Research applications involving design tool development for multi phase material design are at an early stage of development. The computational requirements of advanced numerical tools for simulating material behavior such as the finite element method (FEM) and the molecular dynamics (MD) method can prohibit direct integration of these tools in a design optimization procedure where multiple iterations are required. One, therefore, requires a design approach that can incorporate multiple simulations (multiphysics) of varying fidelity such as FEM and MD in an iterative model management framework that can significantly reduce design cycle times. In this research a material design tool based on a variable fidelity model management framework is presented. In the variable fidelity material design tool, complex “high-fidelity” FEM analyses are performed only to guide the analytic “low-fidelity” model toward the optimal material design. The tool is applied to obtain the optimal distribution of a second phase, consisting of silicon carbide (SiC) fibers, in a silicon-nitride (Si3N4) matrix to obtain continuous fiber SiCSi3N4 ceramic composites with optimal fracture toughness. Using the variable fidelity material design tool in application to two test problems, a reduction in design cycle times of between 40% and 80% is achieved as compared to using a conventional design optimization approach that exclusively calls the high-fidelity FEM. The optimal design obtained using the variable fidelity approach is the same as that obtained using the conventional procedure. The variable fidelity material design tool is extensible to multiscale multiphase material design by using MD based material performance analyses as the high-fidelity analyses in order to guide low-fidelity continuum level numerical tools such as the FEM or finite-difference method with significant savings in the computational time.

1.
Haftka
,
R. T.
,
Gürdal
,
Z.
, and
Kamat
,
M. P.
, 1990,
Elements of Structural Optimization
, 2nd ed.,
Kluwer
,
Waterloo
, pp.
341
376
.
2.
Deka
,
D. J.
,
Sandeep
,
G.
,
Chakraborty
,
D.
, and
Dutta
,
A.
, 2005, “
Multiobjective Optimization of Laminated Composites Using Finite Element Method and Genetic Algorithm
,”
J. Reinf. Plast. Compos.
0731-6844,
24
(
3
), pp.
273
285
.
3.
Bruyneel
,
M.
, 2005, “
A General and Effective Approach for the Optimal Design of Fiber Reinforced Composite Structures
,”
Compos. Sci. Technol.
0266-3538,
66
, pp.
1303
1314
.
4.
Rahul
,
Chakraborty
,
D.
, and
Dutta
,
A.
, 2005, “
Optimization of FRP Composites Against Impact Induced Failure Using Island Model parallel genetic algorithm
,”
Compos. Sci. Technol.
0266-3538,
65
, pp.
2003
2013
.
5.
Pelletier
,
J. L.
, and
Vel
,
S. S.
, 2006, “
Multi-Objective Optimization of Fiber Reinforced Composite Laminates for Strength, Stiffness and Minimal Mass
,”
Comput. Struct.
0045-7949,
84
, pp.
2065
2080
.
6.
Olson
,
G. B.
, 1997, “
Computational Design of Hierarchically Structured Materials
,”
Science
0036-8075,
277
(
5330
), pp.
1237
1412
.
7.
Ashby
,
M. F.
, 2000, “
Multi-Objective Optimization in Material Design and Selection
,”
Acta Mater.
1359-6454,
48
, pp.
359
369
.
8.
Ashby
,
M. F.
, and
Bréchet
,
P.
, 2003, “
Designing Hybrid Materials
,”
Acta Mater.
1359-6454,
51
, pp.
5801
5821
.
9.
Davidson
,
G. G.
, and
Labib
,
A. W.
, 2003, “
Learning From Failures: Design Improvement Using a Multiple Criteria Decision-Making Process
,”
Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng.
,
217
(
4
), pp.
207
216
.
10.
Zhang
,
X. J.
,
Chen
,
K. Z.
, and
Feng
,
X. A.
, 2004, “
Optimization of Materials Properties Needed for Material Design of Component Made of Multi-Heterogeneous Materials
,”
Mater. Des.
0264-1275,
25
, pp.
369
378
.
11.
Seepersad
,
C. C.
,
Kumar
,
R. S.
,
Allen
,
J. K.
,
Mistree
,
F.
, and
McDowell
,
D. L.
, 2004, “
Multifunctional Design of Prismatic Cellular Materials
,”
J. Comput.-Aided Mater. Des.
0928-1045,
11
, pp.
163
181
.
12.
Seepersad
,
C. C.
,
Allen
,
J. K.
,
McDowell
,
D. L.
, and
Mistree
,
F.
, 2006, “
Robust Design of Cellular Materials With Topological and Dimensional Imperfections
,”
ASME J. Mech. Des.
1050-0472,
128
, pp.
1285
1297
.
13.
