Search results for: optimization analysis

Authors: Ibrahim Cicek, Melike Nikbay

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Electrically propelled vehicles, either road or aerial vehicles are studied on contemporarily for their robust maneuvers and cost-efficient transport operations. The main power generating systems of such vehicles electrified by selecting proper components and assembled as e-powertrain. Generally, e-powertrain components selected considering the target performance requirements. Since the main component of propulsion is the drive unit, e-motor control system is subjected to achieve the performance targets. In this paper, the optimization of e-motor control parameters studied by Integral Squared Method (ISE). The overall aim is to minimize power consumption of such vehicles depending on mission profile and maintaining smooth maneuvers for passenger comfort. The sought-after values of control parameters are computed using the Optimal Control Theory. The system is modeled as a closed-loop linear control system with calibratable parameters.

Keywords: optimization, e-powertrain, optimal control, electric vehicles

Procedia PDF Downloads 108

Authors: Arkadiusz Mezyk, Wojciech Klein, Mariusz Pawlak, Jacek Gnilka

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The aortic heart valve stents should fulfill many criterions. These criteria have a strong impact on the geometrical shape of the stent. Usually, the final construction of stent is a result of many year experience and knowledge. Depending on patents claims, different stent shapes are produced by different companies. This causes difficulties for biomechanics engineers narrowing the domain of feasible solutions. The paper present optimization method for stent geometry defining by a specific analytical equation based on various mathematical functions. This formula was implemented as APDL script language in ANSYS finite element environment. For the purpose of simulation tests, a few parameters were separated from developed equation. The application of the genetic algorithms allows finding the best solution due to selected objective function. Obtained solution takes into account parameters such as radial force, compression ratio and coefficient of expansion on the transverse axial.

Keywords: aortic stent, optimization process, geometry, finite element method

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Authors: Mrinmoy Majumder, Apu Kumar Saha

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The ever-increasing human population and its demand for energy is placing stress upon conventional energy sources; and as demand for power continues to outstrip supply, the need to optimize energy distribution and utilization is emerging as an important focus for various stakeholders. The distribution of available energy must be achieved in such a way that the needs of the consumer are satisfied. However, if the availability of resources is not sufficient to satisfy consumer demand, it is necessary to find a method to select consumers based on factors such as their socio-economic or environmental impacts. Weighting consumer types in this way can help separate them based on their relative importance, and cognitive optimization of the allocation process can then be carried out so that, even on days of particularly scarce supply, the socio-economic impacts of not satisfying the needs of consumers can be minimized. In this context, the present study utilized fuzzy logic to assign weightage to different types of consumers based at an educational campus in India, and then established optimal allocation by applying the non-linear mapping capability of neuro-genetic algorithms. The outputs of the algorithms were compared with similar outputs from particle swarm optimization and differential evolution algorithms. The results of the study demonstrate an option for the optimal utilization of available energy based on the socio-economic importance of consumers.

Keywords: power allocation, optimization problem, neural networks, environmental and ecological engineering

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Authors: Sara A. Pakvis, Giulia Scalet, Stefania Marconi, Ferdinando Auricchio, Matthijs Langelaar

