Optimization Toolbox Research Articles

Optimal Dispatch of Concentrating Solar Thermal Power (CSP)-Wind Combined Power Generation System

The random nature of renewable energy leads to the instability of the balance state of the power grid, and the frequency and voltage of the grid also fluctuate. Considering that wind energy and solar thermal power generation can complement each other in terms of temporal output power, the heat storage system of the solar thermal power station is used to regulate the joint operation of the solar thermal power station and wind power generation, and an optimal scheduling method for promoting new energy consumption of solar thermal power station is proposed. Based on the solar thermal-wind combined power generation system, the method considers the operating characteristics and constraints of each unit and uses the MATLAB optimization toolbox to simulate and verify the IEEE-14 node system. The results show that the participation of wind power grid-connected time thermal power stations can alleviate the pressure of rapid output adjustment of traditional units and can ensure the economy of the system, and the operating cost is reduced by 51.5%. The curtailment air volume was reduced from the original 2.5015 MW to 0.83015 MW, maximizing new energy consumption.

Multi-objective optimization model of transmission error of nonlinear dynamic load of double helical gears

Abstract In order to address the impact of reduced transmission stability and reliability caused by volume reduction on the quality of gear transmission, this article proposes a multi-objective optimization model for nonlinear dynamic load transmission errors of double helical gears. This study aims to introduce a multi-objective design method for gear transmission, using the volume and smooth reliability of helical gears as objective functions, and establish a multi-objective optimization design mathematical model for helical cylindrical gear transmission. In order to solve this multi-objective optimization problem, we utilized the optimization toolbox in the scientific calculation software MATLAB with examples. The results show that after the joint optimization design of volume and coincidence degree, it is calculated that the volume after the joint optimization design is still 2.2624 × 107 mm3, and the coincidence degree is 5.9908. After rounding, the design result is m n = 3 , Z 1 = 31 , β = 20 ∘ , Ψ d = 1.2 {m}_{{\rm{n}}}=3,{Z}_{1}=31,\beta ={20}^{\circ },{\Psi }_{{\rm{d}}}=1.2 . The optimization design results show that the joint optimization design with the minimum volume and the maximum coincidence as the objective function can reduce the volume and improve the output stability of the helical gear.

A distributed model parameter optimization toolbox performing multisite calibration in the lump and distributed mode for the SWAT model

A distributed model parameter optimization toolbox performing multisite calibration in the lump and distributed mode for the SWAT model

Research on Multi-Objective Optimization Model of Foundation Pit Dewatering Based on NSGA-II Algorithm

This study focuses on optimizing the foundation pit dewatering scheme using the foundation pit dewatering theory and the principles of multi-objective optimization. It explores the development of a multi-objective optimization model and efficient solution technology for foundation pit dewatering. This research focuses on the foundation pit dewatering project at the inverted siphon section of Xixiayuan canal head, specifically from pile number XZ0+326 to XZ0+500. It establishes an optimized mathematical model for foundation pit dewatering that incorporates three objectives. Additionally, a dewatering optimization program is developed by utilizing the MATLAB optimization toolbox and the multi-objective optimization algorithm program based on the NSGA-II algorithm (Gamultiobj). The multi-objective optimization mathematical model is solved, and a Pareto-optimal solution set with uniform distribution is obtained. The multi-objective optimization evaluation system based on AHP is constructed from the three aspects of dewatering cost, the impact of settlement on the environment, and the safety and stability of the foundation pit. The optimization scheme of the Pareto-optimal solution set is selected as the decision result to provide multiple feasible schemes for the dewatering construction of foundation pits. The optimization scheme is verified by using the GMS software. The simulation results demonstrate that the optimization scheme fulfills the requirements for water level and settlement control. Moreover, the developed optimization program efficiently solves the multi-objective optimization problem associated with foundation pit dewatering. Lastly, an evaluation system incorporating the NSGA-II algorithm and AHP is developed and utilized in the context of dewatering engineering in order to offer multiple viable optimal dewatering schemes.