Liu
,
W. K.
,
Su
,
H.
,
Moran
,
B.
,
Vernerey
,
F.
, and
Olson
,
G. B.
, 2004, “
Multi-Scale Constitutive Model and Computational Framework for the Design of Ultra-High Strength, High Toughness Steels
,”
Comput. Methods Appl. Mech. Eng.
0045-7825,
193
, pp.
1865
1908
.
14.
McVeigh
,
C.
,
Vernerey
,
F.
,
Liu
,
W. K.
, and
Cate
,
L. B.
, 2006, “
Multiresolution Analysis for Material Design
,”
Comput. Methods Appl. Mech. Eng.
0045-7825,
195
, pp.
5053
5076
.
15.
Pelegri
,
A. A.
, and
Tekkam
,
A.
, 2003, “
Optimization of Laminates’ Fracture Toughness Using Design of Experiments and Response Surface
,”
J. Compos. Mater.
0021-9983,
37
(
7
), pp.
579
596
.
16.
Tomar
,
V.
, and
Zhou
,
M.
, 2005, “
Deterministic and Stochastic Analyses of Dynamic Fracture in Two-Phase Ceramic Microstructures With Random Material Properties
,”
Eng. Fract. Mech.
0013-7944,
72
, pp.
1920
1941
.
17.
Gano
,
S. E.
,
Renaud
,
J. E.
, and
Sanders
,
B.
, 2004, “
Variable Fidelity Optimization Using a Kriging Based Scaling Function
,”
Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
,
Albany NY
, Aug. 30–Sept. 1, pp.
1
19
, AIAA Paper No. 2004-4460, pp.
1
9
.
18.
Gano
,
S. E.
,
Agarwal
,
H.
,
Renaud
,
J. E.
, and
Tovar
,
A.
, 2006, “
Reliability Based Design Using Variable Fidelity Optimization
,”
Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycl
,
2
(
3–4
), pp.
247
260
.
19.
Gano
,
S. E.
,
Renaud
,
J. E.
,
Martin
,
J. D.
, and
Simpson
,
T. W.
, 2006, “
Update Strategies for Kriging Models for Use in Variable Fidelity Optimization
,”
Struct. Multidiscip. Optim.
1615-147X,
32
(
4
), pp.
287
298
.
20.
Gano
,
S. E.
,
Sanders
,
B.
, and
Renaud
,
J. E.
, 2006, “
Hybrid Variable Fidelity Optimization Using a Kriging-Based Scaling Function
,”
AIAA J.
0001-1452,
43
(
11
), pp.
2422
2430
.
21.
Rodriguez
,
J. F.
,
Renaud
,
J. E.
, and
Watson
,
L. T.
, 1998, “
Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization
,”
ASME J. Mech. Des.
1050-0472,
120
(
1
), pp.
58
66
.
22.
Rodriguez
,
J. F.
,
Renaud
,
J. E.
, and
Watson
,
L. T.
, 1998, “
Convergence of Trust Region Augmented Lagrangian Methods Using Variable Fidelity Approximation Data
,”
Struct. Optim.
0934-4373,
15
(
3–4
), pp.
141
156
.
23.
Wujek
,
B. A.
, and
Renaud
,
J. E.
, 1998, “
A New Adaptive Move-Limit Management Strategy for Approximate Optimization, Part 2
,”
AIAA J.
0001-1452,
36
(
10
), pp.
1922
1937
.
24.
Wujek
,
B. A.
, and
Renaud
,
J. E.
, 1998, “
A New Adaptive Move-Limit Management Strategy for Approximate Optimization, Part 1
,”
AIAA J.
0001-1452,
36
(
10
), pp.
1911
1921
.
25.
Perez
,
V. M.
,
Renaud
,
J. E.
, and
Watson
,
L. T.
, 2004, “
An Interior-Point Sequential Approximate Optimization Methodology
,”
Struct. Optim.
0934-4373,
27
(
5
), pp.
360
370
.
26.
Rodriguez
,
J. F.
,
Renaud
,
J. E.
,
Wujek
,
B. A.
, and
Tappeta
,
R. V.
, 2000, “
Trust Region Model Management in Multidisciplinary Design Optimization
,”
Comput. Appl. Math.
0101-8205,
124
, pp.
139
154
.
27.
Sobieszczanski-Sobieski
,
J.
, 1990, “
Sensitivity of Complex, Internally Coupled Systems
,”
AIAA J.
0001-1452,
28
(
1
), pp.
153
160
.
28.
Rodriguez
,
J. F.
,
Perez
,
V. M.
,
Padmanabhan
,
D.
