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Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.

This article aims to investigate the structural behavior of beam–column joints subjected to axial force. The geometry used is the addition of a number of beam connections to the column, and the differences in the numbers of beams used are 1, 2, 4, and 4, denoted as V1, V2, V3, and V4, respectively.

The dynamic relationship between current and pressure in magnetorheological (MR) valves is essential for the design of adaptive rehabilitation devices aimed at health rehabilitation for disabled individuals, yet it remains under-explored in existing modeling approaches. Accurately capturing this relationship is vital to predict the pressure drop response to current variations, facilitating the development of effective control systems in such rehabilitation applications. This study employs a linear black-box modeling approach to characterize the current–pressure dynamics of an annular MR valve. Experimental data are used to develop a set of transfer function models, with parameters identified through MATLAB's system identification tools, utilizing invariant variable regression and the Levenberg–Marquardt (LM) iteration. The modeling yielded a 14th-order transfer function, labeled TF14, which closely aligns with experimental data, achieving a root mean square error of 12.64%. These findings contribute valuable insights into the current–pressure dynamics of MR valves and establish a foundational model for adaptive rehabilitation devices designed for individuals with disabilities. [ABSTRACT FROM AUTHOR]

Rim wheel testing through the SAE standard is necessary for driving safety. This study focused on rim wheel tests carried out using the dynamic radial fatigue test method, which has been included in the SAE standard using Fusion360 for the design and ANSYS for the simulation. With different parameters for the rim wheel type, only some parameters of the tested rim wheels were able to pass the standardization by SAE; 16 rim wheels passed the test, while the other 11 rim wheels did not pass. Simulation results suggested that variations in the thickness, geometry, and material affected the displacement of the safety factor, which was inversely proportional. In addition, the variation in the rim wheel produced a change in the safety factor due to changes in its mass and cost, which were directly proportional. The results of this study will aid in rim wheel design, not only in terms of achieving the best performance but also with regard to cost efficiency.

The development of the global economy has led to a rise in ship traffic. As a result, the risk of accidents, such as collisions between ships and grounding, has also increased. Different failure criteria to capture these accidents have been introduced by researchers. Therefore, the purpose of this study was to determine the essential distinction between these failure criteria. The simulations suggest that failure criteria based on the maximum stress result in a slightly higher rupture strain value, greater crack propagation, and higher internal energy than those based on the maximum strain. Furthermore, using a larger mesh size compared with the size of the test specimen appears to greatly affect the validity of the simulation results.

Magnetorheological elastomers (MREs) are smart materials. Their rheological properties can be controlled by applying an external magnetic field. MRE is a solid rubbery material that can change its modulus through the application of a magnetic field. This article presents the research on the simulation, manufacturing, and testing of vibration insulators. The first step in MRE vibration isolator design. A sufficient magnetic field for the stiffness change is considered in the material selection of the vibration isolator. The effectiveness of electromagnetic circuits in generating magnetic fields to minimize vibration is demonstrated using finite element method magnetics software. A vibration isolator test rig is installed as a second step. Lastly, different effects of current input on MRE isolators have been examined. The higher current input is more effective in eliminating vibration using the MRE isolation system. [ABSTRACT FROM AUTHOR]

This research presents a simulation study on electromagnetic behaviour of magnetic flux density distribution in a magnetorheological elastomer (MRE) bushing. The design concept of MRE bushing is based on the design of the bushing used in the conventional car, only the natural rubber is being replaced by the MRE compound. Furthermore, the electromagnetic simulations were conducted by using Finite Element Method Magnetics (FEMM) software where the main aim is for more magnetic flux density in the MRE, which indicates better performances for MRE bushing in this study. The best configuration of the MRE bushing for this study is using single coil, magnetic material for all parts except for coil bobbin, and the thickness of ring plate of 4 mm, which yield the highest magnetic flux density of 0.205 T. By using this configuration, the dynamic stiffness of this MRE bushing is ranging from 2259.13 N/mm to 2671.06 N/mm with the applied currents of 0.5 A to 2.5 A and frequencies from 1 Hz to 15 Hz. All in all, the optimized configurations improve the performance of MRE bushing remarkably. [ABSTRACT FROM AUTHOR]

Many studies focus on the torque-to-dimension ratio when designing magnetorheological brakes (MRB), especially for ankle foot orthosis (AFO) devices. Vane MRB is one type of MRB with a limited angle of motion that is naturally suitable to be applied to AFO. However, very few implement quality function deployment (QFD) when making MRB, whereas QFD is an essential factor in making product designs. In this study, a tiny vane-type MR brake (TVMRB) was successfully made using the QFD method. Torque characteristics are determined by analysis of magnetic flux density, theoretically, by 3D simulation, and by using Ansys Maxwell experimentally. For consideration, the analysis was carried out with fluid gap variations (0.5 mm, 0.75 mm, and 1 mm) and current variations (0.5–2 A with 0.5 A increments). As a result, ignoring the leakage of MR fluid (MRF), at a constant rotation of 10 rpm, the smallest torque of 6.14 Nm was obtained at the fluid gap variation of 1 mm and input current of 0.5 A, whereas the largest torque was 46.71 Nm at the fluid gap variation of 0.5 mm and input current of 2 A. Apart from torque, this article will also discuss other brake performances in the form of operational range and power consumption. Finally, the structure of the TVMRB design is compared with other designs presented in the House of Quality (HOQ). [ABSTRACT FROM AUTHOR]