SELECTION OF THE SET OF ALLOWABLE VALUES OF THE VARIABLE PARAMETERS OF THE STABILIZER OF A COMPLEX DYNAMIC OBJECT

Topicality. One of the most important tasks in the application of computational methods for the parametric synthesis of controllers of complex dynamic objects is the task of determining the set of permissible values of the variable parameters of the controller, where the target function is calculated based on the solutions of the mathematical model of the disturbed motion of the dynamic object with its subsequent minimization. The purpose of the work is to construct the set of permissible values of variable parameters of the stabilizer of a complex dynamic object when applying the algorithmic combined method of parametric synthesis of stabilizers of complex dynamic objects, the essence of which is the direct calculation of the integral quadratic functional on the solutions of a closed dynamic system with subsequent finding of its global minimum through a sequential combination of two algorithms – the Sobol grid algorithm and the Nelder-Mead algorithm. Results. With the help of the Sobol grid construction algorithm, the starting point of the computational process is brought to the node of the Sobol grid, which is located in the small vicinity of the point of the global minimum. At the second stage of optimization, the found Sobol grid node is selected as the starting point for applying the Nelder-Mead method, which is implemented by the Optimization Toolbox software product of the MATLAB package or the Minimize software product of the MATHCAD package and leads the computational process to the point of the global minimum. Conclusion. The paper proves a theorem according to which the stability region of a closed system of the first approximation can be taken as such a set, and also gives an example of a solution to the problem of parametric synthesis of the stabilizer of the car's course stability system during its emergency braking.

Optimization of Rectangular and Box Sections in Oblique Bending and Eccentric Compression

The article presents a solution to the problem of finding the optimal ratio of the height of the cross-section to the width for a rectangular and box-shaped section in the case of oblique bending and eccentric compression. Optimization is performed according to the strength criterion, and for the case of oblique bending of a rectangular beam, a solution was also obtained from the condition of a minimum full deflection. For a rectangular section, the solution is made analytically, and for a box section, numerically using the MATLAB environment and the Optimization Toolbox package. As a numerical method of nonlinear optimization, the interior point method is used. To simplify the solution, the box section is assumed to be thin-walled, i.e. it is assumed that the wall thickness is significantly less than the height and width of the cross section. An estimate of the error of such an assumption is also performed. It has been established that in the case of oblique bending of a rectangular beam, when optimizing according to the strength criterion, the optimal ratio of the cross-sectional height to width is equal to the cotangent of the angle between the force plane and the vertical axis, and when optimizing according to the rigidity criterion, it is the square root of the cotangent of this angle. In the case of eccentric compression of a rectangular beam with eccentricities in two planes, the optimal ratio of the height of the cross section to the width is equal to the ratio of the eccentricity along the vertical and horizontal axes. For a box-shaped section, graphs of the change in optimal parameters depending on the angle between the force plane and the vertical axis in the case of oblique bending, as well as depending on the ratio of eccentricities along the axes in the case of eccentric compression, are plotted.

Switching Frequency Minimized Harmonic Mitigation: A Multiobjective Optimized Modulation Strategy for High- Power Converters

In order to explore the minimum switching frequency required to regulate certain amount of harmonic components, a novel switching frequency minimized harmonic mitigation (SFMHM) model is proposed in this paper, which can regulate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \mathit{N}\mathbf{-1}$</tex-math></inline-formula> harmonics with far less than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> switching angles. By flexibly adjusting the threshold values, the proposed model can achieve both harmonic elimination and mitigation objectives. Based on PWM discretization and supported by quadratic programming, the switching frequency in the proposed model is expressed as a quadratic objective function of the PWM waveform. The objectives as fundamental control and selected harmonic mitigations are realized by treating the numerical approximations of the Fourier coefficients as constraints, which are finally transformed into an optimization model with binary variables and can be easily solved by some optimization toolboxes such as YALMIP. Some computing results show that, compared with the conventional selective harmonic elimination/mitigation (SHE/SHM) methods, the number of switching angles required to mitigate the same number of harmonics under the proposed method has been significantly reduced. The switching frequency can be reduced a lot compared with the conventional SHE/SHM methods, e.g., by 40% for some modulation indexes. Simulations and experiments verify the correctness of proposed SFMHM model.

