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As the development of most domestic and international oilfields progresses, many fields have entered a mature phase characterized by high water cut and high recovery, with water cut levels often exceeding 90%. Carbon/oxygen ratio logging has proven to be an indispensable tool for distinguishing oil layers from water layers in complex environments, especially where salinity is low, unknown, or highly variable. This logging method has become one of the most effective techniques for determining residual oil saturation in cased wells, providing critical insights into the oil–water interface. In this study, we evaluate two key interpretation models for carbon/oxygen ratio logging: the fan chart method and the ratio chart method. We optimize the interpretation parameters in the ratio chart model using an improved genetic algorithm, which significantly enhances interpretation precision. The optimized parameters enable a more seamless integration of logging results with reservoir and conventional logging data, reducing the influence of lithological variations and physical property differences on the measurements. This research establishes a robust theoretical foundation for enhancing the interpretation accuracy of carbon/oxygen ratio logging, which is crucial for effectively identifying water-flooded layers. These advancements provide vital technical support for monitoring oil–water dynamics, optimizing reservoir management, and improving production efficiency in oilfield development. [ABSTRACT FROM AUTHOR]

As enterprises increasingly rely on cloud-based Human Resource Management Systems (HRMS) deployed on the Hadoop platform, the optimization of weighting algorithms becomes imperative to enhance system efficiency. This paper addresses the complex challenge of load balancing in the cloud environment by proposing Effective Load Balancing Strategy (ELBS) a hybrid optimization approach that integrates both Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). The optimization objective involves the allocation of N jobs submitted by cloud users to M processing units, each characterized by a Processing Unit Vector (PUV). The PUV encapsulates critical parameters such as Million Instructions Per Second (MIPS), execution cost α, and delay cost L. Concurrently, each job submitted by a cloud user is represented by a Job Unit Vector (JUV), considering service type, number of instructions (NIC), job arrival time (AT), and worst-case completion time (wc). The proposed hybrid GA-GWO aims to minimize a cost function ζ, incorporating weighted factors of execution cost and delay cost. The challenge lies in determining optimal weights, a task addressed by assigning user preferences or importance as weights. The hybrid algorithm iteratively evolves populations of processing units, applying genetic operators, such as crossover and mutation, along with the exploration capabilities of GWO, to efficiently explore the solution space. This research contributes a comprehensive algorithmic solution to the optimization of weighting algorithms in enterprise HRMS on the cloud and Hadoop platform. The adaptability of the hybrid ELBS to dynamic cloud environments and its efficacy in handling complex optimization problems position it as a promising tool for achieving load balancing in HRMS applications. The proposed approach provides a foundation for further empirical validation and implementation in practical enterprise settings. [ABSTRACT FROM AUTHOR]

In this paper, different modeling approaches to the drain current, including analytical and artificial neural network (ANN) modeling, are investigated. The adopted models address the inherent self-heating and kink effects, especially in high-power GaN-based high electron mobility transistors (HEMTs). Different optimization algorithms were demonstrated for extracting the model parameters, including genetic algorithm optimization (GAO), gray wolf optimization (GWO), growth optimization (GO), and particle swarm optimization (PSO). The modeling approaches are applied to DC IV measurements of 1-mm, 4-mm, and 2-mm GaN HEMTs on SiC and Si substrates. An improved optimization procedure was applied to the analytical models to find the main parameters responsible for fitting the general nonlinear behavior of the device. Then, the thermal or self-heating parameters are tuned for best fitting in the high-power dissipation region. The kink effect has been counted by adding another factor to the analytical formula to characterize the voltage dependency of this effect. The ANN modeling provides an efficient and cost-effective solution to accurately simulate the IV characteristics with less effort. In this technique, there is no need for a predefined closed formula or a complicated fitting parameter extraction process. Also, the model training was enhanced by using a genetic algorithm augmented backpropagation technique. The investigated analytical and ANN techniques were demonstrated by modeling the IV characteristics of the considered GaN HEMTs. The results obtained confirm the advantages of using ANN modeling for solving such problems and large-signal modeling applications. [ABSTRACT FROM AUTHOR]

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover's algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), where n is the number of qubits in the individuals' quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals' register and fixing the remaining ones as classical values of '0' and '1' with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in O(2(n−k)/2) oracle queries, where k is the number of fixed classical bits in the individuals' register. [ABSTRACT FROM AUTHOR]

