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Applied Sciences
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Hysteresis in Engineering Systems
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Hysteresis in Engineering Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 6526

Special Issue Editors

Dr. Mohammad Noori

E-Mail Website
Guest Editor
Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
Interests: hysteretic systems; structural health monitoring; AI-based methods for damage detection and signal processing; seismic isolation; random vibrations
Special Issues, Collections and Topics in MDPI journals
Dr. Wael A. Altabey

E-Mail Website
Co-Guest Editor
International Institute for Urban Systems Engineering, Southeast University, Nanjing, 210096, China
Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt
Interests: hysteretic systems; structural health monitoring; AI-based schemes for damage detection and signal processing; damage identification; structural control; fatigue behavior of composite structures; mechanical vibrations; micro/nanoelectromechanical systems (MEMS/NEMS)
Special Issues, Collections and Topics in MDPI journals
Dr. Chunfeng Wan

E-Mail Website
Co-Guest Editor
School of Civil Engineering, Southeast University, Nanjing, 210096, China
Interests: structural health monitoring; AI-based schemes for damage detection and signal processing; system identification; smart operation and maintenance for civil engineering structures
Dr. Sin Chi Kuok

E-Mail Website
Co-Guest Editor
1. State Key Laboratory on Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau 999078, China
2. Department of Engineering Science, University of Cambridge, Cambridge CB21PZ, UK
Interests: Bayesian inference; data-centric analysis; structural health monitoring; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ramin Ghiasi

E-Mail Website
Co-Guest Editor
Adjunct Research Assistant Professor, International Institute for Urban Systems Engineering, Southeast University, Nanjing, China
Interests: structural health monitoring; AI-based schemes for damage detection and signal processing; system identification; probabilisitc SHM; hysteretic systems; surrogate models; optimization

Special Issue Information

Dear Colleagues,

Understanding the phenomenon of hysteresis and the mechanism of hysteretic behavior is important in the design and analysis of numerous types of engineering systems. Hysteretic behavior is observed in the study of magnetic fields, the dynamic response of many structures under high-intensity cyclic or random loadings, in the force–displacement relationships of vibration-control systems, and in the dynamic response behavior of various connections and fasteners, to cite a few examples. Over the past few decades, a large body of work has been published on the development of mathematical models that can reproduce this behavior for various engineering design and analysis applications, as well as those predicting, for instance, the energy absorption or dissipation characteristics of various hysteretic materials. In recent years, an increasing body of work has also been published on the use of various data analysis methods for modeling and analyzing this highly nonlinear behavior. The main objective of this proposed Special Issue is to collect contributions from active researchers in the field of hysteresis, and from structural, electrical, materials and other engineering fields. It will act as a platform for sharing, for instance, the latest developments in this field, including new modeling techniques, or the system identification of complex hysteresis models. Given the interdisciplinary nature of this topic, the proposed Issue will be a collection of contributions from scholars in several fields, and will cover topics such as:

  • Nonlinear phenomena in hysteretic systems;
  • Hysteresis in the study of magnetic fields;
  • Analytical models for predicting and analyzing hysteretic behavior;
  • The role of hysteretic restoring force on modal interactions;
  • Hysteresis in mechanical systems modeling and dynamic response;
  • Hysteresis modeling applications in electrical engineering;
  • Advances in hysteresis modelling;
  • The use of smart materials in the modelling of hysteresis systems;
  • Hysteresis and its measurement;
  • Artificial-intelligence-based methods for modeling hysteresis;
  • System identification of hysteretic systems;
  • Hysteresis in seismic analysis;
  • Study of Bouc–Wen–Baber–Noori model and its applications;
  • Prandtl–Ishlinski model for modeling asymmetric hysteresis;
  • Hysteresis in control systems;
  • Random vibration of hysteretic systems.


Dr. Mohammad Noori
Guest Editor

Dr. Wael A. Altabey
Dr. Chunfeng Wan
Dr. Sin Chi Kuok
Dr. Ramin Ghiasi
Co-Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hysteresis
  • hysteretic restoring force
  • magnetic fields
  • modeling hysteresis
  • material hysteresis
  • hysteretic energy
  • system identification of hysteretic systems
  • artificial intelligence methods and hysteresis

Published Papers (4 papers)

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Editorial

Jump to: Research

4 pages, 623 KiB  
Editorial
Hysteresis in Engineering Systems
by Mohammad Noori and Wael A. Altabey
Appl. Sci. 2022, 12(19), 9428; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199428 - 20 Sep 2022
Cited by 15 | Viewed by 1871
Abstract
The phenomenon of hysteresis in engineering systems has been with us for ages and has been attracting the attention of many investigators for a long time [...] Full article
(This article belongs to the Special Issue Hysteresis in Engineering Systems)
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Research

