Variable Geometry Research Articles

Feasibility Study of a Nonlinear Wave Energy Converter With Variable Buoy Geometry

Feasibility Study of a Nonlinear Wave Energy Converter With Variable Buoy Geometry

Crafting copper complexes of variable nuclearity and coordination geometry through solvent tailoring: Unveiling novel structure, magnetic insight and computational marvels

The solvent tuning technique has been employed to synthesize two new copper complexes [Cu3(L)(NO3)2(DMSO)4] (1) and [Cu6(L)2(NO3)2(DMF)4(H2O)2(µ-HCOO)2] (2) which display variable nuclearity and coordination geometries. The synthesized complexes further characterized by using spectroscopic, single crystal and powder X-ray diffraction techniques. Supramolecular assembly of the complexes in the solid state is stabilized by a range of intermolecular interactions including O-H⋅⋅⋅O, C-H⋅⋅⋅O, π⋅⋅⋅π and C-H⋅⋅⋅π. Magnetic susceptibility measurements (χT vs T curves) over a wide range of temperature indicate dominant antiferromagnetic interactions between the Cu(II) centres in both complexes, while DFT calculations reveal the nature and amplitude of the main magnetic interactions. Theoretical study further shows strong antiferromagnetic interactions inside the trimeric units of both complexes as well as both weak ferro- and antiferromagnetic interactions among the trimers. The so-obtained J values have been employed to fit the χT vs T curves, using simplified spin models which correctly reproduce the main features of the experimental curves. Thus, this combined experimental and theoretical study provides insight into the magnetic structure of the copper complexes.

An efficient multi-fidelity simulation approach for performance prediction of adaptive cycle engines

Adaptive cycle engine (ACE) with multi-stream, modulated bypass ratio, and high-thrust/high-efficiency mode provides the capability of multiple mission adaptation for the next-generation aviation propulsion system. The low-fidelity zero-dimensional (0D) performance simulation method is commonly adopted in conventional engines such as turbofan and turbojet. However, it is hard to accommodate well to ACE with complex variable geometry schemes and strong interaction between components. This paper presents an efficient multi-fidelity simulation approach, often referred to as component zooming, for predicting the performance of ACE with the three-stream configuration. The one-dimensional (1D) mean-line models of the adaptive fan and low-pressure turbine (LPT) are integrated into the 0D ACE model by the iteratively-coupled method. The performance predicted by the multi-fidelity ACE models with different component zooming strategies is compared. The maximum deviations of thrust, specific fuel consumption, turbine inlet temperature, and bypass ratio between the 0D/1D Adaptive Fan/1D LPT coupled model and 0D ACE model are −10.8%, −4.4%, −5.4% (−75 K), and 1.6%, respectively, which are considerable and non-negligible for the application in the design stage of ACE. The computing time of all the multi-fidelity models is less than 2 minutes. The effects of the key aerodynamic parameters of the adaptive fan and LPT on the engine performance are also evaluated. The proposed approach provides a generic and efficient solution for multi-fidelity ACE performance prediction with acceptable computing resource requirements and time cost, which is applicable in the engine conceptual and preliminary design stage.

HCP-PIGN: Efficient heat conduction prediction by physics-informed graph convolutional neural network

This work proposes a novel surrogate model (noted as HCP-PIGN) combining two groups of neural networks: i.e., the physics-informed and the graph convolutional neural networks (noted as PINN and GCN). It aims to tackle the existing challenges: pixelated pre-processing of data and large amounts of training data. For predicting 2D steady-state heat conduction, the GCN acting as the prediction module, considering the interdependence between unstructured and neighboring nodes. The PINN serving as the physical constraint module, embeds governing equations into the neural network’s loss function. The HCP-PIGN model obtains precise predictions with diverse geometries and within milliseconds. The predictive performance of HCP-PIGN was further compared with three network structures: i.e., the physics-informed fully connected neural network (noted as FNN), purely data-driven based FNN, and GCN. The results indicate that HCP-PIGN has the lowest error of temperature field predictions, which are below 3 % and 1.3 % for the max and mean relative errors, respectively. The improvements of 28.1% and 34.6% in accuracy are achieved over the pure data-driven GCN, and the physics-driven FNN, respectively. Therefore, the proposed HCP-PIGN model improves the physical prior knowledge and model’s adaptabilities to geometry variations, resulting in superior performances.

Enhancing sustainable food packaging design: A machine learning approach to predict ventilated corrugated paperboard strength

In the food packaging industry, ventilated corrugated paperboard boxes are crucial for sustainable transport of fresh products. While these boxes' ventilation holes advance air circulation, they also impact the material's compression or buckling strength. Variations in hole geometry and location affecting this strength are explored, considering the composite material, multi-layered structure. Traditional mechanical analyses, which often require simplifications, may not fully capture this complexity, leading to less accurate predictions of the paperboard's strength. To address these challenges, a machine learning (ML) approach was utilized, employing the Light Gradient Boosting Machine (LGBM) to develop a predictive model for the buckling strength of corrugated paperboard boxes with ventilation cutouts. This physics-informed ML model, trained on a compression dataset resulting from experimental tests for plates with a single cutout in three shapes and Finite Element Method (FEM) simulations for plates with various patterns of circular cutouts, provides highly accurate estimates of the plates' buckling strength. It achieved 91.7% accuracy on experimental data and 94.68% on FEM simulation data, showcasing its reliability. A new tool for predicting the buckling strength of corrugated paperboard is provided by this research, along with insights that can inform the design of more sustainable packaging solutions. Furthermore, the methodology and findings have broader applications, potentially benefiting sectors like aerospace and construction, where similar structural materials are used.

Greedy identification of latent dynamics from parametric flow data

Projection-based reduced-order models (ROMs) play a crucial role in simplifying the complex dynamics of fluid systems. Such models are achieved by projecting the Navier-Stokes equations onto a lower-dimensional subspace while preserving essential dynamics. However, this approach requires prior knowledge of the underlying high-fidelity model, limiting its effectiveness when applied to black-box data. This article introduces a novel, non-intrusive, data-driven method–Greedy Identification of Latent Dynamics (GILD)–for constructing parametric fluid ROMs. Unlike traditional methods, GILD constructs models directly from data, without relying on specific high-fidelity model information. It also employs interpolation within the manifold R∗N×q/Oq to accommodate parameter variability. Numerical experiments on various fluid dynamics scenarios, including lid-driven cavity flow, flow past a cylinder with varying Reynolds number, and Ahmed body flow with variable geometry, demonstrate GILD’s robust performance across both training and unseen parameter values. GILD’s ability to accurately capture system dynamics and its adaptability to diverse data sources highlight its potential as a powerful tool for constructing parametric reduced-order models in an easy and general way for complex fluid dynamics and beyond.

Experimental study on backward erosion piping of a double-layer dike foundation under variable exit geometries

Double-layer dike foundation is composed of a weakly permeable overlying clay layer and a highly permeable underlying sand layer, which is one of the most common stratum types in dike engineering with the highest probability of catastrophic damage, and the main danger is backward erosion piping. Existing research on backward erosion piping of double-layer dike foundation has not fully considered the influence of the exit on the erosion process. Therefore, a self-designed test device is used to assess the influences of the size, position and type of different exits, and the circular exit is connected with the slot exit via the exit area to explore the critical identification conditions and the pipe development mechanism toward the upstream direction under different exit geometry conditions. The results show that both the local and global hydraulic gradients borne by the exit are inversely proportional to the exit area and are less notably affected by the location of the exit. The development process of slot exit pipes differs from that of circular exit pipes, and pipes are usually developed alternately at the two corners of the exit near the upstream end and then converge into one pipe. The average pipe depth and width are proportional to the exit size and the seepage length. With increasing average pipe area of the slot exit, pipes develop more rapidly after head enhancement, and the damage to the dike foundation increases.

An inline point-tracking approach for the real-time monitoring of the free-form bending process

In order to make free-form bending a process of choice for the manufacturing of structural components, a robust strategy for process monitoring is required. Although the technology is particularly suitable for the production of bending components with variable and complex geometry, fluctuations in the process conditions, as well as in the quality of the semi-finished products can results in geometrical deviations from the target geometry. Currently, the quality assessment of the bent components can be done only offline by random sampling, with a considerable time and cost effort. In this contribution, a real-time process monitoring is realised and applied to free-form bending for the first time. First of all, an inline strategy based on single-point tracking for the assessment of the geometry is investigated through an extensive numerical sensitivity analysis. Successively, the method is implemented experimentally and validated with real tests. Finally, a small-batch series of deviating components is produced, and the developed strategy is adopted to perform a real-time process monitoring. The study highlights the potential of an inline measurement strategy for the process monitoring in free-form bending, and its advantages compared to the current offline methods.

Entropy generation in newtonian vs non-newtonian nanofluid flow under vibration

Numerical investigation into the effects of vibration on heat transfer and entropy generation in Newtonian and Non-Newtonian nanofluid flows through pipes reveals enhanced heat transfer via intensified fluid agitation and improved particle dispersion. Thermal entropy generation analysis shows reduced irreversibility in vibrated flow, indicating improved flow mixing. Vibration enhances heat transfer by intensifying fluid agitation and promoting particle dispersion near the wall, resulting in a significantly more uniform temperature distribution along the pipe, approximately 100 times more than steady-state flow. This study underscores vibration’s potential to optimize heat transfer and reduce entropy generation in nanofluid systems, emphasizing velocity and rheological impacts. Comparison of vibrated flow to steady-state flow for Newtonian and non-Newtonian fluids reveals significant improvements under vibration, particularly at lower Reynolds numbers where non-Newtonian fluids exhibit pronounced effects. Future research directions include exploring thermal radiation’s impact on entropy generation, analyzing different nanofluid compositions, and investigating varied boundary conditions and geometries to advance understanding in this field. This study provides valuable insights into the complex interplay among vibration, fluid dynamics, and heat transfer in nanofluid flows. Its findings have practical implications for optimizing thermal management systems in diverse engineering applications.

Theoretical uncovering of the chalcogen element regulated ESDPT behaviors for 2,5-bis(2-benzoxazolyl)-hydroquinone derivatives.

Inspired by the captivating allure of exquisitely regulated characteristics exhibited by 2-(2-hydroxyphenyl)-benzoxazole and its derivatives in the realms of photochemistry and photophysics, our current endeavor primarily revolves around delving into the intricacies of photo-induced excited state reactions for derivatives of 2,5-bis(2-benzoxazolyl)-hydroquinone (BBHQ). Given the significant impact of chalcogen element doping, herein we predominantly focus on exploring the excited state behaviors of BBHQ-OO, BBHQ-SS, and BBHQ-SeSe fluorophores. Our simulations, resulting from variations in geometry and vertical excitation charge reorganization, reveal atomic-electronegativity-dependent hydrogen bonding interactions and charge recombination induced by photoexcitation that can significantly enhance the excited state intramolecular double proton transfer (ESDPT) reaction for BBHQ-OO, BBHQ-SS, and BBHQ-SeSe fluorophores. By constructing potential energy surfaces (PESs) and identifying transition states (TS), we unveil the ultrafast stepwise ESDPT mechanism due to the low potential barriers. Additionally, by employing heterosubstituted BBHQ-OS and BBHQ-OSe compounds, we rigorously validate the stepwise ESDPT mechanism regulated by chalcogen atomic electronegativity. We sincerely anticipate that the modulation of solvent polarity on excited state behaviors will pave the way for groundbreaking advancements in luminescent materials.

Additive Manufacturing of Architected Battery Components for Fast Charging Batteries

Fast charging batteries require facile Li+ ion transport through the porous electrodes. Tape casted electrodes typically demonstrate sluggish ion transport with the increase of layer thickness due to the limited porosity and high tortuosity. Architecting the electrodes to introduce fast ion transport highways is a promising strategy to enhance the power density with limited sacrifice of energy density. In this work, we report our recent efforts in additive manufacturing (AM) of architected electrodes and separators. Two AM techniques are employed for the fabrication of electrodes and separators, including direct ink writing and projection micro stereolithography, respectively. A variety of geometries are designed, fabricated, and compared for the fast charging capabilities. The results show that proper design of the cell configuration and choice of material chemistry are critical to show the benefits from AM. The limitations, and future directions of AM application in batteries will also be discussed.

Evaluation of Nickel-Iron (Oxy)Hydroxide Catalysts in Varied Electrode Geometries Under Industrially Realistic Conditions for Enhanced Alkaline Water Electrolysis

In pursuit of realizing a hydrogen-based economy, the challenge lies in enhancing the efficiency of crucial water electrolysis technologies. Alkaline water electrolysis (AWE) holds promise as an ecologically friendly method for H2 production, primarily due to its capacity for employing non-precious metals as electrocatalysts, in contrast to more costly options which can only switch to noble metals such as ruthenium or iridium. However, this technology faces a significant challenge arising from the sluggish kinetics of the oxygen evolution reaction (OER), which constrains the overall efficiency. [1] Furthermore, the limited stability of many OER catalysts alternatives developed at lab scale and less harsh conditions poses a substantial obstacle to the commercialization of this technology.[2] To overcome this issue, the development of highly active OER catalysts that maintain their activity under industrial conditions is of major importance explaining the growing attention that this topic is gaining in literature. Nevertheless, most of the reported systems are tested at low current densities under easily manageable conditions in the laboratory (≤ 100 mA cm-2, 1 M KOH, RT). Without disagreeing that this approach is necessary to identify promising options among the numerous catalyst systems, it is not sufficient when competing with existing technologies that already achieve high current densities. In this context, we investigate a low-cost, abundantly available non-precious metal based OER-catalyst under industrially relevant conditions (≤ 1.2 A cm-2, 30 wt.-% KOH, 80 °C). The synthesis is based on a multistep galvanic deposition method introduced by Wu et al [3], receiving a highly porous NiFeOOH catalyst. Addressing the complexity of the multiphase process of AWE that involves gas-liquid-solid interfaces, the catalyst system is transferred to numerous nickel electrodes with different 3D geometries (plain woven mesh, knitted mesh, expanded metal mesh) for the investigation of their impact on bubble formation and detachment. To further optimize the catalyst system a variation of the iron content with respect to its’ molar fraction relatively to nickel has been carried out from x(Fe)=0 to x(Fe)=0.75, which after synthesis were first examined using SEM and EDS. The structure can be considered as pompom-shaped particles, forming a porous framework, as can be seen exemplarily in Figure 1a) on a knitted nickel-wire mesh coated with NiFeOOH. Observing variations in iron content, it becomes evident that a higher iron content results in larger particle sizes, thereby altering pore sizes. However, the surface morphologies of each catalyst until a molar fraction x(Fe)=0.5 is reached, can be considered as very similar. When comparing the SEM images of the mesh with a molar fraction x(Fe)=0.75 to the other molar fractions, a secondary phase becomes apparent on the surface. Notably, the porous structure persists within the crystalline phase. It could be shown by EDS that the target values for the molar fractions set in the galvanic bath and the actual molar fractions nearly show no deviations. Further, a uniform dispersion of Ni, Fe and O is observed.The electrochemical investigation of the prepared catalysts was done in a 3-electrode PTFE beaker cell setup in 30 wt % KOH and at 80 °C. The measurement protocol we introduce consists of galvanostatic steps between 0-1.2 A cm-2. The stability is tested chronopotentiometrically as well by maintaining a current density of 500 mA cm-2 for 100 h. It becomes apparent that no optimum is reached in the tested range of iron content and that the potential continues to drop as the molar fraction of Fe increases, achieving the lowest potential with 1.38 V vs. RHE at 50 mA cm-2 and 1.42 V vs. RHE at 750 mA cm-2 with x(Fe)=0.75. It could be further proved that the NiFeOOH catalyst with x(Fe)=0.75 is a highly stable catalyst, resulting in a potential increase of 27 mV over 100 h of testing at 500 mA cm-2, making it to an appropriate OER catalyst for the application in the AWE. The highly porous structure is maintained after testing, proved by SEM imaging. The iron contents after testing are also very similar, which means that there is almost no iron loss in the coating. For the different tested electrode geometries coated with NiFeOOH (x(Fe)=0.5), similar activity is observed, shown in Figure 1b) with a ranging potential between 1.38 V to 1.41 V vs. RHE at 50 mA cm-2 and 1.42 V to 1.45 V vs. RHE at 750 mA cm-2.[1] a) Cao et al., Energy Environ. Sci. 2012 b) Zhao et al., ACS Chemical Reviews 2023.[2] Zeng et al., Journal of Energy Chemistry 2022.[3] Wu et al., Applied Surface Science 2021. Figure 1

