Multi-parameter Optimization Method Research Articles

Design and Optimization of Heat Dissipation for a High-Voltage Control Box in Energy Storage Systems

Abstract To address the issue of excessive temperature rises within the field of electronic device cooling, this study adopts a multi-parameter optimization method. The primary objective is to explore and realize the design optimization of the shell structure of the high-voltage control box, aiming to effectively mitigate the temperature rise in internal components and enhance their thermal management efficacy without altering the fan performance, environmental conditions, or spatial layout. Initially, the study employs computational fluid dynamics methods to investigate the heat dissipation characteristics of the high-voltage control box, subsequently verifying the simulation parameters' accuracy through temperature rise tests. Building upon this foundation, the article conducts a thorough analysis of how the position and shape of the box's openings impact the device's temperature rise. The findings suggest that configuring circular openings on the front and rear sides can optimize the heat dissipation effect. Moreover, the SHERPA algorithm was employed to refine the size and distribution of the openings on the outer shell of the high-voltage control box through multi-parameter optimization, yielding locally optimal structural parameters. Post-optimization, the temperature measurement points within the high-voltage control box exhibited a maximum reduction in temperature rise of 27.16%. The pivotal contribution of this methodology is the application of a data-driven decision-making process for the enhancement of conventional heat dissipation designs. This research offers invaluable practical insights and novel perspectives on the optimization of thermal management designs for box-type electronic devices, significantly advancing the field of thermal management technology in electronic devices.

Multi-Parameter Inversion Optimization for Transverse Rib Profile Enhanced Convective Heat Transfer

The convective heat transfer performance of the rib-element approach in the channel is influenced by the transverse rib profile. In present work, a nonlinear multi-parameter optimization method based on inversion optimization is proposed and further used to obtain the optimal rib profile in a rectangular channel. Firstly, four conventional profiles as the original ribbed channel were selected to obtain the convective heat transfer performance of the channel by experiment and numerical solution; secondly, several control points were selected along the profile, and the polar radius was updated, and curve fitting was carried out. The results show that the inversion optimization can optimize the rib profile to improve the convective heat transfer performance. As the number of control points increases, the optimized profile changes hardly. The inversion optimization yields a similar optimized profile regardless of the original rib profile. With the increase of original rib height, the enhanced heat transfer performance of optimized profile is better. For the ribbed channel with original height 0.4 × channel height, as the Reynolds number increases from 6000 to 33,000, the performance evaluation criteria of the optimized profile increase by 14.94% to 21.2% compared with the original triangular profile.

Structural optimization design of connection between centrifugal impeller and shaft end based on finite element method

A centrifugal compressor is the core equipment of the chemical industry. To solve the problems of assembly difficulties and poor mechanical properties caused by the connection between impeller and shaft end in existing compressor, this paper takes a small geared air compressor designed by Shenyang Borui Turbine Technical Service Co., Ltd. as an example and designs its first-stage impeller and shaft end into three structural forms of tie rod–pin connection, tie rod–end key connection, and flange connection, respectively, to find an optimal connection mode between the centrifugal impeller and shaft end. Firstly, the calculation method is verified. Then, the stress, strain, deformation, and critical rotor speed of these three structures are compared and analyzed based on multi-parameter genetic optimization method. The results show that when the impeller and shaft end are connected by the tie rod–end key structure, compared to the tie rod–pin connection structure and flange connection structure, the maximum stress is reduced by 43.8% and 61.2%, the strain is reduced by 43.8% and 61.8%, the deformation is reduced by 3.64 times and 6.27 times, respectively, and the resonance margin will also be greatly improved. The research results in this paper provide a theoretical basis for the reliable design of the connection between the centrifugal impeller and shaft end.

