scholarly journals Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques

2021 ◽  
Vol 11 (21) ◽  
pp. 10057
Author(s):  
Visar Farhangi ◽  
Hashem Jahangir ◽  
Danial Rezazadeh Eidgahee ◽  
Arash Karimipour ◽  
Seyed Alireza Nedaei Javan ◽  
...  
Keyword(s):  
Cross Sections ◽  
Numerical Models ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Group Method ◽  
Nonlinear Behaviour ◽  
Isolation System ◽  
Cyclic Behaviour ◽  
Artificial Neural Network Ann ◽  
Hysteretic Dampers

Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN- and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre- and post-yield parts with low errors and high accuracy and consistency.

scholarly journals A review on multi-physics numerical modelling in different applications of magnetorheological fluids

2020 ◽  
Vol 31 (16) ◽  
pp. 1855-1897 ◽  
Author(s):  
Wael Elsaady ◽  
S Olutunde Oyadiji ◽  
Adel Nasser
Keyword(s):  
Fluid Flow ◽  
State Of The Art ◽  
Numerical Models ◽  
Magnetorheological Fluid ◽  
Magnetorheological Fluids ◽  
Analytical Models ◽  
Nonlinear Behaviour ◽  
Phase Flow ◽  
Two Phase ◽  
Single Versus

Magnetorheological fluids involve multi-physics phenomena which are manifested by interactions between structural mechanics, electromagnetism and rheological fluid flow. In comparison with analytical models, numerical models employed for magnetorheological fluid applications are thought to be more advantageous, as they can predict more phenomena, more parameters of design, and involve fewer model assumptions. On that basis, the state-of-the-art numerical methods that investigate the multi-physics behaviour of magnetorheological fluids in different applications are reviewed in this article. Theories, characteristics, limitations and considerations employed in numerical models are discussed. Modelling of magnetic field has been found to be rather an uncomplicated affair in comparison to modelling of fluid flow field which is rather complicated. This is because, the former involves essentially one phenomenon/mechanism, whereas the latter involves a plethora of phenomena/mechanisms such as laminar versus turbulent rheological flow, incompressible versus compressible flow, and single- versus two-phase flow. Moreover, some models are shown to be still incapable of predicting the rheological nonlinear behaviour of magnetorheological fluids although they can predict the dynamic characteristics of the system.

scholarly journals Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Field Measurements ◽  
Inverse Modelling ◽  
Machine Learning Techniques ◽  
Interaction Patterns ◽  
Support Structures ◽  
Interaction Processes ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

scholarly journals Gaining a better understanding of the extrusion process in fused filament fabrication 3D printing: a review

2021 ◽  
Author(s):  
Bahaa Shaqour ◽  
Mohammad Abuabiah ◽  
Salameh Abdel-Fattah ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
...  
Keyword(s):  
3D Printing ◽  
Numerical Models ◽  
Extrusion Process ◽  
Analytical Models ◽  
Limiting Factor ◽  
Fused Filament Fabrication ◽  
Promising Tool ◽  
Relevant Work ◽  
3D Printing Technique ◽  
Required Quality

AbstractAdditive manufacturing is a promising tool that has proved its value in various applications. Among its technologies, the fused filament fabrication 3D printing technique stands out with its potential to serve a wide variety of applications, ranging from simple educational purposes to industrial and medical applications. However, as many materials and composites can be utilized for this technique, the processability of these materials can be a limiting factor for producing products with the required quality and properties. Over the past few years, many researchers have attempted to better understand the melt extrusion process during 3D printing. Moreover, other research groups have focused on optimizing the process by adjusting the process parameters. These attempts were conducted using different methods, including proposing analytical models, establishing numerical models, or experimental techniques. This review highlights the most relevant work from recent years on fused filament fabrication 3D printing and discusses the future perspectives of this 3D printing technology.

