CFD2CFD: How computational fluid Dynamics can be a means towards sustainable conscious field dynamics

M Hilman Gumelar Syafei; Illa Rizianiza; Ahmad Indra Siswantara; Angga Septiyana; Adi Syuriadi; Muhammad Agung Santoso; Ahmad Syihan Auzani

doi:10.71452/eeff4w73

Authors

M Hilman Gumelar Syafei Universitas Indonesia Author
Illa Rizianiza Universitas Indonesia Author
Prof. Ir. Ahmad Indra Siswantara, Ph.D Universitas Indonesia Author
Angga Septiyana Universitas Indonesia Author
Adi Syuriadi Universitas Indonesia Author
Muhammad Agung Santoso Universitas Indonesia Author
Ahmad Syihan Auzani Universitas Indonesia Author

DOI:

https://doi.org/10.71452/eeff4w73

Keywords:

conscious , computation, computational fluid dynamics, conscious field dynamics, DAI5

Abstract

The role of Computational Fluid Dynamics (CFD) across diverse applications has been significantly reinforced by rapid advancements in numerical computation, artificial intelligence, and high-performance computing. Nevertheless, prevailing applications of CFD predominantly operate as simulation tools and technical solutions, often neglecting dimensions of awareness, intentionality, and ethical considerations. This study introduces the CFD2CFD paradigm (Computable Field Dynamics to Conscious Field Dynamics), which is conceptualized within the DAI5 framework—comprising Deep Awareness of I, Intention, Initial Thinking, Idealization, and Instruction Set. In this paradigm, Computable Field Dynamics is defined as the material field that can be mathematically represented and numerically resolved, while Conscious Field Dynamics denotes the immaterial field of awareness. To demonstrate this approach, case studies are conducted on representative material fluid phenomena, namely pipe flow, airfoil aerodynamics, and biomass pyrolysis. The integration of Computable Field Dynamics and Conscious Field Dynamics through the DAI5 framework establishes a novel methodological pathway. The contribution of the CFD2CFD paradigm lies in its provision of a computational approach that extends beyond scientific accuracy to encompass sustainability considerations. More importantly, this paradigm is formulated as an epistemic bridge uniting material and immaterial fields, thereby offering a framework for methods that are simultaneously rigorous in scientific validity and oriented toward sustainability.

References

D. Zhang, T. Anjum, Z. Chu, J. S. Cross, and G. Ji, “Simulation of multiphase flow with thermochemical reactions: A review of computational fluid dynamics (CFD) theory to AI integration,” Renew. Sustain. Energy Rev., vol. 221, no. June, 2025, doi: 10.1016/j.rser.2025.115895.

J. Ahn, J. Kim, and J. Kang, “Development of an artificial intelligence model for CFD data augmentation and improvement of thermal environment in urban areas using nature-based solutions,” Urban For. Urban Green., vol. 104, no. April 2024, p. 128629, 2025, doi: 10.1016/j.ufug.2024.128629.

S. Sahibzada, F. S. Malik, S. Nasir, and S. K. Lodhi, “AI-Augmented Turbulence and Aerodynamic Modelling: Accelerating High-Fidelity CFD Simulations with Physics-informed Neural Networks,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 13, no. 1, pp. 91–97, 2025, doi: 10.55524/ijircst.2025.13.1.14.

J. Afful, “A Review of HPC-Accelerated CFD in National Security and Defense”, [Online]. Available: https://arxiv.org/abs/2504.07837

A. Siswantara, I. Rizianiza, M. H. Syafei, A. Syuriadi, and M. Andira, “DAI5: Framework penyelesaian masalah berbasis conscious thinking,” vol. 22, pp. 26–30, 2025, doi: 10.71452/590885.

Siswantara, A. I., Syafei, M. H. G., Rizianiza, I., Syuriadi, A., & Andira, M. A. Implementasi DAI5: Framework penyelesaian masalah berbasis conscious thinking pada studi kasus mekanika fluida. In Seminar Nasional Tahunan Teknik Mesin XXII 2024 (pp. 432-439). Badan Kerja Sama Teknik Mesin Indonesia.

Munson, B. R., Okiishi, T. H., Huebsch, W. W., & Rothmayer, A. P. (2013). Fluid mechanics. Wiley.

Kalliadasis, S., Ruyer-Quil, C., Scheid, B., & Velarde, M. G. (2011). Falling liquid films (Vol. 176). Springer Science & Business Media.

M. Tegmark, “Consciousness as a state of matter,” Chaos, Solitons and Fractals, vol. 76, pp. 238–270, 2015, doi: 10.1016/j.chaos.2015.03.014.

G. Tononi, “An information integration theory of consciousness,” BMC Neurosci., vol. 5, pp. 1–22, 2004, doi: 10.1186/1471-2202-5-42.

J. Kleiner, “Mathematical models of consciousness,” Entropy, vol. 22, no. 6, 2020, doi: 10.3390/E22060609.

M. J. D. Ramstead et al., “The inner screen model of consciousness: applying the free energy principle directly to the study of conscious experience,” PsyArXiv, pp. 1–34, 2023.

J. H. Ferziger, M. Perić, and R. L. Street, Computational Methods for Fluid Dynamics, Fourth Edition. books.google.com, 2019. doi: 10.1007/978-3-319-99693-6.

J. H. Ferziger and M. Perić, “Further discussion of numerical errors in CFD,” Int. J. Numer. Methods Fluids, vol. 23, no. 12, pp. 1263–1274, 1996, doi: 10.1002/(sici)1097-0363(19961230)23:12<1263::aid-fld478>3.0.co;2-v.

S. B. Pope, “Turublent Flows,” 2001.

D. C. Dennett, Consciousness and Explained. 1991.

S. Hameroff and R. Penrose, “Consciousness in the universe: A review of the ‘Orch OR’ theory,” Phys. Life Rev., vol. 11, no. 1, pp. 39–78, 2014, doi: 10.1016/j.plrev.2013.08.002.

T. Hunt et al., Editorial: Electromagnetic field theories of consciousness: opportunities and obstacles, vol. 17. 2023. doi: 10.3389/fnhum.2023.1342634.

N. Martínez-Molina, Y. Sanz-Perl, A. Escrichs, M. L. Kringelbach, and G. Deco, “Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury,” Front. Neuroinform., vol. 18, no. 1507, 2024, doi: 10.3389/fninf.2024.1382372.