The study of thermo-fluid dynamics plays a critical role in advancing technologies for energy efficiency, decarbonisation, and sustainable combustion. Despite significant theoretical and computational developments, the complexity of multiphase flows, chemical reactions, and unsteady heat and mass transfer still poses major experimental challenges. Recent progress in high-fidelity diagnostics and visualization tools, such as hyperspectral imaging, schlieren photography, and thermal metrology, is enabling researchers to probe these phenomena with unprecedented detail and to integrate them into the system design in practical scenarios. When coupled with emerging artificial intelligence (AI) methods, these tools open new pathways for real-time analysis, predictive modeling, and adaptive system control.

This Research Topic aims to showcase recent advances in diagnostics and visualization techniques that are transforming the way we understand and control thermo-fluid systems. We invite contributions that highlight the development or application of novel measurement methods, including, but not limited to, hyperspectral imaging, thermal and infrared metrology, chemiluminescence imaging, schlieren/shadowgraph systems, and data-enhanced diagnostics powered by machine learning or computer vision.

We are particularly interested in experimental and hybrid methodologies applied to complex environments such as gas-phase combustion, droplet evaporation, biomass conversion, multiphase and reactive flows, and renewable energy systems. This collection also welcomes studies addressing fundamental mechanisms such as flame instability, thermal-acoustic interactions, fire safety, and heat transfer in coupled media. The overarching goal is to bridge the gap between advanced diagnostics and practical design, ultimately supporting low-carbon technologies and sustainable engineering solutions.

We welcome original research articles, reviews, perspectives, and methodological papers related to:

• Innovative diagnostics in thermo-fluid systems
• Visualization techniques for multiphase and reactive flows
• Integration of AI/machine learning with thermo-fluid data
• Experimental studies in combustion, fire, biomass, and renewables
• Measurement and modeling of thermoacoustic phenomena
• Sensor development and in-situ process control

Submissions should demonstrate strong methodological innovation, interdisciplinary thinking, or practical relevance to energy and environmental challenges. This collection aims to foster collaboration between researchers in fluid mechanics, combustion, materials, metrology, and data science.

Are you interested in contributing a manuscript to this Research Topic?

Transforming Thermo-Fluids: Novel Diagnostics and Intelligent Visualization

Topic Coordinators