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In recent developments in the field of multi-material additive manufacturing, differences in material properties are exploited to create printed shape-memory structures, which are referred to as 4D-printed structures. New printing techniques allow for the deliberate introduction of prestresses in the specimen during manufacturing, and, in combination with the right design, this enables new functionalities. This research focuses on bi-polymer 4D-printed structures, where the transformation process is based on a heat-induced glass transition in one material lowering its Young’s modulus, combined with an initial prestress in the other material. Upon the decrease in stiffness, the prestress is released, which results in the realization of an essentially pre-programmed deformation. As the design of such functional multi-material structures is crucial but far from trivial, a systematic methodology to find the design of 4D-printed structures is developed, where a finite element model is combined with a density-based topology optimization method to describe the material layout. This modeling approach is verified by a convergence analysis and validated by comparing its numerical results to analytical and published data. Specific aspects that are addressed include the interplay between the definition of the prestress and the material interpolation function used in the density-based topology description, the inclusion of a temperature-dependent stiffness relationship to simulate the glass transition effect, and the importance of the consideration of geometric nonlinearity in the finite element modeling. The efficacy of topology optimization to design 4D-printed structures is explored by applying the methodology to a variety of design problems, both in 2D and 3D settings. Bi-layer designs composed of thermoplastic polymers are printed by means of the fused deposition modeling (FDM) technology. Acrylonitrile butadiene styrene (ABS) polymer undergoes the glass transition transformation, while polyurethane (TPU) polymer is prestressed by means of the 3D-printing process itself. Tests inducing shape transformation in the printed samples through heating are performed to calibrate the prestress and validate the modeling approach by comparing the numerical results to the experimental findings. Using the experimentally obtained prestress values, more complex designs have been generated through topology optimization, and samples have been printed and tested to evaluate their performance. This study demonstrates that by combining topology optimization and 4D-printing concepts, stimuli-responsive structures with specific properties can be designed and realized.

Keywords: 4D-printing, glass transition, shape memory polymer, topology optimization

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Authors: Mamidi Ramakrishna Rao

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Doubly-fed induction generators (DFIG) due to their advantages like speed variation and four-quadrant operation, find its application in wind turbines. DFIG besides supplying power to the grid has to support reactive power (kvar) under grid voltage variations, should contribute minimum fault current during faults, have high efficiency, minimum weight, adequate rotor protection during crow-bar-operation from +20% to -20% of rated speed.  To achieve the optimum performance, a good electromagnetic design of DFIG is required. In this paper, a simple and heuristic global optimization – Differential Evolution has been used. Variables considered are lamination details such as slot dimensions, stack diameters, air gap length, and generator stator and rotor stack length. Two operating conditions have been considered - voltage and speed variations. Constraints included were reactive power supplied to the grid and limiting fault current and torque. The optimization has been executed separately for three objective functions - maximum efficiency, weight reduction, and grid fault stator currents. Subsequent calculations led to the conclusion that designs determined through differential evolution help in determining an optimum electrical design for each objective function.

Keywords: design optimization, performance, DFIG, differential evolution

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Hassan Hajabdollahi

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Economic optimization of shell and tube heat exchanger (STHE) is presented in this paper. To increase the rate of heat transfer, copper oxide (CuO) nanoparticle is added into the tube side fluid and their optimum results are compared with the case of without additive nanoparticle. Total annual cost (TAC) is selected as fitness function and nine decision variables related to the heat exchanger parameters as well as concentration of nanoparticle are considered. Optimization results reveal the noticeable improvement in the TAC and in the case of heat exchanger working with nanofluid compared with the case of base fluid (8.9%). Comparison of the results between two studied cases also reveal that the lower tube diameter, tube number, and baffle spacing are needed in the case of heat exchanger working with nanofluid compared with the case of base fluid.

Keywords: shell and tube heat exchanger, nanoparticles additive, total annual cost, particle volumetric concentration

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Authors: Tia Kristian Tajnšek, Matjaž Mazaj, Nataša Zabukovec Logar

