Search results for: optimization techniques

Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

Abstract:

This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

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Authors: Sourabh Joshi, Geetinder Kaur, Sarabjit Kaur, Gulwatanpreet Singh, Geetika Mannan

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In this paper, we have use an algorithm that able to obtain an optimal solution to travelling salesman problem from a huge search space, quickly. This algorithm is based upon the ant colony optimization technique and employees roulette wheel selection mechanism. To illustrate it more clearly, a program has been implemented which is based upon this algorithm, that presents the changing process of route iteration in a more intuitive way. In the event, we had find the optimal path between hundred cities and also calculate the distance between two cities.

Keywords: ant colony, optimization, travelling salesman problem, roulette wheel selection

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Authors: Mohamed Abdelwahed

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We propose an optimization algorithm for the geometric control of fluid flow. The used approach is based on the topological sensitivity analysis method. It consists in studying the variation of a cost function with respect to the insertion of a small obstacle in the domain. Some theoretical and numerical results are presented in 2D and 3D.

Keywords: sensitivity analysis, topological gradient, shape optimization, stokes equations

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Authors: Yue Hu, Xilu Zhao, Takao Yamaguchi, Manabu Sasajima, Yoshio Koike, Akira Hara

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This study tried optimization of design parameter of a cone loudspeaker unit as an example of the high flexibility of the products design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to each design the parameter of the loudspeaker. To overcome the limitation of the design problem in practice, this paper proposes a new an acoustic analysis algorithm to optimize design the parameter of the loudspeaker. The material character of cone paper and the loudspeaker edge was the design parameter, and the vibration displacement of the cone paper was the objective function. The results of the analysis were compared with the predicted value. They had high accuracy to the predicted value. These results suggest that, though the parameter design is difficult by experience and intuition, it can be performed comparatively easily using the optimization design by the developed acoustic analysis software.

Keywords: air viscosity, loudspeaker, cone paper, edge, optimization

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Authors: Saqib Ali

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Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

Keywords: distributed generation, demand-side management, hybrid power system, micro grid, renewable energy resources, supply-side management

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Authors: Thomas Weber, Nina Strobel, Thomas Kohne, Eberhard Abele

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In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality.

Keywords: CHP, Energy 4.0, energy storage, MILP, optimization, virtual power plant

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Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

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Keywords: structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames

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Authors: C. G. Jinimole, A. Harsha

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One of the key problems facing in the analysis of Computed Tomography (CT) images is the poor contrast of the images. Image enhancement can be used to improve the visual clarity and quality of the images or to provide a better transformation representation for further processing. Contrast enhancement of images is one of the acceptable methods used for image enhancement in various applications in the medical field. This will be helpful to visualize and extract details of brain infarctions, tumors, and cancers from the CT image. This paper presents a comparison study of five contrast enhancement techniques suitable for the contrast enhancement of CT images. The types of techniques include Power Law Transformation, Logarithmic Transformation, Histogram Equalization, Contrast Stretching, and Laplacian Transformation. All these techniques are compared with each other to find out which enhancement provides better contrast of CT image. For the comparison of the techniques, the parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used. Logarithmic Transformation provided the clearer and best quality image compared to all other techniques studied and has got the highest value of PSNR. Comparison concludes with better approach for its future research especially for mapping abnormalities from CT images resulting from Brain Injuries.

Keywords: computed tomography, enhancement techniques, increasing contrast, PSNR and MSE

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Authors: S. Wakim, M. Nemer, B. Zeghondy, B. Ghannam, C. Bouallou

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Designing the optimal heat exchanger is still an essential objective to be achieved. Parametrical optimization involves the evaluation of the heat exchanger dimensions to find those that best satisfy certain objectives. This method contributes to an enhanced design rather than an optimized one. On the contrary, topology optimization finds the optimal structure that satisfies the design objectives. The huge development in metal additive manufacturing allowed topology optimization to find its way into engineering applications especially in the aerospace field to optimize metal structures. Using topology optimization in 3d heat and mass transfer problems requires huge computational time, therefore coupling it with CFD simulations can reduce this it. However, existed CFD models cannot be coupled with topology optimization. The CFD model must allow creating a uniform mesh despite the initial geometry complexity and also to swap the cells from fluid to solid and vice versa. In this paper, a porous media approach compatible with topology optimization criteria is developed. It consists of modeling the fluid region of the heat exchanger as porous media having high porosity and similarly the solid region is modeled as porous media having low porosity. The switching from fluid to solid cells required by topology optimization is simply done by changing each cell porosity using a user defined function. This model is tested on a plate and fin heat exchanger and validated by comparing its results to experimental data and simulations results. Furthermore, this model is used to perform a material reallocation based on local criteria to optimize a plate and fin heat exchanger under a constant heat duty constraint. The optimized fin uses 20% fewer materials than the first while the pressure drop is reduced by about 13%.

