Jie Wang

A highly committed and results-oriented researcher with a recently awarded PhD in Computational Fluid Dynamics (CFD). My core expertise lies in patient-specific cardiovascular hemodynamics, advanced turbulence modeling, and the application of machine learning in biomedical simulations. I excel in transnational and cross-disciplinary environments, fostering collaborations that bridge engineering and medicine to advance global healthcare. My current work involves developing computationally efficient models for both carotid artery disease and novel industrial applications, such as the thermal analysis of railway depots to support national decarbonisation strategies. As a Postgraduate Researcher (PGR) Ambassador for the University of Manchester, I am passionate about creating a supportive and dynamic research community. I am dedicated to leveraging high-performance computing (HPC) and developing automated in-silico simulation pipelines to translate complex data into actionable insights for diagnostics and treatment planning. I am actively expanding my skills in AI for cardiovascular modelling and am highly interested in contributing to medical therapy development through modelling and simulation augmented by big data and machine learning.

Technical Specialist (CFD and Data Science) | May 2025–Present Faculty of Science and Engineering, University of Manchester, UK

• Appointed to a fixed-term position to meet a specific demand for advanced CFD and data science expertise.
• Leading the development of Computational Fluid Dynamics models for a railway depot, creating an initial, refinable model of a depot shed to analyze innovative heating technologies.
• Developing thermodynamic energy balance models in MATLAB/Simulink to evaluate and narrow down multiple heating technologies to the most promising candidates for decarbonisation.
• Engaged in a collaborative project with Q Sustain, targeting the decarbonisation of National Rail infrastructure by assessing the feasibility of replacing current gas-fired heating systems with sustainable alternatives like waste heat.

Research Assistant (MAHSC & Health Innovation Manchester) | 2023–2024

• Validated computational models against 4D-flow MRI data, significantly improving diagnostic precision for cardiovascular simulations.
• Employed statistical correlation methods to meticulously compare CFD and MRI velocity fields, ensuring the accuracy of simulation results.
• Contributed to research engagement in modeling carotid artery disease through a MAHSC & Health Innovation Manchester Grant.

PhD Researcher in Cardiovascular CFD | 2020–2025

• International Research Exchange (University of Cape Town): Engaged in a significant research collaboration in Cape Town as part of the PROTEA (Partnerships for Children with Heart Disease in Africa) study. Worked directly with leading clinicians and academics, including Prof. Malebogo Ngoepe, Prof. Liesl Zühlke, and Prof. Bernard Keavney, to address the profound lack of data on congenital heart disease (CHD) in Africa. This partnership focused on enhancing CHD research infrastructure, building a multicentre registry, and applying advanced computational modeling to improve clinical management for African patients.
• Developed a novel CFD framework to analyze the impact of heart rate on wall shear stress in aortic coarctation.
• Successfully applied high-fidelity simulation techniques, including Large Eddy Simulation (LES) and Wind Kessel models.
• Established an automated, image-based CFD pipeline designed for effective clinical translation.
• Forged key collaborations with the University of Cape Town, NHS, and the British Heart Foundation (BHF) to validate computational models.

Ongoing Research & Development

Published Articles

• Wang, J., Manchester, E., Skillen, A., Ngoepe, M., Keavney, B., & Revell, A. (2025). An in silico analysis of heart rate impact on wall shear stress hemodynamic parameters in aortic coarctation. Scientific Reports, 15(1), 2747.
• Hampwaye, N., Wang, J., Revell, A., Manchester, E., Aldersley, T., Zuhlke, L., Keavney, B., & Ngoepe, M. (2025). Growth in a two-dimensional model of coarctation of the aorta: A CFD-informed agent-based model. Journal of Biomechanics, 112514.
• Wang, J., et al. (2021). Planform geometry and excitation effects of PVDF-based vibration energy harvesters. Energies, 14(1), 211.
• Blackall, J. L., Wang, J., et al. (2020). Development of a Passive Spore Sampler for Capture Enhancement of Airborne Crop Pathogens. Fluids, 5(2), 97.
• Hirst, J., Wang, J., et al. (2020). Long-term power degradation testing of piezoelectric vibration energy harvesters for low-frequency applications. Engineering Research Express, 2(3), 035026.
• Wang, J., et al. (2019). Solar panels as tip masses in low frequency vibration harvesters. Energies, 12(20), 3815.

Submitted & In-Preparation

• Sengupta, N., Manchester E., Wang, J., et al. (2025). An in silico approach to analyze the influence of carotid hemodynamics on cardiovascular events using 3D tomographic ultrasound and computational fluid dynamics. (Submitted, under review).
• In Preparation: Bin-Based Phase Average Method for Coarctation of the Aorta: A efficient study of Turbulence Impact for cardiovascular flow analysis. .
• Automated CFD Software: Currently developing a proprietary, automated software solution for CFD analysis, aimed at accelerating simulation workflows and enhancing model accuracy. The software is undergoing rigorous testing and expansion.
• Efficient LES Methods: Preparing a new manuscript on a computationally efficient Large Eddy Simulation (LES) technique, poised to significantly reduce the computational cost of high-fidelity blood flow simulations.

• Oral Presentation: 9th World Congress of Biomechanics (2022)
• Oral Presentation: UK Fluids Conference (2022)
• Oral Presentation: 5th African Conference on Computational Mechanics (2023)
• Poster Presentation: American Heart Association Scientific Sessions (2024)
• Poster Presentation: Global Young Scientists Summit (2024)