Review of Modern Destruction Simulation Methods and their Application in Voxel-Based Environments

Authors

  • M. S. Danylenko Vinnytsia National Technical University
  • I. S. Kolesnyk Vinnytsia National Technical University

Keywords:

voxel graphics, deformation simulation, destruction, material point method, MPM, mass-spring systems, numerical modeling, crack propagation algorithms, hybrid approaches, GPU optimization, interactive applications

Abstract

An extensive analysis of modern methods for deformation simulation in voxel environments is presented in the article, particularly focusing on techniques used for modeling destructions and complex material deformations. Both traditional approaches, such as simple voxel removal and crack propagation algorithms, as well as more advanced methods—including mass-spring systems, procedural destruction generation, and the material point method (MPM) — are examined. The former are characterized by high performance and ease of implementation; however, their application is limited by insufficient physical realism and an inability to adequately reproduce smooth deformations. In contrast, the MPM method enables the modeling of large plastic deformations and destructions with high accuracy, although it demands significant computational resources and is complex to implement.

Additionally, the work presents a comparative analysis of physically-based modeling methods, highlighting the finite element method (FEM), mass-spring systems, and smoothed particle hydrodynamics (SPH). Special attention is paid to hybrid approaches, particularly the PIC, FLIP, and APIC methods, which combine the advantages of both particle-based and grid-based techniques. These methods help reduce numerical dissipation and preserve detailed flow features when simulating complex physical processes, thereby contributing to a more accurate reproduction of turbulent structures and the interaction between fluids and solids—a factor crucial for applications in cinematic effects and scientific research.

The article outlines the main advantages and disadvantages of each examined approach, shedding light on the limitations of current deformation simulation technologies in voxel environments. Promising directions for future research are proposed, including algorithm optimization through GPU technologies and the application of neural networks for predicting material behavior. The obtained results are relevant not only for the development of interactive applications and video games but also for scientific and engineering studies aimed at improving methods for modeling complex physical phenomena.

Author Biographies

M. S. Danylenko, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Computer Engineering

I. S. Kolesnyk, Vinnytsia National Technical University

Cand. Sc. (Eng.), Associate Professor, Associate Professor of the Chair of Computer Engineering

References

J. Zadick, B. Kenwright, and K. Mitchell, “Integrating Real-Time Fluid Simulation with a Voxel Engine,” The Computer Games Journal, no. 5, pp. 56-64, September. 2016, https://doi.org/10.1007/s40869-016-0020-5 .

H. N. Iben, and J. F. O’Brien, “Generating Surface Crack Patterns,” Graphical Models, vol. 71, no. 6, pp. 198-208, January. 2009, https://doi.org/10.1016/j.gmod.2008.12.005 .

O. C. Zienkiewicz, R. L. Taylor, and J. Z. Zhu, The Finite Element Method. Butterworth-Heinemann, United Kingdom, 2013, https://doi.org/10.1016/C2009-0-24909-9 .

T. Chakkour, “Finite element modelling of complex 3D image data with quantification and analysis,” Oxford Open Materials Science, vol. 4, no. 1, pp. 51-71, February. 2024, https://doi.org/10.1093/oxfmat/itae003 .

A. Nealen, M. Müller, R. Keiser, E. Boxerman, and M. Carlson, “Physically Based Deformable Models in Computer Graphics,” Computer Graphics Forum, vol. 25, no. 4, pp. 809-836, December. 2006, https://doi.org/10.1111/j.1467-8659.2006.01000.x .

A. Lagae, and P. Dutré, “A Comparison of methods for procedural noise,” Computer Graphics Forum, vol. 27, no. 1, pp. 114-129, 2008. October. 2007. https://doi.org/10.1111/j.1467-8659.2007.01100.x .

A. Stomakhin, C. Schroeder, L. Chai, J. Teran, and A. Selle, “A Material Point Method for Snow Simulation,” Transactions on Graphics, vol. 32, no. 102, pp.1-10, July. 2013, https://doi.org/10.1145/2461912.2461948 .

F. H. Harlow, “The Particle-In-Cell Computing Method for Fluid Dynamics,” Methods in Computational Physics, vol. 3, pp. 319-343, March. 1962, https://doi.org/10.2172/4769185 .

J. U. Brackbill, and H. M. Ruppel, “A Method for Adaptively Zoned, Particle-In-Cell Calculations of Fluid Flows in Two Dimensions,” Journal of Computational Physics, vol. 65, no. 2, pp. 314-343, August. 1986, https://doi.org/10.1016/0021-9991(86)90211-1 .

C. Jiang, C. Schroeder, A. Selle, J. Teran, and A. Stomakhin, “The Affine Particle-In-Cell Method,” Transactions on Graphics, vol. 34, no. 4, pp. 1-10, July. 2015, https://doi.org/10.1145/2766996 .

H. Lu, W. Chang, T. Hedstrom, and Tzu-M. Li, “Real-Time Path Guiding Using Bounding Voxel Sampling,” Transactions on Graphics, vol. 43, no. 125, pp. 1-14, July. 2024, https://doi.org/10.1145/3658203 .

M. Müller, R. Keiser, A. Nealen, M. Pauly, M. Gross, and M. Alexa “Point based animation of elastic, plastic and melting objects,” Eurographics Symposium on Computer Animation, no. 4, pp. 141-151, August. 2004. https://doi.org/10.1145/1028523.1028542.

