A developed microstructure-based internal state variable (ISV) plasticity damage model is for the first time used for simulating penetration mechanics of aluminum to find out its penetration properties. The ISV damage model tries to explain the interplay between physics at different length scales that governs the failure and damage mechanisms of materials by linking the macroscopic failure and damage behavior of the materials with their micromechanical performance, such as void nucleation, growth, and coalescence. Within the continuum modeling framework, microstructural features of materials are represented using a set of ISVs, and rate equations are employed to depict damage history and evolution of the materials. For experimental calibration of this damage model, compression, tension, and torsion straining conditions are considered to distinguish damage evolutions under different stress states. To demonstrate the reliability of the presented ISV model, that model is applied for studying penetration mechanics of aluminum and the numerical results are validated by comparing with simulation results yielded from the Johnson-Cook model as well as analytical results calculated from an existing theoretical model.

Yangqing Dou

Yucheng Liu

Youssef Hammi

Wilburn Whittington

Application of a Microstructure-Based ISV Plasticity Damage Model to Study Penetration Mechanics of Metals and Validation through Penetration Study of Aluminum

An automatic framework for tuning plastic constitutive models is proposed. It is based on multistart global optimisation method, where the objective function is provided by the results of multiple elastoplastic finite element analyses, executed concurrently. Wrapper scripts were developed for fully automatic preprocessing, including model and mesh generation, analysis, and postprocessing. The framework is applied to an isotropic power hardening plasticity using real load/displacement data from multiple steel buckling tests. M. J. D. Powell’s BOBYQA constrained optimisation package was used for local optimisation. It is shown that using the real data presents multiple problems to the optimisation process because <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-2.26807pt" id="M1" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 15.3928 11.5564" width="15.3928pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g><g transform="matrix(.013,0,0,-0.013,10.738,0)"><path id="g113-42" d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"/></g></svg> the objective function can be discontinuous, yet <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-2.26807pt" id="M2" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 15.3928 11.5564" width="15.3928pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g113-51" d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"/></g><g transform="matrix(.013,0,0,-0.013,10.738,0)"><use xlink:href="#g113-42"/></g></svg> relatively flat around multiple local minima, with <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-2.26807pt" id="M3" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 15.3928 11.5564" width="15.3928pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g113-52" d="M285 378C315 398 338 416 353 432C373 451 384 474 384 503C384 579 325 635 236 635H235C182 635 136 610 108 579L65 516L85 496C110 533 150 575 205 575C258 575 300 543 300 481C300 407 232 369 141 339L147 310C163 315 188 321 211 321C268 321 338 284 338 192C338 94 288 40 217 40C160 40 119 68 93 91C85 98 77 97 69 91C60 84 47 71 46 58C44 46 48 35 62 22C75 10 116 -12 162 -12C234 -12 424 62 424 224C424 297 373 359 285 376V378Z"/></g><g transform="matrix(.013,0,0,-0.013,10.738,0)"><use xlink:href="#g113-42"/></g></svg> similar values of the objective function for different local minima. As a consequence the estimate of the global minimum is sensitive to the amount of experimental data and experimental noise. The framework includes the verification step, where the estimate of the global minimum is verified on a different geometry and loading. A tensile test was used for verification in this work. The speed of the method critically depends on the ability to effectively parallelise the finite element solver. Three levels of parallelisation were exploited in this work. The ultimate limitation was the availability of the finite element commercial solver license tokens.

Anton Shterenlikht

Mohammad M. Kashani

Nicholas A. Alexander

Gethin Williams

Adam J. Crewe

Autotuning of Isotropic Hardening Constitutive Models on Real Steel Buckling Data with Finite Element Based Multistart Global Optimisation on Parallel Computers

Angelo Marcello Tarantino

Julius Kaplunov

Raimondo Luciano

Carmelo Majorana

Theodoros C. Rousakis

Kasper Willam

Structural Modelling at the Micro-, Meso-, and Nanoscales

In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

Saud Altaf

Muhammad Waseem Soomro

Mirza Sajid Mehmood

Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

An enhanced secure image chaotic cryptosystem has been proposed based on hybrid CMT-Lanczos algorithm. We have achieved fast encryption and decryption along with privacy of images. The pseudorandom generator has been used along with Lanczos algorithm to generate root characteristics and eigenvectors. Using hybrid CMT image, pixels are shuffled to accomplish excellent randomness. Compared with existing methods, the proposed method had more robustness to various attacks: brute-force attack, known cipher plaintext, chosen-plaintext, security key space, key sensitivity, correlation analysis and information entropy, and differential attacks. Simulation results show that the proposed methods give better result in protecting images with low-time complexity.

