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An interactive simulation framework for mobile devices. The 3 key components are integrated through the implementation of prescribed interfaces.
Source publication
The ability to perform interactive CFD simulations on mobile devices allows the development of portable, affordable simulation tools that can have a significant impact in engineering design as well as teaching and learning. This work extends existing work in the area by developing and implementing a GPU-accelerated, interactive simulation framework...
Citations
... Jia-Rui et al. [8] explore the use of augmented reality (AR) technology in mobile devices for visualizing and interacting with CFD simulation results in the context of indoor thermal environment design. Harwood et al. [9] developed a GPU-accelerated, interactive simulation framework suitable for mobile devices, enabling the visualization of flow around particles. ...
... Previously mentioned work [9] utilized a static domain with fixed inlets and outlets to create 2D simulations on tablets, which is limited to NVIDIA GPU-based devices. ...
... Single-threaded execution provides sufficient cross-device performance for the intended two-dimensional flow simulations. On the lowest end, we conducted a performance comparison between paint2sim and the results presented in [9]. Due to hardware availability issues, we were unable to use the same experimental setup and instead relied on a low-end Huawei P8 Lite with inferior specifications compared to the initial high-end NVIDIA Shield K1 Tablet. ...
The present state of research in computational fluid dynamics (CFD) is marked by an ongoing process of refining numerical methods and algorithms with the goal of achieving accurate modeling and analysis of fluid flow and heat transfer phenomena. Remarkable progress has been achieved in the domains of turbulence modeling, parallel computing, and mesh generation, resulting in heightened simulation precision when it comes to capturing complex flow behaviors. Nevertheless, CFD faces a significant challenge due to the time and expertise needed for a meticulous simulation setup and intricate numerical techniques. To surmount this challenge, we introduce paint2sim—an innovative mobile application designed to enable on-the-fly 2D fluid simulations using a device’s camera. Seamlessly integrated with OpenLB, a high-performance Lattice Boltzmann-based library, paint2sim offers accurate simulations. The application leverages the capabilities of the Lattice Boltzmann Method (LBM) to model fluid behaviors accurately. Through a symbiotic interaction with the open-source OpenCV library, paint2sim can scan and extract hand-drawn simulation domains, affording the capability for instant simulation and visualization. Notably, paint2sim can also be regarded as a digital twin, facilitating just-in-time representation and analysis of 2D fluid systems. The implications of this technology extend significantly to both fluid dynamics education and industrial applications, effectively lowering barriers and rendering fluid simulations more accessible. Encouragingly, the outcomes of simulations conducted with paint2sim showcase promising qualitative and quantitative results. Overall, paint2sim offers a groundbreaking approach to mobile 2D fluid simulations, providing users with just-in-time visualization and accurate results, while simultaneously serving as a digital twin for fluid systems.
... Jia-Rui at al. [8] explore the use of augmented reality (AR) technology on mobile devices for visualizing and interacting with CFD simulation results in the context of indoor thermal environment design. Harwood et al. [9] developed a GPU-accelerated, interactive simulation framework suitable for mobile devices, enabling visualization of flow around particles. ...
... Previously mentioned work [9] utilizes a static domain with fixed inlets and outlets to create 2D simulations on tablets, which is limited to NVIDIA GPU-based devices. ...
... On the lowest end, we conducted a performance comparison between paint2sim and the results presented in [9]. Due hardware availability issues, we were unable to use the same experimental setup and instead relied on a low-end Huawei P8 Lite with inferior specifications compared to the initially high-end NVIDIA Shield K1 Tablet. ...
The present state of research in Computational Fluid Dynamics (CFD) is marked by an ongoing process of refining numerical methods and algorithms with the goal of achieving accurate modeling and analysis of fluid flow and heat transfer phenomena. Remarkable progress has been achieved in the domains of turbulence modeling, parallel computing, and mesh generation, resulting in heightened simulation precision when it comes to capturing complex flow behaviors. Nevertheless CFD faces a significant challenge due to the time and expertise needed for meticulous simulation setup and intricate numerical techniques. To surmount this challenge, we introduce paint2sim—an innovative mobile application designed to enable on-the-fly 2D fluid simulations using a device’s camera. Seamlessly integrated with OpenLB, a high-performance Lattice Boltzmann-based library, paint2sim offers accurate simulations. The application leverages the capabilities of the Lattice Boltzmann Method (LBM) to model fluid behaviors accurately. Through a symbiotic interaction with the open-source OpenCV library, paint2sim can scan and extract hand-drawn simulation domains, affording the capability for instant simulation and visualization. Notably, paint2sim can also be regarded as a digital twin, facilitating just-in-time representation and analysis of 2D fluid systems. The implications of this technology extend significantly to both fluid dynamics education and industrial applications, effectively lowering barriers and rendering fluid simulations more accessible. Encouragingly, the outcomes of simulations conducted with paint2sim showcase promising qualitative and quantitative results. Overall, paint2sim offers a groundbreaking approach to mobile 2D fluid simulations, providing users with just-in-time visualization and accurate results, while simultaneously serving as a digital twin for fluid systems.
