This deposit contains the three-page summary accepted for a presentation at the 19th SPHERIC World Conference of the Smoothed Particle Hydrodynamics Research and Engineering International Community (SPHERIC) held in Barcelona, Spain on 17-19 June 2025. The figures redrawn with improved features and at higher resolution are also available as standalone files. The slideshow supporting the conference talk presents additional research progress and is available from a separate Zenodo deposit. Document distribution The DOI 10.5281/zenodo.14674511 always retrieves the latest version of this record and is the most convenient pointer to this work. The summary is immediately available for reading and downloading in the default preview below. The figures can be previewed and downloaded by selecting them in the Files block farther below. All files are open-access under a Creative Commons Attribution 4.0 International. This summary is also part the Book of Abstracts of the 19th SPHERIC World Conference Barcelona. 17-19 June 2025 . This work can and should be cited as conveniently reported in the Citation box at the right of this web page. Document research content In general CFD, setting up a simulation entails decisions on parameters aplenty that define the physics to model, the algorithms to follow and the codes to compile and run. Specifically in SPH, increasing the particle-number density and/or the simulated time makes the descriptions of flow fields voluminous, especially due to the stability constraint in the time stepping. In case of weakly compressible fluids, reading the density/pressure signals recorded at fixed points can be an effective diagnostic tool for the modellers to monitor the simulation development and cross-check their expectations, even during runtime. Especially when the sound of speed is artificially set at a constant value, the temporal and spatial features of density signals are simply tied one to another. The frequency content of these signals clearly separates the scales associated to grand motion patterns and those affecting the particles populating the kernel support. The absence of air and surface tension are the main modelling limitations in the current study case of a dam break against a vertical wall. However, the unrealistic shedding of droplets, appearance of voids and pervasive whirling are a blessing in disguise for the spectral analysis. Those chaotic outcomes reproduce known shortcomings of SPH, and provide the playground to investigate the activity and removal of undesired phenomena that leave a trace in some frequency band of the pressure spectra. The advantages of interpreting spectra may stand out as the simulation size grows into millions of particles and time steps. Therefore, pressure signals appear to have nice properties to assist the workflow of SPH modellers. Consistency, exactitude, lightness, quickness, visibility, and multiplicity of use is the set identified at first recognition. Document design and composition Description coming soon. Deposit versions The DOI 10.5281/zenodo.14674511 always retrieves the latest version of this record. In the table below, major version numbers indicate changes in the deposit content, for which Zenodo always issues a new DOI. I use minor version numbers to indicate substantive changes in the text you are reading just now, the deposit's Description. v3.2 2025-06-30 Add section on document research content 10.5281/zenodo.14743217 v3.1 2025-01-26 Combine v1.1 and v2.1 (fix) 10.5281/zenodo.14743217 v2.1 2025-01-26 Add standalone figures with improved rendering 10.5281/zenodo.14743062 v1.1 2025-01-21 Add extended summary submitted for consideration 10.5281/zenodo.14674512 Deposit documents File name Description Content Versionof addition Version of last modification SPHERIC25_extended_abstract_Lipari_Giordano_2.pdf Extended summary submitted for consideration text and images v1.1 v1.1 SPHERIC25_figure_1A_Lipari_Giordano.pdf Improved Fig. 1: pressure signal, all stages plot v2.1 v2.1 SPHERIC25_figure_1B_Lipari_Giordano.pdf Improved Fig. 1: pressure spectrum, all stages plot v2.1 v2.1 SPHERIC25_figure_2Al_Lipari_Giordano.pdf Improved Fig. 2: pressure spectrum, stage 1 plot v2.1 v2.1 SPHERIC25_figure_2Ar_Lipari_Giordano.pdf Improved Fig. 2: flow field, stage 1 map v2.1 v2.1 SPHERIC25_figure_2Bl_Lipari_Giordano.pdf Improved Fig. 2: pressure spectrum, stage 2 plot v2.1 v2.1 SPHERIC25_figure_2Br_Lipari_Giordano.pdf Improved Fig. 2: flow field, stage 2 map v2.1 v2.1 SPHERIC25_figure_2Cl_Lipari_Giordano.pdf Improved Fig. 2: pressure spectrum, stage 3 plot v2.1 v2.1 SPHERIC25_figure_2Cr_Lipari_Giordano.pdf Improved Fig. 2: flow field, stage 3 map v2.1 v2.1 SPHERIC25_figure_2Dl_Lipari_Giordano.pdf Improved Fig. 2: pressure spectrum, stage 4 plot v2.1 v2.1 SPHERIC25_figure_2Dr_Lipari_Giordano.pdf Improved Fig. 2: flow field, stage 4 map v2.1 v2.1 Acknowledgements Compute power supporting this document has been provided by NCC Netherlands, the EuroCC Project implementation in the Netherlands funded by the European High-Performance Computing Joint Undertaking (Grant Agreement 101101903).

Introduction Material and methods Results and discussion A. Results B. Discussion Conclusion Acknowledgement References

This commentary is authored by Giordano Lipari. No LLM used.