Variable Geometry Research Articles

Experimental study of flow control on tip leakage flow in variable geometry linear turbine cascades with different pivot layouts

Variable geometry turbines are essential for adjusting operational conditions in industrial gas turbines and variable cycle engines. These adjustments necessitate partial gaps at both ends of the variable guide vanes to alter the turning angle, consequently introducing an aerodynamic performance penalty. Moreover, the pivot layout profoundly influences aerodynamic losses. Research on turbine cascades that considers various partial gap layouts is limited, particularly in terms of experimental studies, which are rarely conducted. This study aims to diminish aerodynamic losses and augment the efficiency of gas turbines by examining the impact of pivot layouts on partial gap clearance and secondary flow. It further investigates the effectiveness of flow control strategies at the blade tip across different pivot configurations within a variable geometry turbine cascade, utilizing pneumatic probe scanning and surface oil flow visualization techniques. The results reveal that employing a cavity at the tip can significantly reduce aerodynamic losses in schemes both with and without a pivot, achieving maximum loss reductions of 15.8% and 3.7%, respectively. Additionally, a narrower squealer width can further decrease these losses. However, with a pivot located at the tip, the resulting separation flow and wake vortex become predominant sources of losses. The presence of the pivot weakens the tip leakage flow rate and the intensity of the tip leakage vortex (TLV), thus diminishing the effectiveness of cavity tip flow control. The cavity moderates TLV and enhances the interaction between TLV and the wake vortex, leading to increased aerodynamic losses.

Monodisperse Porous Emitter Materials for Ion Electrospray Propulsion

Electrospray spacecraft propulsion typically uses commercially available porous glass materials for their emitter components. The quality of this emitter material is crucial to the operation of the thrusters, as nonuniformities in the pore distribution can lead to differential propellant flow rates, variation in tip geometries, and less-than-optimal performance of the thruster. The focus of this work is on the development of new porous materials that are more uniform and can mitigate these issues caused by nonuniformities present in current commercially available porous materials. A silica nanoparticle photo-curable resin solution is developed to create high-uniformity porous substrates to improve tip geometry and overall thruster performance.

Optimization of tool wear and surface roughness in ST-37 steel turning process with varying tool angles and machining parameters

The process of cutting low carbon steel (ST-37) typically utilizes High-Speed Steel (HSS) tools owing to their high hardness, affordability, and ease of shaping tool geometry. In machining, tool geometry plays a crucial role in the material cutting process and determines the quality of the final product, particularly surface roughness. The objective of this research is to achieve optimal surface roughness by varying the tool geometry and nose radius. This study employed an experimental approach using ST-37 and HSS tools. The variations in tool geometry include side rake angles of 12°, 15°, and 18°; side cutting edge angles of 85°, 80°, and 75°; and nose radii of 0 mm, 0.4 mm, and 0.8 mm. The machining parameters applied consist of a cutting depth of 1 mm and 2 mm, spindle rotation speeds of 185 rpm, 425 rpm, and 624 rpm, and a feed rate of 0.05 mm/rev, 0.075 mm/rev, and 0.1 mm/rev. Tool wear measurements were captured using a USB camera, whereas the surface roughness was assessed using a surface roughness tester. The impact of the tool geometry on the surface roughness was analyzed using the Taguchi-Grey Relational Analysis (Taguchi-GRA) and ANOVA methods. The optimal combination for ST-37 lathe machining with a sharpening tool is: A1 (cutting depth 1 mm), B1 (cutting speed 17.42 m/min), C3 (feed 0.05 mm/rev), D1 (corner radius 0 mm), E3 (side rake angle γ 18°), and F3 (side cutting edge angle γ 75°). According to the Analysis of Variance (ANOVA), three factors—cutting speed, tool tip angle, and chip angle—should be considered to achieve minimal tool wear and desirable surface roughness during machining

Principal dimensions of voice production and their role in vocal expression.

