Computational Design of Microstructures With Stochastic Property Closures

2019 ◽  
Author(s):  
Pınar Acar
Keyword(s):  
Material Properties ◽  
Computational Design ◽  
Polycrystalline Materials ◽  
Stochastic Property ◽  
Analytical Uncertainty ◽  
Magnetostrictive Strain ◽  
Material Systems ◽  
Stochastic Solution ◽  
Deformation Processes ◽  
Computational Solution

Abstract The present work addresses a stochastic computational solution to define the property closures of polycrystalline materials under uncertainty. The uncertainty in material systems arises from the natural stochasticity of the microstructures and the variations in deformation processes, and impacts the performance of engineering components by causing unanticipated anisotropy in properties. We utilize an analytical uncertainty quantification algorithm to describe the microstructural stochasticity and model its propagation to the volume-averaged material properties. The stochastic solution will be integrated into linear programming to generate the property closure that shows all possible values of the volume-averaged material properties under the uncertainty. We demonstrate example applications for stiffness parameters of a-Titanium, and multi-physics parameters (stiffness, yield strength, magnetostrictive strain) of Galfenol. Significant differences observed between stochastic and deterministic closures imply the importance of considering the microstructural uncertainty when modeling and designing materials.

Computational Design of Microstructures With Stochastic Property Closures

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Pinar Acar
Keyword(s):  
Material Properties ◽  
Computational Design ◽  
Polycrystalline Materials ◽  
Stochastic Property ◽  
Analytical Uncertainty ◽  
Magnetostrictive Strain ◽  
Material Systems ◽  
Stochastic Solution ◽  
Deformation Processes ◽  
Computational Solution

Abstract The present work addresses a stochastic computational solution to define the property closures of polycrystalline materials under uncertainty. The uncertainty in material systems arises from the natural stochasticity of the microstructures as a result of the fluctuations in deformation processes. The microstructural uncertainty impacts the performance of engineering components by causing unanticipated anisotropy in properties. We utilize an analytical uncertainty quantification algorithm to describe the microstructural stochasticity and model its propagation on the volume-averaged material properties. The stochastic solution will be integrated into linear programming to generate the property closure that shows all possible values of the volume-averaged material properties under the uncertainty. We demonstrate example applications for stiffness parameters of α-Titanium, and multi-physics parameters (stiffness, yield strength, magnetostrictive strain) of Galfenol. Significant differences observed between stochastic and deterministic closures imply the importance of considering the microstructural uncertainty when modeling and designing materials.

A New Sampling Approach for the Multi-Scale Design of Metallic Materials

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Pinar Acar
Keyword(s):  
Material Properties ◽  
Sampling Method ◽  
Solution Space ◽  
Computational Time ◽  
Polycrystalline Materials ◽  
Microstructure Design ◽  
Multi Scale ◽  
Generate Property ◽  
Scale Design ◽  
Sampling Approach

Abstract We present a new sampling method for the multi-scale design of polycrystalline materials, which improves the computational time efficiency compared to the existing computational approaches. The solution strategy aims to find microstructure designs that optimize component-scale mechanical properties. The microstructure is represented with a probabilistic texture descriptor that quantifies the volume fractions of different crystallographic orientations. However, the original microstructure design space is high-dimensional and thus optimization in this domain is not favorable. Instead, we generate property closures, which are the reduced spaces of volume-averaged material properties that are computed in terms of the microstructural texture descriptors. We observe that the traditional design approaches which are based on sampling in the original microstructure space and sampling on the property closure are inefficient as they lead to highly concentrated design samples in the solution space. Therefore, we introduce a new sampling method in the property closure, which creates simplexes using the triangulation of the property hull and then generating samples for each simplex. Example problems include the optimization of Galfenol and α-titanium microstructures to improve non-linear material properties. The new sampling approach is shown to obtain better solutions while decreasing the required computational time compared to the previous microstructure design methods.

