My current and previous research is/was supported by the following agencies and companies. The support is gratefully acknowledged.











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Information fusion for real-time national air transportation system prognostics under uncertainty


Status: Active, 2017-2022


Sponsor: NASA


Brief: The objectives of the proposed study are to develop an integrated real-time system-wide information fusion methodology for prognostics and safety assurance of the NAS. Various sources of uncertainties and their coupling effects will be systematically investigated for accurate failure and risk assessment of the extremely large-scale, complex NAS. A community-based collaborative simulation platform will be developed and deployed for continued sustainable prognostics technology evolution for the NAS safety research.




 EAGER: Reconstruction and Optimal Design of Multi-scale Material Systems through Deep Networks


Status: Active, 2016-2017


Sponsor: NSF


Brief: This EArly-concept Grant for Exploratory Research (EAGER) grant supports fundamental research to develop scalable computational design tools to enable efficient and effective materials design. Computational material design (CMD), such as identifying optimal material microstructures to achieve desirable performance, receives a growing interest as sophisticated material designs can be subsequently realized using advanced processing techniques such as additive manufacturing. Conceptually, solving CMD problems involves iterative search for the best solutions in a problem space. Since the cost of solution searching is sensitive to the size of the space, the lack of cost-efficiency hampers the application of existing CMD approaches to complex material systems, where the goodness of the material design depends on numerous details of the microstructure on multiple length scales. The use of CMD tools will enable the discovery of critical microstructure patterns and the reduction of the dimensionality of the problem space. The research will lead to efficient microstructure design and validation for high performance structural materials with superior durability and structural health. Therefore, results from this research will benefit various U.S. industries, and its economy and society. The required seamless integration of material science, engineering design, manufacturing, and data science will help to broaden student participation and positively impact engineering education.




Collaborative Research: Fatigue Damage Prognosis for Slender Coastal Bridges

Status: Active, 2015-2018

Sponsor: NSF

A novel maximum entropy-based Bayesian network for multi-scale corrosion-fatigue damage prognosis for slender coastal bridges is planned. The framework fuses the information and knowledge from the material level, the structural level, and the system level for the probabilistic prognosis and reliability assessment. The inter-correlations among different levels of nodes in the network are developed by using coupled dynamic analysis and corrosion-fatigue damage analysis. Advanced experimental testing for fatigue and simulation methods will be combined together for the physics-based prediction of remaining useful life of costal bridges. Uncertainties will be propagated through the Bayesian network and the system level reliability will be updated and reassessed. The Bayesian network can update itself with information from experimental measurements, field observation, and historical experiences. In this methodology, coupled structure dynamics model will capture the realistic service and environmental loads during the lifetime span of the bridges.

Bayesian Network Inference and Information Fusion for Accurate Pipe Strength and Toughness Estimation

Status: Active, 2015 – 2018

Sponsor: Federal DOT Program: PHMSA

Pipeline infrastructure and its safety are critical for the recovering of U.S. economy and our standard of living. Accurate pipe material strength estimation is critical for the integrity and risk assessment of aging pipeline infrastructure systems. Existing techniques focus on the single modality deterministic estimation of pipe strength and ignores inhomogeneousity and uncertainties. In view of this, this project is a novel information fusion framework using multimodality diagnosis for pipe materials for accurate probabilistic strength and toughness estimation under uncertainties. The first task will be chemical composition, material microstructure, and basic surface mechanical properties are detected using various in situ and ex situ techniques. Advanced data analysis using Gaussian Processing model will be performed for surrogate modeling and uncertainty quantification. Following this, advanced sensing techniques using acoustic and electromagnetic sensing will be considered. Both simulation and prototype testing are proposed for model validation and demonstration. Finally, a generalized Bayesian network methodology is planned to fuse multiple sources of information from the multimodality diagnosis results. Probabilistic pipe strength and toughness estimation is inferred based on the posterior distribution after information fusion. If successful, this study can help to accurately and effectively assess the reliability of pipeline systems, and eventually help the decision making process to balance the pipeline safety and economical operations.