An Open Source Design Optimization Toolbox Evaluated on a Soft Finger

An Open Source Design Optimization Toolbox Evaluated on a Soft Finger

Performance enhancement of a Wells turbine using CFD-optimization algorithms coupling

Wells turbine is a popular device for converting wave energy. The current work aims to maximize the power a Wells turbine generates using a response surface optimization method based on computational fluid dynamics (CFD). The ANSYS software's response surface optimization toolbox was employed in this investigation. The Wells turbine received a performance boost using the variable tip clearance design. Input parameters for the optimization technique included two variable tip clearance design parameters. The major objective of the optimization process was to maximize the torque coefficient (CT) of the Wells turbine. The results showed that the optimal design boosted the average and maximum torque coefficients of the modified Wells turbine by 37.31% and 76.16%, respectively.Furthermore, the improved turbine has a 33.33% wider operating range (OR) than the baseline. At φ equals 0.225, 0.250, and 0.275, the improved Wells turbine's efficiency rose by 19.53%, 71.67%, and 80.38%, respectively. Using the optimal design, 92.2% more power was generated from the modified Wells turbine. Based on a detailed flow analysis, the flow separation zone around the blade tip was reduced, and the optimal variable tip clearance design postponed the stall onset point.

V2G Scheduling of Electric Vehicles Considering Wind Power Consumption

The wind power (WP) has strong random volatility and is not coordinated with the load in time and space, resulting in serious wind abandonment. Based on this, an orderly charging and discharging strategy for electric vehicles (EVs) considering WP consumption is proposed in this paper. The strategy uses the vehicle-to-grid (V2G) technology to establish the maximum consumption of WP in the region, minimizes the peak–valley difference of the power grid and maximizes the electricity sales efficiency of the power company in the mountainous city. The dynamic electricity prices are set according to the predicted values and the true values of WP output, and the improved adaptive particle swarm optimization (APSO) and CVX toolbox are used to solve the problems. When the user responsiveness is 30%, 60% and 100%, the WP consumption is 72.1%, 81.04% and 92.69%, respectively. Meanwhile, the peak shaving and valley filling of the power grid are realized, and the power sales benefit of the power company is guaranteed.

Design optimization to minimize wake of wide-body transport aircraft

A cutting-edge sustainable airplane can improve performance and enhance profitability are the most significant future concerns for the aviation industry. This research aims to analyse and assess aircraft performance of mid-size transport aircraft through retrofit designs. An optimal design was employed using a non-linear optimization approach under two cases fuselage alone (case 1) and a combination of fuselage-wing-empennage (case 2). These two retrofit concepts were evaluated and compared with the baseline A310–200 aircraft. To compute the objective function for various cases, non-linear solver was used from MATLAB optimization tool box. Additionally, the parametric studies of aerodynamics, structural and operational performance analysis are carried out by using Advanced Aircraft Analysis (AAA) 5.0 software. Sensitivity analyses are performed to assess the overall effectiveness of the optimal design. The key findings of this paper obtained the following parameters as Maximum take-off Weight, empty weight, fuel weight, parasite drag coefficient, rate of climb, take-off distance and landing distance are improved by 0.36%, 26.02%, 10.12%, 4.48%, 27.24%, 13.13% and 9.2% respectively. At the end, the paper provides a clear statement on the managerial perceptions and future insights for the upcoming challenges towards sustainable design.

Optimizing the performance of the ZnO-based varistors using the neural networks technique

This study aimed to propose a scientific method depending on neural networks technique to determine the optimal proportions of some impurities (Bi2O3, Nb2O5, MnO2, Co3O4, Cr2O3, NiO, La2O3, Ce2O3) used in manufacturing the ZnO-based varistors. The technique enables obtaining varistors with high structural homogeneity, high performance, and surge energy absorption capability. A nonlinear neural model between the nonlinear coefficient and the proportion of each impurity was accurately created. Subsequently, the neural models have been trained to approximate the correlation factor to the optimum value (R = 1), which proves the accuracy of the models. Then, using the optimization toolbox in MATLAB, the proportion of each impurity producing the highest nonlinear coefficient was determined. Afterwards, a nonlinear neural model representing the relationship between the most influential impurities combined (MnO2, NiO, Ce2O3, La2O3) and the nonlinear coefficient was built. Through training the neural model, the correlation factor was R = 0.99516, and the mean square error was MSE = 0.1078, proving the model's accuracy. Depending on the optimal solutions based on the Lagrange-Newton algorithm in MATLAB, the optimal proportions of impurities combined corresponding to the highest value of the nonlinear coefficient were determined. The accuracy of the proposed method was practically confirmed by preparing a varistor according to the optimal proportions and performing the measurements. The relative error between the theoretical value (α = 99.7522) and the practical value (α = 99.41) of the nonlinear coefficient was RE = 0.0035. That confirms that the proposed method has high reliability and accuracy to be adopted.