, and
Renaud
,
J. E.
, 2001, “
Sequential Approximate Optimization Using Variable Fidelity Response Surface Approximations
,”
Struct. Multidiscip. Optim.
1615-147X,
22
, pp.
24
34
.
29.
Perez
,
V. M.
,
Renaud
,
J. E.
, and
Watson
,
L. T.
, 2002, “
Reduced Sampling for Construction of Quadratic Response Surface Approximations Using Adaptive Experimental Design
,”
Proceedings of the 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
,
Denver, CO
, April 22–25, AIAA Paper No. 2002-1587.
30.
Leary
,
S.
,
Bhaskar
,
A.
, and
Keane
,
A.
, 2000, “
A Constraint Mapping Approach to the Structural Optimization of an Expensive Model Using Surrogates
,”
Proceedings of the First International Workshop on Surrogate Modelling and Space Mapping for Engineering Optimization
,
Lyngby, Denmark
, Nov. 2000.
31.
Qian
,
Z.
,
Seepersad
,
C. C.
,
Joseph
,
V. R.
,
Allen
,
J. K.
, and
Wu
,
C. F. J.
, 2006, “
Building Surrogate Models Based on Detailed and Approximate Simulations
,”
ASME J. Mech. Des.
1050-0472,
128
(
4
), pp.
668
677
.
32.
Osio
,
I. G.
, and
Amon
,
C. H.
, 2005, “
An Engineering Design Methodology With Multistage Bayesian Surrogates and Optimal Sampling
,”
Res. Eng. Des.
0934-9839,
8
(
4
), pp.
189
206
.
33.
Anderson
,
T. L.
, 1994,
Fracture Mechanics: Fundamentals and Applications
,
CRC
,
Boca Raton, FL
.
34.
Tomar
,
V.
, and
Zhou
,
M.
, 2004, “
Deterministic and Stochastic Analyses of Fracture Processes
,”
International Conference on Heterogeneous Material Mechanics
,
Chongqing University
,
China
.
35.
COMSOL MULTIPHYSICS, 2005, Finite Element Software, Ver. 3.2.
36.
ASTM
, 1997, Standard Test Method for Plane-Strain Fracture Toughness of Metallic Materials, E399, Annual Book of ASTM Standards.
37.
Ragab
,
A.-R.
, and
Bayoumi
,
S. E.
, 1998,
Engineering Solid Mechanics
, 3rd ed.,
CRC
,
Boca Raton, FL
.
38.
Chen
,
C. R.
,
Pascual
,
F.
,
Fischer
,
F. D.
,
Koledink
,
O.
, and
Danzer
,
R.
, 2007, “
Prediction of the Fracture Toughness of a Ceramic Multilayer Composite, Modeling and Experiments
,”
Acta Mater.
1359-6454,
55
, pp.
409
421
.
39.
Vanderplaats
,
G. N.
, 1999,
Numerical Optimization Techniques for Engineering Design
, 3rd ed.,
VR&D
,
Colorado Springs, CO
, pp.
261
267
.
40.
Guo
,
S.
,
Mamiya
,
T.
, and
Kagawa
,
Y.
, 2006, “
In Situ Nondestructive Evaluation of the Accumulative Damage in Continuous Ceramic Fiber-Ceramic Matrix Composites (CFCCs) Using Submillimiter Range Electromagnetic Wave
,”
Adv. Eng. Mater.
1438-1656,
6
(
8
), pp.
681
683
.
41.
Conn
,
A. R.
,
Gould
,
N. I. M.
, and
Toint
,
P. L.
, 2000,
Trust-Region Methods
,
Society for Industrial and Applied Mathematics and Mathematical Programming
,
Philadelphia, PA
.
42.
Alexandrov
,
N.
, 1996, “
Robustness Properties of a Trust Region Framework for Managing Approximations in Engineering Optimization
,”
Proceedings of the Sixth AIAA/NASA/USAF Multidisciplinary Analysis & Optimization Symposium
,
Bellevue, WA
, Sept. 4-6, pp.
1056
1059
, AIAA Paper No. 96-4102.
43.
Dennis
,
J. E.
, and
Torczon
,
T.
, 1996, “
Approximation Model Management for Optimization
,”
Proceedings of the Sixth AIAA/NASA/USAF Multidisciplinary Analysis & Optimization Symposium
,
Bellevue, WA
, Sept. 4–6, pp.
1044
1046
, AIAA Paper No. 96–4046.
44.
Booker
,
A. J.
,
Dennis
,
J. E.
,
Frank
,
P. D.
,
Serafini
,
D. B.
,
Torczon
,
T.
, and
Trosset
,
M. W.
, 1999, “
A Rigorous Framework for Optimization of Expensive Functions by Surrogates
,”
Struct. Optim.