Computational Methods for the Design of Deformation Processes of Porous Materials

2002 ◽  
Author(s):  
Nicholas Zabaras ◽  
Shankar Ganapathysubramanian
Keyword(s):  
Porous Materials ◽  
Thermal Analyses ◽  
Computational Design ◽  
Design Problems ◽  
Sensitivity Equations ◽  
Material State ◽  
Deformation Processes ◽  
Continuum Sensitivity ◽  
Updated Lagrangian ◽  
Sensitivity Method

An updated Lagrangian framework of the continuum sensitivity method (CSM) is presented to address important computational design problems in the deformation processing of porous materials. Weak sensitivity equations are developed that are consistent with the kinematic, constitutive, contact and thermal analyses used in the solution of the direct thermomechanical problem. The CSM is here used to analyze die and preform computational design problems in industrial metal forming processes wherein temperature and the accumulated damage play an important role in influencing the deformation mechanism, material state and shape of the deformed workpiece.

A Method for the Validation of Predictive Computations Using a Stochastic Approach

2004 ◽  
Vol 126 (3) ◽  
pp. 227-234 ◽  
Author(s):  
Manuel Pellissetti ◽  
Roger Ghanem
Keyword(s):  
Finite Element ◽  
Finite Element Methods ◽  
Material Properties ◽  
Stochastic Approach ◽  
Stochastic Property ◽  
Limited Data ◽  
Bernoulli Beam ◽  
Direct Differentiation Method ◽  
Stochastic Properties

Stochastic finite element methods provide predictions of the behavior of mechanical systems with randomly fluctuating material properties. Limited data is typically available for the characterization of these properties, introducing errors in their representation. In the present paper, the sensitivity of the response predictions with respect to the stochastic properties is analyzed, by means of the direct differentiation method (DDM). Explicit expressions for the dependence of certain statistics of the response on the statistics of the material property are obtained. The response sensitivities are then used to estimate the error in the response predictions, caused by the error in the representation of the stochastic property. Numerical results for a simple Bernoulli beam are presented.

Material Properties of III–V Semiconductors for Lasers and Detectors

MRS Bulletin ◽  
2003 ◽  
Vol 28 (5) ◽  
pp. 345-349 ◽  
Author(s):  
Charles W. Tu ◽  
Paul K.L.Yu
Keyword(s):  
Material Properties ◽  
Fiber Optic ◽  
Light Emission ◽  
Energy Levels ◽  
Avalanche Photodiodes ◽  
Band Offset ◽  
Type Ii ◽  
Intersubband Transitions ◽  
Fiber Optic Communications ◽  
Material Systems

AbstractWe describe how the material properties of III–V semiconductors, including bandgap, band structure, band offset, refractive index, absorption, and ionization coefficient, are exploited for lasers and photodetectors for fiber-optic communications. The material systems discussed for 1.3 μm and 1.55 μm light emission include the more traditional GaInAsP and AlGaInAs on InP, the more recently investigated GaInAs quantum dots and low-bandgap GaInNAs on GaAs as well as GaAsSb/GaAs Type II structures, and the potentially viable GaN/AlGaN from intersubband transitions (i.e., between quantized conduction-band energy levels). As an example of photodetector applications, GaInAsP/InP and wafer-fused GaInAs/Si are discussed in terms of gain and noise factor for use in avalanche photodiodes with separate absorption and multiplication regions.

A Comprehensive Creep Model

1967 ◽  
Vol 89 (3) ◽  
pp. 496-502 ◽  
Author(s):  
T. Z. Harmathy
Keyword(s):  
Creep Curve ◽  
Equations Of State ◽  
Tertiary Creep ◽  
Creep Model ◽  
Polycrystalline Materials ◽  
Creep Theory ◽  
Deformation Processes ◽  
Stress Dependent ◽  
Explicit Expressions

Based on Dorn’s creep theory, a comprehensive creep model has been developed that is applicable to the calculation of deformation processes at steadily increasing temperatures and slowly varying load. It is shown that the entire course of the creep curve (excepting the tertiary creep) of structural (polycrystalline) materials is uniquely determined by two stress-dependent parameters, εt0 and Z. Explicit expressions for the description of creep processes are proposed. These expressions cannot strictly be regarded as equations of state, but yield an accuracy acceptable for engineering calculations even if dσ/dt ≠ 0.

scholarly journals A Molecular Mechanics Study of Morphologic Interaction between Graphene and Si Nanowires on a SiO2Substrate

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Zhao Zhang ◽  
Teng Li
Keyword(s):  
Molecular Mechanics ◽  
Material Properties ◽  
Graphene Nanoribbon ◽  
Si Nanowires ◽  
Graphene Flake ◽  
Nanowire Diameter ◽  
Si Nanowire ◽  
Material Systems ◽  
Quantitative Results ◽  
Simulation Results

