MATLAB Genetic Algorithm Research Articles

Achieving Dual Objectives of Maximizing Transmitted Solar Energy and Minimizing UV Transmission at Vertical Incidence

Abstract. In northern China, exterior window glass significantly impacts heating energy consumption during winter by collecting heat and providing insulation. Additionally, it plays a crucial role in shading by attenuating ultraviolet rays. This paper presents an intelligent optimization algorithm based on genetic algorithms for optimizing the thickness of three-layer insulating glass to maximize solar radiation transmission and minimize ultraviolet radiation below 400nm wavelength, thereby enhancing the glass's heat collection and shading capabilities. The study utilizes MATLAB's genetic algorithm function, running 20 iterations to overcome local optima, achieving an optimal glass thickness configuration of 12mm for each layer. This configuration allows maximum solar energy transmission while substantially reducing UV radiation, balancing energy efficiency with health protection. Future research directions include examining different glass materials' effects on UV blocking, considering climate change impacts on glass performance, and scaling the optimization method for large-scale architectural applications to assess its practical and economic viability. The findings contribute to the development of energy-efficient and health-protective building materials.

Development of a Model for Generating Trajectories for an Autonomous Naval Vehicle Using Genetic Algorithms in MATLAB

Development of a Model for Generating Trajectories for an Autonomous Naval Vehicle Using Genetic Algorithms in MATLAB

Gansu, Qinghai and Ningxia regions of the new western land and sea corridor Freight network optimization

The new western land and sea corridor makes use of a variety of transport modes to reach major ASEAN countries such as Singapore to the south, connects Southeast Asia and North America to the east, and connects Chongqing, Lanzhou and Xinjiang to the north with the China-European Union (CEU) liner train, which is a composite opening-up corridor in the western region for realising the regional linkage and international co-operation with ASEAN and other countries, and organically connecting with the "One Belt, One Road". In order to explore the regional freight network of Gan-Qing-Ning region of the new western land and sea corridor, we establish the model of node socio-economic attractiveness, topological charisma and comprehensive utility maximization, and analyze the index factors by using entropy weighting, Delphi and comprehensive evaluation methods through the Matlab Genetic Algorithm Toolbox to study the influence of node socio-economic attractiveness, topological charisma, node freight volume, logistics and transportation costs and construction costs on the utility of nodes of the new western land and sea corridor. The influence of the regional node utility of Gansu, Qinghai and Ningxia in the corridor. Analyze the problems and factors affecting the selection of nodes in the existing freight transport corridors in the three provinces. Completing the site selection of Gan-Qing-Ning regional node of the new western land and sea corridor and combining the import and export cargo volume of the three provinces to propose the construction of the South-Middle East three-lane freight corridor, and completing the optimization plan of the freight network. The results show that: scientific optimization and improvement of Gan-Qing-Ning regional freight network can promote the construction of freight network in the northwest region of the new western land and sea corridor, to meet the demand for freight transportation in the corridor, and to promote the development of the corridor industry and economic growth. Thus, it provides reference for the development of Gansu, Qinghai and Ningxia region and freight network optimization.

Comparison of entransy loss analysis and the first law of thermodynamics in the optimization of organic Rankine cycle coupled with parabolic trough collector