0934-4373,
17
(
1
), pp.
1
13
.
45.
Alexandrov
,
N. M.
, and
Lewis
,
R. M.
, 2001, “
An Overview of First-Order Model Management for Engineering Optimization
,”
Optim. Eng.
1389-4420,
2
, pp.
413
430
.
46.
Alexandrov
,
N. M.
, and
Lewis
,
R. M.
, 2001,
First-Order Approximation and Model Management in Optimization, in Large-Scale Pde-Constrained Optimization
,
Springer-Verlag
,
Berlin
.
47.
Haftka
,
R. T.
, 1991, “
Combining Global and Local Approximations
,”
AIAA J.
0001-1452,
29
, pp.
1523
1525
.
48.
Lewis
,
R. M.
, and
Nash
,
S. G.
, 2000, “
A Multigrid Approach to the Optimization of Systems Governed by Differential Equations
,” AIAA Paper No. 2000-4890.
49.
Chang
,
K. J.
,
Haftka
,
R. T.
,
Giles
,
G. L.
, and
Kao
,
P.-J.
, 1993, “
Sensitivity-Based Scaling for Approximating Structural Response
,”
J. Aircr.
0021-8669,
30
(
2
), pp.
283
288
.
50.
Gano
,
S. E.
,
Perez
,
V. M.
, and
Renaud
,
J. E.
, 2004, “
Multi-Objective Variable-Fidelity Optmization of a Morphing Unmanned Aerial Vehicle
,”
Proceedings of the 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference
,
Palm Springs, CA
, April 19-22, AIAA Paper No. 2004-1763.
51.
Eldred
,
M. S.
,
Giunta
,
A. A.
,
Collis
,
S. S.
,
Alexandrov
,
N. A.
, and
Lewis
,
R. M.
, 2004, “
Second-Order Corrections for Surrogate-Based Optimization With Model Hierarchies
,”
Proceedings of the Tenth AIAA/ISSMO Multidisciplinary Analysis & Optimization Conference
,
Albany, NY
, Aug. 30–Sept. 1, AIAA Paper No. 2004-4457.
52.
Booker
,
A. J.
, 1998, “
Design and Analysis of Computer Experiments
,”
Proceedings of the Seventh AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
,
St. Louis, MO
, Sept. 2–4, Vol.
1
, pp.
118
128
.
53.
Jones
,
D. R.
,
Schonlau
,
M.
, and
Welch
,
W. J.
, 1998, “
Efficient Global Optimization of Expensive Black-Box Functions
,”
J. Global Optim.
0925-5001,
13
, pp.
455
492
.
54.
Sasena
,
M. J.
,
Papalmbros
,
P.
, and
Goovaerts
,
P.
, 2002, “
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization
,”
Eng. Optimiz.
0305-215X,
34
(
3
), pp.
263
278
.
55.
Simpson
,
T. W.
,
Maurey
,
T. M.
,
Korte
,
J. J.
, and
Mistree
,
F.
, 2001, “
Kriging Meta-Models for Global Approximation in Simulation-Based Design
,”
Eng. Comput.
0177-0667,
17
(
2
), pp.
129
150
.
56.
Martin
,
J. D.
, and
Simpson
,
T. W.
, 2003, “
A Study on the Use of Kriging Models to Approximate Deterministic Computer Models
,”
Proceedings of ASME 2003 Design Engineering Technical Conferences and Computers and Information in Engineering Conference
,
Chicago, IL
, Sept. 2–6, Paper No. DET2003/DAC-48762.
57.
Martin
,
J. D.
, and
Simpson
,
T. W.
, 2005, “
On the Use of Kriging Models to Approximate Deterministic Computer Models
,”
AIAA J.
0001-1452,
43
(
4
), pp.
853
863
.
58.
MATLAB, 2006, Software for Technical Computing, R2006a.
59.
Han
,
S. P.
, 1997, “
A Globally Convergent Method for Nonlinear Programming
,”
J. Optim. Theory Appl.
0022-3239,
22
(
3
), pp.
297
309
.
60.
Powell
,
M. J. D.
, 1978,
The Convergence of Variable Metric Methods for Nonlinearly Constrained Optimization Calculations
,
Academic
,
New York
.
61.
Powell
,
M. J. D.
, 1978, “
A Fast Algorithm for Nonlineary Constrained Optimization Calculations
,”
Numerical Analysis
Lecture Notes in Mathematics
Vol.
630
,
Springer
,
New York
.
62.
Gill
,
P. E.
,
Murray
,
W.
, and
Wrigth
,
M. H.
, London, 1981,
Practical Optimization
,
Academic
,
New York
.
You do not currently have access to this content.