... Compared to batch, real-time simulations should solve and process at least 24 frames per second (fps) [52]. The potential of real-time CFD simulations has been investigated targeting different contexts such as games [46,58], fire simulation [21], chemical site evacuation scenarios [53] and fluid flow modeling [18]. Although, real-time simulations in design and modeling are still in their infancy, thus mostly impractical to use in multiphysics CFD modeling. ...
Computational fluid dynamics (CFD) simulations can provide meaningful technical content in engineering education, broad engineering and business. However, computationally demanding data production and complex data processing environments of CFD simulations turn them into esoteric tools for potential non-expert users. This consequently limits applications and communications of CFD simulations and results. Augmented and virtual reality (AR/VR) technologies are opening new gates for visualization and interaction techniques. Despite the many recent attempts, the literature lacks an inclusive system development procedure for CFD simulations with AR/VR. The present study proposes a component-oriented system architecture to generate dedicated workflows for any kind of AR/VR environment supported by CFD simulations. The study further explores the potential of data processing options throughout the preparation of the simulation dataset with AR/VR. An automated data coupling strategy is additionally introduced to ease multiplatform integration. We provide an integration strategy with simple, easy-to-implement, end-to-end, automated and free-to-use utilities that the practitioners can readily pursue.
... plots, bar charts, histograms) are used (e.g. Harwood and Revell, 2018;Li and Tate, 2013;Wang et al., 2014). ...
Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS.
We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines.
The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities.
... (3) Because of the influence of the light and the speed of the ball, the color, size, and shape of the football will change, so it is difficult to build an effective model to detect and track the ball. (4) When soccer is tied to the ground or shielded by players, it is more difficult to detect and track [7][8][9][10]. ...
Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking.
... Computationally intensive parts of the algorithms are off-loaded to the accelerators which have onboard memory, and the computed results are copied back to the CPU memory. GPU, in particular, has achieved great success in the grid-based LBM [45][46][47][48][49][50][51] and particle-based DSMC method [52][53][54]. For more general DVM simulations, GPUs have not been fully exploited [55][56][57][58][59][60][61]. ...
This paper presents a Graphics Processing Unit (GPU) acceleration of an iteration-based discrete velocity method (DVM) for gas-kinetic model equations. Unlike the previous GPU parallelization of explicit kinetic schemes, this work is based on a fast converging iterative scheme. The memory reduction techniques previously proposed for DVM are applied for GPU computing, enabling full three-dimensional (3D) solutions of kinetic model equations in the contemporary GPUs usually with a limited memory capacity that otherwise would need terabytes of memory. The GPU algorithm is validated against the direct simulation Monte Carlo (DSMC) simulation of the 3D lid-driven cavity flow and the supersonic rarefied gas flow past a cube with the phase-space grid points up to 0.7 trillion. The computing performance profiling on three models of GPUs shows that the two main kernel functions can utilize 56%∼79% of the GPU computing and memory resources. The performance of the GPU algorithm is compared with a typical parallel CPU implementation of the same algorithm using the Message Passing Interface (MPI). The comparison shows that the GPU program on K40 and K80 achieves 1.2∼2.8 and 1.2∼2.4 speedups for the 3D lid-driven cavity flow, respectively, compared with the MPI parallelized CPU program running on 96 CPU cores.
... The energy-efficient nature of embedded systems has even led to researchers exploring their potential in the form of a HPC system ( [11] and references therein). Our earlier work [12,13] has considered the embedded architectures of the current generation of mobile phones and tablets, developing and profiling CFD simulations for mobile CPUs and GPUs. This work provides some benchmark data for single-device performance but, despite tablets and mobile phones being interactive, portable, energy-efficient computing devices with a unique user experience, recognises that they are limited in computing power individually. ...
... As with earlier work [12,13], we choose to implement our software for Android-powered devices due to their popularity, accessibility, affordability and flexibility. ...
... This is more representative of the pool of devices which could be used in practice and maximises the utility of the application. To achieve this, it is necessary to remove the limitation of using only NVIDIA Tegra GPUs imposed by earlier work [13]. This restriction came about due to the decision to rely on the CUDA toolkit for ease and efficiency of implementation. ...