Steel Ladle Slag Zone Lining Optimization Considering Irreversible Material Behavior

The optimization of refractory linings is a complex task due to the many factors that influence the thermomechanical behavior. This study applies the multiparameter optimization method technique for order preference by similarity to the ideal solution to a commercially used slag zone lining of a secondary metallurgical steel ladle, considering the influence of different material models in a finite element (FE) simulation. The dataset is acquired by varying the working lining brick shape, initial expansion allowance (IEA), and isolation layer thickness. The maximum irreversible strain, joint opening at the hot face, and steel shell temperature are determined from the FE simulation results and used as the input values for the optimization. A larger circumferential brick dimension combined with low IEA is favorable for the von Mises creep model. The main contribution is from the IEA (98.53%). The optimized lining for the Drucker–Prager criterion has a brick with a small circumferential dimension and larger IEA. The main influencing factor is the brick shape (85.06%). The differences in the optimization results and factor contributions can be explained by the contribution of the constitutive models to the simulation results before and after the thermal shock.

A comprehensive multi-parameter optimization method of squeeze film damper-rotor system using hunter-prey optimization algorithm

The squeeze film damper (SFD) is a damping device that is widely used in turbomachinery. A well-designed SFD structure can effectively mitigate vibration at critical speeds, while a poorly designed SFD may increase vibration. The comprehensive multi-parameter optimization method is used for optimizing the vibration response at critical speed and improving the reliability and overall performance by designing the crucial structural parameters of the SFD. Firstly, the coupling dynamic model is proposed for the rotor and SFD, taking into account the influence of non-linear oil film forces of SFD. The test rig is used to verify the vibration response in a typical SFD-rotor system. To provide the optimal vibration reduction effect within a specific SFD parameter range for a particular rotor, a method for comprehensive multi-parameter optimization is introduced. This method introduces the hunter-prey intelligent optimization (HPO) algorithm and compares its results with the PSO algorithm. The comprehensive optimization method revealed that the key parameters of the SFD, when designed using this approach, can effectively alleviate the vibration response of the entire rotor system, achieving the rotor system amplitude reduction ratio of up to13.89%.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

Parameters optimum of multiple underframe suspended equipment on high-speed railway vehicle carbody vibration control by using an improved genetic algorithm

Carbody vibration control and ride comfort improvement are important research parts of high-speed railway vehicle dynamic. The effectiveness of utilizing carbody underframe suspended equipment as dynamic vibration absorbers for carbody vibration suppression has been validated. While previous research has focused mainly on a single suspended equipment without considering its vibration tolerance, a balance must be struck between reducing carbody vibration and maintaining equipment performance. To address the challenging, a multi-objective and multi-parameter optimization method is employed, integrating an improved niche genetic algorithm (INGA) into the numerical simulation process. Considering carbody flexible and multi-suspended equipment, a rigid-flexible dynamics model of a high-speed railway vehicle is established and validated on a vehicle dynamics test rig. Based on the numerical analysis, the optimal parameter combination of the stiffness and the longitudinal installation position is obtained from an evaluation function that comprehensively assesses the vibration performance of both the vehicle carbody and the equipment. Comparative analysis between the results obtained from the optimal and primitive parameters demonstrates that the optimization process not only achieves significant improvements in carbody vibration reduction but also effectively controls equipment vibration within reasonable limits. The multi-objective and multi-parameter optimization method applied on carbody underframe suspended system proves to be valuable in improving the dynamic performance of high-speed railway vehicles.

Study on heat transfer coupling mechanism of direct thermoelectric conversion with regenerative cooling for supersonic combustor applications

Direct thermoelectric conversion is a promising technology that converts aerodynamic and combustion heat into electric energy for thermal management of supersonic combustors. However, the heat transfer process of direct thermoelectric conversion coupled with regenerative cooling of supersonic combustors is rarely studied and the coupling mechanism is not clear. In this paper, a novel “SANDWICH” thermoelectric/cooling integrated model (TCIM) proposed by the present work based on Skutterudite (SKD) or Half-Heusler (HH) thermoelectric conversion devices is analytically studied. The energy conversion and heat transfer mechanism of the direct thermoelectric conversion coupled with regenerative cooling are revealed. Using the analytical model, the effects of structural parameters of thermoelectric conversion device and the heat transfer coefficient of regenerative cooling on the energy conversion efficiency and output power are investigated for two typical thermal conditions corresponding to the isolation part and the combustion part of a Mach 2.5 supersonic combustor. The analytical results showed that larger size of the thermoelectric conversion device can effectively improve the conversion efficiency and output power per unit area. Increasing the heat transfer coefficient of regenerative cooling can lower the structural temperature and increase the output power per unit area by more than 12%. Furthermore, with a multi-parameter optimization method based on Genetic Algorithm, optimization for the output power per unit area is conducted with variations of the structural parameters of TCIM under constraints of the tolerance temperature and the types of thermoelectric conversion devices. A more than 10% increase in the output power per unit area for both SKD and HH TCIMs are achieved via the optimization.