We study the morphologic interaction between graphene and Si nanowires on a SiO2substrate, using molecular mechanics simulations. Two cases are considered: (1) a graphene nanoribbon intercalated by a single Si nanowire on a SiO2substrate and (2) a blanket graphene flake intercalated by an array of Si nanowires evenly patterned in parallel on a SiO2substrate. Various graphene morphologies emerge from the simulation results of these two cases, which are shown to depend on both geometric parameters (e.g., graphene nanoribbon width, nanowire diameter, and nanowire spacing) and material properties (e.g., graphene-nanowire and graphene-substrate bonding strength). While the quantitative results at the atomistic resolution in this study can be further used to determine the change of electronic properties of graphene under morphologic regulation, the qualitative understandings from this study can be extended to help exploring graphene morphology in other material systems.

scholarly journals Fish-inspired flexible protective material systems with anisotropic bending stiffness

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Katia Zolotovsky ◽  
Swati Varshney ◽  
Steffen Reichert ◽  
Eric M. Arndt ◽  
Ming Dao ◽  
...  
Keyword(s):  
Material Properties ◽  
Mechanical Anisotropy ◽  
Design Strategies ◽  
Protective Material ◽  
Design Rules ◽  
Complex Shapes ◽  
Material Systems ◽  
3D Printed ◽  
Polypterus Senegalus ◽  
Protective Systems

AbstractBiological structures integrate morphometry (shape-based rules) with materials design to maximize organism survival. The exoskeleton of the armored fish, Polypterus senegalus, balances flexibility with protection from predatory and territorial threats. Material properties of the exoskeleton are known; however, the geometric design rules underlying its anisotropic flexibility are uncharacterized. Here, we show how scale shape, articulation, and composite architecture produce anisotropic mechanics using bio-inspired, multi-material 3D-printed prototypes. Passive loading (draping) shows that compliant connections between the scales contribute to mechanical anisotropy. Simulated and experimental active loading (bending) show orientation-dependent stiffness ranging over orders of magnitude, including ‘mechanical invisibility’ of the scales where they do not add stiffness to the exoskeleton. The results illustrate how morphometry provides a powerful tool to tune flexibility in composite architectures independent of varying constituent materials composition. We anticipate that introducing morphometric design strategies will enable flexible, protective systems tuned to complex shapes and functions.

Descriptor-Based Methodology for Designing Heterogeneous Microstructural Materials System

2013 ◽  
Author(s):  
Hongyi Xu ◽  
Yang Li ◽  
Catherine Brinson ◽  
Wei Chen
Keyword(s):  
Polymer Nanocomposites ◽  
Material Properties ◽  
Target Material ◽  
Computational Design ◽  
Material Behavior ◽  
Design Evaluation ◽  
Optimization Approach ◽  
Microstructure Design ◽  
Element Analysis ◽  
Small Set

In designing a microstructural materials system, there are several key questions associated with design representation, design evaluation, and design synthesis: how to quantitatively represent the design space of a heterogeneous microstructure system using a small set of design variables, how to efficiently reconstruct statistically equivalent microstructures for design evaluation, and how to quickly search for the optimal microstructure design to achieve the desired material properties. This paper proposes a new descriptor-based methodology for designing microstructural materials systems. A descriptor-based characterization method is proposed to provide a quantitative representation of material morphology using a small set of microstructure descriptors covering features of material composition, dispersion status, and phase geometry at different levels of representation. A descriptor-based multi-phase microstructure reconstruction algorithm is developed which allows efficient stochastic reconstruction of microstructures for Finite Element Analysis (FEA) of material behavior. The choice of descriptors for polymer nanocomposites is verified by establishing a mapping between the finite set of descriptors and the infinite dimensional correlation function. Finally, the descriptor-based representation allows the use of parametric optimization approach to search the optimal microstructure design that meets the target material properties. To improve the search efficiency, this paper employs state-of-the-art computational design methods such as Design of Experiment (DOE), metamodeling, statistical sensitivity analysis, and multi-objective optimization. The proposed methodology is demonstrated using the design of a polymer nanocomposites system.

Sign in / Sign up

Export Citation Format

Share Document