Due to the numerous environmental issues associated with fossil fuel power plants, using solar energy to generate electricity is a viable alternative. The organic Rankine cycle (ORC) is a thermodynamic process used to convert low- and medium-temperature heat sources into electricity, often utilizing organic fluids as the working medium. Entransy is a relatively new concept that many readers may not be familiar with. Moreover, entransy loss (Ġloss) is derived from the entransy concept, which quantifies the inefficiency in transferring thermal energy through a system. In this study, (Ġloss) is used for the first time when designing an ORC cycle coupled with parabolic trough collectors. The entransy loss relations were driven with assumption that the heat capacity is a function of temperature. A genetic algorithm is a search heuristic inspired by natural selection. It is used to find optimal or near-optimal solutions to complex problems by evolving a population of candidate solutions. Two scenarios utilized the genetic algorithm in MATLAB to optimize the system (scenario 1: maximizing the output power and scenario 2: maximizing the Ġloss). In addition, the optimization parameters included turbine inlet temperature (Ttur), boiler pressure (Pboil), condenser pressure (Pcond), and the temperature of the collector fluid at the boiler outlet (Thf,out). This optimization was performed for the temperature of the collector fluid at the boiler inlet (Thf,in) in the range 310–400 °C at 10 °C intervals with four working fluids (i.e., toluene, cyclohexane, MM, and water). The land area and the beam solar radiation were considered to be 100 hectares and 800 W/m2, respectively. The results indicated that according to scenario one, at temperatures of 310–320 °C, the maximum power was obtained for the case of toluene fluid with values 59.8 and 63.5 MW. For the collector fluid temperature from 330 to 400 °C, water had the most optimal power with values ranging from 66.2 to 88.2 MW. Furthermore, toluene exhibited superiority to two other organic fluids in the 330–400 °C temperature range after water, with net power values ranged between 65.7 and 76.3 MW. The results indicated that the maximum entransy loss does not correspond to the maximum output power because the application preconditions of the entransy loss concept are not all satisfied. Across all working fluids and Thf,in, scenario 2 resulted in lower optimal output power, cycle efficiency, and system efficiency compared to scenario 1.

Optimal planning and partitioning of multiple distribution Micro-Grids based on reliability evaluation.

Power system researcher have turned to Micro-Grids (MG) for higher reliability, greater flexibility, lower operating costs and losses, and lower CO2 emissions at the distribution system. This paper presents a single-level stochastic optimization framework for planning and partitioning of a distribution system including Multiple Micro-Grids (MMGs). The main objective is to minimize the total cost of the system including investment, operation, total losses and reliability costs of the distribution network. The proposed model takes into account the viewpoints of MG owners and distribution system operators, simultaneously. The voltage stability index is introduced to identify the optimal site of MG investment. To deal with uncertainties caused by renewable generations, the Firefly Algorithm (FA) and probability-tree method is used to create various operation scenarios of Photo-Voltaic (PVs) and Wind Turbines (WTs). This model is solved through the genetic algorithm in MATLAB, and to evaluate its effectiveness, numerical studies have been carried out on the experimental IEEE distribution network with four specified locations for investing MGs and seven Tie Switches (TS) for network partitioning. Simulation results reveal that optimal locations for MG investment are determined in such a way all MGs connect to the buses near the beginning of the feeder and as a result, load point reliability is improved, total active power losses are reduced, and the energy program becomes more optimized.

Micromechanism insights and constitutive modeling for deformation in a novel high manganese austenitic steel

We explore the mechanical behavior and microstructural evolution of a novel high manganese austenitic steel (HMAS), extensively applied in underground engineering and structural construction. Our investigation focuses on two variants of HMAS, namely HMAS-1.9 and HMAS-10, which exhibit distinct yield strengths and elongation behaviors. Electron backscatter diffraction (EBSD), transmission electron microscopy (TEM) and X-ray diffraction (XRD) were employed to elucidate the evolution of microstructures during plastic deformation. Based on these experimental results, we developed a new phenomenological multiscale constitutive model that captures the mechanical behavior of HMAS, integrating micromechanisms and microstructural evolutions. Optimized with the MATLAB genetic algorithm toolbox, this model was successfully implemented in a uniaxial tensile finite element model (FEM) within ABAQUS, proving its efficacy in predicting both macroscopic flow stress and microstructural evolutions. Our findings highlight the important roles of initial grain size, dislocation density, and twin fraction in determining the mechanical properties of HMAS. Throughout the deformation process, both HMAS variants demonstrate similar plastic deformation behaviors, characterized by comparable rates of increase in dislocation density and twinning fraction, accompanied by a consistent trend of grain refinement. These findings not only deepen our understanding of the complex relationship between microstructure and mechanical properties of the HMAS steel, but also provide an effective multiscale constitutive modeling approach for predicting its deformation behavior.