This article develops novel application software which implements interactive, GPU-powered flow simulation on a group of wirelessly-connected mobile devices. Interactive simulation is an emerging field in engineering with use cases appearing in design, analysis and communication. Herein, we present a new Android-based, interactive flow solver capable of running on a wider range of multiple, wirelessly-connected mobile GPUs. The software consists of a 2D Lattice-Boltzmann Method flow physics solver, implemented using OpenGL ES 3.2, as well as a communication library which uses Wi-Fi Direct to communicate between connected devices. We compare the performance of the OpenGL-based solver against existing implementations in CUDA and demonstrate similar computational throughput. We also test a variety of communication strategies based on configurations of GPU memory mapping and communication frequency. Results confirm that passing large amounts of data infrequently offers the best overall efficiency. However, due to the extended time required to pass larger amounts of data to adjacent devices, this configuration can introduce an undesirable stuttering in an interactive application. Finally, comparisons between two and three device networks to the serial case show that, despite the inevitable cost of communication, it is possible to maintain an interactive frame rate across multiple devices; the extension of calculations across multiple devices in this way, allows the tackling of problems which are larger and of higher-resolution that previous.
... In recent years, GPUs have become an important tool for more efficient computations in many applications, including in fluid simulation [Goswami et al. 2010;Harris 2005;Navarro-Hinojosa et al. 2018]. Furthermore, such interactive fluid simulations can now run on mobile devices [Harwood and Revell 2018], enabling the user to control the simulation in real-time, for instance via the touch interface, [Chen et al. 2015;Stuyck et al. 2017]. Nevertheless, and to the best of our knowledge, there do not currently exist real time fluid simulators that are capable of generating the viscous thin film effects that we demonstrate. ...
We propose a novel discrete scheme for simulating viscous thin films at real-time frame rates. Our scheme is based on a new formulation of the gradient flow approach, that leads to a discretization based on local stencils that are easily computable on the GPU. Our approach has physical fidelity, as the total mass is guaranteed to be preserved, an appropriate discrete energy is controlled, and the film height is guaranteed to be non-negative at all times. In addition, and unlike all existing methods for thin films simulation, it is fast enough to allow realtime interaction with the flow, for designing initial conditions and controlling the forces during the simulation.
... This would allow the use of fluid simulations in more diverse fields, such as in teaching, or for Augmented Reality applications. Hardwood and Revell [35] presented CFD simulations on mobile devices, specifically for devices with a Tegra K1 GPU. The authors integrated CUDA, C++, JNI and Java into a typical Android app, while reporting better throughput and power consumption when compared to a CPU implementation. ...
The rapid increase in performance, programmability, and availability of graphics processing units (GPUs) has made them a compelling platform for computationally demanding tasks in a wide variety of application domains. One of these is real-time computational fluid dynamics, which are computationally expensive due to a large number of grid points that require calculations. One commonly used tool to simulate fluid flows is the Lattice Boltzmann method (LBM), mainly due to its simpler formulation when compared to solving the Navier–Stokes equations, and because of its scalability on parallel processing systems. In this paper, we give an up-to-date survey on the research regarding the LBM for fluid simulation using GPUs. We discuss how the method was implemented with different GPU architectures and software frameworks, focusing on optimization techniques and their performance. Additionally, we mention some applications of the method in different areas of study.
... Finally, a fork of the LUMA LBM core has been used for 'interactive', mobile CFD simulations [28,29]. This is an emerging use mode of simulation and in [29], a GPU-accelerated LUMA core is used to conduct the simulations, which will be included in a future release of LUMA. ...
... Finally, a fork of the LUMA LBM core has been used for 'interactive', mobile CFD simulations [28,29]. This is an emerging use mode of simulation and in [29], a GPU-accelerated LUMA core is used to conduct the simulations, which will be included in a future release of LUMA. The interactive fork of LUMA benefits from all the support structure of the trunk code with no modification which illustrates the modularity of the original software. ...
The Lattice-Boltzmann Method at the University of Manchester (LUMA) project was commissioned to build a collaborative research environment in which researchers of all abilities can study fluid–structure interaction (FSI) problems in engineering applications from aerodynamics to medicine. It is built on the principles of accessibility, simplicity and flexibility. The LUMA software at the core of the project is a capable FSI solver with turbulence modelling and many-core scalability as well as a wealth of input/output and pre- and post-processing facilities. The software has been validated and several major releases benchmarked on supercomputing facilities internationally. The software architecture is modular and arranged logically using a minimal amount of object-orientation to maintain a simple and accessible software.