How we produce and perceive voice is constrained by laryngeal physiology and biomechanics. Such constraints may present themselves as principal dimensions in the voice outcome space that are shared among speakers. This study attempts to identify such principal dimensions in the voice outcome space and the underlying laryngeal control mechanisms in a three-dimensional computational model of voice production. A large-scale voice simulation was performed with parametric variations in vocal fold geometry and stiffness, glottal gap, vocal tract shape, and subglottal pressure. Principal component analysis was applied to data combining both the physiological control parameters and voice outcome measures. The results showed three dominant dimensions accounting for at least 50% of the total variance. The first two dimensions describe respiratory-laryngeal coordination in controlling the energy balance between low- and high-frequency harmonics in the produced voice, and the third dimension describes control of the fundamental frequency. The dominance of these three dimensions suggests that voice changes along these principal dimensions are likely to be more consistently produced and perceived by most speakers than other voice changes, and thus are more likely to have emerged during evolution and be used to convey important personal information, such as emotion and larynx size.

Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations

AbstractPhysics‐informed neural networks (PINNs) provide a new class of mesh‐free methods for solving differential equations. However, due to their long training times, PINNs are currently not as competitive as established numerical methods. A promising approach to bridge this gap is transfer learning (TL), that is, reusing the weights and biases of readily trained neural network models to accelerate model training for new learning tasks. This work applies TL to improve the performance of PINNs in the context of magnetostatic field simulation, in particular to resolve boundary value problems with geometrical variations of the computational domain. The suggested TL workflow consists of three steps. (a) A numerical solution based on the finite element method (FEM). (b) A neural network that approximates the FEM solution using standard supervised learning. (c) A PINN initialized with the weights and biases of the pre‐trained neural network and further trained using the deep Ritz method. The FEM solution and its neural network‐based approximation refer to an computational domain of fixed geometry, while the PINN is trained for a geometrical variation of the domain. The TL workflow is first applied to Poisson's equation on different 2D domains and then to a 2D quadrupole magnet model. Comparisons against randomly initialized PINNs reveal that the performance of TL is ultimately dependent on the type of geometry variation considered, leading to significantly improved convergence rates and training times for some variations, but also to no improvement or even to performance deterioration in other cases.

Morphological traits and seed yield of non-branching monostem sesame VRI 5 under varied crop geometry

Morphological traits and seed yield of non-branching monostem sesame VRI 5 under varied crop geometry

Parametric study on the effect of material properties, tool geometry, and tolerances on preform quality in wind turbine blade manufacturing

Increasing throughput in wind turbine blade production can be achieved by separately manufacturing pre-shaped binder-stabilised dry preforms, and subsequently placing them in the blade mould. To avoid manufacturing defects, a trade-off between the formability and the handleability of the preform is necessary. In this paper, an experimentally validated preform model is used to study how variations in material properties, tool geometry, and placement tolerances influence defect generation. The results from three studies are presented. In the first study, a preform is formed over a ramp transition with variations in geometry. The results from this study indicate that a short ramp promotes transverse shearing of the preform. In the second study, the material properties of the preform are varied. The results indicate that a high mode I cohesive law of the binder and a high bending stiffness of the fabric promote transverse shearing and remove wrinkles. In the last study, placement tolerances for a pre-shaped preform are studied. The results show that if the preform can shear between the preform edge and the tool edge, it can conform to the mould even with large placement offsets. Process engineers and blade designers can readily use these results to help reduce forming-induced wrinkles.

Extensive validation of a combustion and pollutant emission model of a pre-chamber engine including different pre-chamber geometries