J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum, “Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling,” Advances in Neural Information Processing Systems, vol. 82, no. 90, pp. 1-11, Jan. 2017, https://doi.org/10.48550/arXiv.1610.07584 .

C. Yuksel, D. H. House, and J, Keyser, “Wave Particles,” Transactions on Graphics, vol. 26, no. 3, pp. 99-110, July. 2007, https://doi.org/10.1145/1275808.1276501 .

Маттіас Мюллерм, Майлз Маклін, Нуттапонг Чентанез, Стефан Єшке, i Те-Йонг Кім, «Детальне моделювання твердого тіла з розширеною динамікою на основі позиції,» Форум комп’ютерної графіки, т. 39, вип. 8, с. 101-112, 2020.

T. Pfaff, M. Fortunato, A. Sanchez-Gonzalez, and P.W. Battaglia, “Learning Mesh-Based Simulation with Graph Networks,” International Conference on Learning Representations, no. 4, pp.1-12, Jun. 2021, https://doi.org/10.48550/arXiv.2010.03409 .

Ben Mildenhall, et all., UC Berkeley. Google Research. UC San Diego. “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,” rXiv:2003.08934v2 [cs.CV] 3 Aug 2020.

Y. Zhou, H. Lu, G. Wang, and W. Li, J. Wang, “Voxelization modelling based finite element simulation and process parameter optimization for Fused Filament Fabrication,” International Journal of Modeling and Simulation, no. 4.0, pp. 187-210, December, 2019. https://doi.org/10.1016/j.matdes.2019.108409 .

X. Provot, “Deformation Constraints in a Mass-Spring Model to Describe Rigid Cloth Behavior,” Institut National de Recherche en Informatique et Automatique, no. 105, pp. 99-104, September. 1995.

L. L. Chang, and D. S. Liu, “Deformable object simulation in virtual environment,” Virtual reality continuum and its applications, no. 6, pp. 327-330, June. 2006. https://doi.org/10.1145/1128923.1128979 .

M. Müller, J. Stam, D. James, and N. Thürey, “Real Time Physics: Class Notes,” Siggraph Classes, no. 88, pp. 1-90, August. 2008. https://doi.org/10.1145/1401132.1401245 .

J. J. Monaghan, “Smoothed Particle Hydrodynamics,” Reports on Progress in Physics, vol. 68, no. 8, pp. 34-84, July. 2005, https://doi.org/10.1088/0034-4885/68/8/R01 .

N. Akinci, M. Ihmsen, G. Akinci, B. Solenthaler, and M. Teschner, “Versatile Rigid-Fluid Coupling for Incompressible SPH,” Transactions on Graphics, vol. 31, no. 62, pp. 1-8, July. 2012, https://doi.org/10.1145/2185520.2185558 .

B. Solenthaler, and R. Pajarola, “Density Contrast SPH Interfaces,” Eurographics Symposium on Computer Animation, no. 8, pp. 211-218, July. 2008, https://doi.org/10.5167/uzh-9734 .

T. McGraw, “Gram-Schmidt voxel constraints for real-time destructible soft bodies,” Interaction and Games, no. 4, pp. 1-10, November. 2024. https://doi.org/10.1145/3677388.3696322 .

R. Keiser, B. Adams, D. Gasser, P. Bazzi, P. Dutré, and M. Gross, “A Unified Lagrangian Approach to Solid-Fluid Animation,” VGTC conference on Point-Based Graphics, no. 5, pp. 125-133, June. 2005.

F. Losasso, J. O. Talton, N. Kwatra, R. Fedkiw, “Two-way Coupled SPH and Particle Level Set Fluid Simulation,” Transactions on Visualization and Computer Graphics, vol. 14, no. 4, pp. 797-804, July. 2008. https://doi.org/10.1109/TVCG.2008.37 .

Y. Zhu, and R. Bridson, “Animating Sand as a Fluid,” Transactions on Graphics, no. 5, pp. 965-972, July. 2005. https://doi.org/10.1145/1186822.1073298 .

C. Fu, Q. Guo, T. Gast, C. Jiang, and J. Teran, “A Polynomial Particle-In-Cell Method,” Transactions on Graphics, vol. 36, no. 222, pp. 1-12, November. 2017. https://doi.org/10.1145/3130800.3130878 .

D. Sulsky, Shi-J. Zhou, and H. L. Schreyer, “Application of a Particle-in-Cell Method to Solid Mechanics,” Computer Physics Communications, vol. 96, no. 1, pp. 105-106, June. 1995. https://doi.org/10.1016/0010-4655(94)00170-7 .

A. Stomakhin, C. Schroeder, C. Jiang, L. Chai, J. Teran, and A. Selle, “Augmented MPM for Phase-Change and Varied Materials,” Transactions on Graphics, vol. 33, no. 4, pp. 1-11, July. 2014. https://doi.org/10.1145/2601097.2601176 .

M. Gao, et all., “Animating Fluid Sediment Mixture in Particle-Laden Flows,” Transactions on Graphics, vol. 37, no. 149, pp. 1-11, July. 2018. https://doi.org/10.1145/3197517.3201309 .

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Published

2026-03-25

How to Cite

[1]
M. S. Danylenko and I. S. Kolesnyk, “Review of Modern Destruction Simulation Methods and their Application in Voxel-Based Environments”, Вісник ВПІ, no. 1, pp. 108–115, Mar. 2026.

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