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Calibration of a hypoplastic model using genetic algorithms

ACTA GEOTECHNICA 2021年第7期16卷 2031-2047页

作者： Mendez, Francisco Jose Pasculli, Antonio Mendez, Miguel Alfonso Sciarra, Nicola Univ G DAnnunzio Dept Engn Geol INGEO I-66013 Chieti Italy Von Karman Inst Fluid Dynam Dept Environm & Appl Fluid Dynam B-1640 Rhode St Genese Belgium Univ G DAnnunzio INDAM Res Grp GNCS Natl Inst Adv MathNatl Grp Sci Comp Chieti Italy

This article proposes an optimization framework, based on genetic algorithms (GA), to calibrate the constitutive law of von Wolffersdorff. This constitutive law, known as Sand Hypoplasticity (SH), allows for robust and accurate modelling of the soil behaviour but requires a complex calibration involving eight parameters. The proposed optimization can automatically fit these parameters from the results of an oedometric and a triaxial drained compression test, by combining the GA with a numerical solver that integrates the SH in the test conditions. By repeating the same calibration several times, the stochastic nature of the optimizer enables the uncertainty quantification of the calibration parameters and allows studying their relative importance on the model prediction. After validating the numerical solver on the ExCalibre-Laboratory software from the SoilModels' website, the GA calibration is tested on a synthetic dataset to analyse the convergence and the statistics of the results. In particular, a correlation analysis reveals that two couples of the eight model parameters are strongly correlated. Finally, the calibration procedure is tested on the results from von Wolffersdorff, 1996, and Herle and Gudehus, 1999, on the Hochstetten sand. The model parameters identified by the GA optimization improves the matching with the experimental data and hence lead to a better calibration.

关键词： genetic algorithm optimization Hypoplasticity model calibration Nonlinear regression

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Design of a New Balanced Side Slotted Vivaldi Antenna with Director using genetic algorithm

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2023年第11期38卷 886-894页

作者： Zhang, Xiaoyan Hu, Yuxu Zhan, Aiyun East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Peoples R China State Key Lab Millimeter Waves Nanjing Peoples R China

Generally, side slotting and directional techniques can improve the performance of a conventional Vivaldi antenna (CVA), but the optimal structure and distribution of slots and directors may be irregular or even complex, requiring significant manual effort, thus limiting the design possibilities. In this paper, a genetic algorithm (GA) is introduced to assist in designing and optimizing a new type of balanced side slotted Vivaldi antenna with director (SSVAD). The methods of artificial intelligence make the process of searching for the optimal structure of such a multi-objective and multi-dimensional problem simpler and more diverse. The GA-generated SSVAD antenna consists of a CVA and 34 slots with varying lengths, as well as 5 metal strips. It has a compact size of 38.2x49x0.8 mm(3) (or 0.32 lambda(L)x0.41 lambda(L)x0.007 lambda(L), where lambda(L) is the lowest operating frequency of 2.48 GHz). The measured results show that the antenna has a peak gain >0 dBi over 2.48-10.88 GHz and >5 dBi over 4.6-10.88 GHz with S-11<-10 dB standard, and exhibits directional characteristics at most of the operating frequencies. Since the measured results are basically consistent with the simulation ones, the effectiveness of the designed scheme has been proven.

关键词： Balanced side slotted Vivaldi antenna director genetic algorithm optimization ultra-wide band

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Classification of wellhead device defects based on CNN-GA-BP model

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2023年 572卷

作者： Minghui, Wei Aihua, Deng Hongjun, Chen Qiuyang, Tao Yilin, He Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China

During the long-term operation of wellhead devices, various types of defects appear due to scouring and corrosion, poor construction, and other reasons. Timely and accurate identification of wellhead device defects is an important factor in ensuring wellhead integrity. In this paper, we design a wellhead device defect classifi-cation model based on a combination of convolutional neural network (CNN) and genetic algorithm optimization-bp neural network (GA-BP) using the finite element simulation data of leakage detection and experimental data as samples. Based on the characteristics of leakage defect data with three dimensions: axial, radial and circumferential, the three-axis components of leakage defects are used instead of the color image R, G and B channels, and input to the convolutional neural network to achieve data fusion and automatic feature extraction of the three-axis components of leakage defects. In order to improve the problem that bp neural networks are prone to fall into local optima, a genetic algorithm is used for optimization to obtain optimal initial weights and thresholds. The GA-BP model is used instead of the self-contained softmax classifier in CNN to accurately classify the extracted features and achieve intelligent classification and recognition of wellhead device defects. Based on the leakage data for experimental analysis and comparison of various classification methods, the CNN-GA-BP combination model established in this paper has the highest classification accuracy, reaching 91.25%. The experimental results illustrate that the CNN-GA-BP model established in this paper has a good classification and recognition effect on various types of defects of wellhead devices.

