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1. Experimental Investigation of the Tractive Performance of Pneumatic Tires on Ice

   https://doi.org/10.2346/tire.18.460409  

   Jimenez, Emilio; Sandu, Corina (January 2020, Tire Science and Technology)

   null (Ed.)

   ABSTRACT This investigation was motivated by the need for performance improvement of pneumatic tires in icy conditions. Under normal operation, the pneumatic tire is the only force-transmitting component between the terrain and the vehicle. Therefore, it is critical to grasp the understanding of the contact mechanics at the contact patch under various surfaces and operating conditions. This article aims to enhance the understanding of the tire-ice contact interaction through experimental studies of pneumatic tires traversing over smooth ice. An experimental design has been formulated that provides insight into the effect of operational parameters, specifically general tire tread type, slip ratio, normal load, inflation pressure, ice surface temperature, and traction performance. The temperature distribution in the contact patch is recorded using a novel method based on thermocouples embedded in the contact patch. The drawbar pull is also measured at different conditions of normal load, inflation pressure, and ice temperatures. The measurements were conducted using the Terramechanics Rig at the Advanced Vehicle Dynamics Laboratory. This indoor single-wheel equipment allows repeatable testing under well-controlled conditions. The data measured indicates that, with the appropriate tread design, the wheel is able to provide a higher drawbar pull on smooth ice. With an increase in ice surface temperature, a wet film is observed, which ultimately leads to a significant decrease in traction performance. 

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2. Deep learning closure models for large-eddy simulation of flows around bluff bodies

   https://doi.org/10.1017/jfm.2023.446  

   Sirignano, Justin; MacArt, Jonathan F. (July 2023, Journal of Fluid Mechanics)

   Near-wall flow simulation remains a central challenge in aerodynamics modelling: Reynolds-averaged Navier–Stokes predictions of separated flows are often inaccurate, and large-eddy simulation (LES) can require prohibitively small near-wall mesh sizes. A deep learning (DL) closure model for LES is developed by introducing untrained neural networks into the governing equations and training in situ for incompressible flows around rectangular prisms at moderate Reynolds numbers. The DL-LES models are trained using adjoint partial differential equation (PDE) optimization methods to match, as closely as possible, direct numerical simulation (DNS) data. They are then evaluated out-of-sample – for aspect ratios, Reynolds numbers and bluff-body geometries not included in the training data – and compared with standard LES models. The DL-LES models outperform these models and are able to achieve accurate LES predictions on a relatively coarse mesh (downsampled from the DNS mesh by factors of four or eight in each Cartesian direction). We study the accuracy of the DL-LES model for predicting the drag coefficient, near-wall and far-field mean flow, and resolved Reynolds stress. A crucial challenge is that the LES quantities of interest are the steady-state flow statistics; for example, a time-averaged velocity component $$\langle {u}_i\rangle (x) = \lim _{t \rightarrow \infty } ({1}/{t}) \int _0^t u_i(s,x)\, {\rm d}s$$ . Calculating the steady-state flow statistics therefore requires simulating the DL-LES equations over a large number of flow times through the domain. It is a non-trivial question whether an unsteady PDE model with a functional form defined by a deep neural network can remain stable and accurate on $$t \in [0, \infty )$$ , especially when trained over comparatively short time intervals. Our results demonstrate that the DL-LES models are accurate and stable over long time horizons, which enables the estimation of the steady-state mean velocity, fluctuations and drag coefficient of turbulent flows around bluff bodies relevant to aerodynamics applications. 

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3. Modeling Underwater Propulsion of a Helical Drive Using Computational Fluid Dynamics for an Amphibious Rover

   https://doi.org/10.1115/IMECE2023-113954  

   Donohue, Brigid; Beknalkar, Sumedh; Bishop, Riley; Bryant, Matthew; Mazzoleni, Andre (October 2023, American Society of Mechanical Engineers)

   Abstract The Multi-terrain Amphibious ARCtic explOrer (MAARCO) rover is an amphibious arctic rover designed to explore arctic regions in otherwise unsafe or restricted environments. The MAARCO rover consists of a propulsion system with two helical drives made up of hollow cylinder ballasts wrapped in auger or screw shaped blades that provide thrust to propel the vehicle as the drives rotate. Computational fluid dynamic methods provide a better understanding of the helical drives properties effects on hydrodynamic forces. In this paper, the computational fluid dynamic simulations are performed in ANSYS Fluent to observe the hydrodynamic properties of a helical drive. The drag and thrust on the helical drives are simulated for various helical drives with different blade heights and pitch lengths to determine general trends and characteristics of helical drives in water to optimize the vehicle’s abilities to navigate underwater. The helical drive drag is simulated using bluff body drag simulations with a prescribed velocity. The helical drive thrust is simulated using a multi-reference frame (MRF) mesh model with a frame motion replicate flow rotating around a stationary helical drive at a prescribed angular velocity. A convergence study was conducted to test different meshes and turbulence models to determine the most accurate drag and thrust simulation methods. The results demonstrate the effects the blade height and pitch length have on the helical drive thrust and drag properties, while maintaining a constant ballast diameter. From these results a helical drive design can be determined to optimize the net force and therefore the overall vehicle performance. 