In this work, a 0D/1D model of a single-cylinder pre-chamber spark ignition (PCSI) engine is extensively validated with experimental data in terms of performance, combustion, and emissions by varying the pre-chamber geometry and operating conditions.In the first stage, an experimental study is carried out on the PCSI engine at 1600 rpm and wide-open throttle, exploring different pre-chamber (PC) designs and various relative air/fuel (A/F) ratios, λ, in the main chamber.In the second stage, a phenomenological combustion model for PCSI engines is coupled with additional user-defined sub-models of in-cylinder turbulence, heat transfer, and pollutant emissions. These are integrated into a 1D engine model and used to reproduce a set of 16 measured data for a reference PC geometry. The model adequately describes the performance, pollutant production, and burn rate in both the pre- and main-chamber considering the effects of jet-induced turbulence and distributed multiple flame kernels in the main chamber.The predictive capability of the 1D model is further tested on an extended dataset composed of 163 points, including variations in the PC geometry, proving to satisfactorily reproduce experiments in terms of performance, combustion, and emissions. This last aspect represents the novelty of the present work, demonstrating the reliability of the physical background included within the in-cylinder sub-models.

Microscale geometrical modulation of PIEZO1 mediated mechanosensing through cytoskeletal redistribution

The microgeometry of the cellular microenvironment profoundly impacts cellular behaviors, yet the link between it and the ubiquitously expressed mechanosensitive ion channel PIEZO1 remains unclear. Herein, we describe a fluorescent micropipette aspiration assay that allows for simultaneous visualization of intracellular calcium dynamics and cytoskeletal architecture in real-time, under varied micropipette geometries. By integrating elastic shell finite element analysis with fluorescent lifetime imaging microscopy and employing PIEZO1-specific transgenic red blood cells and HEK cell lines, we demonstrate a direct correlation between the microscale geometry of aspiration and PIEZO1-mediated calcium signaling. We reveal that increased micropipette tip angles and physical constrictions lead to a significant reorganization of F-actin, accumulation at the aspirated cell neck, and subsequently amplify the tension stress at the dome of the cell to induce more PIEZO1’s activity. Disruption of the F-actin network or inhibition of its mobility leads to a notable decline in PIEZO1 mediated calcium influx, underscoring its critical role in cellular mechanosensing amidst geometrical constraints.

Controlled Surface Textures of Elastomeric Polyurethane Janus Particles: A Comprehensive Review.

Colloidal particle research has witnessed significant advancements in the past century, resulting in a plethora of studies, novel applications, and beneficial products. This review article presents a cost-effective and low-tech method for producing Janus elastomeric particles of varied geometries, including planar films, spherical particles, and cylindrical fibers, utilizing a single elastomeric material and easily accessible chemicals. Different surface textures are attained through strain application or solvent-induced swelling, featuring well-defined wavelengths ranging from sub-microns to millimeters and offering easy adjustability. Such versatility renders these particles potentially invaluable for medical applications, especially in bacterial adhesion studies. The coexistence of "young" regions (smooth, with a small surface area) and "old" regions (wrinkled, with a large surface area) within the same material opens up avenues for biomimetic materials endowed with additional functionalities; for example, a Janus micromanipulator where micro- or nano-sized objects are grasped and transported by an array of wrinkled particles, facilitating precise release at designated locations through wrinkle pattern adjustments. This article underscores the versatility and potential applications of Janus elastomeric particles while highlighting the intriguing prospects of biomimetic materials with controlled surface textures.

Combined spin orientation and phase function of asteroids

Context. Large sky surveys provide numerous non-targeted observations of small bodies of the Solar System. The upcoming LSST of the Vera C. Rubin Observatory will be the largest source of small body photometry in the next decade. With non-coordinated epochs of observation, colors – and therefore taxonomy and composition – can only be computed by comparing absolute magnitudes obtained in each filter by solving the phase function (evolution of brightness of the small body against the solar phase angle). Current models in use in the community (HG, HG12*, and HG1G2), however, fail to reproduce the long-term photometry of many targets due to the change in the aspect angle between apparitions. Aims. We aim to derive a generic yet simple phase function model accounting for the variable geometry of the small bodies over multiple apparitions. Methods. As a spinoff of the HG1 G2 model, we propose the sHG1G2 phase function model in which we introduce a term describing the brightness changes due to spin orientation and polar oblateness. We applied this new model to 13 245 908 observations of 122 675 Solar System objects (SSOs). These observations were acquired in the g and r filters with the Zwicky Transient Facility between November 1, 2019 and December 1, 2023. We retrieved them and implemented the new sHG1G2 model in FINK, a broker of alerts designed for the LSST. Results. The sHG1G2 model leads to smaller residuals than other phase function models, providing a better description of the photometry of asteroids. We determined the absolute magnitude, H, and phase function coefficients (G1, G2) in each filter, the spin orientation (α0, δ0), and the polar-to-equatorial oblateness, R, for 95 593 SSOs, which constitutes about a tenfold increase in the number of characterized objects compared to the current census. Conclusions. The application of the sHG1 G2 model to ZTF alert data using the FINK broker shows that the model is appropriate for extracting physical properties of asteroids from multi-band and sparse photometry, such as the forthcoming LSST survey.

