Nonlinear material design using principal stretches

2015 ◽  
Vol 34 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Hongyi Xu ◽  
Funshing Sin ◽  
Yufeng Zhu ◽  
Jernej Barbič
Keyword(s):  
Material Design ◽  
Nonlinear Material ◽  
Principal Stretches

Design of an Isotropic Metamaterial With Constant Stiffness and Zero Poisson's Ratio Over Large Deformations

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
A. Delissen ◽  
G. Radaelli ◽  
L. A. Shaw ◽  
J. B. Hopkins ◽  
J. L. Herder
Keyword(s):  
Young’S Modulus ◽  
Material Properties ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Sufficient Conditions ◽  
Material Design ◽  
Poisson's Ratio ◽  
Nonlinear Material ◽  
Large Strains ◽  
Planar Lattice

A great deal of engineering effort is focused on changing mechanical material properties by creating microstructural architectures instead of modifying chemical composition. This results in meta-materials, which can exhibit properties not found in natural materials and can be tuned to the needs of the user. To change Poisson's ratio and Young's modulus, many current designs exploit mechanisms and hinges to obtain the desired behavior. However, this can lead to nonlinear material properties and anisotropy, especially for large strains. In this work, we propose a new material design that makes use of curved leaf springs in a planar lattice. First, analytical ideal springs are employed to establish sufficient conditions for linear elasticity, isotropy, and a zero Poisson's ratio. Additionally, Young's modulus is directly related to the spring stiffness. Second, a design method from the literature is employed to obtain a spring, closely matching the desired properties. Next, numerical simulations of larger lattices show that the expectations hold, and a feasible material design is presented with an in-plane Young's modulus error of only 2% and Poisson's ratio of 2.78×10−3. These properties are isotropic and linear up to compressive and tensile strains of 0.12. The manufacturability and validity of the numerical model is shown by a prototype.

scholarly journals Material Design for Low-Loss Non-Oriented Electrical Steel for Energy Efficient Drives

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6588
Author(s):  
Nora Leuning ◽  
Markus Jaeger ◽  
Benedikt Schauerte ◽  
Anett Stöcker ◽  
Rudolf Kawalla ◽  
...  
Keyword(s):  
Grain Size ◽  
Magnetic Properties ◽  
Electrical Steel ◽  
Sheet Thickness ◽  
Material Design ◽  
Material Behavior ◽  
Nonlinear Material ◽  
Design Stage ◽  
Electrical Machine ◽  
Electrical Steels

Due to the nonlinear material behavior and contradicting application requirements, the selection of a specific electrical steel grade for a highly efficient electrical machine during its design stage is challenging. With sufficient knowledge of the correlations between material and magnetic properties and capable material models, a material design for specific requirements can be enabled. In this work, the correlations between magnetization behavior, iron loss and the most relevant material parameters for non-oriented electrical steels, i.e., alloying, sheet thickness and grain size, are studied on laboratory-produced iron-based electrical steels of 2.4 and 3.2 wt % silicon. Different final thicknesses and grain sizes for both alloys are obtained by different production parameters to produce a total of 21 final material states, which are characterized by state-of-the-art material characterization methods. The magnetic properties are measured on a single sheet tester, quantified up to 5 kHz and used to parametrize the semi-physical IEM loss model. From the loss parameters, a tailor-made material, marked by its thickness and grain size is deduced. The influence of different steel grades and the chance of tailor-made material design is discussed in the context of an exemplary e-mobility application by performing finite-element electrical machine simulations and post-processing on four of the twenty-one materials and the tailor-made material. It is shown that thicker materials can lead to fewer iron losses if the alloying and grain size are adapted and that the three studied parameters are in fact levers for material design where resources can be saved by a targeted optimization.

scholarly journals Cluster-Based Optimization of Cellular Materials and Structures for Crashworthiness

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Kai Liu ◽  
Duane Detwiler ◽  
Andres Tovar
Keyword(s):  
Global Optimization ◽  
Structural Optimization ◽  
Conceptual Design ◽  
Nonlinear Models ◽  
Material Design ◽  
Cellular Materials ◽  
Cellular Material ◽  
Nonlinear Material ◽  
Optimization Approach ◽  
Material Models

The objective of this work is to establish a cluster-based optimization method for the optimal design of cellular materials and structures for crashworthiness, which involves the use of nonlinear, dynamic finite element models. The proposed method uses a cluster-based structural optimization approach consisting of four steps: conceptual design generation, clustering, metamodel-based global optimization, and cellular material design. The conceptual design is generated using structural optimization methods. K-means clustering is applied to the conceptual design to reduce the dimensional of the design space as well as define the internal architectures of the multimaterial structure. With reduced dimension space, global optimization aims to improve the crashworthiness of the structure can be performed efficiently. The cellular material design incorporates two homogenization methods, namely, energy-based homogenization for linear and nonlinear elastic material models and mean-field homogenization for (fully) nonlinear material models. The proposed methodology is demonstrated using three designs for crashworthiness that include linear, geometrically nonlinear, and nonlinear models.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