When planning a hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources--those that rely on fossil fuels--the primary considerations are the total cost of the system and the CO2 emissions. In this paper, we will investigate the typical hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources involving a detailed simulation process that may require specific inputs, models, and data. Then, we employed dual optimization methods: genetic algorithm (GA) and particle swarm optimization (PSO). The consequences of GA and PSO execution in the bus timetabling problem depict that the GA algorithm is better at finding the optimal solution in terms of accuracy and iteration. Additionally, the GA algorithm is also superior to the straightforwardness of the techniques used. So, in this work, we employed a Genetic Algorithm Optimization (GA)---based optimal sizing technique for HES configurations that include sustainability wind turbines (WTs), battery storage (BS), and diesel generators (DGs). HES improved power delivery to a rural community in the Wasit Province, Iraq, situated at 46° - 36° and 32° - 31° in the country's southeastern central region. Throughout the project's 25-year lifespan, the optimization primarily aims to minimize the total cost (CT) and total CO2 emissions (ECO2T). The outcomes demonstrate that the GA algorithm may, with continuous electricity supply, minimize the objectives while meeting the load demand. [ABSTRACT FROM AUTHOR]

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

The seismic signals collected underground in coal mines often exhibit complex waveforms accompanied by significant noise interference, leading to a reduction in the accuracy of first arrival time picking of seismic signals and thereby impacting the inversion and interpretation of seismic data. In response to the low signal-to-noise ratio seismic signals collected in complex interference environments, a method for seismic noise suppression and first arrival extraction based on the Variational Mode Decomposition (VMD) and the Genetic Algorithm-optimized Support Vector Machine (GA-SVM) is proposed to enhance the quality of seismic signals under complex noise conditions in coal mines. The approach employs the Variational Mode Decomposition for adaptive decomposition of the noisy seismic signals, yielding several Variational Mode Components (IMF). The feature extraction is applied to the IMFs obtained from VMD decomposition, utilizing the extracted signal features as the basis for discerning signal validity. Genetic Algorithm is utilized to optimize the Support Vector Machine model, obtaining the optimal penalty factor (c) and kernel function parameter (g). The optimized SVM model is then employed for the validity discrimination of the IMF components, reconstructing them into high signal-to-noise ratio signals. By applying the noise suppression algorithm to artificially noised seismic signals, the common types of noise encountered in coal mines are effectively suppressed, validating the feasibility of the algorithm. Noise suppression processing is applied to the seismic records obtained from mine roadways, successfully mitigating interference noise in the data and significantly improving the signal-to-noise ratio of the seismic records, thereby enhancing the accuracy of first arrival time picking. The results indicate that the seismic noise suppression and first arrival extraction method based on the VMD and GA-SVM can effectively separate and extract valid signals from noisy seismic records, improving first arrival time picking accuracy. This approach demonstrates a significant potential for its application in complex interference conditions in mines, which is of significance for the seismic exploration in the mining environments with complex interference conditions.

Cryogenic energy storage (CES) is a viable method for grid-scale electrical energy storage. Considering the high energy density and mature application of liquefied natural gas (LNG), we proposed an LNG cryogenic energy storage (LNGES) system. A steady-state process model of the LNGES system was established using Aspen HYSYS. The effects of the natural gas composition and key operating parameters such as the charging pressure, discharging pressure, throttling temperature, and liquid storage pressure on the system performance were investigated. A multi-parameter genetic algorithm model built using the MATLAB software was used to optimize the LNGES system to optimize the round-trip efficiency (RTE). Then, an exergy analysis of the optimal configuration was conducted. The results suggested that the LNGES system could achieve optimal RTE and exergy efficiency values of 60.14% and 71.64%, respectively. Exergy destruction mainly occurred during the compression, throttling, expansion, and heat exchange. The proposed LNGES system could be a promising candidate for the large-scale application of CES technology in power grids and gas networks.

A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning. [ABSTRACT FROM AUTHOR]

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover’s algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome (i.e., the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), where n is the number of qubits in the individuals’ quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals’ register and fixing the remaining ones as classical values of ‘0’ and ‘1’ with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum Algorithm with Genetic Optimization (HQAGO) solves search problems in O(2(n−k)/2) oracle queries, where k is the number of fixed classical bits in the individuals’ register.

Education's evolution in the context of energy systems is essential for addressing sustainable energy challenges and developing a workforce equipped for future innovations, emphasizing both formal curricula and informal lifelong learning through successful energy case studies. As the global energy sector transforms to reduce carbon emissions and reliance on fossil fuels, innovations in renewable technologies like solar thermal are pivotal for promoting energy security and economic stability, supported by an educational foundation that fosters awareness and technical skills for sustainable development. Exposure to successful renewable energy systems, such as solar-powered refrigeration, offers an informal educational experience that enhances understanding and supports global educational goals, initiating with the innovative design and optimization of these systems using artificial intelligence (neural networks). Based on this formulation, the current article developed two sustainable energy systems by comparing the refrigeration cycles with two different operating fluids and various arrangements, and multi-objective optimization with an evolutionary genetic algorithm is performed for the proposed systems. The studied systems are refrigeration cycles using an ejector and without an ejector with two working fluids of lithium bromide and lithium chloride. The present work's main aim is to examine the working fluid and refrigeration system arrangements. Energy and economic modeling were performed for the proposed systems, and then parametric analysis and two-objective optimization were extracted. Parameters such as generator temperature, condenser temperature, absorber temperature, and evaporator temperature, which significantly impact the proposed system's performance, have been selected as decision parameters, and parametric analysis has been extracted for them. In addition to the mentioned parameters, diffusion mixing efficiency, nozzle efficiency, and heat exchanger have also been studied in the ejector asset system. To find the best values of decision variables, multi-objective optimization for both arrangements is conducted, and results are presented. The results have indicated that the refrigeration system using lithium chloride working fluid without an ejector achieves a coefficient of performance of 0.766 and a cost of 0.922 $/h at the optimal point, while the system with an ejector yields a higher coefficient of performance (1.047) and a slightly lower cost rate (0.991 $/h). The outcomes of this work can play a critical role for higher education institutions in advancing innovative solutions to pressing energy challenges. Lifelong learning, at the heart of educational innovation, can benefit from the integration of sustainable energy systems as a core component of informal education through the optimization of ejector refrigeration systems.

