Acoustic Tomography for Temperature and Flow Field Measurement
Summary
Acoustic tomography exploits the dependence of sound speed on temperature and flow velocity to reconstruct two- and three-dimensional maps of thermal and dynamic fields. Arrays of acoustic transducers emit and receive signals across a region of interest, measuring time-of-flight along multiple paths. These travel times are related through an inverse problem to the spatial distribution of sound speed, which in turn is translated into temperature or flow information. The non-intrusive nature of the method, its ability to cover large volumes with relatively few sensors and its resilience in harsh or opaque environments have made it attractive for monitoring combustion chambers, furnaces, atmospheric boundary layers and industrial reactors. Advances in computational algorithms, regularisation techniques and sparse representations have improved resolution, noise immunity and real-time capability. The global significance of acoustic tomography spans process control in power plants, environmental monitoring of airflow and heat exchange in meteorology, and safety assessments in chemical processing.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Acoustic Tomography for Temperature and Flow Field Measurement publication trend
The graph below shows the total number of articles in acoustic tomography for temperature and flow field measurement across all publications each year (not limited to Nature Index journals).
Technical terms
Acoustic tomography: A technique that reconstructs spatial distributions of sound speed from multiple acoustic travel-time measurements.
Time-of-flight (TOF): The travel time of an acoustic pulse between a transmitter and receiver, dependent on medium properties.
Inverse problem: A mathematical procedure that infers field variables (temperature, flow) from indirect measurements (acoustic travel times).
Radial basis function (RBF): A smooth interpolation function centred on sensor positions, used to approximate continuous fields.
Regularisation: A strategy that stabilises the solution of an ill-posed inverse problem by imposing smoothness or penalty constraints.
Dictionary learning: A machine-learning process that constructs a set of basis functions for sparse representation of signals in an inverse reconstruction context.
References
- Acoustic tomography in the atmospheric surface layer. Annales Geophysicae (1998).
- A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography. Mathematical Problems in Engineering (2021).
- Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning. Sensors (2022).
- 3D Temperature Distribution Reconstruction in Furnace Based on Acoustic Tomography. Mathematical Problems in Engineering (2019).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.