Thermodynamic Analysis of Wind Energy Systems

This chapter studies the efficiency performance of wind energy systems evaluated by energy and exergy analyses. The theories of energy and exergy analyses along with efficiency calculation for horizontal-axis wind turbines (WTs) are provided by a lucid explanation. A 1.5 MW WT is selected for the thermodynamic analysis using reanalyzed meteorological data retrieved from the National Aeronautics and Space Administration’s (NASA) Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), data set. Matlab scripts are developed to calculate the energy and exergy efficiencies using the MERRA-2 data set. The energy efficiency presents higher magnitude than the exergy efficiency based on the theoretical derivation and the calculated time series of efficiencies. Comparison of impacts of four meteorological variables (wind speed, pressure, temperature, and humidity ratio) on WT efficiencies shows that although wind speed dominates the turbine’s efficiency performance, other meteorological variables also play important roles. In addition, uncertainties of the meteorological variables are represented by the best-fit distributions, which are critically important for evaluating the reliability of wind power performance considering realistic meteorological uncertainty

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

AbstractProduct platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs

An Adaptive Maintenance Model Oriented to Process Environment of the Manufacturing Systems

We explored an adaptive maintenance model of the process environment to diagnose progressive faults in manufacturing systems. Progressive faults are usually caused by deterioration of the operating environment or aging and show stochastic properties. Many researchers have reported how to detect faults on the machine body in manufacturing systems. However, little research has been conducted on the process environment which causes progressive faults. To tackle this problem, we explored an adaptive maintenance model to detect progressive faults and repair the process environment on the E-repair location. When a difference of the environmental factor state is detected, it will combine the transcription factor and the state enzyme to locate fault source. Then the comprehensive maintenance program is derived to repair the operating environment while eliminating progressive faults. For the purpose of validation, this model was implemented on the process environment of the air separation plant. And the simulation experiments validated the feasibility and effectiveness of this method

Physical Logic Enhanced Network for Small-Sample Bi-Layer Metallic Tubes Bending Springback Prediction

Bi-layer metallic tube (BMT) plays an extremely crucial role in engineering applications, with rotary draw bending (RDB) the high-precision bending processing can be achieved, however, the product will further springback. Due to the complex structure of BMT and the high cost of dataset acquisi-tion, the existing methods based on mechanism research and machine learn-ing cannot meet the engineering requirements of springback prediction. Based on the preliminary mechanism analysis, a physical logic enhanced network (PE-NET) is proposed. The architecture includes ES-NET which equivalent the BMT to the single-layer tube, and SP-NET for the final predic-tion of springback with sufficient single-layer tube samples. Specifically, in the first stage, with the theory-driven pre-exploration and the data-driven pretraining, the ES-NET and SP-NET are constructed, respectively. In the second stage, under the physical logic, the PE-NET is assembled by ES-NET and SP-NET and then fine-tuned with the small sample BMT dataset and composite loss function. The validity and stability of the proposed method are verified by the FE simulation dataset, the small-sample dataset BMT springback angle prediction is achieved, and the method potential in inter-pretability and engineering applications are demonstrated