Stochastic resonance induced weak signal enhancement in a second-order tri-stable system with single-parameter adjusting

Weak signal detection methods based on stochastic resonance (SR) have been extensively studied due to their capability to utilize noise energy for enhancing weak signals. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak-signal detection with better output performance compared to other SR models. To optimize the output performance of the second-order tri-stable systems, a variety of parameter optimization methods have been proposed to optimize the parameters of the system. However, these optimization methods often require to optimize multiple parameters, which leads to an increase in computational costs and reduces the real-time processing efficiency of signal processing. Such multi-parameter optimization methods cannot meet the demands for timely signal processing in the context of big data. To address this challenge, this paper proposes two single-parameter-adjusting SR models. The proposed models can attain an ideal output performance by adjusting a single parameter in the SR system. The spectral amplification as an indicator is derived to quantitatively analyze the effects of the proposed models on SR output. On this basis, the influences of the proposed models on the SR output under different potential well parameters, noise intensities, signal frequency, and damping ratio are fully investigated through numerical simulations. At last, the proposed models are employed to process an experimental signal with a weak fault feature, and the experimental results verify the feasibility of the proposed models in optimizing the SR output. The research results can guide the design of tri-stable SR models and support the application of the SR-based signal processing model in the context of big data.

Optimization Method of Multi-parameter Coupling for a Hydraulic Rolling Reshaper Based on Factorial Design

A hydraulic rolling reshaper is an advanced shaping technology with superior protection for casings, and the structural parameters of the reshaper affect its shaping effect on deformed casing directly. To improve the shaping capacity of the reshaper, a multi-parameter coupling optimization method of hydraulic rolling reshaper is proposed to optimize the design of the factors with significant influence under the premise of screening multi-structural parameters. In this paper, according to the working principle of the reshaper, considering the contact nonlinearity between the hydraulic rolling reshaper and deformed casing, as well as the material nonlinearity of the casing, a parametric finite element model of the hydraulic rolling reshaper repairing the shrinkage deformation of casings was developed. The remarkable factors were screened by factorial design, the sample points were generated by optimal Latin hyper-cube design (OLHD), and the response surface models were established by stepwise regression. Therefore, with the maximum plastic deformation of casings as the objective function, the maximum equivalent stress, residual stress, and the plastic deformation of casings as the constrained conditions, an optimized mathematical model for a reshaper was constructed, and the genetic algorithm (GA) is performed to obtain the optimal combination of parameters. The results showed that the optimal reshaper made the shaping process safe and effective, the plastic deformation of casings after single shaping was increased by 11.38 %, and the shaping effect was better (96.48 %), which can effectively improve the safety performance and shaping ability of the reshaper.

Design and Analysis of a Novel Redundant Parallel Mechanism for Long Bone Fracture Reduction

Abstract In recent years, parallel robots have become a hot research topic in trauma fracture treatment because of their high precision, high load capacity, and compact structure. However, compared with serial robots, parallel robots have disadvantages like smaller workspaces and more complex dexterity. In this paper, a novel redundant parallel mechanism (RPM) for long bone fracture reduction is proposed based on Stewart parallel mechanism (SPM). 6 kinematically redundant DOFs (Degrees of freedom) are added to the RPM. First, the kinematics of the RPM is established, and its workspace is calculated. The analysis results indicate that the position workspace of the RPM is about 19 times larger than that of the SPM. The RPM has a similar range of torsion angles as the SPM, but a more extensive range of tilt angles than the SPM. Second, the singularities of the two parallel mechanisms are compared based on the dimensionally homogeneous Jacobian matrix. The results show that the dexterity of the RPM is much better than the SPM. Third, a multi-parameter multi-objective optimization method is proposed to optimize the geometry parameters of the RPM. The statics of the RPM is analyzed by finite element analysis. To further expand the performance of the RPM, the unfixed RPM (URPM) is proposed. The analysis results show that the URPM is superior to the RPM in terms of workspace and dexterity. Finally, experiments are conducted to verify the effectiveness of the proposed methods in this paper.

