Shape optimization of parametric curve-based magnetorheological damper valve for augmented performance in prosthetic ankle applications
Source abstract
Abstract
Abstract Magnetorheological (MR) dampers are extensively utilized across several technical fields owing to their capacity to modify the rheological characteristics of MR fluid. Significant effort has been invested on enhancing MR damper (MRD). A prevalent method to enhance the performance of the MRD is by structural optimization. Equations governing shapes can provide other opportunities and yield superior results, despite the fact that most MRD optimizations concentrate on regions with conventional geometries. This study develops and modifies a novel MRD utilizing parametric Bezier and B-spline curves to optimize the regulated output damping force while minimizing power and mass of the MR valve for the prosthetic ankle. Initially, we provide many basic design concepts based on the two primary categories of MRD components. Subsequently, parametric curves are employed to generate the piston form, a crucial element of the magnetic circuit. Several geometric factors within the magnetic circuit, together with the optimal parametric curve, are determined following optimization and simulation. The shape of the MR valve is enhanced through the integration of an optimization with multiple goals functions combined using NSGA-II in MATLAB and finite element electromagnetic analysis in the ANSYS-APDL environment. Furthermore, we have developed and assessed the improved MRD. Finally, we assess the proposed MRD against our previous studies. The experimental characterization outcomes and the modeling results are significantly complementary. The proposed MRD exhibits a significant improvement compared to conventional MRD.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Diabetic Foot Ulcer Assessment and Management · Prosthetics and Rehabilitation Robotics · Stroke Rehabilitation and Recovery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
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