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Article Citation - WoS: 17Citation - Scopus: 24Optimization and Energy Analysis of a Novel Geothermal Heat Exchanger for Photovoltaic Panel Cooling(Pergamon-elsevier Science Ltd, 2021) Jafari, Rahim; Jafari, Rahim; Jafari, Rahim; Automotive Engineering; Automotive EngineeringElectrical energy and conversion efficiency of the photovoltaic (PV) solar panels are measured under standard test conditions in some microseconds at the room temperature (25 degrees C). It also is seen that the actual working conditions, on the other hand, with higher ambient temperature and continuous generated heat in the PV cells can lead to reduction in reduce their electricity generation and long-term sustainability. In the current work, the coolant (water + ethylene glycol) circulates between two heat exchangers; the minichannel heat exchanger is bounded to the PV cells and geothermal heat exchanger is buried underground, and it is set to remove the heat from PV cells to the ground. Six control factors of the geothermal cooling system are considered for the purpose of optimization using Taguchi design and main effect analysis. These parameters are pipe length, soil thermal conductivity, coolant flow rate, adjacent coil distance, pipe inner diameter and pipe thickness. The experimental results show that the average net electricity generation of the cooled PV panel is improved 9.8% compared to the PV panel without cooling system. However, with the same geothermal heat exchanger it drops to 6.2% as the cooled panel number is doubled. The simulation results reveal that the optimum configuration of the geothermal cooling system is capable of enhancing the net electricity generation of the twin cooled panels up to 11.6%. The LCOE of the optimized geothermal cooling system was calculated 0.089 euro/kWh versus the reference panel of 0.102 euro/kWh for the case study of 30 kW PV solar plant.Article Citation - WoS: 15Citation - Scopus: 17Performance Evaluation of Laser Induced Breakdown Spectroscopy in the Measurement of Liquid and Solid Samples(Pergamon-elsevier Science Ltd, 2018) Bilge, Gonca; Sezer, Banu; Boyaci, Ismail Hakki; Eseller, Kemal Efe; Berberoglu, HalilLiquid analysis by using LIBS is a complicated process due to difficulties encountered during the collection of light and formation of plasma in liquid. To avoid these, some applications are performed such as aerosol formation and transforming liquid into solid state. However, performance of LIBS in liquid samples still remains a challenging issue. In this study, performance evaluation of LIBS and parameter optimizations in liquid and solid phase samples were performed. For this purpose,milk was chosen as model sample; milk powder was used as solid sample, and milk was used as liquid sample in the experiments. Different experimental setups have been constructed for each sampling technique, and optimizations were performed to determine suitable parameters such as delay time, laser energy, repetition rate and speed of rotary table for solid sampling technique,and flow rate of carrier gas for liquid sampling technique. Target element was determined as Ca, which is a critically important element in milk for determining its nutritional value and Ca addition. In optimum parameters, limit of detection (LOD), limit of quantification (LOQ) and relative standard deviation (RSD) values were calculated as 0.11%, 0.36% and 8.29% respectively for milk powders samples; while LOD, LOQ and RSD values were calculated as 0.24%, 0.81%, and 10.93% respectively for milk samples. It can be said that LIBS is an applicable method in both liquid and solid samples with suitable systems and parameters. However, liquid analysis requires much more developed systems for more accurate results. (C) 2018 Elsevier B.V.All rights reserved.Article Citation - WoS: 89Citation - Scopus: 99Optimization of Electric Vehicle Recharge Schedule and Routing Problem With Time Windows and Partial Recharge: a Comparative Study for an Urban Logistics Fleet(Elsevier, 2021) Bac, Ugur; Baç, Uğur; Erdem, Mehmet; Erdem, Mehmet; Baç, Uğur; Erdem, Mehmet; Industrial Engineering; Industrial EngineeringThe use of electric vehicles (EVs) is becoming more and more widespread and the interest in these vehicles is increasing each day. EVs promise to emit less air pollution and greenhouse gas (GHG) emissions with lower operational costs when compared to fossil fuel-powered vehicles. However, many factors such as the limited mileage of these vehicles, long recharging times, and the sparseness of available recharging stations adversely affect the preferability of EVs in industrial and commercial logistics. Effective planning of EV routes and recharge schedules is vital for the future of the logistics sector. This paper proposes an electric vehicle routing problem with the time windows (EVRPTW) framework, which is an extension of the well-known vehicle routing problem (VRP). In the proposed model, partial recharging is considered for the EVRPTW with the multiple depots and heterogeneous EV fleet and multiple visits to customers. While routing a set of heterogeneous EVs, their limited ranges, interdependent on the battery capacity, should be taken into consideration and all the customers' deliveries