Закони керування авіаційним газотурбінним двигуном з турбовентиляторною приставкою

The main aspects of the general task of integrating an aviation engine (AE) with a turbofan additional unit (TAU) of a multi-mode aircraft are the selection of the scheme and design parameters of the AE with the TAU (including parameters of the work process), as well as methods and means of automatic control of the AE with the TAU for better matching its characteristics with certain flight modes. These two tasks are closely interrelated: on the one hand, when determining the appearance of the aircraft and its power plant (PP), it is necessary to consider what means will ensure the adaptation of its characteristics to the variable flight mode, and on the other hand, the purpose of the aircraft, its parameters and parameters of the PP, and the flight modes mostly determine the choice of control laws. The subject of the research is the formation of the laws of control of the aviation gas turbine engine with TAU. The goal is to improve the dynamic characteristics of the aviation gas turbine engine by applying adaptive control of the gas turbine engine with the use of wireless information exchange technologies. Objectives: to generalize the concept of adaptive control at the stage of determining the appearance of an engine with TAU; to determine the methods of regulating the GTE with TAU and their influence on the speed characteristics; to describe the process of formation of the laws governing AE with TAU; to investigate thermal processes in order to find functional dependencies in the optimal control of gas turbines with TAU. Research methods: system analysis, mathematical and computer modeling were used in the formation of control laws; the methods of philosophical knowledge were used to build an approach to the design of adaptive control systems of gas turbines with TAU. The theory of aircraft engines, the theory of differential equations, difference grids, and numerical methods were used to optimize the control of gas turbines with TAU. Results: methods of regulating the GTE with TAU and their influence on the speed characteristics of the aviation engine; formulas for researching the thermal processes of the working blades of the GTE with TAU in order to form the influence of the regulator on the executive mechanisms. Scientific and practical novelty: formation of a paradigm for the development of models of adaptive control of GTE with TAU, considering different flight modes of the aircraft and engine operation modes. The selection of control laws using multi-parameter optimization methods for finding the relationship between structural and component schemes of the gas generator and turbofan additional unit. The character of the engine thrust change depending on the input temperature is shown, which in turn, will allow to increase the efficiency of the fan and obtain a thrust reserve. The research directions of the temperature field and stress field were formed.

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics.

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

Multi-parameter optimization design method for energy system in low-carbon park with integrated hybrid energy storage

Low-carbon parks composed of concentrated and contiguous low-carbon buildings has the characteristics of the high proportion of renewable energy penetration and low-carbon operation, requiring the transformation of relevant energy supply systems from being centralized to being centralized and distributed. However, the centralized and distributed energy supply method faces low levels of renewable energy utilization. Therefore, in this study, a low-carbon park energy system is constructed with a core of photovoltaics, wind power, lithium batteries, and heat storage tanks. A multi-parameter collaborative optimization method is proposed with the goals of cost reduction, carbon reduction, and independence by considering the parameters of equipment configuration and operation. The comprehensive benefits of a low-carbon park energy system under each low-carbon park are explored. The advantages of the system in terms of monthly carbon emission intensity, operation cost, maintenance cost, and interactive power are analysed. For the system, the balance of its energy supply and demand on a typical day and the role of energy storage in improving its utilization of renewable energy are analysed. Finally, the feasibility and benefits of the system's energy supply in different climate zones are further studied. The results show that the energy supply cost, carbon emissions, and interactive power are 31.0 CNY/m2, 4.1 kg/m2, and 2.4 kWh/m2, respectively, resulting in the best benefits for a unit area in a low-carbon park energy system in a high-rise residential park (Beijing). Energy storage can effectively improve the levels of renewable energy utilization, energy conservation and carbon reduction in the system. The low-carbon park energy system proposed in this article can significantly contribute to reducing carbon emissions in the construction industry.