Experimental investigation of the matching boundary and optimization strategy of a variable geometry turbocharged direct-injected hydrogen engine

With the higher demand for torque, power, brake thermal efficiency (BTE), and low NOx emissions of hydrogen engines, direct injection and turbocharging are the most effective methods to improve performance. However, due to the large air flow rate variation range with lean combustion and low exhaust gas energy, the turbine needs to be adjusted significantly, and the performance of the conventional waste gate turbochargers is limited. In this study, a 2.0 L direct-injected turbocharged hydrogen engine was studied experimentally and matched with a variable geometry turbocharger (VGT). The working boundary of the VGT determined by abnormal combustion and exhaust back pressure is proposed at all working conditions as it changes from 10-50 % @1500 rpm to 40–75 % @4500 rpm. Control of the VGT to balance combustion and gas exchange was achieved to reach the highest BTE of 42.57 %. Further, high torque of 326 Nm and high power of 125 kW are explored and different VGT opening strategies are compared. An optimized VGT opening control strategy is proposed, which is divided into five regions at different loads to ensure best performance. The VGT opening control strategy based on λ control and the trade-off between combustion and gas exchange efficiency at all working conditions can be valuable in the development of high-performance hydrogen engines.

Computational explorations about the solvent-polarity-associated excited state proton transfer behaviors for the novel F-BSD compound.

Inspired by the excellent potential application prospects from the precisely controlled attributes displayed by fluorine-substituted-bis(salicylidene)-1,5-diaminonaphthalene (F-BSD) and its derivatives in the domains of photochemistry and photophysics, our present undertaking predominantly focuses on exploring the complexities of photo-induced excited state reactions for F-BSD fluorophores dissolved in solvents with diverse levels of polarity. Our simulations reveal that the excited state intramolecular double proton transfer (ESIDPT) reaction for F-BSD chemosensor can be significantly regulated by solvent polarity-dependent hydrogen bonding interactions and charge recombination induced by photoexcitation, which result from variations in geometries and vertical excitation charge reorganizations. By constructing potential energy surfaces (PESs), we also demonstrate that the stepwise ESIDPT reaction of F-BSD occurs with alternative dual intramolecular hydrogen bonds (O1-H2···N3 or O4-H5···N6). Interestingly, we affirm polar solvents should be conducive to the first-step of ESIDPT process, while nonpolar solvents are in favor of the second step. We sincerely hope solvent polarity-dependent ESIDPT behavior will pave the way for future design of novel luminescent materials. The molecular geometries were optimized by DFT//TDDFT D3-B3LYP/TZVP theoretical level with IEFPCM solvent model in S0 and S1 states, respectively. This work also explores the energy level of target molecules with the computational vertical absorption spectra by TDDFT. All the simulations were carried out based on Gaussian 16 software. The core-valence bifurcation (CVB) indexes were obtained by using Multiwfn 3.8. Potential energy surfaces were constructed by the DFT//TDDFT D3-B3LYP/TZVP level based on restricted optimization, also the transition state (TS) forms were searched using the same level.

Rectification of heat current in Corbino geometry.

