SBIR-STTR Award

Global Engineering and Materials, Inc., along with University of Illinois Urbana-Champaign, Wichita State University, and YB Numerics Inc., proposes to develop an integrated mechanics/data-driven multiphysics framework to predict the time-dependent/space-varying local boundary conditions during autoclave processes of composite parts. A high-fidelity thermal fluid-structure interaction (FSI) model will be developed to determine the local flow, temperature, and pressure for parts in the presence of the thermal-mechanical coupling on bagging-tooling surfaces. An immersogeometric approach will be used for efficient FSI simulations of multiple parts with arbitrary placement directly using parts CAD model to capture the flow-part interaction with a universal fluid mesh. The spatial-temporal distribution of heat fluxes on the part surface will be included in the FSI model. Actual autoclave runs will be performed using single and multiple parts with in-situ sensors to measure the local/remote flow and temperature for model validation and physics-informed data fusion to further improve the models accuracy and fidelity. The validated framework can quickly and reliably evaluate the local boundary conditions and intelligently place multiple parts to achieve the correct cure profile. It paves the way towards the teams ultimate goal to achieve: efficient, optimized, and adjustable autoclave processes by building a multiphysics simulation-based dynamic control system.

Benefit:
The research will result in a versatile, user-friendly, and computationally efficient toolkit for virtual simulation of local environmental conditions in an autoclave with multiple parts to maximize the throughput with the correct cure profile of each part. The toolkit will allow composite manufacturers to determine the optimal manufacturing procedures to reduce the trial-and-error testing required to develop the recipes for each unique combination of autoclave and part batch. The developed physics-based data fusion process can provide an accelerated insertion of an active loop to control the processing parameters via the collected data from the in-situ sensors such as pressure transducers, thermocouples, and vacuum gauges. The developed toolkit can be linked with other physics models to describe cure kinetics, cure history-dependent viscoelastic constitutive relations, the micromechanics model, and progressive damage analysis with a given distribution of fabrication-induced defects. The integrated technology will also allow composite manufacturers to determine the optimal manufacturing procedures to minimize process-induced defects and variations while significantly reducing the trial-and-error testing on expensive physical prototyping. This will enable faster design and process validation and verification, leading to low development costs and adding the values of lightweight, high-performance composites to a long-term production cycle.

Keywords:
fluid-structure interaction, fluid-structure interaction, Autoclave processing, Thermal-Mechanical Coupling, Immersogeometric Analysis, date fusion, In-Situ Sensors

Global Engineering and Materials, Inc. (GEM), along with its team members, Professor Jinhui Yan at University of Illinois Urbana-Champaign (UIUC), National Institute for Aviation Research (NIAR) at Wichita State University, Professor Dianyun Zhang at Composite Manufacturing and Simulation Center at Purdue University (PU), and Professor Yuri Bazilevs at Brown University, proposes to develop an integrated multiphysics and data-driven autoclave process simulation toolkit (SMARTCLAVE) for multiple composite parts with curved geometries. The novelties of the proposed autoclave process simulation and response prediction tool (SMARTCLAVE) include: 1) a coupled immersogeometric thermal CFD and aerodynamics-aided heat transfer module with a consistent LES turbulence model (without ad hoc viscosity) to capture the turbulence-induced local boundary conditions and the resulting thermal and pressurization response of multiple parts in an autoclave; 2) a hybrid shell and solid element modeling approach in CFD and its associated immersogeometric modeling for a multi-layer composite curing system; 3) an efficient coupling strategy between the CFD module and the customized Abaqus for temperature and pressure mapping; 4) micro-macro characterization of the degree of cure, a temperature-dependent constitutive model, and mechanical performance with fabrication-induced defects; 5) an application programming interface connecting to the commercial composite process simulation software COMPRO for defects prediction; 6) hybrid physics-informed neural networks (PINNs) for data fusion to enhance the accuracy of the high-fidelity model and generate surrogate models for process tailoring; and 7) optimization of autoclave processing and placement of multiple parts in an autoclave. The resulting SMARTCLAVE toolkit will be validated first by using data collected from PU and NIAR at coupon and component levels. The high-fidelity toolkit along with its multi-level PINNs will be demonstrated using data collected from representative composite structures during and after autoclaving.

Benefit:
The research will result in a versatile, user-friendly, and computationally efficient toolkit for virtual simulation of local environmental conditions in an autoclave with multiple parts to maximize the throughput with the correct cure profile of each part. The toolkit will allow composite manufacturers to determine the optimal manufacturing procedures to reduce the trial-and-error testing required to develop the recipes for each unique combination of autoclave and part batch. The developed physics-based data fusion process can provide an accelerated insertion of an active loop to control the processing parameters via collected data from in-situ sensors such as pressure transducers, thermocouples, and vacuum gauges. The developed toolkit can be linked with other physics models to describe the cure kinetics, cure history-dependent viscoelastic constitutive relations, micromechanics model, and progressive damage analysis with a given distribution of fabrication-induced defects. The integrated technology will also allow composite manufacturers to determine the optimal manufacturing procedures to minimize process-induced defects and variations while significantly reducing the trial-and-error testing on expensive physical prototyping. This toolkit will enable faster design and process validation and verification, leading to lowered development costs and adding the values of lightweight, high-performance composites to a long-term production cycle.

Keywords:
In-Situ Sensors, Immersogeometric Analysis, Autoclave processing, fluid-structure interaction, Thermal-Mechanical Coupling, date fusion