An augmented reality platform for interactive aerodynamic design and analysis

Badías, Alberto; Curtit, Sarah; González, David; Alfaro, Icíar; Chinesta, Francisco

doi:10.1002/nme.6127

Cited by 19 publications

(18 citation statements)

References 40 publications

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“…Such predictions are straightforward to show the aerodynamic performance of a moving object and are easily understood by the designer. While Badias et al [37] use the LLE algorithm [38] to estimate the airflow around a car, we propose to use LLE to predict the drag coefficients of newly generated car models from the 3D mesh generative design module. The reasons for adopting LLE in this study are 1) LLE has the potentials to estimate the drag coefficient of a car model with good accuracy; 2) the estimation of the drag coefficient of a car model using LLE is fast.…”

Section: Data-driven Cfd Evaluationmentioning

confidence: 99%

Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

Li¹,

Xie²,

Sha³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts' interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance.

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Section: Data-driven Cfd Evaluationmentioning

confidence: 99%

Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

Li¹,

Xie²,

Sha³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Consequently, interaction between reality and the replica, as well as user control and interpretation, are straightforward. AR tools have also been successfully implemented for nonrigid representation, such as aerodynamics [21], or deformable objects [16]. On the other and, existing digital twins for sloshing problems revealed limitations, such time computing and localization in the scene, that inhibit their implementation [22].…”

Section: Related Workmentioning

confidence: 99%

Physically sound, self-learning digital twins for sloshing fluids

et al. 2020

Self Cite

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In this paper, a novel self-learning digital twin strategy is developed for fluid sloshing phenomena. This class of problems is of utmost importance for robotic manipulation of fluids, for instance, or, in general, in simulation-assisted decision making. The proposed method infers the (linear or non-linear) constitutive behavior of the fluid from video sequences of the sloshing phenomena. Real-time prediction of the fluid response is obtained from a reduced order model (ROM) constructed by means of thermodynamics-informed data-driven learning. From these data, we aim to predict the future response of a twin fluid reacting to the movement of the real container. The constructed system is able to perform accurate forecasts of its future reactions to the movements of the containers. The system is completed with augmented reality techniques, so as to enable comparisons among the predicted result with the actual response of the same liquid and to provide the user with insightful information about the physics taking place.

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“…In this work we focus on deformable solids, but our method can be applied to any other physical problem, such as fluid mechanics with the estimation of aerodynamics in a car 41 or even electromagnetic problems. 42 In general, and without loss of generality, we consider the virtual solids to be hyperelastic.…”

Section: Physical Description Of Virtual Solidsmentioning

confidence: 99%

Real‐time interaction of virtual and physical objects in mixed reality applications

Badías

González

Alfaro

et al. 2020

Numerical Meth Engineering

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We present a real-time method for computing the mechanical interaction between real and virtual objects in an augmented reality environment. Using model order reduction methods we are able to estimate the physical behavior of deformable objects in real time, with the precision of a high-fidelity solver but working at the speed of a video sequence. We merge tools of machine learning, computer vision, and computer graphics in a single application to describe the behavior of deformable virtual objects allowing the user to interact with them in a natural way. Three examples are provided to test the performance of the method. K E Y W O R D S model order reduction, nonlinear materials, real-time interaction, solids contact 1 INTRODUCTION New technologies are bringing better tools to improve the augmented and mixed reality experience. These tools are in the form of both hardware and software. On the hardware side we have the development of new devices to show information to the user, such as smartphones, or high-resolution virtual reality glasses (eg, StereoLabs ZED Mini on Oculus Rift, see https://www.stereolabs.com/zed-mini/setup/rift/); we also notice great efforts on the development of devices to capture information around us such as RGB-D systems (Microsoft Kinect for Windows, for instance, see https:// developer.microsoft.com/en-us/windows/kinect), or stereo cameras; and even systems that include both the capture and visualization of information, such as Microsoft Hololens 2 (https://www.microsoft.com/en-us/hololens) or Magic Leap One (see https://www.magicleap.com/magic-leap-one), which allow the capture of the environment, the visualization of virtual objects and the interaction thanks to built-in controls. From the software point of view, a great work has been done to generate new environments to reduce complexity in the process of capturing the data, such as the new development kits Apple ARKit 3 (https://developer.apple.com/augmentedreality/) or Google ARCore (https://developers.google.com/ar/); other software development kits are more focused and integrated into helmets such as Hololens; there are also great advances in visualization libraries such as OpenGL (https:// www.opengl.org/) or other proprietary libraries like Nvidia CUDA (https://developer.nvidia.com/cuda-zone) or Apple Metal (https://developer.apple.com/metal/); and also there has been a big development in new techniques to take robust measures from a scene, such as ORB-SLAM 1 or LSD-SLAM, 2 among others. This paper leverages some of these technologies and develops real-time computational mechanics techniques so as to provide mixed reality systems the ability to seamlessly integrate virtual and physical objects and make them interact F I G U R E 1 Mixed Reality (MR) as the interaction of three sciences: machine learning, computer graphics, and computer vision [Color figure can be viewed at wileyonlinelibrary.com] according to physical laws. The interest of adding physical realism to the interaction between real and virtual elements in a scene is ...

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An augmented reality platform for interactive aerodynamic design and analysis

Cited by 19 publications

References 40 publications

Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

Physically sound, self-learning digital twins for sloshing fluids

Real‐time interaction of virtual and physical objects in mixed reality applications

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