We prove analytically the ballistic thermal rectification effect (BTRE) in the Corbino disk characterized by an annular shape. We derive the thermal rectification efficiency (RE) and show that it can be expressed as the product of two independent functions, the first dependent on the temperatures of the heat baths and the second on the system's geometry. It follows that a perfect BTRE can be reached with the increase of the ratios of the heat baths' temperatures and of the radius of the outer edge to the inner edge of the disk. We also show that, by introducing a potential barrier into the Corbino disk, the RE can be greatly improved. Quite remarkably, by an appropriate choice of parameters, the thermal diode effect can be reversed. Our results are robust under variation of the Corbino geometry, which may provide a flexible route to manipulate the heat flow at the nanoscale.

Design and aerodynamic characteristics of variable-geometry hypersonic inlet based on shock wave elimination

To solve the problem that the control of shock wave elimination is weakened under off-design conditions, the design concept of a variable-geometry inlet scheme that combines the variable-geometry cowl (translating and diagonalizing) with regulating shock wave elimination is introduced in this paper. The variable-geometry inlet is designed by the theories of oblique shock wave and isentropic wave as well as the Oswatitsch theory. Regulatory law of the variable-geometry cowl based on shock wave elimination is obtained by the geometric relationships between cowl compression angle, cowl shock wave angle, and optimal control point or range. Numerical simulations are conducted to investigate flow field characteristics, control mechanism, and working performance of the inlet. Results reveal that expansion waves have a significant impact on the cowl shock wave and boundary layer interaction, and flow separation. Furthermore, variable-geometry inlet with translating and diagonalizing cowl based on the regulation of shock wave elimination effectively controls and even completely eliminates the flow separation. In terms of inlet performance, the total pressure loss of the variable-geometry inlet decreases such that the total pressure recovery coefficients of the translating cowl and diagonalizing cowl inlets are increased by maximum values of 3.39 % and 9.97 %, respectively. However, the mass flow coefficient of translating cowl inlet decreases, whereas that of the diagonalizing cowl inlet is equivalent to that of the fixed-geometry inlet. The working range can be widened by changing the internal contract ratio of the inlet through translating or diagonalizing the cowl. The results confirm that the scheme of variable geometry inlet with diagonalizing cowl is practicable and reliable and has important guiding significance and value for inlet design.

Introduction. Variable Geometries: Hexagonal France in Post-war French and Francophone Culture

Introduction. Variable Geometries: Hexagonal France in Post-war French and Francophone Culture

Chiral Kirigami for Bend-Tolerant Reconfigurable Hologram with Continuously Variable Chirality Measures.

Despite the commonality of static holograms, the holography with multiple information layers and reconfigurable grey-scale images at communication frequencies remain a confluence of scientific challenges. One well-known difficulty is the simultaneous modulation of phase and amplitude of electromagnetic wavefronts with a high modulation depth. A less appreciated challenge is scrambling of the information and images with hologram bending. Here, this work shows that chirality-guided pixelation of plasmonic kirigami sheets enables tunable multiplexed holography at terahertz (THz) frequencies. The convex and concave structures with slanted Au strips exhibit gradual variations in geometries facilitating modulation of light ellipticity reaching 40deg. Real-time switching of 3D images of the letter "M" and the Mona Lisa demonstrates the possibility of complex grey-scale information content and importance of continuously variable mirror asymmetry. Microscale chirality measures of each pixel experiences little change with bending while retaining controllable reconfigurability upon stretching, which translates to remarkable resilience of chiral holograms to bending. Simplicity of their design with local chirality measures opens the door to information technologies with fault-tolerant THz encryption, wearable holographic devices, and new communication technologies.