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Metal-organic frameworks (MOFs) with their specific properties and the possibility of tuning the structure represent excellent candidates for use in the biomedical field. Their advantage lies in large pore surfaces and volumes, as well as the possibility of using bio-friendly or bioactive constituents. So-called bioMOFs are representatives of MOFs, which are constructed from at least one biomolecule (metal, a small bioactive molecule in metal clusters and/or linker) and are intended for bio-application (usually in the field of medicine; most commonly drug delivery). When designing a bioMOF for biomedical applications, we should adhere to some guidelines for an improved toxicological profile of the material. Such as (i) choosing an endogenous/nontoxic metal, (ii) GRAS (generally recognized as safe) linker, and (iii) nontoxic solvents. Design and synthesis of bioNICS-1 (bioMOF of National Institute of Chemistry Slovenia – 1) consider all these guidelines. Zinc (Zn) was chosen as an endogenous metal with an agreeable recommended daily intake (RDI) and LD50 value, and ascorbic acid (Vitamin C) was chosen as a GRAS and active linker. With these building blocks, we have synthesized a bioNICS-1 material. The synthesis was done in ethanol using a solvothermal method. The synthesis protocol was further optimized in three separate ways. Optimization of (i) synthesis parameters to improve the yield of the synthesis, (ii) input reactant ratio and addition of specific modulators for production of larger crystals, and (iii) differing of the heating source (conventional, microwave and ultrasound) to produce nano-crystals. With optimization strategies, the synthesis yield was increased. Larger crystals were prepared for structural analysis with the use of a proper species and amount of modulator. Synthesis protocol was adjusted to different heating sources, resulting in the production of nano-crystals of bioNICS-1 material. BioNICS-1 was further activated in ethanol and structurally characterized, resolving the crystal structure of new material.

Keywords: ascorbic acid, bioMOF, MOF, optimization, synthesis, zinc ascorbate

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Authors: H. Hentati, R. Abdelmoula, Li Jia, A. Maalej

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In this work, we present numerical simulations of the quasi-static crack propagation based on the variation approach. We perform numerical simulations of a piece of brittle material without initial crack. An alternate minimization algorithm is used. Based on these numerical results, we determine the influence of numerical parameters on the location of crack. We show the importance of trying to optimize the time of numerical computation and we present the first attempt to develop a simple numerical method to optimize this time.

Keywords: fracture mechanics, optimization, variation approach, mechanic

Procedia PDF Downloads 588

Authors: Imen Boudali, Marwa Ragmoun

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The Vehicle Routing Problem with Hard Time Windows (VRPHTW) is a basic distribution management problem that models many real-world problems. The objective of the problem is to deliver a set of customers with known demands on minimum-cost vehicle routes while satisfying vehicle capacity and hard time windows for customers. In this paper, we propose to deal with our optimization problem by using a new hybrid stochastic algorithm based on two metaheuristics: Chemical Reaction Optimization (CRO) and Greedy Randomized Adaptive Search Procedure (GRASP). The first method is inspired by the natural process of chemical reactions enabling the transformation of unstable substances with excessive energy to stable ones. During this process, the molecules interact with each other through a series of elementary reactions to reach minimum energy for their existence. This property is embedded in CRO to solve the VRPHTW. In order to enhance the population diversity throughout the search process, we integrated the GRASP in our method. Simulation results on the base of Solomon’s benchmark instances show the very satisfactory performances of the proposed approach.

Keywords: Benchmark Problems, Combinatorial Optimization, Vehicle Routing Problem with Hard Time Windows, Meta-heuristics, Hybridization, GRASP, CRO

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Authors: M. A. Grigoryev, A. N. Shishkov, D. A. Sychev

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The article defines the necessity of choosing the optimal power circuits scheme of the electric drive with field regulated reluctance machine. The specific weighting factors are calculation, the linear regression dependence of specific losses in semiconductor frequency converters are presented depending on the values of the rated current. It is revealed that with increase of the carrier frequency PWM improves the output current waveform, but increases the loss, so you will need depending on the task in a certain way to choose from the carrier frequency. For task of optimization by criterion of the minimum electrical losses regression dependence of the electrical losses in the frequency converter circuit at a frequency of a PWM signal of 0 Hz. The surface optimization criterion is presented depending on the rated output torque of the motor and number of phases. In electric drives with field regulated reluctance machine with at low output power optimization criterion appears to be the worst for multiphase circuits. With increasing output power this trend hold true, but becomes insignificantly different optimal solutions for three-phase and multiphase circuits. This is explained to the linearity of the dependence of the electrical losses from the current.

