Buildings, Duoc T. Phan, James B. P. Lim, Tiku T. Tanyimboh, Wei Sha

CCFSS Proceedings of International Specialty Conference on Cold-Formed Steel Structures (1971 - 2018)

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The objective function is based on the cost of the members for the main frame and secondary members (i.e., purlins, girts, and cladding for walls and roofs) per unit area on the plan of the building. A realcoded niching genetic algorithm is used to minimize the cost of the frame and secondary members that are designed on the basis of ultimate limit state. It is shown that the proposed algorithm shows effective and robust capacity in generating the optimal solution, owing to the population’s diversity being …

An Efficient Genetic Algorithm For The Design Optimization Of Cold-Formed Steel Portal Frame Buildings, Duoc T. Phan, James B. P. Lim, Tiku T. Tanyimboh, Wei Sha

CCFSS Proceedings of International Specialty Conference on Cold-Formed Steel Structures (1971 - 2018)

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The real-coded genetic algorithm (GA) optimizer proposed considers both building’s topology (i.e. frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables that are optimized. Previous GAs in the literature were characterized by poor convergence including slow progress that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented …