Shape Deformation Research Articles

On the Water Jet Quality at Part-Load Operation of Pelton turbines

Abstract Pelton turbines with multiple nozzles are characterised by their excellent performance over a wide operating range of mass flow rates. Thus, Pelton turbine manifolds must also be designed to deliver good flow quality to the nozzles at part-load operating conditions to accomplish the expectations. A reduced number of nozzles are opened at low mass flow rate operating conditions, where the choice of the open nozzles depends on the water jet quality generated at the individual nozzles. We investigate the flow phenomena occurring in the manifold with the operation of a reduced nozzle number employing numerical simulations to analyse beneficial nozzle opening selections. The turbulent two-phase flow is predicted by the Volume-Of-Fluid (VOF) approach and the k–ω SST turbulence closure model. Different nozzle operating scenarios are simulated, and the implications on the water jet quality, i.e. deviation and shape deformation, are reported. The results show that the free water jet quality criteria can change drastically at individual nozzle operating conditions because of the velocity changes at the branch line junctions.

Estimation of waveform deformation with the matched filter

In many particle physics experiments the data processing is based on the analysis of the digitized waveforms provided by the detector. While the waveform amplitude is usually correlated to the event energy, the shape may carry useful information for event discrimination. Thanks to the high signal to noise ratio they provide, matched filters are often applied. Their original design is however intended for the estimation of the waveform amplitude only. In this work we introduce an analytical extension of the original matched filter for the estimation of a possible shape deformation with respect to a reference template. The new filter is validated on simulations and, with respect to shape parameters calculated on unfiltered waveforms or derived from the original matched filter, it improves the discrimination capability by at least a factor 2 both at low and high signal to noise ratios, making it applicable to the data of several experiments.

Shape-Memory Polymers Based on Carbon Nanotube Composites.

For the past two decades, researchers have been exploring the potential benefits of combining shape-memory polymers (SMP) with carbon nanotubes (CNT). By incorporating CNT as reinforcement in SMP, they have aimed to enhance the mechanical properties and improve shape fixity. However, the remarkable intrinsic properties of CNT have also opened up new paths for actuation mechanisms, including electro- and photo-thermal responses. This opens up possibilities for developing soft actuators that could lead to technological advancements in areas such as tissue engineering and soft robotics. SMP/CNT composites offer numerous advantages, including fast actuation, remote control, performance in challenging environments, complex shape deformations, and multifunctionality. This review provides an in-depth overview of the research conducted over the past few years on the production of SMP/CNT composites with both thermoset and thermoplastic matrices, with a focus on the unique contributions of CNT to the nanocomposite's response to external stimuli.

93‐1: Invited Paper: Meta‐Elastomer for Biaxially Stretchable Displays Without Image Distortion

Stretchable displays are garnering attention due to their ability to undergo reversible shape deformation. However, due to Poisson's ratio, image distortion issues caused by undesired substrate deformations during stretching remain challenging. Here, we propose meta‐elastomers with biaxially zero Poisson's ratio for distortion‐free stretchable displays. To program the Poisson's ratio according to biaxial stretching, elastomeric mechanical metamaterial (MM) frames with a negative Poisson's ratio are integrated into an elastomer matrix. By leveraging competitive interactions between the elastomeric MM frame and elastomer matrix deformation, we have successfully implemented the biaxial near‐zero Poisson's ratio substrates.

Assessment of intracranial aneurysm neck deformation after contour deployment.

The contour neurovascular system (CNS) is a novel device to treat intracranial wide-necked bifurcation aneurysms, with few studies assessing its long-term effects. Particularly its impact on aneurysm morphology has not been explored yet. We present a preliminary study to explore this impact for the first time, focusing on the neck curve and ostium of the aneurysm. We investigated seven aneurysms treated with the CNS to assess ostium deformation after CNS deployment by comparing models extracted from in vivo medical pre-treatment and follow-up scans via morphological analysis. Time between pre- and follow-up scans was ten months on average. Size and shape indices like area, neck diameter, ellipticity index, undulation index, and more were assessed. Ostium size was reduced after treatment. On average, ostium area was reduced at a rate of 0.58 (± 4.88)mm2 per year, from 15.52 (± 3.51)mm2 to 13.30 (± 2.27)mm2, and ostium width from 5.01 (± 0.54)mm to 4.49 (± 0.45)mm, with an average reduction of 0.59 (± 0.87)mm. This shrinking positively correlated with time passing. Shape deformation was low, though notably mean ellipticity index was reduced by 0.06 (± 0.15) on average, indicating ostia were less elongated after treatment. We interpret the shrinking of the ostium as part of the healing process. Shape changes were found to be small enough to conclude no shape deformation of the ostium from CNS deployment, but the analysis of more cases with more parameters and information is necessary.

Fish Species Detection Using Deep Learning

Abstract: This research explores the application of deep learning techniques for fish species detection in underwater environments. convolutional neural networks (CNNs) trained on extensive datasets, the study aims to enhance the accuracy and efficiency of species identification. The proposed model demon- strates promising results in differentiating diverse fish species, contributing to advancements in aquatic ecology monitoring and biodiversity conservation. The integration of deep learning in fish species detection holds potential for improving our understanding of underwater ecosystems and supporting sustainable fisheries management. The relative abundance of fish pieces in their habitats on a regular basis and keeping an eye on population fluctuations, this are a crucial task for marine scientists andconservationists diverse automatic computer based fish sample methods have been demonstrated in underwater photos and videos as alternatives to time consuming hand sampling there isn’t however a perfect method for automatically detecting fish and classifying the species this is mostly due to the difficulties in producing clear underwater images and videos which include environmental fluctuations in lightning fish camouflage Dynamicbackdrops murky water low resolution shape deformations of moving fish.

Self-propulsion of a periodically forced shape-deforming submillimeter gas bubble.

The self-propulsion (translational instability) of a gas bubble in a liquid undergoing parametrically induced axisymmetric shape distortion due to being forced by a temporally sinusoidal, spatially constant acoustic field is investigated. Employing a model which accounts for the nonlinear coupling between the spherical oscillations, the axial translation and shape deformation of the bubble, the parametric excitement of two neighboring shape modes by the fundamental resonance, at the same driving frequency is studied. It is shown that provided pertinent driving pressure threshold values are exceeded, the respective shape modes are excited on different timescales. The growth of the shape mode on the faster timescale saturates giving rise to sustained constant amplitude oscillations, while the growth of the shape mode on the slower timescale is both modulated and unbounded. During the growth of the second shape mode, growing, oscillatory bubble translation is also observed.

Observation of dense collisional soliton complexes in a two-component Bose-Einstein condensate

Solitons are nonlinear solitary waves which maintain their shape over time and through collisions, occurring in a variety of nonlinear media from plasmas to optics. We present an experimental and theoretical study of hydrodynamic phenomena in a two-component atomic Bose-Einstein condensate where a soliton array emerges from the imprinting of a periodic spin pattern by a microwave pulse-based winding technique. We observe the ensuing dynamics which include shape deformations, the emergence of dark-antidark solitons, apparent spatial frequency tripling, and decay and revival of contrast related to soliton collisions. For the densest arrays, we obtain soliton complexes where solitons undergo continued collisions for long evolution times providing an avenue towards the investigation of soliton gases in atomic condensates.

Ultra‐Fast Shape‐Deformation and Highly‐Sensitive Detection of 4D Printed Electromagnetic Architectures

Abstract4D printing is widely used in soft actuators, flexible electronic devices, and soft robotics. However, existing 4D printed architectures suffer from drawbacks such as long shape‐deformation response time or low sensitivity to external stimuli, which greatly limit their applications. To address these shortcomings, 4D printed electromagnetic architectures (EMAs) with both ultra‐fast shape‐deformation and highly‐sensitive detection capabilities have been proposed in this study. The EMA is composed of a 4D printed polymeric scaffold, along with magnetic and conductive parts. Experiments show that the EMA exhibits not only an ultra‐fast shape‐deformation response time as low as 0.007 s but also a high sensitivity to detect an external pressure down to 0.4 Pa, which is beyond records of all current 4D printing reports. Numerical simulation reveals the rapid shape‐deformation and sensing mechanism of EMAs. The effects of cell structures on the performances of EMAs are investigated by parameter optimization. Finally, a “sensing‐to‐deforming” system has been demonstrated, which can smartly distinguish objects by pressures and then trap or release them by the shape deformation. This work realized the effective coupling of ultra‐rapid shape‐deformation and ultra‐low stress sensing of 4D printed architectures and provided an effective method for integrated development of smart devices in deformation and sensing.

Vapor and Light Responsive Biocompatible Soft Actuator.

Bioinspired smart polymeric materials that undergo three-dimensional shape deformation in response to specific stimuli have gained significant attention in the field of soft robotics and intelligent devices. Despite the substantial advancements in soft robotics, there is a growing demand for the design of multistimuli-responsive soft actuators using a single layer of material due to its reduced complexity and ease of manufacturing and durability. Here, we report the actuation characteristics of a single-layer, dual-responsive soft actuator that overcomes the commonly encountered delamination issues often associated with bilayer systems by incorporating PEDOT:PSS with cassava starch. This soft actuator exhibits deformations in response to various solvent vapors, such as water, alcohol, and acetone. Remarkably, it demonstrates opposite deformations upon exposure to water and alcohol vapors. Additionally, the actuator responds to light triggers and folds upon exposure to sunlight and infrared light. The degree of folding can be precisely controlled by adjusting the intensity of the light source. Furthermore, the periodic geometric patterns imposed on the surface of the actuator provide an additional handle to control the bending axis. For proof of concept, we leverage the actuation capabilities of our actuator to showcase a range of potential applications, including its usage in wearable textiles, crawler robots, smart curtains, push-and-pull machines, and smart lifts.

Viscous Mechano‐Electric Response of Ferroelectric Nematic Liquid

AbstractDirect viscous mechano‐electric response is demonstrated for a room‐temperature ferroelectric nematic liquid, which combines large spontaneous electric polarization with 3D fluidity. The mechano‐electric transduction is observed in the frequency range 1 −200 Hz via a simple demonstrator device. The liquid is placed into a deformable container with electrodes and the electric current induced by both periodic and irregular actuation of the container is examined. The experiments reveal a rich interplay of several distinct viscous mechano‐electric phenomena, where both shape deformations and material flow cause changes in the electric polarization structure of a ferroelectric nematic liquid. The results show that the mechano‐electric features of the material promise a considerable applicative perspective spanning from sensitive tactile sensors to energy harvesting devices.

Development of smart materials with versatile bending capabilities using microcellular foaming process: influence of foaming and desorption time

Abstract Herein, a smart material with versatile bending capability is developed using a microcellular foaming process (MCPs). In contrast to previous hydrogel-based approaches, the bi-layered smart material is fabricated using typical thermoplastics, polyethylene terephthalate glycol (PETG) and polymethyl methacrylate (PMMA), to achieve shape deformation in response to thermal stimuli. Further, the theoretical model for bi-layered smart materials based on the modified Timoshenko’s model is employed to predict and comprehend this thermal response phenomenon. Due to the distinct foaming characteristics of the two polymers, a reversal in the bending direction is achieved by manipulating the foaming and desorption time. The length variation after foaming differs depending on the desorption time for each polymer. PMMA decreases in length after foaming, measuring 56.25 mm at a desorption time of 40 min and 53.16 mm at 80 min. On the other hand, PETG shows an increase in length after foaming, measuring 53.33 mm at 40 min and 58.25 mm at 80 min. Consequently, when the two polymers are bonded and foamed, bending occurs depending on the desorption time, and a reversal in the bending direction is observed at the critical desorption time of around 60 min. Based on this result, the folding direction of a five-leafed flower-shaped object is successfully altered under thermal stimuli. This innovative approach extends the category of smart materials beyond the hydrogels and showcases the potential of the MCPs for the creation of smart materials for various applications that require versatile shape changes in response to temperature.

Experiment-free multiscale simulation of residual deformation in non-crimp fabric composites

The effectiveness of multiscale modeling, consisting of quantum chemical reaction path calculations, molecular dynamics simulations, and microscopic and macroscopic finite element analysis, developed for the process-induced residual deformation of carbon fiber-reinforced plastic was experimentally evaluated. In this study, non-crimp fabric composites with an arbitrary thermoset resin were fabricated, and process-induced deformation was measured. Process-induced residual deformations due to cure shrinkage and thermal shrinkage of cross-ply laminates under conditions similar to those in the experiment were predicted using multiscale modeling. The predicted deformation shape and maximum deformation value are in good agreement with the fabrication experiments. Multiscale modeling can consider the combined factors that affect the deformation (i.e. size, elastic moduli, coefficient of thermal expansion, and fiber volume fraction) and can provide important knowledge regarding the development of high-performance composite structures and for stable manufacturing.

Electric-driven shape memory deformation control and verification of composite films based on PVA/WPU/SCFs

Based on the research of Polyvinyl alcohol (PVA)/Waterborne Polyurethane (WPU)/Short Carbon Fibers (SCFs) novel electrochemically shape memory composites, this study delved into an exploration of factors influencing the shape memory deformation of shape memory film samples when subjected to voltage stimulation. These main affecting factors encompassed sample thickness, activation voltage, and wire layout. Subsequently, based on the above conclusions, the viscoelastic constitutive model of PVA/WPU/SCFs composite was established based on Maxwell model and the deformation deflection of the composite film was predicted by shape memory recovery, and the electrothermal coupling simulation analysis and shape memory recovery and deformation simulation verification were carried out. Finally, based on the shape memory recovery performance of the composite film, a new reciprocating moving deformation structure was conceptualized, characterized by distinct cycles of motion, each analyzed individually.

Light- and heat-induced shape deformation polyurethane actuators that combine self-healing capabilities

Materials commonly experience physical damage during regular usage, and the implementation of self-healing materials can effectively repair this damage, thereby prolonging the lifespan of the materials. We have successfully synthesized a polyurethane material, S-PU, by introducing azobenzene and reversible disulfide bonds through co-polymerization. The S-PU film not only exhibits light-induced bending, heat-induced recovery ability, but also good self-healing properties. The disulfide bonds in S-PU accelerate the dynamic exchange as the polymer chains flow under heated conditions, enabling self-repairing without affecting the mechanical properties of the material. S-PU is a self-repairing material that can repair damage in a variety of ways. By combining dual-stimulus response characteristics with self-healing properties, this work provides a new idea for self-healing materials and opens more directions for the practical application of the material.

Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential.

The design of heterogeneous catalysts generally involves optimizing the reactivity descriptor of adsorption energy, which is inevitably governed by the structure of surface-active sites. A prerequisite for understanding the structure-properties relationship is the precise identification of real surface-active site structures, rather than relying on conceived structures derived from bulk alloy properties. However, it remains a formidable challenge due to the dynamic nature of nanoalloys during catalytic reactions and the lack of accurate and efficient interatomic potentials for simulations. Herein, a generalizable deep-learning potential for the Ag-Pd-F system is developed based on a dataset encompassing the bulk, surface, nanocluster, amorphous, and point defected configurations with diverse compositions to achieve a comprehensive description of interatomic interactions, facilitating precise prediction of adsorption energy, surface energy, formation energy, and diffusion energy barrier and is utilized to investigate the structural evolutions of AgPd nanoalloys during fluorination. The structural evolutions involve the inward diffusion of F, the outward diffusion of Ag in Ag@Pd nanoalloys, the formation of surface AgFx species in mixed and Janus AgPd nanoalloys, and the shape deformation from cuboctahedron to sphere in Ag and Pd@Ag nanoalloys. Moreover, the effects of atomic diffusion and dislocation formation and migration on the reconstructing pathway of nanoalloys are highlighted. It is demonstrated that the stress relaxation upon F adsorption serves as the intrinsic driving factor governing the surface reconstruction of AgPd nanoalloys.

Experimental Investigations on the Cavitation Bubble Dynamics near the Boundary of a Narrow Gap

Cavitation bubbles near narrow gaps widely exist within microfluidic control devices. In the present paper, a laser-induced cavitation bubble is arranged in a narrow gap composed of two parallel plates. The inception position of the bubble is set to be at the same distance from the two plates so that the dynamic behaviors of the bubble are symmetrical. The collapse and rebound dynamics of the bubble near the boundary of a narrow gap are investigated through high-speed photography. The bubble behaviors (e.g., shape deformation, translational movement, and jet characteristics) are analyzed while considering the influence of the dimensionless distance between the bubble and the boundary and the dimensionless gap width. The principal findings include the following: (1) When the dimensionless distance is small, a violent jet towards the gap is generated during the bubble collapse stage, along with a weak counter-jet towards the boundary appearing during the rebound stage. (2) As the dimensionless distance increases, the translational distance of the bubble during the collapse stage initially decreases, then increases, and finally decreases to zero. (3) Within the parameter range considered in this paper, the dimensionless width mainly affects the expansion degree and movement direction of the bubble cloud during its rebound and subsequent stages. The above research findings can provide experimental support for bubble-driven flow control, pumping, and liquid mixing in microfluidic channels.