Kinematic modelling of three link robot manipulator and joint torque optimization using genetic algorithm in MATLAB

This research article presents the non-linear dynamic of a three-link robotic manipulator formulated by the Newton-Euler method. The planar manipulator is composed of three links and three revolute joints rotating about the z-axis. The three nonlinear non-homogeneous dynamic equations have been solved graphically with the help of MATLAB by phase variable method. The work represents the graphical solution of the transient response of angular position, and angular velocity of each link member for a predetermined interval of time. With the help of simulated value from MATLAB, torque characteristics have been determined for different torque ratios and optimum torque has been derived using a genetic algorithm to move the manipulator in a proper direction.

Application of Genetic Algorithms for Medical Diagnosis of Diabetes Mellitus

The system of glucose-insulin control and associated problems in diabetes mellitus were studied by mathematical modeling. It is a helpful theoretical tool for understanding the basic concepts of numerous distinct medical and biological functions. It delves into the various risk factors contributing to the onset of diabetes, such as sedentary lifestyle, obesity, family history, viruses, and increasing age. The study emphasizes the importance of mathematical models in understanding the dynamic characteristics of biological systems. The study emphasizes the increasing prevalence of diabetes, especially in India, where urbanization and lifestyle changes contribute to the rising incidence. The present investigation describes the use of John Holland's evolutionary computing approach and the Genetic Algorithm (GA) in diabetes mellitus. The Genetic Algorithm is applied to address issues related to diabetes, offering a generic solution and utilizing MATLAB's Genetic Algorithm tool. The Mathematical Model provides differential equations representing glucose and insulin concentrations in the blood. The results represent testing outcomes for normal, prediabetic, and diabetic individuals, optimized with Genetic Algorithm showcased through fitness value plots. The conclusion highlights the effectiveness of Genetic Algorithm as an optimization tool in predicting optimal samples for diabetes diagnosis. The paper encourages the use of heuristic algorithms, such as Genetic Algorithms, to address complex challenges in the field of diabetes research. Future scope includes further exploration of biomathematics and Genetic Algorithm applications for enhanced understanding and management of diabetes mellitus. It is critical for people with diabetes to consistently check their blood glucose levels and follow their treatment plan.

Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach.

A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB's GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies.

Multi-Objective Optimization of a Three Degree-of-Freedom Translational Parallel Kinematic Machine with Coplanar Rails

Several advancements in the field of parallel manipulators have taken place in recent days as they offer many advantages over serial manipulators in terms of accuracy, agility, stiffness, speed, etc. The Parallel Kinematic Machines (PKMs) with lower Degree of Freedom (DoF) joints are being explored for a variety of industrial applications and, in particular, machining applications as these offer more accuracy, high machining capability, and more stiffness. This research work focuses on the modeling, kinematics, workspace and dexterity analyses of a 3DoF Translational PKM having coplanar rails along the Cartesian axes: -X, +X, +Y. Actuation of sliders, independently along the respective rails, offer the tool platform pure translational motion. Fixed length links are used to connect the sliders and tool platform. The PKM under study is modeled in CATIA. Inverse kinematics and workspace analysis are carried out using the performance indices, namely, Workspace Volume Index (WVI) and Global Condition Index (GCI). Attempts are also made to find the optimal dimensions of the PKM through multi-objective optimization using Genetic Algorithms in MATLAB. The methodology presented is helpful to predict the PKM's performance capability while the results obtained are helpful for the development of a physical prototype necessary for further experimental investigations.

Optimization of antique assembly building frame construction based on BIM technology and Matlab genetic algorithm

Abstract With the rapid development of science and technology, the traditional construction industry is facing the urgent need for transformation and upgrading, and how to achieve the performance optimization of the assembly building frame has become an urgent problem to be solved. The research constructs the antique assembly building frame using BIM technology and uses a Matlab genetic algorithm to optimize the model of the building frame. The efficacy of the optimized assembly building is analyzed, and the safety performance of the BIM-Matlab frame building proposed in this paper is compared with that of the traditional frame building and the anti-buckling frame building to explore the effectiveness of the performance optimization of the BIM-Matlab frame building. The values of the structural non-lateral stiffness ratio of the BIM-Matlab assembly building are all greater than or equal to 1, and the seismic shear weight ratio meets the requirements. The displacement ratio and inter-story displacement ratio meet the code requirements under all working conditions. The building foundation’s zero stress zone is 0.00%, and the minimum stiffness-to-weight ratio value of 38.69 is enough to pass the overturning resistance and stability test. The BIM-Matlab framed building can endure earthquakes with greater intensity and safety reserves than conventional framed buildings and anti-flexural framed buildings, which are more secure in earthquakes.

