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The material point method (MPM) recently demonstrated its efficacy at simulating many materials and the coupling between them on a massive scale. However, in scenarios containing debris, MPM manifests more dissipation and numerical viscosity than traditional Lagrangian methods. We have two observations from carefully revisiting existing integration methods used in MPM. First, nearby particles would end up with smoothed velocities without recovering momentum for each particle during the particle-grid-particle transfers. Second, most existing integrators assume continuity in the entire domain and advect particles by directly interpolating the positions from deformed nodal positions, which would trap the particles and make them harder to separate. We propose an integration scheme that corrects particle positions at each time step. We demonstrate our method's effectiveness with several large-scale simulations involving brittle materials. Our approach effectively reduces diffusion and unphysical viscosity compared to traditional integrators.

3D weaving is an emerging technology for manufacturing multilayer woven textiles. In this work, we present Weavecraft: an interactive, simulation-based design tool for 3D weaving. Unlike existing textile software that uses 2D representations for design patterns, we propose a novel weave block representation that helps the user to understand 3D woven structures and to create complex multi-layered patterns. With Weavecraft, users can create blocks either from scratch or by loading traditional weaves, compose the blocks into large structures, and edit the pattern at various scales. Furthermore, users can verify the design with a physically based simulator, which predicts and visualizes the geometric structure of the woven material and reveals potential defects at an interactive rate. We demonstrate a range of results created with our tool, from simple two-layer cloth and well known 3D structures to a more sophisticated design of a 3D woven shoe, and we evaluate the effectiveness of our system via a formative user study.

3D weaving is an industrial process for creating volumetric material through organized multiaxis interlacing of yarns. The overall complexity and rarity of 3D weaving have limited its market to aerospace and military applications. Current textile design software does not address the ease of iterating through physical trialing so necessary for designers to access this medium. This paper describes the development of a series of volumetric textile samples culminating in the creation of a fully formed shoe and the collaboration with computer scientists to develop a visualization tool that addresses the consumer accessory design opportunities for this medium.

We describe an interactive design tool for authoring, simulating, and adjusting yarn-level patterns for knitted and woven cloth. To achieve interactive performance for notoriously slow yarn-level simulations, we propose two acceleration schemes: (a) yarn-level periodic boundary conditions that enable the restricted simulation of only small periodic patches, thereby exploiting the spatial repetition of many cloth patterns in cardinal directions, and (b) a highly parallel GPU solver for efficient yarn-level simulation of the small patch. Our system supports interactive pattern editing and simulation, and runtime modification of parameters. To adjust the amount of material used (yarn take-up) we support "on the fly" modification of (a) local yarn rest-length adjustments for pattern specific edits, e.g., to tighten slip stitches, and (b) global yarn length by way of a novel yarn-radius similarity transformation. We demonstrate the tool's ability to support interactive modeling, by novice users, of a wide variety of yarn-level knit and woven patterns. Finally, to validate our approach, we compare dozens of generated patterns against reference images of actual woven or knitted cloth samples, and we release this corpus of digital patterns and simulated models as a public dataset to support future comparisons.

3D weaving is a manufacturing technique that creates multilayer textiles with substantial thickness. Currently, the primary use for these materials is in regularly structured carbon-polymer or glass-polymer composites, but in principle a wide range of complex shapes can be achieved, providing the opportunity to customize the fiber structure for individual parts and also making 3D weaving appealing in many soft-goods applications. The primary obstacle to broader use is the need to design intricate weave structures, involving tens to hundreds of thousands of yarn crossings, which are different for every shape to be produced. The goal of this research is to make 3D weaving as readily usable as CNC machining or 3D printing, by providing an algorithm to convert an arbitrary 3D solid model into machine instructions to weave the corresponding shape. We propose a method to generate 3D weaving patterns for height fields by slicing the shape along intersecting arrays of parallel planes and then computing the paths for all the warp and weft yarns, which travel in these planes. We demonstrate the method by generating weave structures for different shapes and fabricating a number of examples in polyester yarn using a Jacquard loom.

Rendering fabrics using micro-appearance models---fiber-level microgeometry coupled with a fiber scattering model---can take hours per frame. We present a fast, precomputation-based algorithm for rendering both single and multiple scattering in fabrics with repeating structure illuminated by directional and spherical Gaussian lights. Precomputed light transport (PRT) is well established but challenging to apply directly to cloth. This paper shows how to decompose the problem and pick the right approximations to achieve very high accuracy, with significant performance gains over path tracing. We treat single and multiple scattering separately and approximate local multiple scattering using precomputed transfer functions represented in spherical harmonics. We handle shadowing between fibers with precomputed per-fiber-segment visibility functions, using two different representations to separately deal with low and high frequency spherical Gaussian lights. Our algorithm is designed for GPU performance and high visual quality. Compared to existing PRT methods, it is more accurate. In tens of seconds on a commodity GPU, it renders high-quality supersampled images that take path tracing tens of minutes on a compute cluster.

Traditional 3D printers fabricate objects by depositing material to build up the model layer by layer. Instead printing only wireframes can reduce printing time and the cost of material while producing effective depictions of shape. However, wireframe printing requires the printer to undergo arbitrary 3D motions, rather than slice-wise 2D motions, which can lead to collisions with already-printed parts of the model. Previous work has either limited itself to restricted meshes that are collision free by construction, or simply dropped unreachable parts of the model, but in this paper we present a method to print arbitrary meshes on a 5DOF wireframe printer. We formalize the collision avoidance problem using a directed graph, and propose an algorithm that finds a locally minimal set of constraints on the order of edges that guarantees there will be no collisions. Then a second algorithm orders the edges so that the printing progresses smoothly. Though meshes do exist that still cannot be printed, our method prints a wide range of models that previous methods cannot, and it provides a fundamental enabling algorithm for future development of wireframe printing.

We introduce a novel approach that uses a generative adversarial network (GAN) to synthesize realistic oil painting brush strokes, where the network is trained with data generated by a high-fidelity simulator. Among approaches to digitally synthesizing natural media painting strokes, physically based simulation produces by far the most realistic visual results and allows the most intuitive control of stroke variations. However, accurate physics simulations are known to be computationally expensive and often cannot meet the performance requirements of painting applications.In our work, we propose to replace the expensive fluid simulation with a neural network. The network takes the existing canvas and a new stroke trajectory as input and produces the height and color of the new stroke as output. We train the network with a dataset generated with a high quality offline simulator. The network is able to produce visual quality comparable to the offline simulator with better performance than the existing real-time oil painting simulator. Finally, we implement a real-time painting system using the trained network.

We present a novel anisotropic Spherical Gaussian (ASG) function, built upon the Bingham distribution [Bingham 1974], which is much more effective and efficient in representing anisotropic spherical functions than Spherical Gaussians (SGs). In addition to retaining many desired properties of SGs, ASGs are also rotationally invariant and capable of representing all-frequency signals. To further strengthen the properties of ASGs, we have derived approximate closed-form solutions for their integral, product and convolution operators, whose errors are nearly negligible, as validated by quantitative analysis. Supported by all these operators, ASGs can be adapted in existing SG-based applications to enhance their scalability in handling anisotropic effects. To demonstrate the accuracy and efficiency of ASGs in practice, we have applied ASGs in two important SG-based rendering applications and the experimental results clearly reveal the merits of ASGs.

An optimal control strategy is proposed to suppress the over-voltage of AC power grids near DC converter station when high voltage direct current (HVDC) in low power operation. Comprehensively considering all kinds of voltage regulation measures, the adjustment order is determined based on their costs and the influence on power system. Besides the difference between leading phase operation and lagging phase operation of generators, the condition that in actual power grids the parameters of transmission lines belonging to one branch may be different from one another is also taken into account in optimization model. Taking the total adjustment amount of reactive power as objective function, the mixed integer nonlinear programming (MINLP) model to suppress over-voltage is built on general algebraic modeling system (GAMS). The simulation tests on the rectifier side of XiZhe (Xiluodu-Zhejiang) and XiangShang (Xiangjiaba-Shanghai) HVDC transmission lines in Sichuan Province verify this optimal control strategy.

Direction comparison principle has problems when applied to wide-area backup protection (WABP): low sensitivity of direction-element because of collected small current or voltage, need of directional messages of all branches and message errors during transmission. In this paper, an improved WABP based on direction comparison principle and fault component current distribution is presented to solve the problems. Combining negative sequence with positive sequence, it selects the branch with maximum amplitude current connected to buses locally and communicates required direction messages to the Center. Then, the decision-making center determines the fault line through the changed elements of state matrix. The principle has strong endurance ability to high-impedance-fault, needs no information synchronization, and has better ability to identify the error message, reducing the influence of insufficient sensitivity or incorrect information on the relay protection. It has better adaptability to multi-terminal, loop network The simulation results show that the method promotes the safe of the grid.

Accurate fault location is important in transmission lines. As location results from two ends of line are conflicting, the operators cannot restore the fault quickly. In this paper, based on fuzzy inference data fusion technology, a new method is proposed to improve the fault location accuracy by using the location results from distance relays installed in the different end of line. The simulation data is trained firstly to establish the data fusion model. During the modeling, the fuzzy inference system (FIS) is formed by using the training data. Then the data fusion model is validated by using the test data. The fault location result is obtained based on the fuzzy inference data fusion technology. The results through PSCAD/EMTDC simulation show that the method can improve the fault location accuracy and is not affected by fault type, fault resistance and data asynchronization.

Current interactive fabrication tools offer tangible feedback by allowing users to work directly on the physical model, but they are slow because users need to participate in the physical instantiation of their designs. In contrast, CAD software offers powerful tools for 3D modeling but delays access to the physical workpiece until the end of the design process. In this paper we propose On-the-Fly Print: a 3D modeling approach that allows the user to design 3D models digitally while having a low-fidelity physical wireframe model printed in parallel. Our software starts printing features as soon as they are created and updates the physical model as needed. Users can quickly check the design in a real usage context by removing the partial physical print from the printer and replacing it afterwards to continue printing. Digital content modification can be updated with quick physical correction using a retractable cutting blade. We present the detailed description of On-the-Fly Print and showcase several examples designed and printed with our system.

With the development of society, the requirement for security and economy in power grid is increasing. Hence, the concept of smart grid has been proposed. Smart grid is a new power network, which integrates the technology of advanced sensor measuring, network, communication, calculation and intelligent control. The application of visualization is a significant part in smart grid. On the other hand, the analysis of power flow in power grid is a traditional part in power system. This paper combines power flow analysis with visualization technology in one platform through C programming language. A detailed explanation is given on how to achieve power flow analysis through the classic Newton-Raphson algorithm and model the power grid with OpenGL programming method in computer. The platform can achieve the function of modeling a power system and dynamic effect in the line.