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students, enterprises and industries. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method.

Abstract To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4 +–N loading rate, etc., and the energy consumption could be reduced by 13–27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants.

To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4+–N loading rate, etc., and the energy consumption could be reduced by 13–27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants. [ABSTRACT FROM AUTHOR]

Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize behavior exploration and minimize the test suite size while covering more server-side vulnerable paths. Three diversity metrics (i.e. phenotypic coupling, intent coupling and competitiveness) are proposed to measure the underlying relationship between test cases from user behavior, code logic and test execution history. Moreover, a 3-dimensional weight graph is designed to model association among metrics, which provides fine-grained guidance for the genetic algorithm to generate diverse test cases from the client-side behavior model. Our empirical evaluation on five web applications shows that DTester can efficiently and robustly generate better test suites than the state-of-the-art TCG method. The maximum improvement is 8 5 % , 6 0 % , 7 1 6 % and 8 8 % in efficiency, test suite size, diversity and robustness. [ABSTRACT FROM AUTHOR]

This study focuses on the Wartsila 9L34DF engine and proposes an integrated system for low-temperature carbon capture using the coupling of cold and hot energy recovery with membrane separation in LNG-powered ships. By utilizing a series dual-pressure organic Rankine cycle (SDPORC) system to recover waste heat from the engine exhaust gases and generate electricity, the system provides power support for the low-temperature carbon capture compression process without consuming additional ship power. To validate the accuracy and reliability of the mathematical model, the simulation results are compared with the literature's data. Once the model's accuracy is ensured, the operational parameters of the integrated system are analyzed. Subsequently, working fluid optimization and genetic algorithm sensitive parameter optimization are conducted. Finally, under the optimal operating conditions, the thermodynamic performance and economic evaluation of the integrated system are assessed. The results demonstrate that the net power output of the integrated system is 100.95 kW, with an exergy efficiency of 45.19%. The unit carbon capture cost (UCC) is 14.24 $/ton, and for each unit of consumed LNG, 1.97 kg of liquid CO2 with a concentration of 99.5% can be captured. This integrated system significantly improves the energy utilization efficiency of ships and reduces CO2 emissions. [ABSTRACT FROM AUTHOR]

This research developed a pressure-based thrust vectoring angle estimation method for fluidic thrust vectoring nozzles. This method can accurately estimate the real-time in-flight thrust vectoring angle using only wall pressure information on the inner surface of the nozzle. We proposed an algorithm to calculate the thrust vectoring angle from the wall pressure inside the nozzle. Non-dominated sorting genetic algorithm II was applied to find the optimal sensor arrays and reduce the wall pressure sensor quantity. Synchronous force and wall pressure measurement experiments were carried out to verify the accuracy and real-time response of the pressure-based thrust vectoring angle estimation method. The results showed that accurate estimation of the thrust vectoring angle can be achieved with a minimum of three pressure sensors. The pressure-based thrust vectoring angle estimation method proposed in this study has a good prospect for engineering applications; it is capable of accurate real-time in-flight monitoring of the thrust vectoring angle. This method is important and indispensable for the closed-loop feedback control and aircraft attitude control of fluidic thrust vectoring control technology. [ABSTRACT FROM AUTHOR]

Component parameters directly affect the dynamic characteristics of suspension systems in small rescue craft. To study and improve the vibration reduction performance of a new suspension system, sensitivity analysis and genetic algorithm (GA) optimization were performed for a three-degree-of-freedom (3-DOF) vibration reduction suspension system. The system performance was analyzed using AMESim multi-condition simulations, and the sensitivity of the system to parameters that affect its dynamic characteristics was analyzed. Furthermore, the parameters were optimized using the GA. The simulation results indicated that the hydraulic cylinder inner diameter, the piston rod diameter, the accumulator volume, the accumulator pre-charge pressure, and the damper valve aperture size all influenced the working performance of a small salvage vessel. The optimization results showed that the stability of the ship was improved by 60% and that the main hull acceleration root mean square value decreased by 2.24% as a result of the optimization. The stability and riding comfort of the small salvage ship were improved, and there was an evident stability optimization effect. The comprehensive performance of the salvage ship was significantly improved. [ABSTRACT FROM AUTHOR]