关键词： Wellhead device Magnetic leakage detection Convolutional neural network genetic algorithm optimization bp neural network Defect classification

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Robot-aided human evacuation optimal path planning for fire drill in buildings

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JOURNAL OF BUILDING ENGINEERING 2023年 72卷

作者： Sriniketh, Konduri Le, Anh Vu Mohan, Rajesh Elara Sheu, Bing J. Tung, Vo Dinh Van Duc, Phan Vu, Minh Bui Singapore Univ Technol & Design ROAR Lab Engn Prod Dev Singapore 487372 Singapore Chang Gung Univ Coll Engn Dept Elect Engn Taoyuan 330 Taiwan Ton Duc Thang Univ Fac Elect & Elect Engn Commun & Signal Proc Res Grp Ho Chi Minh City Vietnam HUTECH Univ HUTECH Inst Engn Ho Chi Minh City Vietnam Van Lang Univ Fac Automot Engn Sch Technol 69-68 Dang Thuy Tram Ho Chi Minh City 700000 Vietnam Nguyen Tat Thanh Univ Fac Engn & Technol 300A Nguyen Tat Thanh StWard 13Dist 4 Ho Chi Minh City Vietnam

Human evacuation during an emergency in high-rise buildings is a life-risking task and has to be performed in the least possible time. With the advancements in robotics, deploying autonomous robots in those situations could ease human efforts in evacuation processes but pose challenges in finding the optimal path to guide the people toward the exits. This paper proposes a novel path planning for the autonomous mobile robot to lead evacuees to the nearest staircase exits. The proposed algorithm is modeled by the moving-target TSP (Traveling Salesman Problem) concept for humans who need to be evacuated and uses the genetic algorithm optimization framework to generate the optimal path in consideration of muti-staircases as exits. We evaluated the performance of the proposed algorithm by benchmarking it with traditional algorithms in a simulated environment. Also, we conducted controlled real-time experiments to validate the proposed method with the conventional methods. In both cases, the proposed algorithm demonstrated significantly better with the gain of 22.4% than other algorithms regarding distance traveled by the robot.

关键词： Human evacuation Robot guided evacuation Moving-target TSP Optimal path planning genetic algorithm optimization

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Artificial neural network based optimization for Ag grated D-shaped optical fiber surface plasmon resonance refractive index sensor

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OPTICS COMMUNICATIONS 2023年 534卷

作者： Dogan, Yusuf Katirci, Ramazan Erdogan, Ilhan Yartasi, Ekrem Sivas Univ Sci & Technol Dept Elect & Elect Engn TR-58000 Sivas Turkiye Sivas Univ Sci & Technol Dept Met & Mat Engn TR-58000 Sivas Turkiye Sivas Univ Sci & Technol Dept Engn Fundamental Sci TR-58000 Sivas Turkiye

This study reports the optimization of fiber optic SPR refractive index sensor parameters with the simulation of finite element method (FEM) and artificial neural network (ANN) model. To demonstrate the applicability of the algorithm, we examined an Ag-grated D-shaped fiber optic sensor configuration with 4 basic input parameters with the aim of reaching the highest sensitivity. Through the conventional optimization, the best parameter set appeared to be a 10 nm air gap distance between the gratings (a), 20 gratings (N), 50 nm residual cladding thickness (d), and 70 nm silver layer thickness (Ag_th) at the indices of 1.35 and 1.39 yielding a sensitivity of 3775 nm/RIU. A close match is found between the actual and predicted sensitivity. 199 input data obtained from FEM are used for training by Leave One Out Cross-Validation (LOOCV) approach with R-squared value of 0.98, and the trained model with R-squared value of 0.97 is implemented in the genetic algorithm. We achieved the sensitivity of 3890 nm/RIU at the predicted a, N, d, and Ag_th of 10 nm, 20, 50 nm, and 75 nm, respectively. Future studies may further improve these results by integrating other algorithms. This method may apply to different and more complex structures to observe the correlation between the parameters, cover an entire range of parameters, and get more accurate results, especially with a high number of inputs requiring less time and computing effort. The proposed method carries great potential to improve the sensing ability and bring a new perspective to the literature.

关键词： Surface plasmon resonance (SPR) Optical fiber sensors genetic algorithm optimization Artificial intelligence

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optimization of Hydraulic Fine Blanking Press Control System Based on System Identification

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PROCESSES 2023年第1期11卷 59页

作者： Shu, Yuwen Liu, Yanxiong Xu, Zhicheng Zhao, Xinhao Chen, Mingzhang Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Collaborat Innovat Ctr Automot Components Te Wuhan 430070 Peoples R China

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.