Multi-parameter cooperative optimization and solution method for regional integrated energy system

As an energy system that uses the power grid and heat network to coordinate the energy interaction between subsystems in the region, the regional integrated energy system can further reduce the system's operating cost and improve the energy utilization rate. However, in the optimization of regional integrated energy systems, there is difficulty in balancing the accuracy of the results and the calculation time. Therefore, a nonlinear collaborative optimization model of a regional integrated energy system is established in this paper, which fully considers the electricity and heat sharing between energy stations. Subsequently, a three-layer-nested co-optimization method for a regional integrated energy system is proposed. The outer layer, middle layer, and inner layer optimize the rated capacity of the internal combustion engine, electric refrigeration ratio, and partial load rate of the internal combustion engine, respectively. Finally, the three-layer-nested co-optimization method is verified from the total system cost, energy utilization rate, and calculation time. The results show that compared with the exhaustive search method and genetic algorithm, the cost per unit area of the system is about 102.99 ¥/m2 (reduced by 1.00 ¥/m2), the energy utilization rate is 70.47% (increased by 2.43%), and the calculation time is about 50–100 h. The three-layer-nested co-optimization method significantly shortens the model's solution time while ensuring the optimization results' accuracy. Finally, this paper can provide a research idea for optimizing regional integrated energy system design.

Comparative study on the globally optimal performance of cryogenic energy storage systems with different working media

Cryogenic energy storage (CES) has garnered attention as a large-scale electric energy storage technology for the storage and regulation of intermittent renewable electric energy in power networks. Nitrogen and argon can be found in the air, whereas methane is the primary component of natural gas, an important clean energy resource. Most research on CES focuses on liquid air energy storage (LAES), with its typical round-trip efficiency (RTE) being approximately 50% (theoretical). This study aims to explore the feasibility of using different gases as working media in CES systems, and consequently, to achieve a high system efficiency by constructing four steady-state process models for the CES systems with air, nitrogen, argon, and methane as working media using Aspen HYSYS. A combined single-parameter analysis and multi-parameter global optimization method was used for system optimization. Further, a group of key independent variables were analysed carefully to determine their reasonable ranges to achieve the ideal system performance, that is, RTE and liquefaction ratio through a single-parameter analysis. Consequently, a multi-parameter genetic algorithm was adopted to globally optimize the CES systems with different working media, and the energy and exergy analyses were conducted for the CES systems under their optimal conditions. The results indicated the high cycle efficiency of methane and a low irreversible loss in the liquefaction cycle. Moreover, the Joule-Thomson valve inlet temperature and charging and discharging pressures considerably affected the system performance. However, exergy loss in the CES system occurred primarily in the compressor, turbine, and liquefaction processes. The maximum optimal RTE of 55.84% was achieved in the liquid methane energy storage (LMES) system. Therefore, the LMES system is expected to exhibit potential for application in the CES technology to realize the integration of natural gas pipelines with renewable power grids on a large scale. Moreover, the results of study have important theoretical significance for the innovation of the CES technology.

Constrained Multi-Objective Optimization for Double-Sided Tubular Machine With Hybrid Segmented PM

Double-sided tubular machines (DSTM) have greatly improved space utilization and power density, and are widely used in tidal power generation and high-power electric drives, while the thrust force ripple is also significant. To get the minimum cogging force with the maximum thrust force, hybrid segmented PM array topology is introduced to the DSTM and used as research object for multi-objective optimization. Due to the constraints among the parameters to be optimized, this paper proposes a new constrained multi-parameter multi-objective optimization method. In this method, a new optimal Latin hypercube(OLH) is put forward to solve the constrained parameter sampling problem. RReliefF is used to distinguish the importance of multi-parameter and divide them into three layers: strong, medium and weak, and surrogate models are established hierarchically to simplify the optimization complexity. In addition, an improved Gaussian process regression(GPR) algorithm is proposed to improve the optimization accuracy of strong significance parameters. The optimization results show that the average thrust force is increased by 4.7%, the cogging force is reduced by 28%, and total harmonic distortion rate of the voltage is reduced by 30.5% without loss of efficiency, which significantly improves the performance of the machine, and verifies the effectiveness of the proposed method, which is suitable for constrained multi-parameter multi-objective optimization.