Jump to: Editorial

11 pages, 2135 KiB  
Article
Considerations for the Design of a Wheelchair Dynamometer Concerning a Dedicated Braking System
by Michał Kończak, Mateusz Kukla, Łukasz Warguła, Dominik Rybarczyk and Bartosz Wieczorek
Appl. Sci. 2023, 13(13), 7447; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137447 - 23 Jun 2023
Viewed by 700
Abstract
As part of ongoing research, a wheelchair dynamometer has been designed and built. This device is a complex test stand, enabling research on the operation of wheelchairs, taking into account a number of biomechanical factors in laboratory conditions. Based on a review of [...] Read more.
As part of ongoing research, a wheelchair dynamometer has been designed and built. This device is a complex test stand, enabling research on the operation of wheelchairs, taking into account a number of biomechanical factors in laboratory conditions. Based on a review of the available literature, the braking system was designed and constructed as a part of a dynamometer drive system. This has resulted in a design issue concerning the optimal selection of the electromechanical drive combined with a hydraulic system as the actuator of the brake. The purpose of the research discussed here is to determine the characteristics of the braking system. For this purpose, a series of tests were carried out using a wheelchair with an electric drive, which allowed the generation of a constant rotational speed in the range between 72 rpm and 222 rpm. Based on the test results, the hysteresis of the developed braking system and the characteristics of the braking power were determined. Full article
(This article belongs to the Special Issue Hysteresis in Engineering Systems)
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20 pages, 4421 KiB  
Article
Stray Magnetic Field Measurement Method of Magnetic Hysteresis Curve of Open-Ended Sensor and Actuator Cores
by Patrik Kašper, Miroslav Šmelko, Jaroslav Kessler, Pavol Lipovský and Katarína Draganová
Appl. Sci. 2023, 13(8), 4885; https://0-doi-org.brum.beds.ac.uk/10.3390/app13084885 - 13 Apr 2023
Viewed by 1370
Abstract
In the design and development of measurement systems, such as magnetometric security systems or sophisticated devices such as satellites, it is necessary to consider the magnetic properties of all its parts and components, especially if it contains any magnetometric subsystem. The magnetic parameters [...] Read more.
In the design and development of measurement systems, such as magnetometric security systems or sophisticated devices such as satellites, it is necessary to consider the magnetic properties of all its parts and components, especially if it contains any magnetometric subsystem. The magnetic parameters of the materials are generally well described by the manufacturers in relation to their unprocessed raw state. However, their magnetic properties change as the subsequent machining or heat treatment is performed. These behavioral reactions of the material may lead to changes in its hysteresis during the magnetization cycles. This effect is necessary to consider, especially in the case of metallic ribbons, the magnetic characteristics of which are usually estimated by measurements performed on toroidal cores. Since the magnetic properties of a toroidal core differ from the magnetic properties of the preferably used open-ended samples, the corresponding measurement method needs to be used to determine its magnetic characteristics. Therefore, in the proposed article, the authors present a stray magnetic field-based method and measuring workstation intended to measure the magnetic hysteresis curves of the ferromagnetic open-ended samples used in the applications concerning the magnetometric systems and stabilization subsystems of small satellites. The physical background of the measurement method is described in detail, as well as the hardware and software used. The magnetic hysteresis curves of a small satellite electromagnetic actuator were measured as an example of an open-ended ferromagnetic component produced from amorphous ribbons, the permeability of which differs in comparison to manufacturer-stated permeability of raw amorphous material. The results are supplemented by measurements of the core characteristics of the used magnetometer probe, as well as by the characteristics of the semi-produced materials used for the production of both investigated cores. The primary advantage of the presented measurement method lies in the ability of design validation prior to the production of the final component and its assembly. Full article
(This article belongs to the Special Issue Hysteresis in Engineering Systems)
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20 pages, 4776 KiB  
Article
A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model
by Zele Li, Mohammad Noori, Chunfeng Wan, Bo Yu, Bochen Wang and Wael A. Altabey
Appl. Sci. 2022, 12(19), 9440; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199440 - 21 Sep 2022
Cited by 13 | Viewed by 1621
Abstract
A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models [...] Read more.
A restoring-force model is a versatile mathematical model that can describe the relationship between the restoring force and the deformation obtained from a large number of experiments. Over the past few decades, a large body of work on the development of restoring-force models has been reported in the literature. Under high intensity cyclic loadings or seismic excitations, reinforced concrete (RC) structures undergo a wide range of hysteretic deteriorations such as strength, stiffness and pinching degradations. These characteristic behaviors can be described by the multi-parameter Bouc-Wen-Baber-Noori (BWBN) model, which offers a wide range of applicability. This model has been applied for the response prediction and modeling restoring-force behavior in structural and mechanical engineering systems, by adjusting the distribution range of this model’s parameters. However, a major difficulty in utilizing the multi-parameter BWBN model is the parameters’ identification. In this paper, a deep neural network model is used to estimate the hysteresis parameters of the BWBN model. This model is one of the most versatile and widely used general hysteresis models that can describe the hysteretic behavior of RC columns. The experimental data of the RC columns used in this paper are collected from the database of the Pacific Earthquake Engineering Research Center (PEER). Firstly, the hysteretic loop obtained from a physical experiment is described by the BWBN model, and the parameters of the BWBN model are identified via a genetic optimization algorithm. Then a neural network is established by a backpropagation (BP) algorithm for associating the identified BWBN model parameters with physical parameters of the RC column. Finally, the regression analysis of the identified parameters is carried out to obtain the regression characteristics of the RC columns. The trained neural network model can directly identify the parameters of BWBN model based on the physical parameters of RC columns, and is effective and computationally efficient for multi-parameter BWBN model identification. The proposed approach overcomes the difficult problem of identifying the parameters of BWBN model and provides a promising approach for a wider application of this multi-parameter hysteresis model. Full article
(This article belongs to the Special Issue Hysteresis in Engineering Systems)
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