S E L ECT PUB LI CATI ONS
PATENTS, PEER-REVIEWED JOURNALS, CONFERENCE PROCEEDINGS,
INVITED TALKS, AND MORE - A PARTIAL LIST OF PUBLICATIONS FROM
SOME OF THE MORE THAN 100 FACULTY, AFFILIATE FACULTY, ADJUNCT
FACULTY, AND INSTRUCTORS SERVING MECHANICAL ENGINEERING
PINAR ACAR
Journal Papers
P. Acar, “Uncertainty Quantification for Ti-7Al Alloy Microstructure
with an Inverse Analytical Model (AUQLin)”, Materials, Vol. 12, No. 11,
1773, 2019; doi: 10.3390/ma12111773
minum Alloys”, 16th Pan-American Congress of Applied Mechanics,
20-23 May 2019, Ann Arbor, MI, USA.
P. Acar, “A Machine Learning Approach for Process Optimization of
Polycrystalline Materials”, 148th TMS Annual Meeting & Exhibition,
10-14 March 2019, San Antonio, TX, USA.
P. Acar, “Machine Learning Approach for Identification of Microstruc-
ture-Process Linkages”, AIAA Journal, accepted, 2019; doi: 10.2514/1.
J058244 M. Ghodrati, P. Acar and R. Mirzaeifar, “A Generalized Nature-Inspired
Optimization Method: Additively Manufactured Materials with Supe-
rior Mechanical Performance”, 148th TMS Annual Meeting & Exhibi-
tion, 10-14 March 2019, San Antonio, TX, USA.
(invited) R. Catania, A. Diraz, D. Maier, A. Tagle and P. Acar, “Math-
ematical Strategies for Design Optimization of Multi-Phase Materials”,
Mathematical Problems in Engineering, Vol. 2019, Article ID 4024637,
2019; doi: 10.1155/2019/4024637 P. Acar and V. Sundararaghavan, “Uncertainty Quantification in Micro-
structural Reconstruction of Additively Manufactured Materials”, 148th
TMS Annual Meeting & Exhibition, 10-14 March 2019, San Antonio,
TX, USA.
(invited) P. Acar, “Multi-Scale Computational Modeling of Lightweight
Aluminum-Lithium Alloys”, Heliyon, Vol. 5, No. 3, e01225, 2019; doi:
10.1016/j.heliyon.2019.e01225 P. Acar, “A Transductive Learning Approach for Identification of
Microstructure-Process Linkages”, AIAA Science and Technology Forum
(AIAA SciTech), 7-11 January 2019, San Diego, CA, USA.
A. Paul, P. Acar, W. Liao, A. Choudhary, V. Sundararaghavan, A. Agrawal,
“Microstructure Optimization with Constrained Design Objectives
using Machine Learning-Based Feedback-Aware Data-Generation”,
Computational Materials Science, Vol. 160, pp: 334-351, 2019; doi:
10.1016/j.commatsci.2019.01.015 P. Acar and V. Sundararaghavan, “Uncertainty Quantification and Sto-
chastic Optimization for Spatially Varying Composite Fiber Paths”, AIAA
Science and Technology Forum (AIAA SciTech), 7-11 January 2019, San
Diego, CA, USA.
P. Acar, “Eliminating mesh sensitivities in microstructure design with an
adjoint algorithm”, Finite Elements in Analysis and Design, Vol. 154, pp:
22-29, February 2019; doi: 10.1016/j.finel.2018.10.001
P. Acar and V. Sundararaghavan, “Do Epistemic Uncertainties Al-
low for Replacing Microstructural Experiments with Reconstruction
Algorithms?”, AIAA Journal, Vol. 57, No. 3, pp: 1078-1091, 2019; doi:
10.2514/1.J057488
P. Acar and V. Sundararaghavan, “Stochastic Design Optimization of
Microstructural Features using Linear Programming for Robust Mate-
rial Design”, AIAA Journal, Vol. 57, No. 1, pp: 448-455, 2019; doi:
10.2514/1.J057377
P. Acar, “Crystal Plasticity Model Calibration for Ti-7Al Alloy with a
Multi-Fidelity Computational Scheme”, Integrating Materials and Man-
ufacturing Innovation, Vol. 7, No. 4, pp: 186-194, 2018; doi: 10.1007/
s40192-018-0120-0
P. Acar, “Reliability Based Design Optimization of Microstructures with
Analytical Formulation”, Journal of Mechanical Design, Vol. 140, No.
11, 111402, 2018; doi: 10.1115/1.4040881
Conference Proceedings and Meeting Presentations
P. Acar, “Uncertainty Quantification for Microstructural Features of
Additively Manufactured Materials”, 15th U.S. National Congress on
Computational Mechanics, 28 July-1 August 2019, Austin, TX, USA.
P. Acar, “A Machine Learning Approach for Crystal Plasticity Modeling
of Ti-7Al Alloy under Uncertainties”, 5th World Congress on Integrated
Computational Materials Engineering (ICME 2019), 21-25 July 2019,
Indianapolis, IN, USA.
P. Acar, “Multi-Fidelity Crystal Plasticity Modeling of Titanium-Alu-
42 Revised and Corrected, Nov. 2019
P. Acar, “Integrating an Analytical Uncertainty Quantification Approach
to Multi-Scale Modeling of Nanocomposites”, ASME International Me-
chanical Engineering Congress & Exposition (IMECE), 11-14 November
2018, Pittsburgh, PA, USA.
A. M. Roy, S. Ganesan, P. Acar, S. Gentry, A. Trump, J. Allison, K. Thorn-
ton, V. Sundararaghavan, “Phase-field Approach Coupled with Crystal
Plasticity for Three-Dimensional Static Recrystallization in Ti-7Al Alloys
and Comparison with Experiment”, Materials Science & Technology
Technical Meeting and Exhibition, 14-18 October 2018, Columbus,
OH, USA.
MEHDI AHMADIAN
Kothari, K., Dixit, J., and Ahmadian, M., “Effect of Variation of Angle of
Attack on Adhesion-Creepage Behavior Using Virginia Tech –Federal
Railroad Administration (VT – FRA) Roller Rig,” Proceedings of the 2018
AREMA Annual Conference, Chicago, IL, September 16 – 20, 2018.
Ghodrati, M., Ahmadian, M., Mirzaeifar R., “Investigating the rolling
contact fatigue in rails using finite element method and cohesive zone
approach,” Proceedings of the 2018 Joint Rail Conference, Pittsburgh,
PA, April 18-20, 2018.
Jazizadeh, F. K., Afzalan, M., and Ahmadian, M., “Determining Track
Condition from Onboard Data in Revenue Service through Machine
Learning: Fondest Hopes, Wildest Dream,” Big Data in Railroad Mainte-
nance Planning 2018, Newark, DE, December 13 – 14, 2018.
Neighborgall, C., Mast, T., Peterson, A. W., and Ahmadian, M., “Qualita-
tive Assessment of Rail Lubricity,” The First Annual Symposium on
Railroad Infrastructure Diagnosis and Prognosis Symposium, University
of Nevada, Las Vegas, NV, October 16 – 17, 2018.