Keywords: computational methods, finite element method, heat exchanger, porous media, topology optimization

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Authors: T. Gomes, J. Manzi

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The inclusion of stirring systems in the calculation and optimization procedures has been undergone a significant lack of attention, what it can reflect in the results because such systems provide an additional energy to the process, besides promote a better distribution of mass and energy. This is meaningful for the reactive systems, particularly for the Continuous Stirred Tank Reactor (CSTR), for which the key variables and parameters, as well as the operating conditions of stirring systems, can play a pivotal role and it has been showed in the literature that neglect these factors can lead to sub-optimal results. It is also well known that the sole use of the First Law of Thermodynamics as an optimization tool cannot yield satisfactory results, since the joint use of the First and Second Laws condensed into a procedure so-called entropy generation minimization (EGM) has shown itself able to drive the system towards better results. Therefore, the main objective of this paper is to determine the effects of key parameters of the stirring system in the optimization procedures by means of EGM applied to the reactive systems. Such considerations have been possible by dimensional analysis according to Rayleigh and Buckingham's method, which takes into account the physical and geometric parameters and the variables of the reactive system. For the simulation purpose based on the production of propylene glycol, the results have shown a significant increase in the conversion rate from 36% (not-optimized system) to 95% (optimized system) with a consequent reduction of by-products. In addition, it has been possible to establish the influence of the work of the stirrer in the optimization procedure, in which can be described as a function of the fluid viscosity and consequently of the temperature. The conclusions to be drawn also indicate that the use of the entropic analysis as optimization tool has been proved to be simple, easy to apply and requiring low computational effort.

Keywords: stirring systems, entropy, reactive system, optimization

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Authors: Suleiman Aliyu

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Integrating IoT applications with Fog systems presents challenges in optimization due to diverse environments and conflicting objectives. This study explores achieving Pareto optimal deployments for Fog-based IoT systems to address growing QoS demands. We introduce Pareto optimality to balance competing performance metrics. Using the FogTorch optimization framework, we propose a hybrid approach (Backtracking search with branch and bound) for scalable IoT deployments. Our research highlights the advantages of Pareto optimality over single-objective methods and emphasizes the role of FogTorch in this context. Initial results show improvements in IoT deployment cost in Fog systems, promoting resource-efficient strategies.

Keywords: pareto optimality, fog application deployment, resource allocation, internet of things

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Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

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In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: parameterization, response surface, structure optimization, pressure hull

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Authors: Aouaouche Elmaouhab, Hassina Beggar

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Solving multi-objective discrete optimization applications has always been limited by the resources of one machine: By computing power or by memory, most often both. To speed up the calculations, the grid computing represents a primary solution for the treatment of these applications through the parallelization of these resolution methods. In this work, we are interested in the study of some methods for solving multiple objective integer linear programming problem based on Branch-and-Bound and the study of grid computing technology. This study allowed us to propose an implementation of the method of Abbas and Al on the grid by reducing the execution time. To enhance our contribution, the main results are presented.

Keywords: multi-objective optimization, integer linear programming, grid computing, parallel computing

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Authors: Bale Baidi Blaise, Gilles Marckmann, Liman Kaoye, Talaka Dya, Moustapha Bachirou, Gambo Betchewe, Tibi Beda

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This work highlights the capabilities of particles swarm optimization (PSO) method to identify parameters of hyperelastic models. The study compares this method with Genetic Algorithm (GA) method, Least Squares (LS) method, Pattern Search Algorithm (PSA) method, Beda-Chevalier (BC) method and the Levenberg-Marquardt (LM) method. Four classic hyperelastic models are used to test the different methods through parameters identification. Then, the study compares the ability of these models to reproduce experimental Treloar data in simple tension, biaxial tension and pure shear.

Keywords: particle swarm optimization, identification, hyperelastic, model

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Authors: E. Behmanesh, J. Pannek

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The distribution-allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution-allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near-optimal solutions particularly for large scales test problems. This paper, presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a novelty in population presentation. To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as a comparison basis for small size problems. In large size cases that we are dealing with in the real world, the Genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

Keywords: integrated logistics network, flexible path, memetic algorithm, genetic algorithm

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Authors: Chee Sun Won

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This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods.

