LISBON 2026 – Spray Monitoring and Droplet Measurement with TSTOF Diagnostics

This contribution was presented at the 22nd International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics, held in Lisbon, Portugal, from June 29 to July 2, 2026. The paper discusses a new interpretation of spray monitoring and droplet measurement based on the memory of measurements in spray diagnostics.

https://www.lisbonsymposia.org/22nd-lxsymp

Spray processes are dynamic systems. During a measurement, the spray can change because pressure, flow rate, droplet size, droplet velocity, or measurement position may vary over time. For this reason, modern spray monitoring needs more than average values. It also needs methods that can detect changes during the measurement itself.

This paper introduces a new way to interpret droplet measurement data in spray diagnostics. The approach treats each measured droplet not only as an isolated event, but also as part of a time-dependent information record. In this context, the present signal can contain traces of the recent past. This concept is described as the “memory of measurements” in spray diagnostics.

The experimental work uses a TSTOF measurement device for droplet characterization. TSTOF combines time-shift and time-of-flight principles. It detects time-dependent light scattering signals from individual droplets as they pass through shaped light beams.

This makes TSTOF a counting measurement technique. Each detected droplet provides information about droplet size, droplet velocity, and arrival time. Therefore, the method is well suited for detailed droplet measurement in sprays and flows.

The experiments used a closed-loop spray chamber with a flat-fan nozzle from Lechler. Pressure, flow rate, and measurement position were monitored during the spray process. In total, 85 measurements were performed, including 81 static and 4 dynamic measurements.

Classical spray diagnostics often uses mean values, distributions, or statistical moments. These parameters are useful, but they may not fully show how the spray changes during the measurement. For example, two different droplet size distributions can have similar mean values while still representing different spray states.

The paper therefore introduces an additional characterization layer based on information theory. This layer describes the informational state of the spray. It can show how much diversity exists in droplet size, droplet velocity, and event timing.

This is important for spray monitoring because it helps identify changes that may remain hidden in classical averaged values.

The study uses the concept of informature to describe the amount of information in the spray state. This parameter can react to changes in the spray, especially when droplet velocity changes due to variations in injection pressure.

In addition, the paper introduces a corrected memory trace. This parameter measures how much information about the next droplet event is contained in the present droplet event. It compares real event sequences with randomized sequences. As a result, it can distinguish real measurement memory from random correlations.

This approach creates a new method for time-resolved spray monitoring. It can help detect sudden changes, gradual transformations, and non-stationary spray behavior.

The paper shows that spray measurement should not only focus on static distributions. Instead, it should also consider how the spray evolves during the measurement. This is especially important for real-time diagnostics, process control, model validation, and sensor development.

By combining droplet measurement with information-based analysis, researchers can gain a deeper understanding of complex spray systems. This approach can support future spray monitoring methods for transient, unstable, or changing operating conditions.

The concept is relevant for optical spray diagnostics, flat-fan sprays, coating processes, atomization research, and industrial process monitoring. It can also support the development of intelligent measurement systems that detect process changes during operation.

The method extends classical TSTOF-based droplet measurement with a new infodynamic layer. This helps characterize sprays not only by droplet size and velocity, but also by how the spray changes and how much of the past remains visible in the present signal.