Slow Crack Growth Evaluation of Vintage Polyethylene Pipes

Status: Active, 2015 – 2017

Sponsor: Federal DOT through GTI

Damage diagnosis and remaining life prediction of pipeline infrastructure systems is still a challenging problem despite tremendous progress made during the past several decades, such as the damage accumulation in plastic gas distribution pipes. The goal of our part of the project is to implement Bayesian network for classification via images taken inside the pipe. And develop a maintenance framework for plastic pipeline system. Various imaging processing techniques and feature extraction algorithms will be developed for the accurate representation of pipe damage. Advanced parallel computing will be developed for the automatic detection of large imaging datasets. Reliability-based optimization framework will be developed for the pipe infrastructure integrity assessment and maintenance.

Multi-resolution in-situ testing and multiscale simulation for creep fatigue damage analysis of Alloy 617

Status: Active, 2014 – 2017

Sponsor: DOE

The overall goal of this project is to develop novel testing and experimentally validated prediction methodologies for creep-dominated creep-fatigue response of structural materials for advanced reactor systems. The investigations will focus on the characterization and testing of Alloy 617, but the proposed testing and life-prediction methodologies are applicable to other structural materials as well. The research objectives of this proposal are: (1) Perform multi-resolution in-situ and ex-situ testing and imaging analysis for the fundamental creep-fatigue damage mechanism investigation; (2) Develop a new procedure for creep-fatigue testing at the coupon level and generate a database for model validation; (3) Formulate and implement models for simulation of creep-fatigue damage mechanisms and their interactions at the microstructure scale; and (4) Conduct microstructure simulations for creep-fatigue mechanism understanding and develop a microstructure-informed and experimentally validated phenomenological creep-fatigue life prediction model.

Optimized Diagnosis and Prognosis for Impingement Failure of PA and PE Piping Materials

Status: Active, 2014 – 2016

Sponsor: Federal DOT through University of Colorado Denver

The objectives of this pipeline safety research Competitive Academic Agreement Program (CAAP) will be well addressed and supported by our research. Development, demonstration and standardization to ensure the integrity of pipeline facilities will be carried out with this multi-university and collaborative effort. Another major objective of this proposed research that is coherent and relevant to the PHMSA’s CAAP program is to engage MS and PhD students who may later seek careers in this field by exposing them to subject matter common to pipeline safety challenges. There are currently six CAAP students fully or partially supported by this excellent program since 2013. If funded, two more PhD students from both universities and several MS students will be trained through this CAAP program and apply their engineering disciplines to pipeline safety and integrity research.

Proactive and Hybrid Sensing based Inline Plastic Pipeline Defects Diagnosis and Prognosis

Status: Finished, 2013 – 2015

Sponsor: Federal DOT through University of Colorado Denver

This proposal seeks support to develop a new form of sensing technique that can identify and characterize injurious pipe body internally and/or externally without contact using near-field microwave probing and induced ultrasonic waves due to microwave absorption and thermal expansion. Different from the current technology and tools such as remote field eddy current (RFEC), magnetic flux leakage (MFL), electromagnetic-acoustic transducer (EMAT) and magneto-strictive (MsS), etc. that can only apply to metallic piping materials or acoustic emission inspection that may lack sensitivity, the proposed hybrid Thermo-Electromagnetic-Acoustic Pipeline Inspection ProbE (TEA-PIPE) system can achieve both superior spatial resolution and high contrast simultaneously due to the innovative nature in laws of physics. The illustration of the TEA-PIPE approach is shown in Figure 1. We will integrate this advance sensor to the inline inspection (ILI) platforms and deliver the system to provide near-term solutions that will improve the safety and enhance the reliability of the pipeline transportation system. Defect characterization through analytics and signal processing will be developed for “in the ditch direct measurement”. The detection results from the proposed advanced NDE sensing methodology can be further integrated with probabilistic methods and mechanical analysis for the accurate time-dependent reliability analysis. An information fusion framework integrating the residual strength calculation, uncertainty quantification and propagation analysis, and Bayesian updating is proposed for the accurate pipeline reliability evaluation and risk assessment using NDE testing results. If successful, the pipeline failure can be significantly reduced.

