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Development of multiscale theories, methods, and software for the design and analysis of large scale engineering material systems, materials design, manufacturing, bio-sensing, and drug delivery.
PI of multi-year multi-million dollars grants from Goodyear Tire and Rubber Company to develop a virtual lab to enable prediction, synthesis, characterization, and acceleration the synthesis of new polymer nanocomposites with enhanced performance and fulfillment of AAA labeling requirements. Collaborative research is also underway with Bridgestone and Japanese Universities.
Technology transfer through DMDII project Elastic Cloud-Based Make: Led by GE, NU is designing training modules for Adaptive Vehicle Make (AVM) toolsets of digital manufacturing technologies developed under the Defense Advanced Research Project Agency’s (DARPA) Adaptive Vehicle Make (AVM) program, such as the instant Foundry Adaptive through Bits (iFAB) tools. The goal is to integrate the most mature elements of the digital manufacturing technologies into an existing commercially available Product Lifecycle Management, Computer-Aided Engineering or Model-Based Engineering software solution, and to apply the resulting new capability to a manufactured product.
Technology transfer: Development of new methods and algorithms that have significantly enhanced the accuracy and speed in software for crashworthiness, manufacturing, and prototype simulations. These contributions have been implemented in many commercial and laboratory software: (a) Solid shell elements in DYNA3D, ABAQUS, LS-DYNA, ANSYS, and Argonne National Laboratory (ANL) software; (b) Explicit/implicit methods in US Ballistic Laboratory EPIC-2/EPIC-3 programs, and ANL software; (c) Lagrangian-Eulerian methods adopted by ANL, Kawasaki, Mitsubishi, Ford Motors, and Grumman; (d) Meshfree methods implemented by Sandia National Labs, Lawrence Livermore National Lab, General Motors, Ford Motors, Delphi, Ball Aerospace, Caterpillar, and among many others; (e) Proprietary multiscale methods adopted by Goodyear, Bridgestone for the design of tires and by Sandia
PI of multi-year multi-million dollars grants from Goodyear Tire and Rubber Company to develop a virtual lab to enable prediction, synthesis, characterization, and acceleration the synthesis of new polymer nanocomposites with enhanced performance and fulfillment of AAA labeling requirements. Collaborative research is also underway with Bridgestone and Japanese Universities.
Technology transfer through DMDII project Elastic Cloud-Based Make: Led by GE, NU is designing training modules for Adaptive Vehicle Make (AVM) toolsets of digital manufacturing technologies developed under the Defense Advanced Research Project Agency’s (DARPA) Adaptive Vehicle Make (AVM) program, such as the instant Foundry Adaptive through Bits (iFAB) tools. The goal is to integrate the most mature elements of the digital manufacturing technologies into an existing commercially available Product Lifecycle Management, Computer-Aided Engineering or Model-Based Engineering software solution, and to apply the resulting new capability to a manufactured product.
Technology transfer: Development of new methods and algorithms that have significantly enhanced the accuracy and speed in software for crashworthiness, manufacturing, and prototype simulations. These contributions have been implemented in many commercial and laboratory software: (a) Solid shell elements in DYNA3D, ABAQUS, LS-DYNA, ANSYS, and Argonne National Laboratory (ANL) software; (b) Explicit/implicit methods in US Ballistic Laboratory EPIC-2/EPIC-3 programs, and ANL software; (c) Lagrangian-Eulerian methods adopted by ANL, Kawasaki, Mitsubishi, Ford Motors, and Grumman; (d) Meshfree methods implemented by Sandia National Labs, Lawrence Livermore National Lab, General Motors, Ford Motors, Delphi, Ball Aerospace, Caterpillar, and among many others; (e) Proprietary multiscale methods adopted by Goodyear, Bridgestone for the design of tires and by Sandia
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npj Computational Materialsno. 1 (2024): 1-14
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2024): 116550-116550
COMPUTATIONAL MECHANICSno. 2 (2023): 363-382
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2023): 116356-116356
Materials Today Communications (2023): 106669-106669
COMPUTATIONAL MECHANICSno. 3 (2023): 593-612
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Abdullah Amin,Yangfan Li,Ye Lu,Xiaoyu Xie,Satyajit Mojumder,Zhengtao Gan, Gregory Wagner,Wing Kam Liu
Research Square (Research Square) (2023)
Computational Mechanicsno. 1 (2023): 1-2
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