In Sweden, steel bridges are the most common solution regarding prefabricated short-span (15-35 m) pedestrian bridges. The most common bridge type for this application is a carbon steel truss bridge with an orthotropic deck consisting of a 10-12 mm deck plate and trapezoidal stiffeners. For these bridges, the deck is the main driver regarding economic and environmental costs, originating from, e.g., high material consumption and maintenance activities. This paper aims to compare the economic aspects between a conventional steel deck and an extruded aluminum deck from an investment cost perspective. The aluminum deck is homogenous, i.e., containing no mechanical connections. To perform this case study, an optimization routine is developed and executed for the aluminum alternative. This optimization study targets the minimum weight and is constrained by the Eurocode design code. A sub-model approach is adopted for the numerical analysis, based on shell and solid elements. The procedure to go from a cross-section defined by a set of parameters to reach the optimized bridge deck is presented. The studies that are reported in this paper show that the developed optimization routine gives satisfactory results. Furthermore, they indicate that homogenous extruded aluminum decks can be a viable deck option for pedestrian bridges. [ABSTRACT FROM AUTHOR]

The cooling capacity of a radiator under complex environmental conditions is improved by controlling the detector temperature within a range of the thermal control index. An optimal design method for the radiation cooler based on the response surface method (RSM) and genetic algorithm is proposed, and the area and location of the radiation cooler of the space ultraviolet imager detector are optimized. First, the physical model and thermal simulation model of the space ultraviolet imager are established. Then, the detector temperature is used as the objective function, and the length, height, and inclination angle of the radiator in space are used as the design variables. The mathematical model of the objective function is established by the RSM, and the rationality of the mathematical model is verified by analysis of variance (ANOVA). Finally, the Pareto optimal frontier is obtained using the genetic algorithm. The optimization results confirm that the optimal design parameters of the radiator are XW = 165.58 mm, XH = 160.29 mm, θ1 = 7.28°, θ2 = 5.87°. Compared with before optimization, the area of the radiator is reduced by 18.18%, and the weight is reduced by 48.78 g. Based on verifying the optimization results, the optimized radiation cooler can control the detector within the target temperature of −20 to −10 °C. Therefore, the optimal design of the radiation cooler meets the requirement of lightweight while improving the cooling capacity of the radiator.

H7N9 avian influenza is a novel virus with high morbidity and mortality that threatens human health and life. Therefore, it is necessary to diagnose H7N9 avian influenza in a timely and rapid manner to prevent further transmission of the virus and greatly reduce the infection and mortality rates. This paper proposes an H7N9 avian influenza diagnostic model that is based on a multilayer belief rule‐based (BRB) inference methodology by considering five typical characteristics of influenza: epidemiology, clinical manifestations, complications, characteristics of imaging tests and positive pathogen test results. Specifically, the severity of H7N9 avian influenza is gradually identified by a multilayer BRB model, and then the diagnostic model is optimized by a genetic algorithm (GA) to improve the diagnostic accuracy. Finally, the feasibility of the model is verified by fivefold cross‐validation with a real clinical dataset. The performance of the proposed diagnostic model is compared with those of the BP neural network (BPNN) model and support vector machine (SVM) model, and the results show that the multilayer BRB model can achieve rapid and satisfactory diagnostic results for H7N9 avian influenza. The experiment shows that the accuracy of the BRB model for H7N9 avian influenza hierarchical diagnosis provided in this paper is 0.903, which is higher than 0.818 of the BP neural network (BPNN) modules and 0.844 of the support vector machine (SVM) models. Especially when diagnosing the suspected and confirmed degree of H7N9 disease, it is more realized satisfactory diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors' previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller. [ABSTRACT FROM AUTHOR]

This article presents a methodology to determine the control objectives of conduction angle control in generating mode of operation in a switched reluctance machine. First, the performance in motoring mode of control is compared with generating mode for different operating points. Then, the key optimization objectives are established to improve a switched reluctance machine's performance in generating mode. A multiobjective optimizer is used to select the conduction angles. The proposed generating-specific objectives are maximizing source current per torque and minimizing torque ripple. These objectives are then compared with the motoring-specific objectives, such as maximizing average torque and minimizing torque ripple for a wide speed range. Finally, the proposed generating objectives have been validated experimentally using a three-phase 12/8 switched reluctance machine.