Dynamic simulation of a triple-mode multi-generation system assisted by heat recovery and solar energy storage modules: Techno-economic optimization using machine learning approaches

Intelligent design and operation optimization allow energy systems to take advantage of the flexibility that multi-generation provides. This study proposes a basic solar-driven system integrated with thermal energy storage for round-the-clock energy harvesting. A modified configuration is then designed incorporating innovative multi-heat recovery approaches to increase the capacity and product diversity of the basic system. The modified system is able to cover vital urban utilities such as electricity, fresh water, cooling, and hydrogen throughout the day. To overcome the time-consuming procedure of dynamic techno-economic simulation as well as the limitation of commercial engineering equation solvers for tri-objective optimization, a deep learning approach is developed to reduce the computational complexity and improve the analysis accuracy. In this regard, the trained neural networks play an intermediary role in coupling the developed code with the MATLAB optimization toolbox. A comparison between the modified and conventional configurations indicates that implementing the multi-heat recovery approach results in a 29% increase in power generation while only increasing the overall system cost by 1.97%. From an economic perspective, the Sankey diagram depicts that the storage unit with a cost rate of 12.25 $/h accounts for 6.77% of the plant's cost rate, which enables the system to operate continuously. According to the sensitivity analysis and contour plots, the number of collectors significantly affects the total cost rate and fresh water production capacity while it has no tangible effect on the exergy efficiency.

Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks

The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40.7% system performance compared to alternative approaches.

Planning of UAV Flight Routes in the Problems of Group Patrolling of the Extended Territories

Currently one of the promising areas of joint use of unmanned aerial vehicles (UAVs) is group air patrolling of large territories. Here the organization of patrolling assumes the solving the planning problem of routes flight of UAV group. The paper considers the problem of optimal planning of flight routes of the same type of UAVs during group patrolling of large territories. The territorial waters or narrow border areas of any State may serve as an example of such territories. It is suggested that the patrolled area has an elongated shape and is divided into a chain of adjacent patrol zones prescribed by a separate UAV. The drone’s flight route passes through adjacent zones. The flight task performed periodically by each drone consists in moving it to a given flight zone, collecting operational data and transmitting this data to a control point (center, station). The optimization aspect of UAV flight route planning is to minimize the maximum time required to complete flight tasks. The considered problem of group patrolling reduced to the multiple traveling salesman problem — one of the classic intractable combinatorial optimization problems. A brief analysis of modern methods for solving the multiple traveling salesman problem is given. Due to the lack of effective exact methods for solving this problem, it is natural to use approximate heuristic and metaheuristic methods focused on solving NP-hard optimization problems, reducing the full search and giving a solution close to the exact one. The multiple traveling salesman problem considered in this paper is reduced to the problem of integer linear programming, for the solution of which a genetic algorithm implemented in MATLAB based on the mathematical package Global Optimization Toolbox is proposed. An illustrative example of patrolling by three UAVs of an extended territory with 11 adjacent zones is considered. Computational experiments confirm the effectiveness of the algorithmic solutions proposed in the work.

Range of application of heat pump systems on various natural refrigerants

GROUNDING: The utilization of heat pumps (variation of vapor compressing machines) for heat transfer between different temperature levels is a promising development trend, considering low cost of heat energy, environmental friendliness of working mediums, economically sufficient solutions and low cost maintenance. Heat pumps are used for heating in building structures where low potential heat sources are wastewater streams, groundwater streams, air, heat of the soil. It is also possible to use low potential heat of heat emissions from industrial plants, that can be cost effectively in terms of production resources usage.&#x0D; AIMS: Selection of the refrigerant and its composition in order to provide operational stability for heat pumps at temperature levels from 48 to 95 ̊C with minimum energy consumption for the most widespread thermodynamical cycles.&#x0D; MATERIALS AND METHODS: Standard thermodynamic cycles of heat pumps were simulated using Aspen HYSYS software. Further optimization and parameter selection were performed using MATLAB module Global Optimization Toolbox.&#x0D; RESULTS: In compliance with demanded design parameters the results of each working medium simulation were obtained. Energy efficient parameters of the common heat pumps thermodynamical cycles were determined.&#x0D; CONCLUSIONS: According to exergy analysis of the thermodynamical cycles it has been determined that mixed refrigerants, pentane and ammonia are the most suitable and energy efficient working fluids for heat pumps.

