草莓视频

Enhancing the Reliability of HDPE Piping Connections through Computational Modeling

This project tackles the critical industrial challenge of leakage in high-density polyethylene (HDPE) piping systems, especially at bolted flange connections widely used in infrastructure and energy sectors. By combining advanced constitutive modeling of HDPE’s time- and temperature-dependent behavior with the innovative Tetra-Parametric Assembly Method (TAM), the research introduces a predictive computational framework that captures both material viscoelasticity and bolt elastic interactions. The methodology, validated through extensive experimental testing and finite element simulations, provides accurate insight into stress relaxation, preload loss, and sealing performance under realistic service conditions, including thermal cycling. Key results demonstrate that conventional tightening methods are insufficient for hybrid HDPE–steel assemblies, while the TAM-based approach ensures more uniform bolt load distribution, delays leakage onset, and guides optimal re-torqueing strategies. These findings not only advance the scientific understanding of polymer–metal joint mechanics but also deliver practical design and maintenance solutions for industry, improving the reliability and longevity of critical piping systems.

Supported by Union Pipes Industry (UPI).

Advanced Phase-Field Modeling of Sulfide Stress Cracking for Safer Oil and Gas Infrastructure

This project tackles the critical industrial challenge of sulfide stress cracking and hydrogen-assisted fracture in oil and gas pipelines and tubular components. The research introduces innovative chemo-thermo-mechanical and residual stress–coupled phase-field modeling frameworks that capture the complex interplay of hydrogen diffusion, temperature, residual stresses, and fracture mechanics with unprecedented accuracy. Validated against industry-standard tests, these models provide new predictive insights into crack initiation and propagation under sour service conditions. The outcomes deliver practical tools for industry, enabling more reliable material selection, improved design against cracking, and enhanced integrity management strategies, ultimately advancing safety and reducing costly failures in harsh operating.

Funded by Abu Dhabi National Oil Company (ADNOC).

Machine Learning-Enhanced Electrical Impedance Tomography (EIT) for Next-Generation Robotic Skin

This study introduces an innovative framework that integrates finite element modeling with machine learning to overcome the long-standing limitations of tactile sensing in robotics. By combining electrical impedance tomography (EIT) with artificial neural networks, the research bypasses traditional imaging challenges and directly predicts key mechanical responses such as touch location, pressure, and force. Using experimentally calibrated models of polyurethane foam with conductive coatings, the team developed a robust data-driven system capable of accurately reconstructing tactile interactions across complex surfaces. The results demonstrate a significant leap toward cost-effective, flexible, and highly accurate “robotic skin” technologies, with wide-ranging implications for robotics, biomedical devices, and structural health monitoring.

Funded through CIRA (internal KU fund).

Optimizing Scanning Strategies to Improve Additive Manufacturing Performance

This research project addresses the challenge of residual stresses and distortions in metal additive manufacturing, which critically affect the reliability of lightweight lattice structures. The study introduces and compares two innovative thermo-mechanical simulation methodologies, e.g. path-dependent and layer-by-layer modeling, validated against experimental data. The path-dependent approach accurately captures detailed stress evolution, while the layer-by-layer method reduces computational cost by nearly 80% without sacrificing accuracy. By applying these models to various lattice designs, the work demonstrates how scanning patterns directly influence residual stress buildup and mechanical properties. Key findings show that strategies such as 45°/–45° and 0°/90° scanning improve strength and stiffness, while “stars” patterns are most detrimental. These insights provides practical guidance for tailoring process parameters to minimize residual stress formation and enhance the performance of additively manufactured parts.

Supported by the Advanced Digital and Additive Manufacturing (ADAM) center at KU.

Innovative Design of Biomechanical Implants for Enhanced Bone Healing

This research work advances the design of orthopedic implants by integrating computational modeling, topology optimization, and mechano-regulation algorithms to improve fracture fixation and healing. Using finite element analysis and additively manufactured prototypes, the studies developed and evaluate porous and topology-optimized titanium bone plates for long bone and mandibular fractures. The innovative methodology captured the complex interaction between implant stiffness, residual stresses, and interfragmentary movement, showing how optimized lattice structures and porous configurations can promote callus formation, reduce stress shielding, and accelerate healing compared to conventional rigid plates. Key outcomes include validated implant designs that balance stability with controlled micromotion, offering practical pathways to next-generation patient-specific implants that enhance recovery and reduce complications.

Supported by the Advanced Digital and Additive Manufacturing (ADAM) center at KU.

Computational Modeling and Experimental Procedure of Butt Fusion Welding (BFW) of Large HDPE Pipes

This research project addresses challenges in HDPE thick-walled large pipes welding used in nuclear power plant applications where butt fusion is the primary joining method. A thermomechanical finite element model incorporating heat transfer, melting behavior, crystallization kinetics, and pipe deformation simulates the BFW stages. Experimental work systematically examines the four-stage BFW process using advanced equipment and Design of Experiments (DOE) methodology to optimize process variables, including temperature and pressure. The primary objectives are to develop a validated multi-physics computational model of the BFW and to develop a novel BFW technique to enhance joint strength and integrity, while evaluating welding performance through failure simulation and full-scale testing under field conditions. This integrated computational and experimental approach aims to ensure reliable, high-performance welded joints for critical nuclear applications.

Funded by Emirate Nuclear Energy Company (ENEC), Emirates Nuclear Tech. Center (ENTC), and Borouge.

Stress Corrosion Cracking (SCC) of Additively Manufactured Metallic Nuclear Components

This project investigates the stress corrosion cracking (SCC) behavior of 3D-printed metal alloys, paying special attention to Inconel 718 and SS316L, two essential materials for nuclear power applications. The main objectives are to integrate an experimental–computational framework to investigate stress corrosion cracking (SCC) in additively manufactured (AM) metallic components for nuclear environments. Especially the affect of process parameters on SCC. The methodology combines advanced mechanical testing, detailed microstructural characterization, and multiphysics finite element modeling to simulate SCC behavior under conditions representative of nuclear power plant environments. The overarching goal is to improve structural integrity assessments of AM components and find optimum AM process parameters for the mitigation of SCC. The work is in line with the UAE's nuclear energy goals to increase AM technology in the nuclear industry. The results will help develop optimized AM processing strategies, enabling safer, more dependable deployment of AM components in nuclear environments.

Funded by Emirates Nuclear Tech. Center (ENTC).

Advancing the Reliability of Reinforced Thermoplastic Pipes through Computational Modeling

This project tackles the urgent industrial challenge of ensuring the mechanical integrity and long-term reliability of reinforced thermoplastic pipes (RTPs), which are emerging as sustainable alternatives to traditional steel pipelines in the oil and gas sector. The research introduces an innovative methodology that integrates rigorous mechanical testing with advanced multi-scale computational modeling to capture the complex behavior of RTPs under real installation and operational conditions. By simulating critical failure modes and validating results with full-scale testing, the project will deliver a predictive framework and roadmap for an Integrity Management System (IMS) tailored to RTPs. The outcomes will enable industry to deploy RTP technologies with greater confidence, reducing corrosion-related costs, extending pipeline lifetimes, and contributing to more efficient, safe, and sustainable energy infrastructure.

Funded by Abu Dhabi National Oil Company (ADNOC).