Keywords: field regulated reluctance machine, the electrical losses, multiphase power circuit, the surface optimization criterion

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Authors: Yue-Tzu Yang, Shu-Ching Liao

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This study presents the numerical simulation of three-dimensional incompressible steady and laminar fluid flow and conjugate heat transfer of a trapezoidal microchannel heat sink using water as a cooling fluid in a silicon substrate. Navier-Stokes equations with conjugate energy equation are discretized by finite-volume method. We perform numerical computations for a range of 50 ≦ Re ≦ 600, 0.05W ≦ P ≦ 0.8W, 20W/cm2 ≦ ≦ 40W/cm2. The present study demonstrates the numerical optimization of a trapezoidal microchannel heat sink design using the response surface methodology (RSM) and the genetic algorithm method (GA). The results show that the average Nusselt number increases with an increase in the Reynolds number or pumping power, and the thermal resistance decreases as the pumping power increases. The thermal resistance of a trapezoidal microchannel is minimized for a constant heat flux and constant pumping power.

Keywords: microchannel heat sinks, conjugate heat transfer, optimization, genetic algorithm method

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Authors: Mehrdad Azimzadeh, Seyed Mohammadreza Jabbari, Mohammadreza Hosseinzadeh Alherd

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This paper includes experimental results and analytical studies about increasing resistance of single-span reinforced concreted frames against impact factor and their modeling according to optimization methods and optimizing the behavior of these frames under impact loads. During this study, about 30 designs for different frames were modeled and made using specialized software like ANSYS and Sap and their behavior were examined under variable impacts. Then suitable strategies were offered for frames in terms of concrete mixing in order to optimize frame modeling. To reduce the weight of the frames, we had to use fine-grained stones. After designing about eight types of frames for each type of frames, three samples were designed with the aim of controlling the impact strength parameters, and a good shape of the frame was created for the impact resistance, which was a solid frame with muscular legs, and as a bond away from each other as much as possible with a 3 degree gradient in the upper part of the beam.

Keywords: optimization, reinforced concrete, optimization methods, impact load, earthquake

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Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

Procedia PDF Downloads 197

Authors: Zilaila Zakaria, Muhd Azri Abdul Razak, Muhammad Murtadha Othman, Mohd Ainor Yahya, Ismail Musirin, Mat Nasir Kari, Mohd Fazli Osman, Mohd Zaini Hassan, Baihaki Azraee

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Energy efficiency can be realized by minimizing the power loss with a sufficient amount of energy used in an electrical distribution system. In this report, a detailed analysis of the energy efficiency of an electric distribution system was carried out with an implementation of the optimal capacitor placement and sizing (OCPS). The particle swarm optimization (PSO) will be used to determine optimal location and sizing for the capacitors whereas energy consumption and power losses minimization will improve the energy efficiency. In addition, a certain number of busbars or locations are identified in advance before the PSO is performed to solve OCPS. In this case study, three techniques are performed for the pre-selection of busbar or locations which are the power-loss-index (PLI). The particle swarm optimization (PSO) is designed to provide a new population with improved sizing and location of capacitors. The total cost of power losses, energy consumption and capacitor installation are the components considered in the objective and fitness functions of the proposed optimization technique. Voltage magnitude limit, total harmonic distortion (THD) limit, power factor limit and capacitor size limit are the parameters considered as the constraints for the proposed of optimization technique. In this research, the proposed methodologies implemented in the MATLAB® software will transfer the information, execute the three-phase unbalanced load flow solution and retrieve then collect the results or data from the three-phase unbalanced electrical distribution systems modeled in the SIMULINK® software. Effectiveness of the proposed methods used to improve the energy efficiency has been verified through several case studies and the results are obtained from the test systems of IEEE 13-bus unbalanced electrical distribution system and also the practical electrical distribution system model of Sultan Salahuddin Abdul Aziz Shah (SSAAS) government building in Shah Alam, Selangor.