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4. A direct method for generating quadratic curvilinear tetrahedral meshes using an advancing front approach

   https://doi.org/10.5281/zenodo.5559211  

   Mohammadi, Fariba; Shontz, Suzanne M. (October 2021, Proc. of the 29th International Meshing Roundtable)

   Computational modeling and simulation of real-world problems, e.g., various applications in the automotive, aerospace, and biomedical industries, often involve geometric objects which are bounded by curved surfaces. The geometric modeling of such objects can be performed via high-order meshes. Such a mesh, when paired with a high-order partial differential equation (PDE) solver, can realize more accurate solution results with a decreased number of mesh elements (in comparison to a low-order mesh). There are several types of high-order mesh generation approaches, such as direct methods, a posteriori methods, and isogeometric analysis (IGA)-based spline modeling approaches. In this paper, we propose a direct, high-order, curvilinear tetrahedral mesh generation method using an advancing front technique. After generating the mesh, we apply mesh optimization to improve the quality and to take advantage of the degrees of freedom available in the initially straight-sided quadratic elements. Our method aims to generate high-quality tetrahedral mesh elements from various types of boundary representations including the cases where no computer-aided design files are available. Such a method is essential, for example, for generating meshes for various biomedical models where the boundary representation is obtained from medical images instead of CAD files. We present several numerical examples of second-order tetrahedral meshes generated using our method based on input triangular surface meshes. 

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5. Investigation of Material Transport Through Triply Periodic Minimal Surface (TPMS) Bone Scaffolds Using Computational Fluid Dynamics (CFD)

   https://doi.org/10.1115/MSEC2024-123878  

   Coburn, Brandon; Salary, Roozbeh “Ross” (June 2024, American Society of Mechanical Engineers (ASME))

   Abstract Cell-laden, scaffold-based tissue engineering methods have been successfully utilized for the treatment of bone fractures and diseases, caused by factors such as trauma, tumors, congenital anomalies, and aging. In such methods, the rate of scaffold biodegradation, transport of nutrients and growth factors, as well as removal of cell metabolic wastes at the site of injury are critical fluid-dynamics factors, affecting cell proliferation and ultimately tissue regeneration. Therefore, there is a critical need to identify the underlying material transport mechanisms and factors associated with cell-seeded, scaffold-based bone tissue engineering. The overarching goal of this study is to contribute to patient-specific, clinical treatment of bone pathology. The overall objective of the work is to establish computational fluid dynamics (CFD) models to identify: (i) the consequential mechanisms behind internal and external material transport through/over porous bone scaffolds and (ii) optimal triply periodic minimal surface (TPMS) scaffold designs toward cell-laden bone fracture treatment. In this study, 10 internal-flow and 10 external-flow CFD models were established using ANSYS, correspondingly based on 10 single-unit TPMS bone scaffold designs, where the geometry of each design was parametrically created using Rhinoceros 3D software. The influence of several design parameters, such as surface representation iteration, merged toggle iso value, and wall thickness, on geometry accuracy as well as computational time, was investigated in order to obtain computationally efficient and accurate CFD models. The fluid properties (such as density and dynamic viscosity) as well as the boundary conditions (such as no-slip condition, inlet flow velocity, and pressure outlet) of the CFD models were set based on clinical/research values reported in the literature as well as according to the fundamentals of internal/external Newtonian flow modeling. Several fluid characteristics, including flow velocity, flow pressure, and wall shear stress, were analyzed to observe material transport internally through and externally over the TPMS scaffold designs. Regarding the internal flow CFD modeling, it was observed that “P.W. Hybrid” (i.e., Design #7) had the highest-pressure output, with “Neovius” (i.e., Design #1) following second to it. These two designs have a relatively flatter surface area. In addition, “Schwarz P” (i.e., Design #2) was the lowest pressure output of all 10 TPMS designs. “Neovius” and “Schwarz P” had the highest and lowest values of wall shear stress. Besides, the velocity streamlines analysis showed an increase in velocity along the curved sections of the scaffolds’ geometry. Regarding the external flow CFD modeling, it was observed that “Neovius” yielded the highest-pressure output within the inlet section, which contains the area of the highest-pressure location. Furthermore, “Diamond” (i.e., Design #8) displayed having the highest values of wall shear stress due to the results of fluid interaction that accrues with complex curved structures. Also, when we look at designs like “Schwarz G”, the depiction of turbulent motion can be seen along the internal curved sections of the structure. As the external velocity streamlines decrease within the inner channels of the designs, this will lead to an increased pressure buildup due to the intrinsic interactions between the fluid with the walls. Overall, the outcomes of this study pave the way for optimal design and fabrication of complex, bone-like tissues with desired material transport properties for cell-laden, scaffold-based treatment of bone fractures. 

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