Quasiparticle Dynamics in a Superconducting Qubit Irradiated by a Localized Infrared Source.

A known source of decoherence in superconducting qubits is the presence of broken Cooper pairs, or quasiparticles. These can be generated by high-energy radiation, either present in the environment or purposefully introduced, as in the case of some hybrid quantum devices. Here, we systematically study the properties of a transmon qubit under illumination by focused infrared radiation with various powers, durations, and spatial locations. Despite the high energy of incident photons, our observations agree well with a model of low-energy quasiparticle dynamics dominated by trapping. This technique can be used for understanding and potentially mitigating the effects of high-energy radiation on superconducting circuits with a variety of geometries and materials.

A Pseudo-Spectral Method for Wall Shear Stress Estimation from Doppler Ultrasound Imaging in Coronary Arteries.

The Wall Shear Stress (WSS) is the component tangential to the boundary of the normal stress tensor in an incompressible fluid, and it has been recognized as a quantity of primary importance in predicting possible adverse events in cardiovascular diseases, in general, and in coronary diseases, in particular. The quantification of the WSS in patient-specific settings can be achieved by performing a Computational Fluid Dynamics (CFD) analysis based on patient geometry, or it can be retrieved by a numerical approximation based on blood flow velocity data, e.g., ultrasound (US) Doppler measurements. This paper presents a novel method for WSS quantification from 2D vector Doppler measurements. Images were obtained through unfocused plane waves and transverse oscillation to acquire both in-plane velocity components. These velocity components were processed using pseudo-spectral differentiation techniques based on Fourier approximations of the derivatives to compute the WSS. Our Pseudo-Spectral Method (PSM) is tested in two vessel phantoms, straight and stenotic, where a steady flow of 15mL/min is applied. The method is successfully validated against CFD simulations and compared against current techniques based on the assumption of a parabolic velocity profile. The PSM accurately detected Wall Shear Stress (WSS) variations in geometries differing from straight cylinders, and is less sensitive to measurement noise. In particular, when using synthetic data (noise free, e.g., generated by CFD) on cylindrical geometries, the Poiseuille-based methods and PSM have comparable accuracy; on the contrary, when using the data retrieved from US measures, the average error of the WSS obtained with the PSM turned out to be 3 to 9 times smaller than that obtained by state-of-the-art methods. The pseudo-spectral approach allows controlling the approximation errors in the presence of noisy data. This gives a more accurate alternative to the present standard and a less computationally expensive choice compared to CFD, which also requires high-quality data to reconstruct the vessel geometry.