Probabilistic Remaining Useful Life Prediction of Composite Aircraft Components

Status: Finished, 2013 – 2015

Sponsor: NASA through GEM

A discrete crack network model coupled with a critical-plane based fatigue delamination growth model under multiaxial loading will be developed and used as a physics-based deterministic solver for its subsequent probabilistic integration. Advanced probabilistic analysis methods with the Bayesian Maximum Entropy (BME) updating will be implemented for its probabilistic integration. Damage detection results are integrated/fused with the physics-based model using the BME framework.

Concurrent structural fatigue damage prognosis under uncertainties

Status: Finished, 2011-2014

Sponsor: AFOSR

Program: Young Investigator Program

This project proposes a fundamentally different and innovative fatigue prognosis methodology based on a small time scale formulation is proposed for the real-time concurrent structural damage prognosis. The proposed novel damage model overcomes the inherent difficulties in existing fatigue theories. One of the most important benefits is that concurrent fatigue analysis across multiple spatial and temporal scales becomes feasible. Pervasive prognosis capability is addressed in this study, from material level up to structure level. Rigorous validation of model hypotheses and prediction will be performed using state-of-the-art experimental techniques, such as in-situ fatigue testing under scanning electron microscopy combined with digital image analysis. A special focus in the proposed study is on the systematic uncertainty modeling through multilevel computational simulations. Advanced reliability methods, Bayesian statistics and information theory are proposed to capture the stochastic nature of fatigue damage accumulation. 

Probabilistic Fatigue Life Prediction and Risk Assessment of Aging Bridges in Cold Regions

Status: Finished, 2009 – 2012

Sponsor: NSF

Program: CMMI

The research objective of this award is to develop an innovative life prediction and risk assessment methodology for aging bridges in cold regions. The detailed fatigue mechanism modeling, corrosive environmental effects due to deicing salts, and advanced probabilistic methods will be systematically integrated into a general framework. Fatigue damage accumulation behavior under general multiaxial random loading will be considered in this project to represent the realistic service conditions of bridges. Coupled corrosion damage growth and fatigue crack growth analysis will be used to investigate the damage interaction of steel bridges in cold regions. A rigorous uncertainty quantification and propagation analysis will be performed using random process theory and Bayesian methodology. Efficient probabilistic methods will be employed to solve the time-dependent reliability problem of bridges considering mechanical and corrosive damage accumulation. 

Validation and uncertainty management of prognostic algorithms

Status: Finished, 2008 – 2012

Sponsor: NASA Ames

The overall goal of the proposed project is to develop, validate and demonstrate a general fatigue damage prognosis and uncertainty management methodology for implementations of the IVHM to aircraft structures. It combines fundamental fatigue mechanism modeling, efficient probabilistic methods, systematical model verification and validation, and hybrid simulation and experimental testing into a single framework for the reliability evaluation and uncertainty management.

Rotorcraft damage tolerance risk assessment and management

Status: Finished, 2007 – 2011

Sponsor: Federal Aviation Administration, William J. Hughes Technical Center

The project aims support FAA rulemaking and the implementation of rotorcraft damage tolerance requirements. It combines uncertainty quantification and propagation analysis, multi-scale fatigue and fracture modeling, risk assessment and reliability-based inspection and maintenance scheduling to develop a general risk management methodology. Demonstration and implementation of the developed methodology are given to show its impact on the applicable FAR regulations. 

IRES in China – Advanced Materials for a Sustainable Development (link)

Status: Finished, 2011~2014

Sponsor: NSF

This award is a grant in response to Program Solicitation, International Research Experiences for Students (IRES). The research topic of this program is the development of advanced materials for applications in energy generation and storage and environmental remediation processes. The Clarkson University program will collaborate with Corning, Inc., in New York State, Corning Research China and host research mentors at Tsinghua University and the Chinese Academy of Science Institute of Physics in Beijing, China. Six to twelve undergraduates and three graduate students will travel to China in each of three summers, starting in 2011. Student recruitment will be national, and an effort will be made to include underrepresented minorities.

