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1. Prediction of Hydroplaning Potential Using Fully Coupled Finite Element-Computational Fluid Dynamics Tire Models

   https://doi.org/10.1115/1.4047393  

   Nazari, Ashkan; Chen, Lu; Battaglia, Francine; Ferris, John B.; Flintsch, Gerardo; Taheri, Saied (October 2020, Journal of Fluids Engineering)

   null (Ed.)

   Abstract Hydroplaning is a phenomenon that occurs when a layer of water between the tire and pavement pushes the tire upward. The tire detaches from the pavement, preventing it from providing sufficient forces and moments for the vehicle to respond to driver control inputs such as breaking, accelerating, and steering. This work is mainly focused on the tire and its interaction with the pavement to address hydroplaning. Using a tire model that is validated based on results found in the literature, fluid–structure interaction (FSI) between the tire-water-road surfaces is investigated through two approaches. In the first approach, the coupled Eulerian–Lagrangian (CEL) formulation was used. The drawback associated with the CEL method is the laminar assumption and that the behavior of the fluid at length scales smaller than the smallest element size is not captured. To improve the simulation results, in the second approach, an FSI model incorporating finite element methods (FEMs) and the Navier–Stokes equations for a two-phase flow of water and air, and the shear stress transport k–ω turbulence model, was developed and validated, improving the prediction of real hydroplaning scenarios. With large computational and processing requirements, a grid dependence study was conducted for the tire simulations to minimize the mesh size yet retain numerical accuracy. The improved FSI model was applied to hydroplaning speed and cornering force scenarios. 

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2. Investigation of Tire Rotating Modeling Techniques Using Computational Fluid Dynamics

   https://doi.org/10.1115/1.4051311  

   Fu, Gen; Untaroiu, Alexandrina (November 2021, Journal of Fluids Engineering)

   Abstract Fuel efficiency becomes very important for new vehicles. Therefore, improving the aerodynamics of tires has started to receive increasing interest. While the experimental approaches are time-consuming and costly, numerical methods have been employed to investigate the air flow around tires. Rotating boundary and contact patch are important challenges in the modeling of tire aerodynamics. Therefore, majority of the current modeling approaches are simplified by neglecting the tire deformation and contact patch. In this study, a baseline computational fluid dynamics (CFD) model is created for a tire with contact patch. To generate mesh efficiently, a hybrid mesh, which combines hex elements and polyhedral elements, is used. Then, three modeling approaches (rotating wall, multiple reference frame, and sliding mesh) are compared for the modeling of tire rotation. Additionally, three different tire designs are investigated, including smooth tire, grooved tire, and grooved tire with open rim. The predicted results of the baseline model agree well with the measured data. Additionally, the hybrid mesh shows to be efficient and to generate accurate results. The CFD model tends to overpredict the drag of a rotating tire with contact patch. Sliding mesh approach generated more accurate predictions than the rotating wall and multiple reference frame approaches. For different tire designs, tire with open rim has the highest drag. It is believed that the methodology presented in this study will help in designing new tires with high aerodynamic performance. 

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3. Bridge Monitoring Utilizing Smart Portable Sensing System

   https://doi.org/10.32548/RS.2018.004  

   Elhattab, Ahmed; Uddin, Nasim; OBrien, Eugene (October 2018, 27th ASNT Research Symposium)

   Natural Frequencies of structures is an elegant intrinsic property that is essential for many Civil Structural applications, as Structural Health Monitoring and Simulation Modeling. The physically tangible relation between the frequency of the structures and its dynamic characteristics was the impetus for using different time/frequency based methods to quantify this fundamental property. Unfortunately, the disruption effect of noise requires incorporating advanced sensors, that provide signals with a low noise-intensity, to accurately identify the fundamental frequencies of the structure. This article solves this bottleneck via exploiting the Stochastic Resonance (SR) phenomena to extract the fundamental frequencies of a bridge using an acceleration recorded by a conventional portable sensor as the sensor implemented in small portable accelerometer. The portable accelerometer device has an M9 motion coprocessor designed mainly for tracking human activities. Human activities have an exaggerated amplitude when it is compared to the structural responses. Therefore, if an iPhone device is used to record the response of the structure (for example a bridge) the structure response will be swamped by severe surrounding noise because of its small amplitude. Therefore, in this vein, the SR phenomena has been employed to use rather than suppress the noise to magnify the feeble bridge response in the recorded acceleration and hence identify the corresponding frequency. The fidelity of the proposed approach has been verified using the data of a field experiment. The bridge frequencies are identified first using conventional vibration analysis, thereafter, the portable accelerometer has been attached to the bridge rail to record the bridge vibration under the passing traffic. The recorded data has been processed using a new Developed Underdamped Pinning Stochastic Resonance (DUPSR) technique to quantify the bridge frequency. 

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4. Efficient Three-Dimensional Model to Predict Time History of Structural Dynamics in Cold Rolling Mills

   https://doi.org/10.1115/1.4052703  

   Patel, Akash; Malik, Arif; Mathews, Ritin (July 2022, Journal of Manufacturing Science and Engineering)

   Abstract Introduced is a new physics-based three-dimensional (3D) mathematical model capable of efficiently predicting time histories of the nonlinear structural dynamics in cold rolling mills used to manufacture metal strips and sheets. The described model allows for the prediction of transient strip thickness profiles, contact force distributions, and roll-stack deformations due to dynamic disturbances. Formulation of the new 3D model is achieved through a combination of the highly efficient simplified-mixed finite element method with a Newmark-beta direct time integration approach to solve the system of differential equations that governs the motion of the roll-stack. In contrast to prior approaches to predict structural dynamics in cold rolling, the presented method abandons several simplifying assumptions and restrictions, including 1D or 2D linear lumped parameter analyses, vertical symmetry, continuous and constant contact between the rolls and strip, as well as the inability to model cluster-type mill configurations and accommodate typical profile/flatness control mechanisms used in industry. Following spatial and temporal convergence studies of the undamped step response, and validation of the damped step response, the new model is demonstrated for a 4-high mill equipped with both work-roll bending and work-roll crown, a 6-high mill with continuously variable crown (CVC) intermediate rolls, and finally a complex 20-high cluster mill. Solution times on a single computing processor for the damped 4-high and 20-high case studies are just 0.37 s and 3.38 s per time-step, respectively. 

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5. Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators

   https://doi.org/10.3390/jimaging9070133  

   Evangelista, Davide; Morotti, Elena; Piccolomini, Elena Loli; Nagy, James (July 2023, Journal of Imaging)

   In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation of neural networks for deblurring is their sensitivity to noise and other perturbations, which can lead to instability and produce poor reconstructions. In addition, networks do not necessarily take into account the numerical formulation of the underlying imaging problem when trained end-to-end. In this paper, we propose some strategies to improve stability without losing too much accuracy to deblur images with deep-learning-based methods. First, we suggest a very small neural architecture, which reduces the execution time for training, satisfying a green AI need, and does not extremely amplify noise in the computed image. Second, we introduce a unified framework where a pre-processing step balances the lack of stability of the following neural-network-based step. Two different pre-processors are presented. The former implements a strong parameter-free denoiser, and the latter is a variational-model-based regularized formulation of the latent imaging problem. This framework is also formally characterized by mathematical analysis. Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise. In particular, the model-based framework represents the most reliable trade-off between visual precision and robustness. 

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