Force-displacement relation for lumped plasticity model of compact square concrete-filled steel tube columns

Integration of steel and concrete benefits in square concrete-filled tube columns (SCFTs) makes them be utilized extensively worldwide. To evaluate the seismic behavior of special moment frames having SCFT columns, an accurate macro-modeling tool with little complexity is necessary. Although the fiber section can consider both linear and nonlinear behavior of SCFT, it fails to allow for deterioration, which is vital in assessing a seismic resisting system. This study proposes a new deterioration tool based on the Ibarra-Krawinkler model for compact SCFT columns using artificial neural networks (ANNs) through numerical simulation. Initially, 96 specimens with variations in geometry, material, and axial loading conditions were simulated in ABAQUS and then were analyzed using displacement control loading protocol to obtain hysteretic curves, which were used to prepare the data sets for training, testing, and validating the neural networks. Finally, the performance of ANN models was evaluated by statistical relations such as regression and mean square error. Through FEMA 356 and Ibarra-Krawinkler approaches, parameters defining linear and nonlinear phases of SCFTs behavior were derived. By employing efficiently trained neural networks, equations predicting the parameters of the deterioration model were established, and the obtained results show the accuracy and efficiency of the proposed model.

Towards Design Optimization of Compliant Mechanisms: A Hybrid Pseudo-Rigid-Body Model-Finite Element Method Approach and an Accurate Empirical Compliance Equation for Circular Flexure Hinges.

Innovative designs such as morphing wings and terrain adaptive landing systems are examples of biomimicry and innovations inspired by nature, which are actively being investigated by aerospace designers. Morphing wing designs based on Variable Geometry Truss Manipulators (VGTMs) and articulated helicopter robotic landing gear (RLG) have drawn a great deal of attention from industry. Compliant mechanisms have become increasingly popular due to their advantages over conventional rigid-body systems, and the research team led by the second author at Toronto Metropolitan University (TMU) has set their long-term goal to be exploiting these systems in the above aerospace applications. To gain a deeper insight into the design and optimization of compliant mechanisms and their potential application as alternatives to VGTM and RLG systems, this study conducted a thorough analysis of the design of flexible hinges, and single-, four-, and multi-bar configurations as a part of more complex, flexible mechanisms. The investigation highlighted the flexibility and compliance of mechanisms incorporating circular flexure hinges (CFHs), showcasing their capacity to withstand forces and moments. Despite a discrepancy between the results obtained from previously published Pseudo-Rigid-Body Model (PRBM) equations and FEM-based analyses, the mechanisms exhibited predictable linear behavior and acceptable fatigue testing results, affirming their suitability for diverse applications. While including additional linkages perpendicular to the applied force direction in a compliant mechanism with N vertical linkages led to improved factors of safety, the associated increase in system weight necessitates careful consideration. It is shown herein that, in this case, adding one vertical bar increased the safety factor by 100N percent. The present study also addressed solutions for the precise modeling of CFHs through the derivation of an empirical polynomial torsional stiffness/compliance equation related to geometric dimensions and material properties. The effectiveness of the presented empirical polynomial compliance equation was validated against FEA results, revealing a generally accurate prediction with an average error of 1.74%. It is expected that the present investigation will open new avenues to higher precision in the design of CFHs, ensuring reliability and efficiency in various practical applications, and enhancing the optimization design of compliant mechanisms comprised of such hinges. A specific focus was put on ABS plastic and aluminum alloy 7075, as they are the materials of choice for non-load-bearing and load-bearing structural components, respectively.