Multi-Parameter Optimization of Heat Dissipation Structure of Double Disk Magnetic Coupler Based on Orthogonal Experimental Design

The existing heat dissipation research on double disk magnetic couplers ignores the coupling influence of electromagnetic temperature–stress and other multiphysics fields, and the error between the calculation and analysis results and the measured values is large. Therefore, a multi-parameter optimization method for heat dissipation structures of double disk magnetic couplers based on orthogonal experimental design is proposed. Based on the double disk magnetic coupler model, a three-dimensional finite element model based on fluid–solid–heat coupling is established, with the axial air gap length, input motor speed, the thickness of the permanent magnet in the magnetizing direction, the thickness of the copper plate, the number of fins of the heat dissipation plate and the length of the fins of the heat dissipation plate as design variables. Six-factor and three-level simulation experiments are designed with the minimum temperature of the heat dissipation plate as the objective function, and additionally, orthogonal experiments were designed according to the actual working conditions by selecting the optimal combination of parameters and modifying the model to perform physical tests. The results show that the variables that have the most significant impact on heat dissipation performance from high to low are as follows: axial air gap length, input motor speed, the length of the fins of the heat dissipation plate, the thickness of the permanent magnet in the magnetizing direction, the number of fins of the heat dissipation plate and the thickness of the copper plate. The increase in axial air gap length can effectively reduce the temperature rise, and the maximum decrease can reach 9.76%. Under the same conditions, the input motor speeds are set to 300 r/min, 400 r/min, 500 r/min, 600 r/min and 700 r/min, respectively, and the simulation results are in good agreement with the physical test results, with a maximum error of 4.8%. The error between the simulation result and the physical test result is only 1.9% under the optimal combination of parameters obtained by the orthogonal experiment, which verifies the correctness of the optimization model. In conclusion, the study is of reference significance for the parameter optimization of the heat dissipation structure of the double disk magnetic coupler.

Design optimization and numerical verification of adaptive composite blades for hydro-turbine

In this paper, an adaptive composite blade is designed based on the composite mechanics and bending torsion coupling theory, and the blade structure is optimized by a multi-parameter and multi-objective optimization method. Furthermore, the dynamic interaction between the flow and structure domain during the bend-twist coupling process was also studied by the two-way fluid-structure coupling method to verify the hydrodynamic performance and structural performance of the optimized blade. The results show that the multi-parameter and multi-objective optimization method is suitable for the design of composite blades. The optimized blade has better hydrodynamic performance and structural performance, manifesting a higher energy utilization and less concentrated stress.

Quantitative measurement of disturbances based on optical-fibre multiparameter optimization method

Phase-sensitive optical time domain reflectometry (φ-OTDR) has been applied in security monitoring. However, φ-OTDR cannot directly measure strain and intensity of an applied disturbance, thereby limiting the choice of features for intrusion pattern recognition and hazard assessment. This paper proposes a quantitative method to measure disturbances using amplitude-based φ-OTDR. The optical-fibre multiparameter optimization (FMPO) method is developed. The FMPO analyses the curve pattern of optical intensity signals to calculate strain. No extra module is needed, instead a specially designed bio-inspired algorithm is introduced. Quantification indices to gauge disturbance intensities are derived for practical use. A piezoelectric ceramic cylinder test demonstrated a linear correlation between the quantification indices and the vibration amplitude (R2 = 0.8956). The calculated strains of 34 driven voltages agreed well with theoretical values within 1000 nε. In a pendulum-impact experiment, five intrusion intensities were successfully identified, which indicated that FMPO performed well in the field.