Srinivas Koppu

V. Madhu Viswanatham

A Fast Enhanced Secure Image Chaotic Cryptosystem Based on Hybrid Chaotic Magic Transform

Regenerative types of cryogenic refrigerators (or cryocoolers) employ magnetic intermetallic compounds of 3d and 4f elements to work well below 10 K. This paper presents the analysis of temperature distribution in regenerators of such cryocoolers under the influence of magnetic fields of 1 T, 3 T, and 4.3 T. Commercial code of finite element analysis (FEA) package, ANSYS (APDL) 14.5, is used to investigate the temperature distribution under above-mentioned fields. <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.29112pt" id="M1" height="11.6791pt" version="1.1" viewBox="-0.0498162 -8.38798 30.2171 11.6791" width="30.2171pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g121-67" d="M517 162C503 123 484 88 467 68C445 42 417 34 341 34C291 34 256 34 237 47C219 59 213 81 213 128V317H308C395 317 402 311 415 240H444V431H415C403 364 398 356 307 356H213V584C213 613 215 616 246 616H322C394 616 421 609 435 587C448 566 458 544 467 502L496 506C493 557 488 625 488 650H42V622C120 616 128 612 128 523V125C128 43 120 35 29 28V0H511C520 31 540 125 546 158L517 162Z"/></g><g transform="matrix(.013,0,0,-0.013,7.384,0)"><path id="g121-112" d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"/></g><g transform="matrix(.0091,0,0,-0.0091,12.253,3.132)"><path id="g50-52" d="M290 377C321 398 342 415 358 430C378 450 389 473 389 502C389 578 329 635 238 635H237C184 635 137 610 109 578L64 515L88 493C112 529 154 573 208 573S303 542 303 482C303 409 233 370 141 341L149 308C165 313 190 319 215 319C272 319 341 283 341 193C342 98 292 43 222 43C163 43 122 72 96 94C88 101 79 100 70 94C61 87 47 73 46 60C44 47 48 37 62 23C76 10 118 -12 165 -12C238 -12 430 62 430 223C430 297 379 359 290 375V377Z"/></g><g transform="matrix(.013,0,0,-0.013,17.199,0)"><path id="g121-76" d="M719 650H483V622C553 619 576 606 581 560C585 532 588 487 588 394V148H585L167 650H20V622C67 618 88 610 109 585C127 562 129 557 129 484V264C129 173 126 124 123 93C118 44 92 31 35 28V0H272V28C204 32 181 44 176 95C173 124 170 172 170 264V515H172L598 -9H629V394C629 487 631 532 635 563C639 606 663 619 719 622V650Z"/></g><g transform="matrix(.013,0,0,-0.013,26.584,0)"><path id="g121-103" d="M135 536C164 536 186 560 186 587C186 617 164 639 136 639C109 639 85 617 85 587C85 560 109 536 135 536ZM252 0V26C188 32 181 38 181 106V451C138 433 90 420 39 412V388C99 379 102 374 102 312V106C102 38 95 32 32 26V0H252Z"/></g></svg> is selected as regenerator material and the criteria for its selection are discussed in detail. The cold end temperature is varied from 4.2 K to 10 K and hot end temperature is fixed at 20 K. The values obtained from FEA clearly show that the ineffectiveness of <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.29112pt" id="M2" height="11.6791pt" version="1.1" viewBox="-0.0498162 -8.38798 30.2171 11.6791" width="30.2171pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g121-67"/></g><g transform="matrix(.013,0,0,-0.013,7.384,0)"><use xlink:href="#g121-112"/></g><g transform="matrix(.0091,0,0,-0.0091,12.253,3.132)"><use xlink:href="#g50-52"/></g><g transform="matrix(.013,0,0,-0.013,17.199,0)"><use xlink:href="#g121-76"/></g><g transform="matrix(.013,0,0,-0.013,26.584,0)"><use xlink:href="#g121-103"/></g></svg> is at 8 K and 10 K at 3 T and 4.3 T.

Rajendra Kumar

Sumit Shoor

Numerical Simulation of Magnetic Field Effect on Cryocooler Regenerators: Temperature Distribution

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Modelling and Simulation in Engineering