Overvoltage transient is the major danger facing large offshore wind farms, as it may lead to a complete failure of the system components. A 150 MW offshore wind farm was modeled using the ATP-EMTP package to analyze the effect of the temporary overvoltage resulting from de-energization conditions, and the ferro resonance phenomenon resulting from the asymmetric opening of circuit breaker pole(s). The results showed that the best scenario to shut down such a system from the grid with minimum values of temporary overvoltage is to disconnect it, feeder, by a feeder. The stuck of one or more poles during the opening operation of the circuit breaker is a dangerous phenomenon, as it induces a high overvoltage value, which lasts for a long time causing more electric stress on the sensitive components of the system. The use of the Pre-Insertion Resistor PIR was recommended as a mitigation technique for the temporary overvoltage in large offshore wind farms, as it proves a significant reduction in the overvoltage values reaches to 79.44 % compared to surge arrester and shunt reactor which reduce the over voltage by 68.33%, 66.29% respectively. The genetic algorithm was used to find the optimum value of the PIR and shunt reactor that leads to the minimum TOV. The optimum value of PIR which was recorded is equal to 57.352 Ohm, which leads to more reduction in the overvoltage value by 9.67%. Also, the optimum values of the resistor and inductor of the shunt reactor were found to be 11.238 Ohms and 37.246 mH which leads to more reduction in the overvoltage value by 21.97% compared to the reduction without optimization.

This study focuses on the Wartsila 9L34DF engine and proposes an integrated system for low-temperature carbon capture using the coupling of cold and hot energy recovery with membrane separation in LNG-powered ships. By utilizing a series dual-pressure organic Rankine cycle (SDPORC) system to recover waste heat from the engine exhaust gases and generate electricity, the system provides power support for the low-temperature carbon capture compression process without consuming additional ship power. To validate the accuracy and reliability of the mathematical model, the simulation results are compared with the literature’s data. Once the model’s accuracy is ensured, the operational parameters of the integrated system are analyzed. Subsequently, working fluid optimization and genetic algorithm sensitive parameter optimization are conducted. Finally, under the optimal operating conditions, the thermodynamic performance and economic evaluation of the integrated system are assessed. The results demonstrate that the net power output of the integrated system is 100.95 kW, with an exergy efficiency of 45.19%. The unit carbon capture cost (UCC) is 14.24 $/ton, and for each unit of consumed LNG, 1.97 kg of liquid CO2 with a concentration of 99.5% can be captured. This integrated system significantly improves the energy utilization efficiency of ships and reduces CO2 emissions.

Near-zero refractive index metamaterials (NZIM) have drawn a lot of attention recently for the development of high directivity antennas. However, their application has been constrained by the tedious and time-consuming manual design process. In this work, we offer an automated optimization design technique for NZIM that combines a modeling process based on pixelated metamaterials with an optimization process based on heuristic algorithms. The interactive CST-Python simulation is utilized for accomplishing the above automated design. In detail, the elite-preserving genetic algorithm (EGA) is specifically employed because of its improved capacity in locating the superior unit, and the fitness function proposed guarantees the performance of bandwidth and optimization process based on the principles of proportionate integration and penalty-like functions, respectively. Utilizing the suggested technique, we designed a NZIM unit that operates between 8.0 and 8.52 GHz. In order to validate the performance of the NZIM, a rectangular microstrip patch antenna (MPA) resonating at 8.2 GHz was employed as the radiation source for studies. According to the simulation results, the developed NZIM has a clearly beam focusing effect which causes the MPA's spherical waves to converge into quasi-plane waves. Furthermore, discussing the NZIM’s application in high directivity antennas designing, the prototypes of the NZIM and MPA were fabricated and tested. The results revealed that the MPA’s gain enhancement achieves 5.3 dB on average and 6.05 dB on maximum. Due to the automated optimization and design, this work is promised to advance NZIM's further applicability in high directivity antennas.

Fine-blanking is a molding process based on the common blanking process, which obtains hydrostatic stress through blank holder reverse jacking, in order to increase material plasticity. It requires special equipment, namely a fine-blanking press, to complete the fine-blanking process. In this paper, the problem of the speed of the slide block fluctuation found in the actual use of a 12,000 kN hydraulic fine-blanking press after multi-stage pressure source optimization is studied. Firstly, the mathematical model of the motion of the slide block in the blanking stage of the hydraulic fine blanking press is established, and the accurate mathematical model in the blanking stage of the hydraulic fine-blanking press is obtained through the least square method system identification experiment. Aiming at the complex working situation of the fine-blanking press, a phased PID control strategy is creatively proposed. The optimal PID control parameters are obtained by a genetic algorithm, and established a fuzzy PID controller for the blanking stage to accurately control the movement speed of the slide block. The results show that the new control strategy is very effective in improving the movement accuracy of the slide block, effectively improving the machining accuracy and reducing the impact vibration of the hydraulic system. [ABSTRACT FROM AUTHOR]