Keywords: image compression, image matching, key-point detection and description, machine-to-machine communication

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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: Muhammad Zaman, Guohua Fang, Muhammad Saifullah,

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The presented study emphasize at an optimal model to compare past and future optimal hydropower generation. In order to get maximum benefits from the Xin’anjiang hydropower station a model is developed. A Particle Swarm Optimization (PSO) has purposed and past and future water flow is used to get the maximum benefits from future water resources in this study. The results revealed that the future hydropower generation is more than the past generation. This paper gives us idea that what could we get in the past using optimal method of electricity generation and what can we get in the future using this technique.

Keywords: PSO, future water resources, optimization, Xin’anjiang,

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Authors: Mandeep Kumar, Hari Singh

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The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents the optimization aspects of a Wire Electrical Discharge Machining operation using Inconel X-750 as work material. The objective considered in this study is minimization of the dimensional deviation. Six input process parameters of WEDM namely spark gap voltage, pulse-on time, pulse-off time, wire feed rate, peak current and wire tension, were chosen as variables to study the process performance. Taguchi's design of experiments methodology has been used for planning and designing the experiments. The analysis of variance was carried out for raw data as well as for signal to noise ratio. Four input parameters and one two-factor interaction have been found to be statistically significant for their effects on the response of interest. The confirmation experiments were also performed for validating the predicted results.

Keywords: ANOVA, DOE, inconel, machining, optimization

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Authors: Yehya Abdellatif, Iyad M. Muslih, Azzah Alkhalailah, Abdallah Muslih

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Hybrid Optimization Model for Electric Renewable (HOMER) software was utilized to find the optimum design of a hybrid off-Grid system, by choosing the optimal solution depending on the cost analysis of energy based on different capacity shortage percentages. A complete study for the site conditions and load profile was done to optimize the design and implementation of a hybrid off-grid power station. In addition, the solution takes into consecration the ambient temperature effect on the efficiency of the power generation and the economical aspects of selection depending on real market price. From the analysis of the HOMER model results, the optimum hybrid power station was suggested, based on wind speed, and solar conditions. The optimization function objective is to minimize the Net Price Cost (NPC) and the Cost of Energy (COE) with zero and 10 percentage of capacity shortage.

Keywords: energy modeling, HOMER, off-grid system, optimization

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Authors: Stefano Frassinelli, Alessandro Niccolai, Riccardo E. Zich

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Sport performance analysis is a technique that is becoming every year more important for athletes of every level. Many techniques have been developed to measure and analyse efficiently the performance of athletes in some sports, but in combat sports these techniques found in many times their limits, due to the high interaction between the two opponents during the competition. In this paper the problem will be framed. Moreover the physical performance measurement problem will be analysed and three different techniques to manage it will be presented. All the techniques have been used to analyse the performance of 22 high level Judo athletes.

Keywords: sport performance, physical performance, judo, performance coefficients

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Authors: Muhammad Umar Kiani, Muhammad Shahbaz

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Performance of any vehicle can be predicted by its design/modeling and optimization. Design optimization leads to efficient performance. Followed by horizontal launch, the air launch re-entry vehicle undergoes a launch maneuver by introducing a carefully selected angle of attack profile. This angle of attack profile is the basic element to complete a specified mission. Flight program of said vehicle is optimized under the constraints of the maximum allowed angle of attack, lateral and axial loads and with the objective of reaching maximum altitude. The main focus of this study is the endo-atmospheric phase of the ascent trajectory. A three degrees of freedom trajectory model is simulated in MATLAB. The optimization process uses evolutionary algorithm, because of its robustness and efficient capacity to explore the design space in search of the global optimum. Evolutionary Algorithm based trajectory optimization also offers the added benefit of being a generalized method that may work with continuous, discontinuous, linear, and non-linear performance matrix. It also eliminates the requirement of a starting solution. Optimization is particularly beneficial to achieve maximum advantage without increasing the computational cost and affecting the output of the system. For the case of launch vehicles we are immensely anxious to achieve maximum performance and efficiency under different constraints. In a launch vehicle, flight program means the prescribed variation of vehicle pitching angle during the flight which has substantial influence reachable altitude and accuracy of orbit insertion and aerodynamic loading. Results reveal that the angle of attack profile significantly affects the performance of the vehicle.

Keywords: endo-atmospheric, evolutionary algorithm, efficient performance, optimization process

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Authors: Mehdi Jalalpour, Mazdak Tootkaboni

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We present a computationally efficient method for reliability-based topology optimization under material properties uncertainty, which is assumed to be lognormally distributed and correlated within the domain. Computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. The proposed algorithm is utilized for design of new structures and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Predictions were verified thorough comparison with results obtained using Monte Carlo simulation.