Hysteresis Loops of Base Isolation System - An Overview

2021 ◽  
Vol 879 ◽  
pp. 189-201
Author(s):  
M.A. Amir ◽  
N.H. Hamid
Keyword(s):  
Earthquake Engineering ◽  
Structural Damage ◽  
Base Isolation ◽  
Numerical Models ◽  
Hysteresis Loops ◽  
Human Beings ◽  
Rc Buildings ◽  
Isolation System ◽  
Base Isolation System ◽  
Isolation Systems

Recently, there are a lot of technological developments in the earthquake engineering field to reduce structural damage and one of them is a base isolation system. The base isolation system is one of the best technologies for the safety of human beings and properties under earthquake excitations. The aim of this paper is to review previous research works on simulation of base isolation systems for RC buildings and their efficiency in the safety of these buildings. Base isolation decouples superstructure from substructure to avoid transmission of seismic energy to the superstructure of RC buildings. The most effective way to assess the base isolation system for RC building under different earthquake excitations is by conducting experiment work that consumes more time and money. Many researchers had studied the behavior of base isolation system for structure through modeling the behavior of the base isolation in which base isolator is modeled through numerical models and validated through experimental works. Previous researches on the modeling of base isolation systems of structures had shown similar outcomes as the experimental work. These studies indicate that base isolation is an effective technology in immunization of structures against earthquakes.

Modelling of interactions between dykes, inclined sheets and faults at Santorini volcano

2021 ◽  
Author(s):  
Kyriaki Drymoni ◽  
John Browning ◽  
Agust Gudmundsson
Keyword(s):  
Tensile Strength ◽  
Fault Zone ◽  
Numerical Models ◽  
Damage Zone ◽  
Sheet Thickness ◽  
Analytical Models ◽  
Energy Conditions ◽  
Dyke Swarm ◽  
Normal Faults ◽  
Santorini Volcano

<p>Dykes and inclined sheets are known occasionally to exploit faults as parts of their paths, but the conditions that allow this to happen are still not fully understood. Here we report field observations from a well-exposed dyke swarm of the Santorini volcano, Greece, that show dykes and inclined sheets deflected into faults and the results of analytical and numerical models to explain the conditions for deflection. The deflected dykes and sheets belong to a local swarm of 91 dyke/sheet segments that was emplaced in a highly heterogeneous and anisotropic host rock and partially cut by some regional faults and a series of historic caldera collapses, the caldera walls providing, excellent exposures of the structures. The numerical models focus on a normal-fault dipping 65° with a damage zone composed of parallel layers or zones of progressively more compliant rocks with increasing distance from the fault rupture plane. We model sheet-intrusions dipping from 0˚ to 90˚ and with overpressures of alternatively 1 MPa and 5 MPa, approaching the fault. We further tested the effects of changing (1) the sheet thickness, (2) the fault-zone thickness, (3) the fault-zone dip-dimension (height), and (4) the loading by, alternatively, regional extension and compression. We find that the stiffness of the fault core, where a compliant core characterises recently active fault zones, has pronounced effects on the orientation and magnitudes of the local stresses and, thereby, on the likelihood of dyke/sheet deflection into the fault zone. Similarly, the analytical models, focusing on the fault-zone tensile strength and energy conditions for dyke/sheet deflection, indicate that dykes/sheets are most likely to be deflected into and use steeply dipping recently active (zero tensile-strength) normal faults as parts of their paths.</p>

scholarly journals Modelling the high temperature oxidation of titanium alloys: review of analytical models and development of a new numerical tool PyTiOx

2020 ◽  
Vol 321 ◽  
pp. 06012
Author(s):  
C. Ciszak ◽  
D. Monceau ◽  
C. Desgranges
Keyword(s):  
Numerical Models ◽  
Ecological Impact ◽  
Operating Costs ◽  
Analytical Models ◽  
Temperature Oxidation ◽  
Ti Alloys ◽  
State Diffusion ◽  
First Results ◽  
Aeronautical Industry ◽  
Numerical Tool

In order to limit the ecological impact of air traffic and its operating costs, the aeronautical industry is looking for improving engines efficiencies and substitutes to high density Ni-based superalloys. Thus, a wider use of Ti-alloys operating at higher temperatures is one of the developed solutions. Being able to predict as accurately as possible the oxidation behavior of Ti-based components at high temperatures appears therefore crucial to improve their sizing and durability. Analytical models based on the solid-state diffusion laws can be found in the litterature. They are fairly accurate in most cases, but they reveal some intrinsic limitations in specific cases such as temperature transients or thin components. Numerical models were later developed to break down these limitations. First results from a new numerical tool called “PyTiOx” (still under development are presented here. They confirm the intrinsic limitations of analytical models. In the case of thin samples, the numerical model predicts an increase of scaling kinetic when metal becomes O-saturated, whereas analytical models do not.

scholarly journals GEOLOGICAL AND GEOPHYSICAL CHARACTERISTICS OF THE ANABAR‐KHATANGA OIL AND GAS PROVINCE; NUMERICAL MODELING OF THE PROCESSES OF FORMATION OF SALT DOMES (SIBERIAN SECTOR OF THE RUSSIAN ARCTICS)

