DESIGN, MODELING, AND CONTROL OF SOFT DYNAMIC SYSTEMS

Soft physical systems, be they elastic bodies, fluids, and compliant-bodied creatures, are ubiquitous in nature. Modeling and simulation of these systems with computer algorithms enable the creation of visually appealing animations, automated fabrication paradigms, and novel user interfaces and control mechanics to assist designers and engineers to develop new soft machines. This thesis develops computational methods to address the challenges emerged during the automation of the design, modeling, and control workflow supporting various soft dynamic systems. On the design/control side, we present a sketch-based design interface to enable non-expert users to design soft multicopters. Our system is endorsed by a data-driven algorithm to generate system identification and control policies given a novel shape prototype and rotor configurations. We show that our interactive system can automate the workflow of different soft multicopters\u27 design, simulation, and control with human designers involved in the loop. On the modeling side, we study the physical behaviors of fluidic systems from a local, collective perspective. We develop a prior-embedded graph network to uncover the local constraint relations underpinning a collective dynamic system such as particle fluid. We also proposed a simulation algorithm to model vortex dynamics with locally interacting Lagrangian elements. We demonstrate the efficacy of the two systems by learning, simulating and visualizing complicated dynamics of incompressible fluid

LightGCNet: A Lightweight Geometric Constructive Neural Network for Data-Driven Soft sensors

Data-driven soft sensors provide a potentially cost-effective and more accurate modeling approach to measure difficult-to-measure indices in industrial processes compared to mechanistic approaches. Artificial intelligence (AI) techniques, such as deep learning, have become a popular soft sensors modeling approach in the area of machine learning and big data. However, soft sensors models based deep learning potentially lead to complex model structures and excessive training time. In addition, industrial processes often rely on distributed control systems (DCS) characterized by resource constraints. Herein, guided by spatial geometric, a lightweight geometric constructive neural network, namely LightGCNet, is proposed, which utilizes compact angle constraint to assign the hidden parameters from dynamic intervals. At the same time, a node pool strategy and spatial geometric relationships are used to visualize and optimize the process of assigning hidden parameters, enhancing interpretability. In addition, the universal approximation property of LightGCNet is proved by spatial geometric analysis. Two versions algorithmic implementations of LightGCNet are presented in this article. Simulation results concerning both benchmark datasets and the ore grinding process indicate remarkable merits of LightGCNet in terms of small network size, fast learning speed, and sound generalization.Comment: arXiv admin note: text overlap with arXiv:2307.0018

Microstructure and Velocity of Field-Driven SOS Interfaces: Analytic Approximations and Numerical Results

The local structure of a solid-on-solid (SOS) interface in a two-dimensional kinetic Ising ferromagnet with single-spin-flip Glauber dynamics, which is driven far from equilibrium by an applied field, is studied by an analytic mean-field, nonlinear-response theory [P.A. Rikvold and M. Kolesik, J. Stat. Phys. 100, 377 (2000)] and by dynamic Monte Carlo simulations. The probability density of the height of an individual step in the surface is obtained, both analytically and by simulation. The width of the probability density is found to increase dramatically with the magnitude of the applied field, with close agreement between the theoretical predictions and the simulation results. Excellent agreement between theory and simulations is also found for the field-dependence and anisotropy of the interface velocity. The joint distribution of nearest-neighbor step heights is obtained by simulation. It shows increasing correlations with increasing field, similar to the skewness observed in other examples of growing surfaces.Comment: 18 pages RevTex4 with imbedded figure

Density-Temperature-Softness Scaling of the Dynamics of Glass-forming Soft-sphere Liquids

The principle of dynamic equivalence between soft-sphere and hard-sphere fluids [Phys. Rev. E \textbf{68}, 011405 (2003)] is employed to describe the interplay of the effects of varying the density n, the temperature T, and the softness (characterized by a softness parameter {\nu}^{-1}) on the dynamics of glass-forming soft-sphere liquids in terms of simple scaling rules. The main prediction is that the dynamic parameters of these systems, such as the {\alpha}-relaxation time and the long-time self-diffusion coefficient, depend on n, T, and {\nu} only through the reduced density n^\ast \equiv n{\sigma}^{3}_{HS}(T, {\nu}),where the effective hard-sphere diameter {\sigma}_{HS}(T, {\nu}) is determined, for example, by the Andersen-Weeks-Chandler condition for soft-sphere-hard-sphere structural equivalence. A number of scaling properties observed in recent simulations involving glass-forming fluids with repulsive short range interactions are found to be a direct manifestation of this general dynamic equivalence principle. The self-consistent generalized Langevin equation (SCGLE) theory of colloid dynamics is shown to accurately capture these scaling rule

Metastability at the Yield-Stress Transition in Soft Glasses

We study the solid-to-liquid transition in a two-dimensional fully periodic soft-glassy model with an imposed spatially heterogeneous stress. The model we consider consists of droplets of a dispersed phase jammed together in a continuous phase. When the peak value of the stress gets close to the yield stress of the material, we find that the whole system intermittently tunnels to a metastable "fluidized" state, which relaxes back to a metastable "solid" state by means of an elastic-wave dissipation. This macroscopic scenario is studied through the microscopic displacement field of the droplets, whose time statistics displays a remarkable bimodality. Metastability is rooted in the existence, in a given stress range, of two distinct stable rheological branches as well as long-range correlations (e.g., large dynamic heterogeneity) developed in the system. Finally, we show that a similar behavior holds for a pressure-driven flow, thus suggesting possible experimental tests.Comment: 13 pages, 11 figure

Real-time Error Control for Surgical Simulation

Objective: To present the first real-time a posteriori error-driven adaptive finite element approach for real-time simulation and to demonstrate the method on a needle insertion problem. Methods: We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA. The refinement strategy relies upon a hexahedron-based finite element method, combined with a posteriori error estimation driven local $h$-refinement, for simulating soft tissue deformation. Results: We control the local and global error level in the mechanical fields (e.g. displacement or stresses) during the simulation. We show the convergence of the algorithm on academic examples, and demonstrate its practical usability on a percutaneous procedure involving needle insertion in a liver. For the latter case, we compare the force displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach. Conclusions: Error control guarantees that a tolerable error level is not exceeded during the simulations. Local mesh refinement accelerates simulations. Significance: Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM

Soft versus Hard Dynamics for Field-driven Solid-on-Solid Interfaces

Analytical arguments and dynamic Monte Carlo simulations show that the microstructure of field-driven Solid-on-Solid interfaces depends strongly on the dynamics. For nonconservative dynamics with transition rates that factorize into parts dependent only on the changes in interaction energy and field energy, respectively (soft dynamics), the intrinsic interface width is field-independent. For non-factorizing rates, such as the standard Glauber and Metropolis algorithms (hard dynamics), it increases with the field. Consequences for the interface velocity and its anisotropy are discussed.Comment: 9 pages LaTex with imbedded .eps figs. Minor revision

DynamO: A free O(N) general event-driven molecular-dynamics simulator

Molecular-dynamics algorithms for systems of particles interacting through discrete or "hard" potentials are fundamentally different to the methods for continuous or "soft" potential systems. Although many software packages have been developed for continuous potential systems, software for discrete potential systems based on event-driven algorithms are relatively scarce and specialized. We present DynamO, a general event-driven simulation package which displays the optimal O(N) asymptotic scaling of the computational cost with the number of particles N, rather than the O(N log(N)) scaling found in most standard algorithms. DynamO provides reference implementations of the best available event-driven algorithms. These techniques allow the rapid simulation of both complex and large (>10^6 particles) systems for long times. The performance of the program is benchmarked for elastic hard sphere systems, homogeneous cooling and sheared inelastic hard spheres, and equilibrium Lennard-Jones fluids. This software and its documentation are distributed under the GNU General Public license and can be freely downloaded from http://marcusbannerman.co.uk/dynamo