Optimized Development of Hohhot’s New Public Cultural Space Layout Construction Based on Genetic Algorithm under the Perspective of Urban Renewal

Abstract The rational layout and scientific configuration of public cultural space is an important criterion to measure the public service function of a city, whether it is perfect or not. In this paper, Hohhot Downtown is selected as the research object, and the spatial layout structure of the city is explored using the indicators of expansion intensity, compactness and fractal dimension. A layout optimization method based on a genetic algorithm is proposed to build a spatial layout model, set the spatial layout optimization constraints, complete the genetic algorithm optimization through the optimization coding process, and finally solve the Pareto optimization solution set by using the genetic algorithm of Matlab, which results in the optimization scheme of the new public cultural spatial layout of Hohhot City. The results show that six new public cultural space layout schemes of Hohhot are obtained after optimization, and the six layout scores are in the interval of [0.9, 1.0], all of which are excellent. Decision makers can choose the final layout scheme based on their actual situation and decision preferences.

Energy, exergy, economic and environmental (4E) analysis of two-stage cascade, Linder-Hampson and reverse Brayton systems in the temperature range from −120 °C to −60 °C

Cryopreservation in the temperature range from −120 °C to −60 °C is an important way to achieve high-quality food storage. In this paper, an in-depth quantitative thermodynamic comparison of two-stage cascade cycle, Linde-Hampson cycle and reverse Brayton cycle was carried out in the temperature range from −120 °C to −60 °C. Aspen HYSYS was used to invoke advanced genetic algorithm in MATLAB to optimize the system parameters. The two-stage cascade system performs best at the evaporation temperatures above −80 °C, achieving a COP of 0.97 at −60 °C. The Linder-Hampson system performs best when the evaporation temperature is below −90 °C, achieving a COP of 0.35 at −120 °C. In order to further explore the impact of environmental change on the system performance, five typical cities in China were selected to analyze the seasonal performances of the three systems. The seasonal analysis demonstrated that Harbin has the highest latitude and its seasonal energy efficiency ratio is significantly higher than other cities, the performance of the two-stage cascade system fluctuates more obvious within 24-h. What's more, the Linder-Hampson system has the lowest annual investment cost and annual operating cost at the evaporation temperature lower than −90 °C, as well as the lowest emissions of CO2 and SO2.

Modeling of neodymium-doped wideband fiber amplifier gain spectrum and numerical simulation optimization based on Matlab genetic algorithm: 1300-1400 nm peak gain maximization

The gain spectrum of the neodymium-doped wideband fiber amplifier was modeled and simulated by Matlab, and the intelligent optimization algorithm (genetic algorithm) included in Matlab was used to optimize the fiber length and doping concentration to maximize the peak gain (find the optimal fiber length and doping concentration within the set range). According to the set signal optical wavelength range (1300 nm~1400 nm) and the corresponding neodymium ions four-level transition structure, the pump optical wavelength (800 nm) is designed and the four-level structure rate equation and power propagation equation are established, and Matlab programming calculates the change of gain with fiber length, neodymium ions doping concentration and pump optical power. According to the obtained gain curve, it can be inferred that under the parameter range set in this study, the gain of the neodymium-doped fiber amplifier increases significantly with fiber length and neodymium ions doping concentration, while the pump optical power has almost no effect on the gain value.