Keywords: particle swarm optimization, pre-determine of capacitor locations, optimal capacitors placement and sizing, unbalanced electrical distribution system

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Authors: K. Kusakana

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A hybrid energy system is a combination of renewable energy sources with back up, as well as a storage system used to respond to given load energy requirements. Given that the electrical output of each renewable source is fluctuating with changes in weather conditions, and since the load demand also varies with time; one of the main attributes of hybrid systems is to be able to respond to the load demand at any time by optimally controlling each energy source, storage and back-up system. The induced optimization problem is to compute the optimal operation control of the system with the aim of minimizing operation costs while efficiently and reliably responding to the load energy requirement. Current optimization research and development on hybrid systems are mainly focusing on the sizing aspect. Thus, the aim of this paper is to report on the state-of-the-art of optimal operation control of hybrid renewable energy systems. This paper also discusses different challenges encountered, as well as future developments that can help in improving the optimal operation control of hybrid renewable energy systems.

Keywords: renewable energies, hybrid systems, optimization, operation control

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Authors: Elena Vinogradova, Aleksei Pleshakov, Aleksei Yakovlev

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During the flight of a supersonic aircraft under various conditions (altitude, Mach, etc.), it becomes necessary to coordinate the operating modes of the air intake and engine. On the supersonic aircraft, it’s been done by changing various control factors (the angle of rotation of the wedge panels and etc.). This paper investigates the possibility of using modern optimization methods to determine the optimal position of the supersonic air intake wedge panels in order to maximize the total pressure recovery coefficient. Modern software allows us to conduct auto-optimization, which determines the optimal position of the control elements of the investigated product to achieve its maximum efficiency. In this work, the flow in the supersonic aircraft inlet has investigated and optimized the operation of the flaps of the supersonic inlet in an aircraft in a 2-D setting. This work has done using ANSYS CFX software. The supersonic aircraft inlet is a flat adjustable external compression inlet. The braking surface is made in the form of a three-stage wedge. The IOSO NM software package was chosen for optimization. Change in the position of the panels of the input device is carried out by changing the angle between the first and second steps of the three-stage wedge. The position of the rest of the panels is changed automatically. Within the framework of the presented work, the position of the moving air intake panel was optimized under fixed flight conditions of the aircraft under a certain engine operating mode. As a result of the numerical modeling, the distribution of total pressure losses was obtained for various cases of the engine operation, depending on the incoming flow velocity and the flight altitude of the aircraft. The results make it possible to obtain the maximum total pressure recovery coefficient under given conditions. Also, the initial geometry was set with a certain angle between the first and second wedge panels. Having performed all the calculations, as well as the subsequent optimization of the aircraft input device, it can be concluded that the initial angle was set sufficiently close to the optimal angle.

Keywords: optimal angle, optimization, supersonic air intake, total pressure recovery coefficient

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Authors: M. Suresh Kumar, K. Ramani

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This paper proposed the harmonic elimination of Cascaded H-Bridge Multi-Level Inverter by using Selective Harmonic Elimination-Pulse Width Modulation method programmed with Particle Swarm Optimization algorithm. PSO method determine proficiently the required switching angles to eliminate low order harmonics up to the 11th order from the inverter output voltage waveform while keeping the magnitude of the fundamental harmonics at the desired value. Results demonstrate that the proposed method does efficiently eliminate a great number of specific harmonics and the output voltage is resulted in minimum Total Harmonic Distortion. The results shown that the PSO algorithm attain successfully to the global solution faster than other algorithms.

Keywords: multi-level inverter, Selective Harmonic Elimination Pulse Width Modulation (SHEPWM), Particle Swarm Optimization (PSO), Total Harmonic Distortion (THD)

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Authors: Yue-Tzu Yang, Peng-Jen Chen

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The present study deals with the numerical optimization of wavy channel with the help of genetic algorithm (GA). Three design variables related to the wave amplitude (A), the wavelength (λ) and the channel aspect ratio (α) are chosen and their ranges are decided through preliminary calculations of three-dimensional Navier-stokes and energy equations. A parametric study is also performed to show the effects of different design variables on the overall performance of the wavy channel. Objective functions related to the heat transfer and pressure drop, performance factor (PF) is formulated to analyze the performance of the wavy channel. The numerical results show that the wave amplitude and the channel aspect ratio have significant effects on the thermal performance. It can improve the performance of the wavy channels by increasing wave amplitude or decreasing the channel aspect ratio. Increasing wavelengths have no significant effects on the heat transfer performance.