Enhancing mechanical performance in SLS-printed PA12-slate composites through amino-silane treatment of mineral waste

The SLS additive manufacturing industry enables the development of products for diverse applications with distinct properties due to its excellent surface finish and ability to create varied part geometries, but it consumes high-performance materials with high acquisition costs. An extensive quarrying of stone leads to the accumulation of mineral residues, posing environmental hazards by contaminating soil and water when disposed of in landfills. The primary objective of the study was to incorporate mineral waste into the SLS technique and investigate the influence of its addition, along with a silane-based chemical treatment, on the mechanical performance of polymer-mineral composites (PA12-slate). Additionally, the feasibility of producing a highly loaded printed prototype, employing 50 wt% of mineral waste, was examined. Samples of PA12, PA12 blended with 50 wt% slate waste, and slate waste treated with silane underwent fabrication via selective laser sintering (SLS) and subsequent mechanical characterization, including tensile, flexural, and compressive tests. Additionally, the samples underwent accelerated aging using a QUV weathering tester, followed by mechanical characterization. The geometric accuracy, stability, and processing feasibility of these formulations were evaluated through SLS-printed composite prototypes utilizing PA12_50Sla_Si. It was found that the addition of 50% of slate to the PA12 presented mechanical properties decreasing compared to the printed PA12 only. However, an increase was verified when using silane-induced mineral bonding. The incorporation of mineral agents and silane enhanced the resistance of PA12 to aging. However, after aging, both tensile and flexural strength decreased across all printed samples. Nonetheless, this study showcased the feasibility of producing complex PA12-slate waste specimens containing up to 50 wt% of mineral waste using the SLS printing technique. Therefore, SLS presents itself as a viable means of adding value to this mineral waste.

Comparison of vortex formation in expanded and curved aortic sinuses: The effect of sinus curvature

Aortic valve disease is a significant health issue. Research on the effects of aortic sinus geometry and aortic valve motion during a heartbeat is imperative, as numerous valvular diseases are associated with blood flow near the heart valve. Changes in the internal space of the sinus and the formation of blood clots have been reported after the implantation of a transcatheter aortic valve, an artificial heart valve. Although the sinus shape and leaflet motion significantly affect the hemodynamic characteristics and platelet aggregation, the blood flow behaviors near the heart valve associated with clot formation have not been fully elucidated. This study conducted in vitro experiments to investigate the flow behavior near an aortic sinus model based on the sinus shape. Additionally, hemodynamic changes associated with variations in the aortic sinus geometry and leaflet length within an aortic sinus model under pulsatile-flow conditions were elucidated. In a curved sinus model, a large single vortex formed near the center of the sinus region. Meanwhile, the center of the vortex in the sinus region shifted during a cycle in an expanded sinus model. Furthermore, a small vortex remained where the leaflet was initiated, thus increasing the likelihood of thrombus formation. Thus, the curved sinus model is advantageous for preventing material accumulation by maintaining a large vortex structure. The experimental results confirmed that the aortic sinus shape and leaflet length affect the likelihood of thrombus formation inside the aortic sinus.

Optimizing crash box design to meet injury criteria: a protocol for accurate simulation and material selection

The design of a deformation element or crash box that meets a given injury criterion based on deceleration requires careful consideration of physical properties and space requirements. Variations in material yield stress or geometry can result in statistical variations in the injury criterion output. Optimizing the crash box to fulfil two different injury criteria and two different energy levels may require more space than initially specified. In this study, we propose a protocol where the crash box is collapsed, and force–displacement is fitted to an equation. This fit is carried out with just two simulations and compared to 30 possible scenarios, obtaining a maximum error of 38.9%. With this initial fit, the appropriate thickness and yield stress can be chosen to perform crashes with two energy levels and monitor four injury values. With the ideal yield stress and sheet metal thickness, we introduce real statistical distributions using Monte Carlo design to perform 200 simulations and obtain 400 injury values for each design proposal. This technique ensures that the design will meet injury requirements for any possible combination of thickness and yield stress accepted by quality inspection. If only one simulation is performed, all designs meet the requirements, but only the last proposed design decreased the average injury to 9.2 g with a standard deviation of 2.68 g and a maximum value of 14.4 g, which is less than the required 15 g. This technique minimizes the risk of finding combinations of yield stress and thickness that produce an undesirable injury criterion.

NeRF-Art: Text-Driven Neural Radiance Fields Stylization.

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially in simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency.