International Journal of Modeling, Simulation, and Scientific Computing

In this paper, a mobile augmented reality (AR) framework for on-site finite element analysis (FEA) is proposed. The proposed framework is achieved using a client–server architecture. The performance of FEA relies on several important components, namely, computational power, visualization technique, and numerical analysis. AR renders intuitive computer-generated contents directly on a user’s surroundings. Integrating FEA with AR helps users through enhancing their perception and interaction with the engineering problems. Correct and effective visualization of these data using an AR platform can reduce the misinterpretation in spatial and logical aspects. Over the past decade, AR has undergone a transition from desktop to phablet computing. Mobile platforms enable user exploration of FEA results in situ. The client side uses a hybrid method to visualize FEA results in the mobile AR environment. In addition, a user can collaborate with other users by using the result sharing function. A prototype with basic functions has been built and a case study has been implemented to demonstrate the visualization method and evaluate the overall performance.

Dome display is expected to be used as effective visualization environment for modeling and simulation due to the features of frameless and high immersive sensation. However, since the users in the dome display can see the projected image in arbitrary direction freely, it is difficult to share information among the viewers. In this research, in order to solve such a problem, the effect of visual attention guidance in the dome environment due to the effect of camera work was examined. As a visualization system, DomePlayer that can express the effect of camera work based on the camera work description language was developed. From the result of evaluation experiments using this system, the constraint condition of the camera work in the dome environment was derived and the effect of visual attention guidance by the camera work was evaluated.

As the development of new visualization systems, within the field of simulation, offers their users more insights from their simulations, immersive systems are becoming a part of the visualization techniques. With the recent advancements of the Head-Mounted Displays (HMDs) and popularity of motion sensors, human beings and computer become more interactive. This study aims to evaluate the potential of such systems as a visualization tool through the development of a new flexible framework for visualization within virtual reality (VR) environment, using an Oculus Rift and a Leap Motion. Two approaches are then compared: high-3D object rendering within the virtual scene approach and a user experience-oriented system with an intuitive interface. To assess the quality of the interface and its relevance for the user, different types of gestures are implemented and tested. From an experiment on users to evaluate the developed system as a proper visualization tool, the HMDs, paired with a motion sensor to make a gesture-controlled interface seem to be promising mediums despite various constraints on development regarding the technology limitations.

The amount of produced data required to be visualized and analyzed has grown year by year, and the traditional approach of using larger computational resources or exploiting task and data parallelism seems to have reached its limit. Therefore, a new paradigm for large-scale data visualization becomes highly desired, and in this paper, we propose a new and optimized visualization pipeline which uses a point rendering-based early visibility testing for reducing the unnecessary rendering in the final stage of the visualization pipeline, and we named this technique “Early Visibility Test Point Rendering”. In a densely populated polygonal mesh scene, where multiple triangles may cover a single pixel, unnecessary and wasteful rendering will occur in the final stage of the traditional visualization pipeline, that is, during the rasterization process. Therefore, we propose an alternative visualization pipeline by introducing the “Early Visibility Test Point Rendering” for selecting only the visible polygonal elements for a given visualization scene. This visibility testing can be executed on the CPU side, and only the visible polygonal elements are needed to be sent to the GPU for an optimized rendering. We could verify the effectiveness of our proposed approach by using synthetic datasets, and also a real-world large-scale simulation result.

Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics. Data-driven method is an efficient way for motion analysis and animation synthesis. As a widely used tool, motion capture has been used in plant motion data acquisition and analysis. The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction. This paper presents a novel physics-based approach to motion capture data processing of plants. Firstly, a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf, and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated. Further, by using the physical model with evaluated parameters, we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence. We take an example of a maize leaf and pachira leaf to examine the proposed approach, and the results show that the physics-based method is feasible and effective for plant motion data processing.

Leading-edge supercomputers, such as the K computer, have generated a vast amount of simulation results, and most of these datasets were stored on the file system for the post-hoc analysis such as visualization. In this work, we first investigated the data generation trends of the K computer by analyzing some operational log data files. We verified a tendency of generating large amounts of distributed files as simulation outputs, and in most cases, the number of files has been proportional to the number of utilized computational nodes, that is, each computational node producing one or more files. Considering that the computational cost of visualization tasks is usually much smaller than that required for large-scale numerical simulations, a flexible data input/output (I/O) management mechanism becomes highly useful for the post-hoc visualization and analysis. In this work, we focused on the xDMlib data management library, and its flexible data I/O mechanism in order to enable flexible data loading of big computational climate simulation results. In the proposed approach, a pre-processing is executed on the target distributed files for generating a light-weight metadata necessary for the elaboration of the data assignment mapping used in the subsequent data loading process. We evaluated the proposed approach by using a 32-node visualization cluster, and the K computer. Besides the inevitable performance penalty associated with longer data loading time, when using smaller number of processes, there is a benefit for avoiding any data replication via copy, conversion, or extraction. In addition, users will be able to freely select any number of nodes, without caring about the number of distributed files, for the post-hoc visualization and analysis purposes.

Vortexes induced by tsunamis, “tsunami vortexes”, pose a potential danger for ship evacuation, and their prediction is important to determine their role in navigation and safe vessel evacuation. This study simulated tsunami-induced vortexes in Osaka Bay and investigated their spatial and temporal scales using a port-resolved, high-resolution tsunami model based on a scenario for the largest tsunamigenic earthquake expected to occur along the Nankai Trough in the near future. Vortexes induced by tidal currents were also evaluated using a realistic tidal simulation for comparison with the magnitudes of the tsunami vortexes. The tsunami simulation identified the dominant areas of the tsunami vortexes characterized by the magnitude of the vorticity. Unified maps of tsunami speeds and vortexes showed the difficult areas for ship evacuation and indicated that these areas can persist after the approach of the leading wave around the entrances to port areas. The areas of tsunami vortex formation were largely deduced by analyzing the vorticity fields in the tidal simulations, because the magnitude of tsunami-induced vorticities was closely correlated with that of the tide-induced vorticities. The combination of tide and tsunami simulations can identify areas of tsunami vortex formation and provide valuable information to guide measures for safer vessel evacuation.

In order to evaluate and recognize plant growth automatically based on photos of plants in plant science and engineering, we propose a diagnostic system for plant growth in which simulated plant growth is used as a reference for real growing plants. In our diagnostic system, we employ the Lindenmayer system [Lecture Notes in Computer Science, 15, 53–68, 1974] to model healthy growing plants. In the Lindenmayer system plant growth is expressed by string expressions designed as a numerical model for the plant. This proposal system provides users the string expressions by using image processing. As users compare with two strings such as DNA sequence genetic code, users obtain developmental change of plat growth. The results show that our automatic plant diagnostic system is effective in checking actual healthy plant growth, and suggest that the diagnostic system should be useful in large-scale plant factories.

Transparent visualization is used in many fields because it can visualize not only the frontal object but also other important objects behind it. Although in many situations, it would be very important for the 3D structures of the visualized transparent images to be perceived as they are simulated, little is known quantitatively as to how such transparent 3D structures are perceived. To address this question, in the present study, we conducted a psychophysical experiment in which the observers reported the perceived depth magnitude of a transparent object in medical images, presented with a multi-view 3D display. For the visualization, we employed a stochastic point-based rendering (SPBR) method, which was developed recently as a technique for efficient transparent-rendering. Perceived depth of the transparent object was smaller than the simulated depth. We found, however, that such depth underestimation can be alleviated to some extent by (1) applying luminance gradient inherent in the SPBR method, (2) employing high opacities, and (3) introducing binocular disparity and motion parallax produced by a multi-view 3D display.