Keywords: wavy channel, genetic algorithm, optimization, numerical simulation

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Authors: Choupani Andisheh, Temucin Elif Sevval, Bediz Bekir

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Due to their various advantages compared to many other drug delivery systems such as hypodermic injections and oral medications, microneedle arrays (MNAs) are a promising drug delivery system. To achieve enhanced performance of the MN, it is crucial to develop numerical models, optimization methods, and simulations. Accordingly, in this work, the optimized design of dissolvable MNAs, as well as their manufacturing, is investigated. For this purpose, a mechanical model of a single MN, having the geometry of an obelisk, is developed using commercial finite element software. The model considers the condition in which the MN is under pressure at the tip caused by the reaction force when penetrating the skin. Then, a multi-objective optimization based on non-dominated sorting genetic algorithm II (NSGA-II) is performed to obtain geometrical properties such as needle width, tip (apex) angle, and base fillet radius. The objective of the optimization study is to reach a painless and effortless penetration into the skin along with minimizing its mechanical failures caused by the maximum stress occurring throughout the structure. Based on the obtained optimal design parameters, master (male) molds are then fabricated from PMMA using a mechanical micromachining process. This fabrication method is selected mainly due to the geometry capability, production speed, production cost, and the variety of materials that can be used. Then to remove any chip residues, the master molds are cleaned using ultrasonic cleaning. These fabricated master molds can then be used repeatedly to fabricate Polydimethylsiloxane (PDMS) production (female) molds through a micro-molding approach. Finally, Polyvinylpyrrolidone (PVP) as a dissolvable polymer is cast into the production molds under vacuum to produce the dissolvable MNAs. This fabrication methodology can also be used to fabricate MNAs that include bioactive cargo. To characterize and demonstrate the performance of the fabricated needles, (i) scanning electron microscope images are taken to show the accuracy of the fabricated geometries, and (ii) in-vitro piercing tests are performed on artificial skin. It is shown that optimized MN geometries can be precisely fabricated using the presented fabrication methodology and the fabricated MNAs effectively pierce the skin without failure.

Keywords: microneedle, microneedle array fabrication, micro-manufacturing structural optimization, finite element analysis

Procedia PDF Downloads 100

Authors: Georges Ghazi, Marc-Henry Devillers, Ruxandra M. Botez

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Aircraft flight trajectory optimization has been identified to be a promising solution for reducing both airline costs and the aviation net carbon footprint. Nowadays, this role has been mainly attributed to the flight management system. This system is an onboard multi-purpose computer responsible for providing the crew members with the optimized flight plan from a destination to the next. To accomplish this function, the flight management system uses a variety of look-up tables to compute the optimal speed and altitude for each flight regime instantly. Because the cruise is the longest segment of a typical flight, the proposed algorithm is focused on minimizing fuel consumption for this flight phase. In this paper, a complete methodology to estimate the aircraft performance and subsequently compute the optimal speed in cruise is presented. Results showed that the obtained performance database was accurate enough to predict the flight costs associated with the cruise phase.