Placement of High Availability Geo-Distributed Data Centers in Emerging Economies

The data center markets in emerging economies are being built at a furious pace. When high availability is required, as it always is in the modern digital economy, the placement of geo-distributed data centers may be influenced by factors such as technician shortage and under-developed infrastructure, both of which are typical in emerging economies. Although the data center availability subject in general has been well studied, it remains unclear how rapid and unbalanced economic development in emerging economies may affect the availability of geo-distributed data centers and their cost of ownership. In this paper, we incorporate the unbalanced availability of infrastructure and technician into the data center placement. The problem is first formulated as a mixed integer nonlinear program (MINLP).To solve this potentially large scale problem, we transform it into a QCQP, capable of handling heterogeneous workloads. The resulting problem can then be efficiently solved by off-the-shelf optimization toolboxes. With real-life data in China, we show how unbalanced development of infrastructure and technician shortage may affect the placement of data centers, and analyze the tradeoff between cost and availability. Our results indicate that technician shortage and unbalanced network infrastructure will lead to increased cost and distinct data center placement strategies.

Consensus Congestion Control for Ad Hoc Networks: Time-Delay and Saturation

This work focuses on the congestion control design for ad hoc networks affected by time-delay and asymmetrical input saturation. First, an innovative general TCP/AQM model for mobile and wireless ad hoc networks (MANET/WANET) is proposed using graph theory and TCP/AQM system dynamics in a dumbbell topology. Next, a new characteristic condition for sector linearity is developed, which plays the role of a specification to be checked and allows us to assume that the dynamics of the nodes are not saturated. Finally, we propose a new consensus-based congestion control protocol while satisfying the input constraints based on the Lyapunov method, the new sector of linearity condition, the Bessel Legendre-based integral inequality, and the reciprocal convex combination. The proposed results have the advantage of being given as a set of linear matrix inequality (LMI) conditions, which can be solved using convex optimization toolboxes. The merits of the theory are evaluated with the help of various real and challenging scenarios with the use of MATLAB and NS3.

Optimal measurement structures for contextuality applications

The Kochen-Specker (KS) theorem is a cornerstone result in the foundations of quantum mechanics describing the fundamental difference between quantum theory and classical non-contextual theories. Recently specific substructures termed 01-gadgets were shown to exist within KS proofs that capture the essential contradiction of the theorem. Here, we show these gadgets and their generalizations provide an optimal toolbox for contextuality applications including (i) constructing classical channels exhibiting entanglement-assisted advantage in zero-error communication, (ii) identifying large separations between quantum theory and binary generalized probabilistic theories, and (iii) finding optimal tests for contextuality-based semi-device-independent randomness generation. Furthermore, we introduce and study a generalization to definite prediction sets for more general logical propositions, that we term higher-order gadgets. We pinpoint the role these higher-order gadgets play in KS proofs by identifying these as induced subgraphs within KS graphs and showing how to construct proofs of state-independent contextuality using higher-order gadgets as building blocks. The constructions developed here may help in solving some of the remaining open problems regarding minimal proofs of the Kochen-Specker theorem.

Vibration Control of the Steel-Concrete Composite Box Girder Bridge with Slip and Shear-Lag Effects by MTMDs under the Train-Bridge Interaction

Steel-concrete composite girder bridges, in essence, are thin-walled structures, the dynamic responses of which will be greater than those of a typical Euler–Bernoulli beam under the train-bridge interaction because of the shear lag and interface slip. Thus, in this study, the dynamic analysis model of a train-composite box girder bridge-multiple tuned mass damper (MTMD) coupling system is proposed, with the derived dynamic equations of this time-varying system. Meanwhile, the program is compiled using the Newmark-β method for the solution, combined with the optimization toolbox to solve the complex optimization design problems of MTMDs involved. Finally, factors affecting the vibration-damping effect are studied, such as the mass ratio of MTMDs, the number of trains, and the slip and shear lag of the composite box girder bridge.