Assessing plastic and biomass-based biochar's potential for carbon sequestration: an energy-water-environment approach

Biochar from waste has emerged as a vital solution for multiple contemporary issues. While the organic content and porous structure of biochar have granted it multiple benefits. Where the use of biochar is proven to be beneficial for enhancing the soil structure and water and nutrients retention ability, therefore, saving water and boosting yields in arid regions. Moreover, biochar is capable to sequester carbon from the atmosphere and permanently store it within the soil. As such, this study evaluates the potential for carbon sequestration through biochar obtained from the pyrolysis of feedstock mixtures including camel manure, date pits, high-density polyethylene (HDPE) and low-density polyethylene (LDPE), and how it can enhance water and food security. Multiple energy and water supplying sources have been considered for different project scenarios to provide a broader understanding of biochar potentials. The lifecycle analysis (LCA) approach is utilized for the assessment of net emissions, while an economic study is conducted in Aspen Process Economic Analyser (APEA) to evaluate the feasibility of the different scenarios. Finally, single-objective optimization and multi-objective optimizations were carried out using excel and MATLAB genetic algorithm respectively to select optimal biomass blending and utilities options to fulfill the low cost and negative emissions targets. The assessment conducted for a Qatar case study indicates that the best waste blending scenario for maximum carbon sequestration potential was obtained at a mixing ratio of 20.4% Camel manure: 27% date pits: 26.3% LDPE: 26.4% HDPE. Furthermore, the optimum char blend for maximum carbon sequestration corresponding to the minimum cost of char mix was computed. The optimal biochar mixing percentage for highest net emission was obtained at a feedstock mixing ratio of 96.8% of date pits, 1.5% of LDPE, and 1.7% of HDPE with 0% of camel manure with an optimal cost of 313.55 $/kg biochar. Solar PV was selected as the best energy source in this pyrolysis study due to its reduced carbon emissions in comparison to other sources studied such as natural gas, coal and diesel. However, natural gas is selected to fulfill the economic objective. Moreover, the optimal water source was investigated including wastewater treatment, multi-stage flash and reverse osmosis desalination, where treated wastewater is selected as the optimal supply to fulfill both, economic and environmental objectives.

Energy, exergy, exergoeconomic and environmental (4E) analysis of cascade heat pump, recuperative heat pump and carbon dioxide heat pump with different temperature lifts

Air source heat pump is recognized as an effective environmental protection and energy-saving technology. The heating temperature lift is one of the key factors affecting the performance of heat pump systems. In this paper, an in-depth quantitative thermodynamic comparison of cascade heat pump, recuperative heat pump and CO2 heat pump for direct-heating and space heating was conducted. Aspen HYSYS was used to invoke advanced genetic algorithm in MATLAB to optimize the system parameters. The temperature matching in heat exchangers was employed to reveal the essence of the performances of different heat pump systems. The recuperative heat pump is more suitable for direct-heating with the temperature lift 60 °C, and its COP is up to 4.81 at the ambient temperature of 20 °C. The cascade heat pump performs best in space heating with the maximum COP of 2.47. The exergoeconomic factor of the compressor is less than 7%, which illustrated that the investment in compressor should be increased to further improve the system efficiency. The annual CO2 and SO2 lowest emissions is 4431 kg and 130 kg of recuperative heat pump for direct-heating, and the cascade heat pump has the lowest annual 4234 kg CO2 and 127 kg SO2 emissions for space heating.

Optimization Design of Thin Wall Angular Contact Ball Bearing Based on MATLAB Genetic Algorithm

The bearing for industrial robots is desired to have a long service life. To address that, an optimization study was conducted on a thin wall angular contact ball bearing used for a robot. Firstly, an optimization model was established with the basic rated life as the objective function and the curvature radius coefficient of the inner ring groove, curvature radius coefficient of the outer ring groove, ball diameter, pitch diameter, and the number of balls as the design variables. Then, taking the angular contact ball bearing ZR76/82C as an example, the optimization model was solved quickly based on a genetic algorithm via MATLAB software in two ways: calling the ga function and optimization toolbox, and the optimal structural parameters of the bearing were obtained. After the optimization, the basic rated life of the bearing was increased by 52%. The orthogonal test optimization design method was applied to validate the calculations, and the validation results showed that the two optimization results were very close to each other. The results show that the two approaches based on MATLAB genetic algorithm are efficient and feasible for solving optimization issues, and are of reference value for the study of solving other optimization issues.