Keywords: Cessna Citation X, cruise speed optimization, flight cost, cost index, and golden section search

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Authors: Shahram Naghizadeh Raeisi, Ali Alghooneh

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The application of whey protein in drink industry to enhance the nutritional value of the products is important. Furthermore, the gelification of protein during thermal treatment and shelf life makes some limitations in its application. So, the main goal of this research is manufacturing of high concentrate whey protein orange drink with appropriate shelf life. In this way, whey protein was 5 to 30% hydrolyzed ( in 5 percent intervals at six stages), then thermal stability of samples with 10% concentration of protein was tested in acidic condition (T= 90 °C, pH=4.2, 5 minutes ) and neutral condition (T=120° C, pH:6.7, 20 minutes.) Furthermore, to study the shelf life of heat treated samples in 4 months at 4 and 24 °C, the time sweep rheological test were done. At neutral conditions, 5 to 20% hydrolyzed sample showed gelling during thermal treatment, whereas at acidic condition, was happened only in 5 to 10 percent hydrolyzed samples. This phenomenon could be related to the difference in hydrodynamic radius and zeta potential of samples with different level of hydrolyzation at acidic and neutral conditions. To study the gelification of heat resistant protein solutions during shelf life, for 4 months with 7 days intervals, the time sweep analysis were performed. Cross over was observed for all heat resistant neutral samples at both storage temperature, while in heat resistant acidic samples with degree of hydrolysis, 25 and 30 percentage at 4 and 20 °C, it was not seen. It could be concluded that the former sample was stable during heat treatment and 4 months storage, which made them a good choice for manufacturing high protein drinks. The Scheffe polynomial model and numerical optimization were employed for modeling and high protein orange drink formula optimization. Scheffe model significantly predicted the overal acceptance index (Pvalue<0.05) of sensorial analysis. The coefficient of determination (R2) of 0.94, the adjusted coefficient of determination (R2Adj) of 0.90, insignificance of the lack-of-fit test and F value of 64.21 showed the accuracy of the model. Moreover, the coefficient of variable (C.V) was 6.8% which suggested the replicability of the experimental data. The desirability function had been achieved to be 0.89, which indicates the high accuracy of optimization. The optimum formulation was found as following: Modified whey protein solution (65.30%), natural orange juice (33.50%), stevia sweetener (0.05%), orange peel oil (0.15%) and citric acid (1 %), respectively. Its worth mentioning that this study made an appropriate model for application of whey protein in drink industry without bitter flavor and gelification during heat treatment and shelf life.

Keywords: croos over, orange beverage, protein modification, optimization

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Authors: Salem El-Tohami Ashoor

Abstract:

Here we show that the reduction of [Cr(ArN(CH2)3NAr)2Cl2] (1) where (Ar = 2,6-Pri2C6H3) and in presence of NaCp (2) (Cp= C5H5 = cyclopentadien), with a center coordination η5 interaction between Cp as co-ligand and chromium metal center, this was optimization by using density functional theory (DFT) and then was comparing with experimental data, also other possibility of Cp interacted with ion metal were tested like η1 ,η2 ,η3 and η4 under optimization system. These were carried out under investigation of density functional theory (DFT) calculation, and comparing together. Other methods, explicitly including electron correlation, are necessary for more accurate calculations; MB3LYP ( Becke)( Lee–Yang–Parr ) level of theory often being used to obtain more exact results. These complexes were estimated of electronic energy for molecular system, because it accounts for all electron correlation interactions. The optimised of [Cr(ArN(CH2)3NAr)2(η5-Cp)] (Ar = 2,6-Pri2C6H3 and Cp= C5H5) was found to be thermally more stable than others of chromium cyclopentadienyl. By using Dewar-Chatt-Duncanson model, as a basis of the molecular orbital (MO) analysis and showed the highest occupied molecular orbital (HOMO) and lowest occupied molecular orbital LUMO.

Keywords: Chromium(III) cyclopentadienyl complexes, DFT, MO, HOMO, LUMO

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Authors: A. A. Okafor, C. H. Achebe, J. L. Chukwuneke, C. G. Ozoegwu

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The performance of an engine whose basic design parameters are known can be predicted with the assistance of simulation programs into the less time, cost and near value of actual. This paper presents a comprehensive mathematical model of the performance parameters of four stroke spark ignition engine. The essence of this research work is to develop a mathematical model for the analysis of engine performance parameters of four stroke spark ignition engine before embarking on full scale construction, this will ensure that only optimal parameters are in the design and development of an engine and also allow to check and develop the design of the engine and it’s operation alternatives in an inexpensive way and less time, instead of using experimental method which requires costly research test beds. To achieve this, equations were derived which describe the performance parameters (sfc, thermal efficiency, mep and A/F). The equations were used to simulate and optimize the engine performance of the model for various engine speeds. The optimal values obtained for the developed bivariate mathematical models are: sfc is 0.2833kg/kwh, efficiency is 28.77% and a/f is 20.75.