New Insights into the Nature of the Band Gap of CuGeO3 Nanoparticles: Synthesis, Electronic Structure, and Optical and Photocatalytic Properties

2019 ◽  
Author(s):  
Victor Y. Suzuki ◽  
Luís Henrique Cardozo Amorin ◽  
Natália H. de Paula ◽  
Anderson R. Albuquerque ◽  
Julio Ricardo Sambrano ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Band Gap ◽  
Material Design ◽  
Photocatalytic Properties ◽  
Critical Parameters ◽  
Synthetic Approach ◽  
Microwave Assisted ◽  
Theoretical Approaches ◽  
Nanoparticles Synthesis ◽  
First Time

<p>We report, for the first time, new insights into the nature of the band gap of <a>CuGeO<sub>3</sub> </a>(CGO) nanocrystals synthesized from a microwave-assisted hydrothermal method in the presence of citrate. To the best of our knowledge, this synthetic approach has the shortest reaction time and it works at the lowest temperatures reported in the literature for the preparation of these materials. The influence of the surfactant on the structural, electronic, optical, and photocatalytic properties of CGO nanocrystals is discussed by a combination of experimental and theoretical approaches, and that results elucidates the nature of the band gap of synthetized CGO nanocrystals. We believe that this particular strategy is one of the most critical parameters for the development of innovative applications and that result could shed some light on the emerging material design with entirely new properties.</p> <p><b> </b></p>

Pengembangan Bahan Ajar Mata Pelajaran Sosiologi Siswa Madrasah Aliyah

2017 ◽  
Vol 2 (1) ◽  
pp. 51
Author(s):  
Wagimin Wagimin
Keyword(s):  
High School ◽  
Small Group ◽  
Field Test ◽  
Instructional Material ◽  
Material Design ◽  
Teaching Sociology ◽  
One To One ◽  
Teaching Learning ◽  
Developing Method ◽  
Test Result

Abstract Penelitian ini bertujuan untuk mengembangkan perangkat materi pembelajaran untuk pengajaran sosiologi guna memotivasi siswa. Penelitian ini menggunakan metode penelitian dan pengembangan guna menformulasi produk dan mengukur efektifitasnya melalui validasi dan pengujian ahli di bidangnya. Ada tiga tipe test yang digunakan yaitu, one to one, small group, dan field test yang menunjukkan bahwa desain materi tersebut efektif. Setelah melalui revisi, desain meteri pembelajaran Sosiologi dianggap bermanfaat dan dapat digunakan dalam proses belajar mengajar Kata Kunci: Perangkat pembelajaran, mata pelajaran Sosiologi, siswa Madrasah Aliyah Abstract [Tittle: The Development of Sosiology Instructional Material for The Students fo Islamic Senior High School] . The research aims at developing a set of instructional material for teaching Sociology in order to motivate the students. The researcher employed a research and developing method to formulate a product and to measure its effectiveness through validation and trial-out by the experts of content-material and material-design. There were three types of trial to test the design; one to one, small group, and field test. Based on the test result, it was found that students in average can achieve 84% of overall score which indicate that the designed material is effective. After being revised several times in each stage, the design of instructional material for Sociology subject is considered as beneficial and able to be used in the teaching learning process

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

Ultrasonic Torsion Welding of Aging Resistant Al/CFRP Joints - Concepts, Mechanical and Microstructural Properties

2017 ◽  
Vol 742 ◽  
pp. 395-400 ◽  
Author(s):  
Florian Staab ◽  
Frank Balle ◽  
Johannes Born
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Materials Science ◽  
Dissimilar Materials ◽  
Material Design ◽  
Ultrasonic Welding ◽  
Fatigue Properties ◽  
Aviation Industry ◽  
Efficient Design ◽  
Engineering Structures

Multi-material-design offers high potential for weight saving and optimization of engineering structures but inherits challenges as well, especially robust joining methods and long-term properties of hybrid structures. The application of joining techniques like ultrasonic welding allows a very efficient design of multi-material-components to enable further use of material specific advantages and are superior concerning mechanical properties.The Institute of Materials Science and Engineering of the University of Kaiserslautern (WKK) has a long-time experience on ultrasonic welding of dissimilar materials, for example different kinds of CFRP, light metals, steels or even glasses and ceramics. The mechanical properties are mostly optimized by using ideal process parameters, determined through statistical test planning methods.This gained knowledge is now to be transferred to application in aviation industry in cooperation with CTC GmbH and Airbus Operations GmbH. Therefore aircraft-related materials are joined by ultrasonic welding. The applied process parameters are recorded and analyzed in detail to be interlinked with the resulting mechanical properties of the hybrid joints. Aircraft derived multi-material demonstrators will be designed, manufactured and characterized with respect to their monotonic and fatigue properties as well as their resistance to aging.

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