Numerical Investigation of Solitary Wave Attenuation by a Vertical Plate-Type Flexible Breakwater Constructed Using Hyperelastic Neo-Hookean Material

This study conducted numerical investigations on solitary wave attenuation by a vertical plate-type flexible breakwater constructed using hyperelastic neo-Hookean material. The wave attenuation performance and elastic behaviors of the flexible breakwater were discussed systematically by considering the effects of three prominent factors: mass coefficient, stiffness coefficient, and Poisson’s ratio. It is indicated that more compressible and flexible materials are beneficial for enhancing efficiency in mitigating solitary wave energy and protecting the structure from damage. In addition, the performance of the hyperelastic neo-Hookean material model was compared with that of a linear elastic isotropic material model coupled with linear and nonlinear geometry analysis (LGEOM and NLGEOM) by evaluating several key targets: wave reflection coefficient, transmission coefficient, horizontal tip displacement, and wave load. Our findings revealed that the hyperelastic neo-Hookean material model showed almost the same predictions as the linear elastic isotropic material model with NLGEOM, but significantly diverged from that with LGEOM. The linear elastic isotropic material model with LGEOM cannot capture the nonlinear variations in structural geometry and stress–strain relationship, resulting in the underestimation and overestimation of horizontal tip displacement under moderate and extreme wave loads, respectively. Moreover, it underestimates the damage inflicted by solitary waves due to inaccurately predicted wave reflection and transmission.

Honeycomb-shaped artificial roughness in solar air heaters: CFD-experimental insights into thermo-hydraulic performance

This research paper presents numerical and experimental investigations to examine the effectiveness of a honeycomb pattern as a form of the geometry of artificial roughness in solar air heaters. Utilizing Computational Fluid Dynamics (CFD) through three-dimensional simulations, the study explores how Thermo-Hydraulic Performance Parameter (THPP) is affected by variations in honeycomb geometry. The research examines various parameters, including the angle of attack (Ø), relative roughness pitch (P/e), and relative roughness height (e/D) within the respective ranges of (90°-120°), (8–12), and (0.03–0.05). The system's performance is evaluated across various flow scenarios, covering Reynolds numbers from (3000) to (21,000). Incorporating the honeycomb design into an absorber is observed to improve the heat transfer rates. The system achieves a maximum Nu of (140.65) at (e/D) of 0.04, (P/e) of 10, (Ø) of 120°, and Re of (21,000). The maximum FF of (0.039) was obtained at (e/D) of 0.05, (P/e) of 9, and (Ø) of 120° at a Reynolds number of (6000). The system exhibited a THPP of (1.7) at a Reynolds number of (6000). This Maximum THPP was associated with specific parameters, including (e/D) of 0.04, (P/e) of 10, and (Ø) of 120°.

Geom-DeepONet: A point-cloud-based deep operator network for field predictions on 3D parameterized geometries

Modern digital engineering design process commonly involves expensive repeated simulations on varying three-dimensional (3D) geometries. The efficient prediction capability of neural networks (NNs) makes them a suitable surrogate to provide design insights. Nevertheless, few available NNs can handle solution prediction on varying 3D shapes. We present a novel deep operator network (DeepONet) variant called Geom-DeepONet, which encodes parameterized 3D geometries and predicts full-field solutions on an arbitrary number of nodes. To the best of the authors’ knowledge, this is the first attempt in the literature and is our primary novelty. In addition to expressing shapes using mesh coordinates, the signed distance function for each node is evaluated and used to augment the inputs to the trunk network of the Geom-DeepONet, thereby capturing both explicit and implicit representations of the 3D shapes. The powerful geometric encoding capability of a sinusoidal representation network (SIREN) is also exploited by replacing the classical feedforward neural networks in the trunk with SIREN. Additional data fusion between the branch and trunk networks is introduced by an element-wise product. A numerical benchmark was conducted to compare Geom-DeepONet to PointNet and vanilla DeepONet, where results show that our architecture trains fast with a small memory footprint and yields the most accurate results among the three. Results show a much lower generalization error of our architecture on unseen dissimilar designs than vanilla DeepONet. Training the model on 2500 cuboid designs is done within 2 h on a single A100 GPU while generating the training dataset through finite element simulations took about 27 h. Once trained, the model can predict vector fields and a single prediction can be over 105 times faster than its corresponding implicit finite element simulation if the mesh is large. The ability of the proposed model to perform efficient and accurate field predictions on variable 3D geometries, especially those discretized by different nodes and elements, makes it a valuable tool for preliminary performance evaluation and design optimizations and is the most significant contribution of the current work.