Keywords: material uncertainty, stochastic perturbation, structural reliability, topology optimization

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Authors: Rojin Bana Derakhshan, Abbas Toloie

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For the purpose of optimizing and developing energy efficiency in building, it is required to recognize key elements of success in optimization of energy consumption before performing any actions. Surveying Principal Components is one of the most valuable result of Linear Algebra because the simple and non-parametric methods are become confusing. So that energy management system implemented according to energy management system international standard ISO50001:2011 and all energy parameters in building to be measured through performing energy auditing. In this essay by simulating used of data mining, the key impressive elements on energy saving in buildings to be determined. This approach is based on data mining statistical techniques using feature selection method and fuzzy logic and convert data from massive to compressed type and used to increase the selected feature. On the other side, influence portion and amount of each energy consumption elements in energy dissipation in percent are recognized as separated norm while using obtained results from energy auditing and after measurement of all energy consuming parameters and identified variables. Accordingly, energy saving solution divided into 3 categories, low, medium and high expense solutions.

Keywords: energy saving, key elements of success, optimization of energy consumption, data mining

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Authors: P. Gunnasegaran, M. Z. Abdullah, M. Z. Yusoff, Nur Irmawati

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This study presents experimental and optimization of nanoparticle mass concentration and heat input based on the total thermal resistance (Rth) of loop heat pipe (LHP), employed for PC-CPU cooling. In this study, silica nanoparticles (SiO2) in water with particle mass concentration ranged from 0% (pure water) to 1% is considered as the working fluid within the LHP. The experimental design and optimization is accomplished by the design of the experimental tool, Response Surface Methodology (RSM). The results show that the nanoparticle mass concentration and the heat input have a significant effect on the Rth of LHP. For a given heat input, the Rth is found to decrease with the increase of the nanoparticle mass concentration up to 0.5% and increased thereafter. It is also found that the Rth is decreased when the heat input is increased from 20W to 60W. The results are optimized with the objective of minimizing the Rt, using Design-Expert software, and the optimized nanoparticle mass concentration and heat input are 0.48% and 59.97W, respectively, the minimum thermal resistance being 2.66(ºC/W).

Keywords: loop heat pipe, nanofluid, optimization, thermal resistance

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Alireza Lavaei, Alireza Lohrasbi, Mohammadali M. Shahlaei

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To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method.

Keywords: optimization, genetic algorithm, neural networks, self-organizing map

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Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: Ahmed E. Hodaib, Muhammed A. Hashem

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High-speed transportation has become a growing concern. To increase high-speed efficiencies and minimize power consumption of a vehicle, we need to eliminate the friction with the ground and minimize the aerodynamic drag acting on the vehicle. Due to the complexity and high power requirements of electromagnetic levitation, we make use of the air in front of the capsule, that produces the majority of the drag, to compress it in two phases and inject a proportion of it through small nozzles to make a high-pressure air cushion to levitate the capsule. The tube is partially-evacuated so that the air pressure is optimized for maximum compressor effectiveness, optimum tube size, and minimum vacuum pump power consumption. The total relative mass flow rate of the tube air is divided into two fractions. One is by-passed to flow over the capsule body, ensuring that no chocked flow takes place. The other fraction is sucked by the compressor where it is diffused to decrease the Mach number (around 0.8) to be suitable for the compressor inlet. The air is then compressed and intercooled, then split. One fraction is expanded through a tail nozzle to contribute to generating thrust. The other is compressed again. Bleed from the two compressors is used to maintain a constant air pressure in an air tank. The air tank is used to supply air for levitation. Dividing the total mass flow rate increases the achievable speed (Kantrowitz limit), and compressing it decreases the blockage of the capsule. As a result, the aerodynamic drag on the capsule decreases. As the tube pressure decreases, the drag decreases and the capsule power requirements decrease, however, the vacuum pump consumes more power. That’s why Design optimization techniques are to be used to get the optimum values for all the design variables given specific design inputs. Aerodynamic shape optimization, Capsule and tube sizing, compressor design, diffuser and nozzle expander design and the effect of the air bearing on the aerodynamics of the capsule are to be considered. The variations of the variables are to be studied for the change of the capsule velocity and air pressure.

Keywords: tube-capsule, hyperloop, aerodynamic design optimization, air compressor, air bearing

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time.

Keywords: swarm-based optimization, local search, Pareto optimality, flexible job shop scheduling, multi-objective optimization

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