关键词： fine-blanking system identification phased PID control genetic algorithm optimization adaptive fuzzy PID control

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Hybrid vibration control of the membrane antenna experiencing maneuver with cable actuators

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THIN-WALLED STRUCTURES 2023年第1期192卷

作者： Shao, Qi Lu, Yifan Shi, Chuang Yue, Honghao Lv, Liangliang Liu, Rongqiang Fang, Guangqiang Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China China Aerosp Sci & Technol Grp Co Ltd Space Struct & Mech Technol Lab Shanghai 201108 Peoples R China

This work investigates a novel hybrid vibration control approach of a membrane antenna with cable actuators considering the rigid-flexible coupling effect induced by maneuver. The dynamic model of the membrane antenna is first formulated using finite element analysis, incorporating nonlinearity and rigid-flexible coupling. Tension cables are selected as actuators due to their wide influence range and lightweight properties, without the need for additional accessories on the membrane. Based on these foundations, a hybrid control strategy is proposed, comprising two sub-controllers for the during-maneuver and after-maneuver stages. The feedforward sub-controller during maneuver is designed based on the system's transfer function and optimized using the genetic algorithm, with the purpose of minimizing shape deflection. The feedback sub-controller for postmaneuver vibration suppression is designed based on the linear quadratic regulator (LQR), with a general controller expression of cable actuators provided. Finally, numerical simulations demonstrate the robustness and efficiency of the proposed control scheme for the membrane antenna. The proposed scheme leverages the rapid responses of feedforward control and reliable suppression of feedback control, offering a new perspective on control scheme design for maneuver-induced vibration and presenting a general controller expression for cable actuators based on state space equations.

关键词： Vibration control Membrane antenna Cable-driven actuator Hybrid control genetic algorithm optimization

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Study on strengthening power generation by using LNG cold energy with multi-stream heat exchange

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APPLIED THERMAL ENGINEERING 2023年第1期234卷

作者： Zhao, Yiming Hu, Dapeng Cai, Dongxu Wang, Yingguang Liang, Yong Dalian Univ Technol Dept Chem Machinery Dalian 116012 Peoples R China PipeChina Engn Technol Innovat Co Ltd Tianjin 300450 Peoples R China

The utilization of cold energy released during the liquefied natural gas vaporization to enhance power generation is an efficient method for energy conservation and emission reduction. In this study, a novel multi-stream cascade combined cycle (MS-CCC) is proposed, which integrates multi-stream heat exchange into the power generation system. In contrast to the cycles of the previous research, MS-CCC improves the utilization efficiency of cold exergy while simultaneously recovering the cold exergy of the working fluid without the need for additional heat exchangers in the system. The thermodynamic performance and economic benefits of MS-CCC were evaluated by comparing them with traditional combined cycle (CC), parallel combined cycle (PCC) and cascade combined cycle (CCC) by genetic algorithm, and the study also investigated the influence of operating parameters and cycle structure on the performance of MS-CCC. Compared to the CC, PCC and CCC, the exergy efficiency of MS-CCC can be higher by 88.6 %, 63.1 % and 12.4 % respectively when the same working fluid is employed. Additionally, the net annual income with the optimal working fluids can be increased by 1.039, 1.036 and 0.405 M$, respectively. The maximum net annual income of MS-CCC reaches 1.225 M$ with an exergy efficiency of 17.46 % when employing the optimum working fluid group of ethane + R32. The exergy efficiency of MS-CCC can be maximized at certain condensing temperatures, and it increases with the decline of the natural gas exhaust pressure of the system. Furthermore, the removal of direct expansion has minimal effect on the system efficiency, and by adding one stage of Rankine cycle, the exergy efficiency can be increased by more than 24.4 %.

关键词： Cold energy power generation Liquefied natural gas Multi-stream heat exchange Cascade combined cycle genetic algorithm optimization

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genetically Optimized Pitch Angle Controller of a Wind Turbine with Fuzzy Logic Design Approach

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ENERGIES 2022年第18期15卷 6705-6705页

作者： Pehlivan, Ahmet Selim Bahceci, Beste Erbatur, Kemalettin Sabanci Univ Fac Engn & Nat Sci Mechatron Engn TR-34956 Istanbul Turkey

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.

关键词： wind energy wind turbine pitch angle controller genetic algorithm optimization fuzzy logic

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Forecast of Coal Demand in Shanxi Province Based on GA-LSSVM under Multiple Scenarios

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ENERGIES 2022年第17期15卷 6475-6475页

作者： Liu, Yujing Du, Ruoyun Niu, Dongxiao North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

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.

关键词： coal demand forecast scenario analysis least squares support vector machine genetic algorithm optimization

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