This research developed a pressure-based thrust vectoring angle estimation method for fluidic thrust vectoring nozzles. This method can accurately estimate the real-time in-flight thrust vectoring angle using only wall pressure information on the inner surface of the nozzle. We proposed an algorithm to calculate the thrust vectoring angle from the wall pressure inside the nozzle. Non-dominated sorting genetic algorithm II was applied to find the optimal sensor arrays and reduce the wall pressure sensor quantity. Synchronous force and wall pressure measurement experiments were carried out to verify the accuracy and real-time response of the pressure-based thrust vectoring angle estimation method. The results showed that accurate estimation of the thrust vectoring angle can be achieved with a minimum of three pressure sensors. The pressure-based thrust vectoring angle estimation method proposed in this study has a good prospect for engineering applications; it is capable of accurate real-time in-flight monitoring of the thrust vectoring angle. This method is important and indispensable for the closed-loop feedback control and aircraft attitude control of fluidic thrust vectoring control technology.

In Sweden, steel bridges are the most common solution regarding prefabricated short-span (15–35 m) pedestrian bridges. The most common bridge type for this application is a carbon steel truss bridge with an orthotropic deck consisting of a 10–12 mm deck plate and trapezoidal stiffeners. For these bridges, the deck is the main driver regarding economic and environmental costs, originating from, e.g., high material consumption and maintenance activities. This paper aims to compare the economic aspects between a conventional steel deck and an extruded aluminum deck from an investment cost perspective. The aluminum deck is homogenous, i.e., containing no mechanical connections. To perform this case study, an optimization routine is developed and executed for the aluminum alternative. This optimization study targets the minimum weight and is constrained by the Eurocode design code. A sub-model approach is adopted for the numerical analysis, based on shell and solid elements. The procedure to go from a cross-section defined by a set of parameters to reach the optimized bridge deck is presented. The studies that are reported in this paper show that the developed optimization routine gives satisfactory results. Furthermore, they indicate that homogenous extruded aluminum decks can be a viable deck option for pedestrian bridges.

Component parameters directly affect the dynamic characteristics of suspension systems in small rescue craft. To study and improve the vibration reduction performance of a new suspension system, sensitivity analysis and genetic algorithm (GA) optimization were performed for a three-degree-of-freedom (3-DOF) vibration reduction suspension system. The system performance was analyzed using AMESim multi-condition simulations, and the sensitivity of the system to parameters that affect its dynamic characteristics was analyzed. Furthermore, the parameters were optimized using the GA. The simulation results indicated that the hydraulic cylinder inner diameter, the piston rod diameter, the accumulator volume, the accumulator pre-charge pressure, and the damper valve aperture size all influenced the working performance of a small salvage vessel. The optimization results showed that the stability of the ship was improved by 60% and that the main hull acceleration root mean square value decreased by 2.24% as a result of the optimization. The stability and riding comfort of the small salvage ship were improved, and there was an evident stability optimization effect. The comprehensive performance of the salvage ship was significantly improved.

Improving spontaneity, shifting control, and efficiency are the main goals of actuators for hydraulic automated transmissions. Friction losses of piston-seals play an essential role in achieving these goals. Therefore, modeling the complex friction behavior of piston seals leads to a better understanding of the determinant factors of energy losses and, consequently, the realization of more efficient transmission actuators. This paper proposes a piston-seal friction model based on the Generalized Maxwell-Slip model. The proposed model introduces an additional hydraulic-pressure dependency that emulates the influence of cylinder-pressure on the displacement variable while accounting for various piston-seal structures. A Genetic Algorithm is also applied to identify and optimize the parameters of the proposed friction model. Simulations with O-Ring, D-Ring, and Bonded Piston seals were developed to show the validity of the proposed model in practical scenarios. The results were also compared with the original Generalized Maxwell Slip friction model to show the superiority of the proposed model in representing the experimental data.

Multi-band microstrip patch antennas are convenient for mm-wave wireless applications due to their low profile, less weight, and planar structure. This paper investigates patch geometry optimization of a single microstrip antenna by employing a binary coded genetic algorithm to attain triple band frequency operation for wireless network application. The algorithm iteratively creates new models of patch surface, evaluates the fitness function of each individual ranking them and generates the next set of offsprings. Finally, the fittest individual antenna model is returned. Genetically engineered antenna was simulated in ANSYS HFSS software and compared with the non-optimized reference antenna with the same dimensions. The optimized antenna operates at three frequency bands centered at 28 GHz, 40 GHz, and 47 GHz whereas the reference antenna operates only at 28 GHz with a directivity of 6.8 dB. Further, the test result exhibits broadside radiation patterns with peak directivities of 7.7 dB, 12.1 dB, and 8.2 dB respectively. The covered impedance bandwidths when S11≤-10 dB are 1.8 %, 5.5 % and 0.85 % respectively.

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved.