View adaptive unified self-supervised technique for abdominal organ segmentation

Automatic abdominal organ segmentation is an essential prerequisite for accurate volumetric analysis, disease diagnosis, and tracking by medical practitioners. However, the deformable shapes, variable locations, overlapping with nearby organs, and similar contrast make the segmentation challenging. Moreover, the requirement of a large manually labeled dataset makes it harder. Hence, a semi-supervised contrastive learning approach is utilized to perform the automatic abdominal organ segmentation. Existing 3D deep learning models based on contrastive learning are not able to capture the 3D context of medical volumetric data along three planes/views: axial, sagittal, and coronal views. In this work, a semi-supervised view-adaptive unified model (VAU-model) is proposed to make the 3D deep learning model as view-adaptive to learn 3D context along each view in a unified manner. This method utilizes the novel optimization function that assists the 3D model to learn the 3D context of volumetric medical data along each view in a single model. The effectiveness of the proposed approach is validated on the three types of datasets: BTCV, NIH, and MSD quantitatively and qualitatively. The results demonstrate that the VAU model achieves an average Dice score of 81.61% which is a 3.89% improvement compared to the previous best results for pancreas segmentation in multi-organ dataset BTCV. It also achieves an average Dice score of 77.76% and 76.76% for the pancreas under the single organ non-pathological NIH dataset, and pathological MSD dataset.

Real-Time Seamless Multi-Projector Displays on Deformable Surfaces.

Prior works on multi-projector displays have focused primarily on static rigid objects, some focusing on dynamic rigid objects. However, works on projection based displays on deformable dynamic objects have focused only on small scale single projector displays. Tracking a deformable dynamic surface and updating projections precisely in real time on it is a significantly challenging task, even for a single projector system. In this paper, we present the first end-to-end solution for achieving a real-time, seamless display on deformable surfaces using mutliple unsychronized projectors without requiring any prior knowledge of the surface or device parameters. The system first accurately calibrates multiple RGB-D cameras and projectors using the deformable display surface itself, and then using those calibrated devices, tracks the continuous changes in the surface shape. Based on the deformation and projector calibration, the system warps and blends the image content in real-time to create a seamless display on a surface that continuously changes shape. Using multiple projectors and RGB-D cameras, we provide the much desired aspect of scale to the displays on deformable surfaces. Most prior dynamic multi-projector systems assume rigid objects and depend critically on the constancy of surface normals and non-existence of local shape deformations. These assumptions break in deformable surfaces making prior techniques inapplicable. Point-based correspondences become inadequate for calibration, exacerbated with no synchronization between the projectors. A few works address non-rigid objects with several restrictions like targeting semi-deformable surfaces (e.g. human face), or using single coaxial (optically aligned) projector-camera pairs, or temporally synchronized cameras. We break loose from such restrictions and handle multiple projector systems for dynamic deformable fabric-like objects using temporally unsynchronized devices. We devise novel methods using ray and plane-based constraints imposed by the pinhole camera model to address these issues and design new blending methods dependent on 3D distances suitable for deformable surfaces. Finally, unlike all prior work with rigid dynamic surfaces that use a single RGB-D camera, we devise a method that involve all RGB-D cameras for tracking since the surface is not seen completely by a single camera. These methods enable a seamless display at scale in the presence of continuous movements and deformations. This work has tremendous applications on mobile and expeditionary systems where environmentals (e.g. wind, vibrations, suction) cannot be avoided. One can create large displays on tent walls in remote, austere military or emergency operations in minutes to support large scale command and control, mission rehearsal or training operations. It can be used to create displays on mobile and inflatable objects for tradeshows/events and touring edutainment applications.

Modeling compliant bistable mechanisms: An energy method based on the high-order smooth curvature model

This paper applies an energy method based on the high-order smooth curvature model to address the challenges of kinetostatically modeling the complicated nonlinear post-buckling behavior of inclined compliant beams. The proposed energy method is grounded in the principle of minimum strain energy, implying that the total strain energy is minimized at the equilibrium configuration. In this work, the high-order smooth curvature model is adopted to accurately model the bending strain energy of large deformation beams. Subsequently, the Lagrange multiplier method is employed to ascertain the minimum of strain energy while concurrently determining the corresponding tip loads. Additionally, the deformation shape and maximum stress are determined via the smooth curvature model. The proposed method is introduced for the first time in modeling compliant bistable mechanisms, and it is proven that the method can be used for modeling compliant beams with inclined angles ranging from 0 to 90 degrees. Following the modeling, finite element analysis and experimental tests are conducted to verify the accuracy of the proposed energy method. A comprehensive comparative analysis between proposed method and existing methods is conducted. The comparison results prove that the model is more computationally efficient without compromising modeling accuracy The proposed modeling method can not only be used for modeling compliant bistable mechanisms but also has extendable applications in modeling initially-curved compliant beams, contact-aid design problems, and distributed load problems.The present work not only advances the practical application of the proposed energy method but also lays the foundation for future research in modeling compliant mechanisms.