Keywords: bivariate models, engine performance, injector engine, optimization, performance parameters, simulation, spark ignition

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Authors: K. Tahir, C. Belfedal, T. Allaoui, C. Gerard, M. Doumi

Abstract:

In this paper, we presented an optimized RST controller using Particle Swarm Optimization (PSO) meta-heuristic technique of the active and reactive power regulation of a grid connected wind turbine based on a wound field synchronous generator. This regulation is achieved below the synchronous speed, by means of a maximum power point tracking algorithm. The control of our system is tested under typical wind variations and parameters variation, fault grid condition by simulation. Some results are presented and discussed to prove simplicity and efficiency of the WRSG control for WECS. On the other hand, according to simulation results, variable speed driven WRSG is not significantly impacted in fault conditions.

Keywords: wind energy, particle swarm optimization, wound rotor synchronous generator, power control, RST controller, maximum power point tracking

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Authors: Nikhil Kumar, Asish Bandyopadhyay

Abstract:

Laser welding process is a capable technology for forming the automobile, microelectronics, marine and aerospace parts etc. In the present work, a mathematical and statistical approach is adopted to study the laser welding of AISI 304 stainless steel. A robotic control 500 W pulsed Nd:YAG laser source with 1064 nm wavelength has been used for welding purpose. Butt joints are made. The effects of welding parameters, namely; laser power, scanning speed and pulse width on the seam width and depth of penetration has been investigated using the empirical models developed by response surface methodology (RSM). Weld quality is directly correlated with the weld geometry. Twenty sets of experiments have been conducted as per central composite design (CCD) design matrix. The second order mathematical model has been developed for predicting the desired responses. The results of ANOVA indicate that the laser power has the most significant effect on responses. Microstructural analysis as well as hardness of the selected weld specimens has been carried out to understand the metallurgical and mechanical behaviour of the weld. Average micro-hardness of the weld is observed to be higher than the base metal. Higher hardness of the weld is the resultant of grain refinement and δ-ferrite formation in the weld structure. The result suggests that the lower line energy generally produce fine grain structure and improved mechanical properties than the high line energy. The combined effects of input parameters on responses have been analyzed with the help of developed 3-D response surface and contour plots. Finally, multi-objective optimization has been conducted for producing weld joint with complete penetration, minimum seam width and acceptable welding profile. Confirmatory tests have been conducted at optimum parametric conditions to validate the applied optimization technique.

Keywords: ANOVA, laser welding, modeling and optimization, response surface methodology

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Authors: Abdolsalam Ghaderi

Abstract:

In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed.

Keywords: location-routing problem, robust optimization, stochastic programming, variable neighborhood search

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Authors: Sachin Aggarwal, Sarah Kassinger, Nicholas Hoffman

Abstract:

Geometry simplification is a key step in performing conjugate heat transfer analysis using CFD. This paper proposes a standard methodology for the geometry simplification of rotating machines, such as electrical generators and electrical motors (both air and liquid-cooled). These machines are extensively deployed throughout the aerospace and automotive industries, where optimization of weight, volume, and performance is paramount -especially given the current global transition to renewable energy sources and vehicle hybridization and electrification. Conjugate heat transfer analysis is an essential step in optimizing their complex design. This methodology will help in reducing convergence issues due to poor mesh quality, thus decreasing computational cost and overall analysis time.

Keywords: CFD, electrical machines, Geometry simplification, heat transfer

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Authors: Alper Akin, Ibrahim Aydogdu

Abstract:

This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teaching-learning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame

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