An important engineering challenge is the design of a wind turbine's pitch angle controller. The dependability, safety, and power output maximization of a wind turbine are all impacted by this controller. In this study, a 2 MW doubly fed induction generator wind turbine's blade angle controller design with a novel fuzzy logic controller is tested in a simulated environment. The evolutionary algorithm technique is used to optimize the fuzzy logic controller with three inputs. A genetic algorithm is used to optimize the specified pitch angle controller for a number of coefficients. After the optimization process, the controller's performance is assessed in terms of power output, overshoot, and steady-state error characteristics. [ABSTRACT FROM AUTHOR]

Under the "carbon peaking and carbon neutrality" goal, Shanxi Province adjusts the power supply structure and promotes the development of a high proportion of new energy, which has a certain impact on the demand for thermal coal. Therefore, constructing a reasonable forecasting model for thermal coal demand can play a role in stabilizing coal supply and demand. This paper analyzes various factors related to coal demand, and uses Pearson coefficient to screen out six variables with strong correlation. Then, based on the scenario analysis method, combined with the "14th Five-Year Plan" of Shanxi Province, different scenarios of economic development and carbon emission reduction development are set. Finally, a multi-scenario GA–LSSVM forecasting model of thermal coal demand in Shanxi Province is constructed, and the future development trend of thermal coal demand in Shanxi Province is predicted. The results show that the demand for thermal coal is the largest in the mode of high-speed economic development and low emission reduction, and the demand for thermal coal is the lowest in the mode of low-speed economic development and strong emission reduction, which provides a scientific basis for the implementation of Shanxi Province's thermal coal supply policy. [ABSTRACT FROM AUTHOR]

Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service.This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time.

Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors’ previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller.

Magnetoplasmonic permittivity-near-zero (ε -near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the ε -near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic ε -near-zero nanostructures' capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic ε -near-zero sensing platform with a sensitivity higher than 56 ∘ / RIU and a figure of merit in the order of 10 2 . These last results, higher than any previous magnetoplasmonic ε -near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer). [ABSTRACT FROM AUTHOR]

The main objective of this work is to propose an approach for improving the performance of miniaturized microstrip patch antennas (MPAs) that are loaded with a thin film consisting of a high relative permittivity material. The method uses a thin film to decrease the antenna's resonance frequency while keeping the antenna's patch dimensions. For the enhancement of the antenna's performance with a thin film, the dimensions of the patch of the designed antenna are optimized utilizing genetic algorithms (GAs). The resonance frequency of the microstrip patch antenna was changed from 5.8 GHz to 4.0 GHz, and the area of the proposed antenna was minimized by around 60%, especially in comparison to a conventional antenna alone without thin film. Most of the performances of the proposed antenna such as the return loss, bandwidth, and voltage standing wave ratio (VSWR) were improved. [ABSTRACT FROM AUTHOR]

The focus of this research is to develop the optimum design of torsional vibration damper using hybrid damping approach to decrease the torsional vibrations in the cranktrain system of internal combustion engines (ICE). For this purpose, a double mass rubber and viscous torsional vibration damper (DMRV-TVD) are combined. The optimization procedure is carried out using genetic algorithm (GA) to determine the best hybrid damping performance on cranktrain system of a four stroke and four cylinder diesel engine. Accordingly, twelve degrees of freedom lumped mass mathematical model of the proposed cranktrain system is created. The stiffness and damping coefficients of viscous and rubber materials used in DMRV-TVD model are verified by modal test and finite element natural frequency analysis. Then, the excitation torque is calculated considering the inertia forces and gas force, and Fourier series expansion is performed to obtain the harmonics of driven torque as the input load on the relevant masses. The relative angular deflection of the front end point of the crankshaft is determined. Additionally, in order to decrease the torsional vibrations of the crankshaft, DMRV-TVD model is optimized depending on the viscous material parameters by defining the boundary conditions and objective function of the genetic algorithm. The comparative results show that the developed hybrid design of optimized DMRV-TVD reduced the torsional vibrations by 50.17% when compared to the non-optimized DMRV-TVD. This achieved reduction in the torsional vibrations is expected to increase the engine performance and its durability as well as providing a better driving comfort and fuel efficiency.

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s ∙ m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications. • The datasets were generated by a laboratory-scale prototype of the IEC system. • The ANN model for IEC considering spray operation was trained and validated. • Effects of nozzle and operation characteristics parameters were investigated. • The significance of the influential parameters was evaluated. • The optimal parameters for IEC were determined based on genetic algorithms. [ABSTRACT FROM AUTHOR]

