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To provide an improved version of SIMPLER algorithm which can enhance the convergence rate of the iterative solution procedure in the field of computational fluid dynamics analysis.

The improved version of SIMPLER algorithm is developed by modifying the coefficients of the velocity correction equation and implementing the correction of pressure within an iteration cycle.

The CSIMPLER algorithm (the improved version) can enhance the convergence rate for almost all cases tested, especially for the low under‐relaxation factor situations. The pressure correction term even can be overrelaxed to further enhance the convergence rate.

The CSIMPLER algorithm can enhance the rate of convergence to different degree for different problems. It can only be adopted to solve the incompressible fluid flow and heat transfer.

CSIMPLER is a simple and effectual method to enhance the convergence rate of the iterative process for the computational fluid dynamics analysis. The existing code of SIMPLER can be easily changed to CSIMPLER.

The paper developed an improved version of SIMPLER algorithm with some minor changes in the existing SIMPLER code.

A comparative study is performed to reveal the convergence characteristics and the robustness of four variants in the semi‐implicit method for pressure‐linked equations (SIMPLE)‐family: SIMPLE, SIMPLE revised (SIMPLER), SIMPLE consistent (SIMPLEC), and SIMPLE extrapolation (SIMPLEX). The focus is concentrated in the solution at fine grid system. Four typical fluid flow and heat transfer problems are taken as the numerical examples (lid‐driven cavity flow, flow in an axisymmetric sudden expansion, flow in an annulus with inner surface rotating and the natural convection in a square enclosure). It is found that an appropriate convergence condition should include both mass conservation and momentum conservation requirements. For the four problems computed, the SIMPLEX always requires the largest computational time, the SIMPLER comes the next, and the computational time of SIMPLE and SIMPLEC are the least. As far as the robustness is concerned, the SIMPLE algorithm is the worst, the SIMPLER comes the next and the robustness of SIMPLEX and SIMPLEC are superior to the others. The SIMPLEC algorithm is then recommended, especially for the computation at a fine grid system. Brief discussion is provided to further reveal the reasons which may account for the difference of the four algorithms.

This paper aims to describe a recently proposed algorithm in terrain‐based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature. It also aims to use an extended Kalman filter (EKF) to estimate the fused position of the UAVs and to apply the 2‐D splinegon technique to build the map of the complex shaped obstacles. The path of the UAVs are dictated by the Dubins path planning algorithm. The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.

An extended Kalman filter is used to estimate the position of the UAVs, and the 2‐D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.

The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2‐D splinegon technique. This is a newly proposed algorithm, the most efficient and a robust way in terrain based mapping of the complex obstacles. The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.

The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2‐D splinegon technique. This is a new approach where the fused EKF estimated positions are used with the limited number of sensors' measurements in building the map of the complex obstacles.

We argue that a fundamental issue regarding how to search and how to switch between different cognitive modes lies in the decision rules that influence the dynamics of learning and exploration. We examine the search logics underlying these decision rules and propose conceptual prompts that can be applied mentally or computationally to aid managers’ decision-making.

By applying Multi-Armed Bandit (MAB) modeling to simulate agents’ interaction with dynamic environments, we compared the patterns and performance of selected MAB algorithms under different configurations of environmental conditions.

We develop three conceptual prompts. First, the simple heuristic-based exploration strategy works well in conditions of low environmental variability and few alternatives. Second, an exploration strategy that combines simple and de-biasing heuristics is suitable for most dynamic and complex decision environments. Third, the uncertainty-based exploration strategy is more applicable in the condition of high environmental unpredictability as it can more effectively recognize deviated patterns.

This study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.

This study offers insights that there are different possibilities for exploration strategies for managers to apply conceptually and that the adaptability of cognitive-distant search may be underestimated in turbulent environments.

Drawing on insights from machine learning and cognitive psychology research, we demonstrate the fitness of different exploration strategies in different dynamic environmental configurations by comparing the different search logics that underlie the three MAB algorithms.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

Describes the solution of implicitly discretized fluid flow equations, for turbulent swirling flows, by means of a new iterative method, based on the PISO algorithm. The technique is based on the splitting of the solution process into a series of steps, where, at each step, pressure operations are dissociated from those on velocities. The split sets of equations are solvable by standard numerical techniques. However, in this work, the influence of turbulent quantities and tangential velocity on the momentum equations is taken into account in the splitting process, through the source terms of the discretized equations. Does this by embedding a predictor and a corrector step for the turbulent quantities and for the tangential velocity into the PISO algorithm. This improvement to the standard PISO algorithm leads to a considerable reduction of the computational effort in achieving the same numerically converged solution. Applies the algorithm, in conjunction with a finite‐volume technique, to simulate two different geometries for two‐dimensional (axisymmetric) isothermal turbulent steady‐flows, with and without swirl. Compares the results with predictions of the same flows obtained other iterative methods (SIMPLE) and (PISO). Shows that the algorithm presented herein always exhibits a better efficiency in getting exactly the same final predictions, as far as the number of iterations for convergence and the required central processing unit time are concerned.

This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW).

The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA.

A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA.

This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

The purpose of this paper is to develop a coupled approach to solve the pressure–velocity-coupled problem efficiently in the three-dimensional injection molding simulation.

A fully coupled pressure–velocity algorithm is developed to solve the coupled problem, by treating the pressure gradient term implicitly. And, the Schur complement preconditioned FGMRES is applied to decompose the resulting coupled pressure–velocity equation into pressure and velocity subsystems. Then, BoomerAMG is adopted to solve the pressure subsystem, and block Jacobi preconditioned FGMRES is applied to the velocity subsystem.

According to the several experiments, the fully coupled pressure–velocity algorithm was demonstrated to have faster convergence than the traditional SIMPLE algorithm, and the calculating time was reduced by up to 70 per cent. And, the Schur complement preconditioned FGMRES worked more efficiently than block Gauss–Seidel preconditioned FGMRES, block-selective AMG and AMG with block ILU(0) smoother and could take at least 47.4 per cent less time. The proposed solver had good scalability for different-size problems, including various cases with different numbers of elements. It also kept good speedup and efficiency in parallel performance.

A coupled solver has been proposed to effectively solve the coupled problem in the three-dimensional injection molding simulation, which is more robust and efficient than existing methods.

Simple algoritims are developed to track the harmonic structure of the voltages or currents in a power system. The algorithms are based on fitting the measured signal adaptively to an appropriate model in a least mean squares (LMS) sense. The update is accomplished employing the well known adaptive LMS and signed LMS algorithms. The input sequence at each iteration consists of samples of harmonic sinusoids with a fundamental equal to the power frequency. It is shown that such input satisfies the persistence of excitation condition. It is also shown that the algorithms are computationally simple, practically tractable, and exhibit guaranteed stability. The performance of the algorithms and their efficiency in digital relaying in power system protection problems are demonstrated.

Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.

In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.

Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.

This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.