Piezoelectric Hysteresis Modeling of Hybrid Driven Three-Dimensional Elliptical Vibration Aided Cutting System Based on an Improved Flower Pollination Algorithm

Three-dimensional elliptical vibration assisted cutting technology has been widely used in the past few years. The piezoelectric stack drive structure is an important part of the three-dimensional elliptical vibration aided cutting system. Its piezoelectric hysteresis characteristics affects the final output of the elliptical trajectory. Aiming at this problem, a piezoelectric hysteresis modeling method based on a generalized Bouc–Wen model is presented in this paper. An improved flower pollination algorithm (IFPASO) was used to identify Bouc–Wen model parameters. Standard test result shows that IFPASO has better algorithm performance. The model identification effect experiment proved that the Bouc–Wen model obtained by IFPASO identification, the highest modeling accuracy of the three axial subsystems, can reach 98.86%. Therefore, the model can describe the piezoelectric hysteresis characteristics of the three axial subsystems of the 3D-EVC system effectively and has higher modeling accuracy and fitting accuracy.

The Effect of Relative Permeability Hysteresis on the Design of an Optimal Water-Alternating-Gas (WAG) Process

Summary Water-alternating gas (WAG) as a tertiary recovery method is applied to oil reservoirs at a later stage of reservoir life to more or less success depending on field and operation. Uncertainty in WAG optimization has been shown to be dependent on several factors including reservoir characterization, WAG timing, and its operation. In this paper, we comprehensively explore WAG optimization in the context of WAG operating parameters and hysteresis, the first paper to explore both simultaneously. WAG operating parameters have been analyzed and optimized at both the core and field scale with a general conclusion that the timing, miscibility, WAG ratio, cycle time, and number of cycles play a varying role in the WAG optimization. Reservoir characterization has considered well configuration, oil type, rock properties, and hysteresis in relative permeability. Due to the cyclic nature of WAG and the dependency of the relative permeability on the saturation history, the relative permeability hysteresis modeling plays a key role in WAG performance whereby different hysteresis models will predict different results, as shown in literature. In this paper, we consider the choice of the hysteresis model and WAG operating parameters on WAG optimization. First, a sensitivity analysis is performed to evaluate the effect of hysteresis models (no hysteresis, Carlson, and Killough) on a large number of WAG development scenarios sampled by the Latin hypercube sampling method. Next, optimizations were conducted to compare and analyze the optimum recovery factor and corresponding optimal WAG operating parameters for various combinations of hysteresis models. The results of the study indicate that excluding hysteresis modeling from simulations would likely lead to a higher predicted produced volume of the nonwetting phases, that is, oil and gas, and a lower predicted produced volume of the wetting phase, that is, water. Also, the optimal recovery factor as well as the optimal WAG operating parameters can be significantly affected by the choice of the hysteresis models.

Hysteresis Modeling of a PAM System Using ANFIS

Pneumatic artificial muscles (PAMs) are excellent environmentally friendly actuators and springs that remain somewhat underutilized in the industry due to their hysteretic behavior, which makes predicting their behavior difficult. This paper presents a novel black-box approach that employs an adaptive-network-based fuzzy inference system (ANFIS) to create pressure-contraction hysteresis models. The resulting models are simulated in a control system toolbox to test their controllability using a simple proportional-integral (PI) controller. The data showed that the models created based on fixed inputs had an average normalized root mean square error (RMSE) of 0.0327, and their generalized counterparts achieved an average normalized RMSE of 0.04087. The simulation results showed that the PI controller was able to achieve mean tracking errors of 8.1 µm and 18.3 µm when attempting to track a sinusoidal and step references, respectively. This work concludes that modeling using the ANFIS is limited to being able to know the derivative of the input pressure or its rate of change, but competently models hysteresis in PAMs across multiple operating ranges. This is the highlight of this work. Additionally, these ANFIS-created models lend themselves well to controller, but exploring more refined control schemes is necessary to fully utilize them.