Probabilistic Remaining Useful Life Prediction of Composite Aircraft Components

Status: Finished, 2011~2014

Sponsor: NASA through GEM

A discrete crack network model coupled with a critical-plane based fatigue delamination growth model under multiaxial loading will be developed and used as a physics-based deterministic solver for its subsequent probabilistic integration. Advanced probabilistic analysis methods with the Bayesian Maximum Entropy (BME) updating will be implemented for its probabilistic integration. Damage detection results are integrated/fused with the physics-based model using the BME framework. 

Self-healing of fatigue damage in metallic materials

Status: Finished, 2010~2011

Sponsor: Clarkson CSoE Seed Grant

This project proposes an innovative and fundamentally different fatigue damage mitigation methodology based on the self-healing mechanism. Detailed mechanism modeling, numerical methods, state-of-the-art experimental techniques, and advanced material synthesis are integrated together for system development and validation. Three major tasks are proposed: 1) Investigate the self-healing mechanism using the newly developed multiscale fatigue damage simulation model for metallic materials; 2) Synthesize a new epoxy/polymer coating material with desired transient temperature and mechanical properties from mechanism modeling; 3) Perform fatigue crack growth testing of Al-7075-T6 specimens with and without self-healing for model validation.

Mesh Independent Probabilistic Residual Life Prediction of Metallic Airframe Structures

Status: Finished, 2011

Sponsor: NASA through GEM

Global Engineering and Materials, Inc. (GEM) along with its team members, Clarkson University and LM Aero, propose to develop a mesh independent probabilistic residual life prediction tool for metallic airframe structures. The deterministic solver of this probabilistic analysis tool will be developed by integrating our cutting edge extended finite element toolkit for Abaqus (XFA) with a novel small time scale fatigue crack growth model for mesh independent fatigue crack growth prediction in a complex airframe structural component subjected to multiaxial and variable amplitude loading. The fast matching and narrow band technique will be implemented to track a curvilinear 3D crack growth without remeshing. 

Innovative approaches for improving progressive damage modeling and structural life prediction of airframes    

Status: finished, 2009 – 2010

Sponsor: NAVAIR through GEM

The project is to develop an integrated numerical simulation tool for structural level damage prognosis. An automatic tool for 3D fatigue crack growth prognosis of structural systems under realistic complex loading will be developed by integrating a unified growth model with a mesh independent extended finite element method. The tool will be able to model arbitrary non-planer crack growth over multiple growth regimes with an arbitrary stress ratio without user intervention or remeshing. 

Advanced modeling capabilities for railroad wheel failure analysis

Status: Finished, 2005 – 2007

Sponsor: Transportation Technology Center, Inc.

The project combines structural failure analysis, finite element methods, and fracture mechanics to develop a methodology to analyze and simulate railroad wheel failure. Experimental tests are performed to quantify initial defects in the wheel material. Failure analyses focuses on both fatigue crack growth and wear to model cracking failure mode and rim thinning, and implement these methods with finite element stress analysis. The results of these analyses lead to future guidance for railroad wheel design optimization. 

Stochastic multiaxial fatigue and fracture modeling (Ph.D. dissertation)

Status: Finished, 2004 – 2006

Sponsor: Union Pacific Railroad

Research combines fatigue theory, fracture mechanics, finite element analysis, and probabilistic methods to develop a general methodology for the fatigue reliability assessment of rotating mechanical components. Both the crack initiation and propagation under low-cycle and high-cycle fatigue loading were included. The damage accumulation and uncertainty quantification under service stochastic loading was studied in detail. The proposed method has been validated for various materials from different industries.

Residual stress and its effects on fatigue failure of spot-weld joint

Status: Finished, 2002 – 2003

Sponsor: Daimler Chrysler.

Developed a general methodology to combine electro-thermal-structural finite element simulation, design of experiments, response surface method, random field expansion and Monte Carlo technique to simulate the residual stress during the manufacturing process. The results were integrated into a strain-based probabilistic fatigue life prediction model to analyze the reliability variation of spot-weld components.

Probabilistic life prediction of composite laminates

Status: Finished, 2003 – 2004

Sponsor: Vanderbilt University

Developed a probabilistic fatigue life prediction framework for multi-directional composite laminates, which enables fatigue-resistant design and maintenance decision-making. The numerical simulation results were validated with experimental observations.