Design and optimization of complex mechanism flip shaping subsystem based on genetic algorithm and rigid-flexible coupled dynamic model.

In this paper, the cam connecting rod system of the high-speed group vertical machine flipping shaping mechanism is the research object. In order to solve the key problem that the flipping shaping mechanism cannot accurately complete the action when the vibration of the mechanism is large. In this paper, the finite element method is used to construct the dynamic model of the connecting rod subsystem of the flipped shaping mechanism. And The dynamic model of cam roller subsystem is established by centralized parameter method. Based on the MATLAB Genetic Algorithm toolbox and using Newmark's method, the dynamic equations of the flipped plastic mechanism system are solved. The optimal parameters of the connecting rod of the mechanism, the cam profile curve and the swing power and swing torque of the mechanism at different speeds are analyzed. The results show that the speed and convex contour line are important factors affecting the performance of the mechanism. And the pendulum force (swing torque) is the main cause of the vibration of the mechanism on the frame. Therefore, the mechanism pendulum dynamic and the swing moment are selected as the objective functions of the optimization model. By selecting the node parameters of the sixth order spline motion law and the cross-section parameters of the connecting rod as the design variables. The cam linkage system is optimally designed to obtain the optimal value. Finally, the optimal design of the flipped shaping mechanism was analyzed and compared with the original mechanism.

Efficient Combination of Complex Chromatography, Molecular Docking and Enzyme Kinetics for Exploration of Acetylcholinesterase Inhibitors from Poria cocos.

Poria cocos (P. cocos) is a traditional Chinese medicinal product with the same origin as medicine and food. It has diuretic, anti-inflammatory and liver protection properties, and has been widely used in a Chinese medicine in the treatment of Alzheimer's disease (AD). This study was conducted to explore the activity screening, isolation of acetylcholinesterase inhibitors (AChEIs), and in vitro inhibiting effect of P. cocos. The aim was to develop a new extraction process optimization method based on the Matlab genetic algorithm combined with a traditional orthogonal experiment. Moreover, bio-affinity ultrafiltration combined with molecular docking was used to screen and evaluate the activity of the AChEIs, which were subsequently isolated and purified using high-speed counter-current chromatography (HSCCC) and semi-preparative high-performance liquid chromatography (semi-preparative HPLC). The change in acetylcholinesterase (AChE) activity was tested using an enzymatic reaction kinetics experiment to reflect the inhibitory effect of active compounds on AChE and explore its mechanism of action. Five potential AChEIs were screened via bio-affinity ultrafiltration. Molecular docking results showed that they had good binding affinity for the active site of AChE. Meanwhile, the five active compounds had reversible inhibitory effects on AChE: Polyporenic acid C and Tumulosic acid were non-competitive inhibitors; 3-Epidehydrotumulosic acid was a mixed inhibitor; and Pachymic acid and Dehydrotrametenolic acid were competitive inhibitors. This study provided a basis for the comprehensive utilization of P. cocos and drug development for the treatment of AD.

Graded index all-dielectric lens antenna designed by phase manipulation and optical path rescaling

A rotationally symmetric all-dielectric lens antenna is designed by defining the phase function inside the dielectric directly via a closed-form series formula. The refractive index of the lens is modified using state-of-the-art optical path rescaling to keep the refractive index within practical values. The lens optimization is done using the genetic algorithm in MATLAB, where each case is evaluated by conducting linked ray tracing and full-wave electromagnetic simulation in COMSOL. A lens prototype is shown to provide a directivity enhancement of 5.1 dB compared to the optimal conical horn antenna, an improvement of 2 dB compared to the Luneburg lens, and an improvement of 3.8 dB compared to a reference graded index lens. All the examined cases shared a similar length of 5λ for the center frequency of 10 GHz. The prototype lens antenna has a maximum refractive index of 2.1, a reflection coefficient of −22 dB, and a side-lobe level of −25 dB at the center frequency of 10 GHz. The lens’ performance is consistent in the 9–11 GHz band supported by the standard circular waveguide. The results from a prototype with a discretized refractive index simulated in CST Studio Suite are in excellent agreement with the ones with a continuous refractive index profile simulated in COMSOL.