should be completed within the predetermined time windows. To deal with this problem, a series of neighbourhood operators are developed for the local search process in the variable neighbourhood search (VNS) and variable neighbourhood descent (VND) heuristics. The proposed solution algorithms are tested in large-scale instances. Results indicate that the proposed heuristics perform well as to this problem in terms of optimizing recharging times, idle waiting times, overtime of operators, compliance with time windows, number of vehicles, depots, and charging stations used.Article Citation - WoS: 24Activity Uncrashing Heuristic With Noncritical Activity Rescheduling Method for the Discrete Time-Cost Trade-Off Problem(Asce-amer Soc Civil Engineers, 2020) Sonmez, Rifat; Aminbakhsh, Saman; Atan, TankutDespite intensive research efforts that have been devoted to discrete time-cost optimization of construction projects, the current methods have very limited capabilities for solving the problem for real-life-sized projects. This study presents a new activity uncrashing heuristic with noncritical activity rescheduling method to narrow the gap between the research and practice for time-cost optimization. The uncrashing heuristic searches for new solutions by uncrashing the critical activities with the highest cost-slope. This novel feature of the proposed heuristic enables identification and elimination of the dominated solutions during the search procedure. Hence, the heuristic can determine new high-quality solutions based on the nondominated solutions. Furthermore, the proposed noncritical activity rescheduling method of the heuristic decreases the amount of scheduling calculations, and high-quality solutions are achieved within a short CPU time. Results of the computational experiments reveal that the new heuristic outperforms state-of-the-art methods significantly for large-scale single-objective cost minimization and Pareto front optimization problems. Hence, the primary contribution of the paper is a new heuristic method that can successfully achieve high-quality solutions for large-scale discrete time-cost optimization problems.Article Citation - WoS: 9Citation - Scopus: 12The Behavior of Warm Standby Components With Respect To a Coherent System(Elsevier Science Bv, 2011) Eryilmaz, SerkanThis paper is concerned with a coherent system consisting of active components and equipped with warm standby components. In particular, we study the random quantity which denotes the number of surviving warm standby components at the time of system failure. We represent the distribution of the corresponding random variable in terms of system signature and discuss its potential utilization with a certain optimization problem. (C) 2011 Elsevier B.V. All rights reserved.Article Citation - WoS: 25Citation - Scopus: 29Optimum Design of Steel Braced Frames Considering Dynamic Soil-Structure Interaction(Springer, 2019) Bybordiani, Milad; Azad, Saeid KazemzadehRecent studies on design optimization of steel frames considering soil-structure interaction have focused on static loading scenarios, and limited work has been conducted to address the design optimization under dynamic soil-structure interaction. In the present work, first, a platform is developed to perform optimization of steel frames under seismic loading considering dynamic soil-structure interaction (SSI) in order to quantify the effects of earthquake records on the optimum design. Next, verification of the adopted modeling technique is conducted using comparison of the results with the reference solution counterparts in frequency domain. For time history analyses, records from past events are selected and scaled to a target spectrum using simple scaling approach as well as spectrum matching technique. For sizing of the steel frames, a recently developed metaheuristic optimization algorithm, namely exponential big bang-big crunch optimization method, is employed. To alleviate the computational burden of the optimization process, the metaheuristic algorithm is integrated with the so-called upper bound strategy. Effects of factors such as the building height, presence of soil domain, and the utilized ground motion scaling technique are investigated and discussed. The numerical results obtained based on 5- and 10-story steel braced frame dual systems reveal that, although dynamic SSI reduced the seismic demands to some extent, given the final design pertains to different load combinations, the optimum weight difference is not considerable.Conference Object A Lithium-Ion Battery Fast Charging Algorithm Based on Electrochemical Model: Experimental Results(Amer Soc Mechanical Engineers, 2024) Anwar, Sohel; Pramanik, Sourav; Amini, AliLithium-Ion batteries have become the principal battery technology for EVs to date. However, one of the principal factors limiting the widespread usage of the EVs is the length of charging times for the lithium-ion battery packs. The appropriate charging algorithm is critical to shorten the battery charging times while keeping the battery safe. In our earlier work, we proposed a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model using A performance index that aimed at achieving a faster charging rate while maintaining safe limits for various battery parameters. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal charging algorithm as opposed to the conventional equivalent circuit models. Simulation results showed that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium-ion cell by as much as 30% when compared with the standard constant current charging. Here we present the results from a number of experiments using Lithium-Ion cylindrical cells that were charged using the proposed algorithm and compared the charging times with the standard constant current-constant voltage (CC-CV) charging algorithms. A Maccor Series 4300 battery testing system was used to carry out the experiments. The experimental results showed that the proposed algorithm offered shorter charging times by up to 16% when compared to the CC-CV charging algorithms under the same battery initial conditions such as SOC and temperature of the cells.Article Citation - WoS: 2A Generic, Multi-Period and Multi-Partner Cost Optimizing Model for Cloud-Based Supply Chain(Igi Global, 2016) Akyuz, Goknur Arzu; Rehan, MohammadCloud technology in a multi-enterprise, collaborative context is one of the most promising topics at IT-Supply Chain (SC) intersection. Cloud comes with well-proven advantages and cost savings; enabling collaboration and business intelligence. Cloud transition is still on-going with individual enterprise-level transitions. However, collaborative paradigm dictates that entire network well-being is to be considered over enterprise-level concerns. Thus, handling the transition of a single enterprise is not sufficient for strategic network leverage. Planning and managing across multiple enterprises is required, taking into consideration various cost items and budget constraints. In this study, a multi-period multi-partner cost-optimizing model is developed for network-level management of cloud transition for a SC. The model produces an optimal transition plan, indicating timing of the transition for each partner to obtain the maximum cost savings across network over a specified planning horizon. The model proposed is generic, flexible and customizable for different sectors/settings.Article Citation - WoS: 14Citation - Scopus: 15An Accurate Optical Gain Model Using Adaptive Neuro-Fuzzy Inference System(Natl inst Optoelectronics, 2009) Celebi, F. V.; Altindag, T.; Computer EngineeringThis paper presents a single, simple, new and an accurate optical gain model based on adaptive neuro-fuzzy inference system (ANFIS) which combines the benefits of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs). The dynamic optical gain model results are in very good agreement with the previously published experimental findings.Article Citation - WoS: 23Citation - Scopus: 29Analysis of Combined Cycle Efficiency by Simulation and Optimization(Pergamon-elsevier Science Ltd, 2017) Balku, SaziyeNatural gas has been regarded as the cleanest fuel when compared to the other fossil fuels because of its low emission of greenhouse gases and no particulate matter after combustion. Around 22% of the world's power production is based on natural gas. Combined gas-steam power plants operating with natural gas are preferred in recent years due to their high efficiency and less emission. To meet the world's increasing energy demand, natural gas will continue to be used in the future in increasing amounts. For this reason, it is very, important to design and operate such systems in optimal conditions. Energy conversion systems can be analyzed in terms of energetic, exergetic, economic, and environmental aspects for a good management. When the overall efficiency is increased, it can be said that these four aspects will also improve. In the present study, the modeling, simulation and optimization studies on the combined gas-steam power plants are performed. The most important parameters which influence the efficiency of such plants are determined. The simulation results indicate that the crucial unit is the combustion chamber. The optimization results show that the most effective parameters in the power production are air/fuel ratio, gas/steam ratio and the pressure ratio for the compressor and, thus, the gas turbine. The thermal efficiency of the plant increases by 22.55% and the exergy destroyed decreases by 22.65% using optimal design variables determined by the optimization algorithm in which the objective function is the thermal efficiency. The study demonstrates that the modeling, simulation and optimization can be used for the optimal design of the plants before invested, for operating the present plants at optimal conditions and for analyzing the systems. The minimum detrimental effect on the environment can be provided by optimal design and operation under optimal conditions. The originality of the study is to use an objective function by defining a new efficiency term for the maximum power production with the minimum exergy destruction which results 23.49% increase in the thermal efficiency and, in the meantime, 23.61% decrease in the exergy destruction. This new efficiency term can be used as an objective function in the solution of the optimization problems related with the efficiency of power generating in order to achieve better results. (C) 2017 Elsevier Ltd. All rights reserved.