2019 ◽  
Vol 10 (2) ◽  
pp. 459-470
Author(s):  
V. A. Kontorovich ◽  
В. V. Lunev ◽  
V. V. Lapkovsky
Keyword(s):  
Cross Sections ◽  
Oil And Gas ◽  
Numerical Models ◽  
Sedimentary Cover ◽  
Ground Surface ◽  
Geological Structure ◽  
Salt Domes ◽  
Software Packages ◽  
Geological Features ◽  
Gas Bearing

The article discusses the geological structure, oil‐and‐gas‐bearing capacities and salt tectogenesis of the Anabar‐Khatanga saddle located on the Laptev Sea shore. In the study area, the platform sediments are represented by the 14‐45 km thick Neoproterozoic‐Mesozoic sedimentary complexes. The regional cross‐sections show the early and middle Devonian salt‐bearing strata and associated salt domes in the sedimentary cover, which may be indicative of potential hydrocarbon‐containing structures. Diapirs reaching the ground surface can be associated with structures capable of trapping hydrocarbons, and typical anticline structures can occur above the domes buried beneath the sediments. In our study, we used the algorithms and software packages developed by A.A. Trofimuk Institute of Petroleum Geology and Geophysics (IPGG SB RAS). Taking into account the structural geological features of the study area, we conducted numerical simulation of the formation of salt dome structures. According to the numerical models, contrasting domes that reached the ground surface began to form in the early Permian and developed most intensely in the Mesozoic, and the buried diapirs developed mainly in the late Cretaceous and Cenozoic.

scholarly journals On the Evaluation of Rhamnolipid Biosurfactant Adsorption Performance on Amberlite XAD-2 Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fengqin Chen ◽  
Jinbo Huang ◽  
Xianjun Wu ◽  
Xiaoli Wu ◽  
Arash Arabmarkadeh
Keyword(s):  
Least Squares ◽  
Machine Learning Techniques ◽  
Lower Percentage ◽  
Support Vector ◽  
Mean Deviation ◽  
Group Management ◽  
Learning Techniques ◽  
Data Points ◽  
Artificial Neural Network Ann ◽  
Rhamnolipid Biosurfactant

Biosurfactants are a series of organic compounds that are composed of two parts, hydrophobic and hydrophilic, and since they have properties such as less toxicity and biodegradation, they are widely used in the food industry. Important applications include healthy products, oil recycling, and biological refining. In this research, to calculate the curves of rhamnolipid adsorption compared to Amberlite XAD-2, the least-squares vector machine algorithm has been used. Then, the obtained model is formed by 204 adsorption data points. Various graphical and statistical approaches are applied to ensure the correctness of the model output. The findings of this study are compared with studies that have used artificial neural network (ANN) and data group management method (GMDH) models. The model used in this study has a lower percentage of absolute mean deviation than ANN and GMDH models, which is estimated to be 1.71%.The least-squares support vector machine (LSSVM) is very valuable for investigating the breakthrough curve of rhamnolipid, and it can also be used to help chemists working on biosurfactants. Moreover, our graphical interface program can assist everyone to determine easily the curves of rhamnolipid adsorption on Amberlite XAD-2.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

ANN-Based System Identification, Modelling and Control of Gas Turbines – A Review

2012 ◽  
Vol 622-623 ◽  
pp. 611-617 ◽  
Author(s):  
Hamid Asgari ◽  
Xiao Qi Chen ◽  
Mohammad Bagher Menhaj ◽  
Raazesh Sainudiin
Keyword(s):  
System Identification ◽  
Gas Turbines ◽  
Experimental System ◽  
Vital Role ◽  
Nonlinear Behaviour ◽  
Industrial Plants ◽  
Research Activities ◽  
Significant Research ◽  
Artificial Neural Network Ann ◽  
And Control

Gas Turbines (GTs) are the beating heart of nearly all industrial plants and specifically play a vital role in oil and power industries. Significant research activities have been carried out to discover accurate dynamics and to approach to the optimal operational point of these systems. A variety of analytical and experimental system identification methods, models and control systems has been investigated so far for gas turbines. Artificial neural network (ANN) has been recognized as one of the successful approaches that can disclose nonlinear behaviour of such complicated systems. This paper briefly reviews major ANN-based research activities in the field of system identification, modelling and control of gas turbines. It can be used as a reference for those who are interested to work and study in this area.

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