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students , enterprises and industries. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (OWTs) are complex systems that may experience excessive vibrations due to winds, waves, rotor torque, or seismic loads during the operation. Tuned mass dampers (TMDs) have been a widely used vibration passive control device in structures, including wind turbines. The TMD works as a damper that transfers the kinetic energy from the main structure to a secondary mass usually attached to the hub. The spectral element method (SEM) is suitable for analyzing dynamic structural problemss with accuracy and low computational cost. This paper presents a monopile wind turbine fitted with a pendulum-TMD (PTMD) modeled by spectral elements. An optimum pendulum design is performed through the genetic algorithm technique. The wind turbine selected is a National Renewable Energy Lab (NREL) monopile 5 MW baseline wind turbine fitted with a PTMD, subjected to winds and waves simulated as random spectra in the analysis. Numerical results show the efficiency of the proposed spectral model and the optimum PTMD design for the OWT under random excitation. [ABSTRACT FROM AUTHOR]

Multi-band microstrip patch antennas are convenient for mm-wave wireless applications due to their low profile, less weight, and planar structure. This paper investigates patch geometry optimization of a single microstrip antenna by employing a binary coded genetic algorithm to attain triple band frequency operation for wireless network application. The algorithm iteratively creates new models of patch surface, evaluates the fitness function of each individual ranking them and generates the next set of osprings. Finally, the fittest individual antenna model is returned. Genetically engineered antenna was simulated in ANSYS HFSS software and compared with the non-optimized reference antenna with the same dimensions. The optimized antenna operates at three frequency bands centered at 28 GHz, 40 GHz, and 47 GHz whereas the reference antenna operates only at 28 GHz with a directivity of 6.8 dB. Further, the test result exhibits broadside radiation patterns with peak directivities of 7.7 dB, 12.1 dB, and 8.2 dB respectively. The covered impedance bandwidths when S11-10 dB are 1.8 %, 5.5 % and 0.85 % respectively. [ABSTRACT FROM AUTHOR]

Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used not only the unconventional method of abrasive water jet but also conventional machining, such as drilling. Delamination factors (Fd) are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three Fd evaluation methods that have been most frequently used in previous investigations. To this end, three spindle and feed speeds and three Brad and Spur drills (BSD) tool diameters were selected (L27) for drilling 155-g/m2 density jute fabric–reinforced polyester biocomposites. The desirability function (DF) was further made to optimize the drilling parameters. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions. [ABSTRACT FROM AUTHOR]

Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service. This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time. [ABSTRACT FROM AUTHOR]

An important engineering challenge is the design of a wind turbine’s pitch angle controller. The dependability, safety, and power output maximization of a wind turbine are all impacted by this controller. In this study, a 2 MW doubly fed induction generator wind turbine’s blade angle controller design with a novel fuzzy logic controller is tested in a simulated environment. The evolutionary algorithm technique is used to optimize the fuzzy logic controller with three inputs. A genetic algorithm is used to optimize the specified pitch angle controller for a number of coefficients. After the optimization process, the controller’s performance is assessed in terms of power output, overshoot, and steady-state error characteristics.

Under the “carbon peaking and carbon neutrality” goal, Shanxi Province adjusts the power supply structure and promotes the development of a high proportion of new energy, which has a certain impact on the demand for thermal coal. Therefore, constructing a reasonable forecasting model for thermal coal demand can play a role in stabilizing coal supply and demand. This paper analyzes various factors related to coal demand, and uses Pearson coefficient to screen out six variables with strong correlation. Then, based on the scenario analysis method, combined with the “14th Five-Year Plan” of Shanxi Province, different scenarios of economic development and carbon emission reduction development are set. Finally, a multi-scenario GA–LSSVM forecasting model of thermal coal demand in Shanxi Province is constructed, and the future development trend of thermal coal demand in Shanxi Province is predicted. The results show that the demand for thermal coal is the largest in the mode of high-speed economic development and low emission reduction, and the demand for thermal coal is the lowest in the mode of low-speed economic development and strong emission reduction, which provides a scientific basis for the implementation of Shanxi Province’s thermal coal supply policy.

Multi-objective optimization of the hydrofoil shaped pin fins for micro heat sinks was studied. Optimization tool utilized a multi objective genetic algorithm (MOGA) and a numerical model that was validated against previously obtained experimental single-phase heat transfer results on a hydrofoil shape micro scale pin fin. The numerical model was built using StarCCM + software. Several turbulence models were examined against single-phase experimental data and the Lag-EB k-epsilon turbulence model was chosen for validation. Optimization tool varied the shape of the hydrofoil pin fin through flexible parametrization utilizing an ellipse and polynomials. An algorithm coupling pin fin parametrization code, validated the 3D CFD solutions and a MOGA interface was implemented. Subsequently, multi-objective optimization was performed to maximize the heat transfer coefficient while minimizing pressure drop. A broad range of novel geometries were obtained with a tradeoff between thermal and hydraulic performance for low Reynolds number. Novel pin fin shape, bird design, is proposed as a result of the study. Additional 3D steady state conjugate CFD simulations including the bird design pin fin were performed using already verified setup, and it was shown that the enhancement in the average heat transfer coefficient dominates over the pressure drop increase at higher Reynolds numbers.