Variable Geometry Research Articles

POD–ANN as digital twins for surge line thermal stratification

This paper describes a hybrid proper orthogonal decomposition (POD) and artificial neural network (ANN) strategy to construct digital twins of a pressurizer surge line under thermal stratification conditions. The one-way coupled conjugate heat transfer and thermal stress analysis was conducted by use of parametric modeling and the introduction of the inverse distance weighted interpolation for the grid mapping, which allows for the mapped grids to have the same number of nodes regardless of variations of surge line geometries. A snapshot-based POD was utilized to obtain truncated lower-order modes and the full-order system response was projected onto these modes with reduced state coefficients. Then the ANN was employed to establish a surrogate model between the five chosen design variables of interest and the reduced state coefficients, resulting in a surrogate-assisted digital twin for a pressurizer surge line. Prediction of fluid–structure interface temperature and thermal stress distribution was thus achieved in an in-line real-time manner for a wide range of parameter variations. We publicly share all code implementations, and we believe that our efforts open a door for the digital twining of thermo–fluid–structure interaction problems

Neural SSS: Lightweight Object Appearance Representation

AbstractWe present a method for capturing the BSSRDF (bidirectional scattering‐surface reflectance distribution function) of arbitrary geometry with a neural network. We demonstrate how a compact neural network can represent the full 8‐dimensional light transport within an object including heterogeneous scattering. We develop an efficient rendering method using importance sampling that is able to render complex translucent objects under arbitrary lighting. Our method can also leverage the common planar half‐space assumption, which allows it to represent one BSSRDF model that can be used across a variety of geometries. Our results demonstrate that we can render heterogeneous translucent objects under arbitrary lighting and obtain results that match the reference rendered using volumetric path tracing.

Third-order nonlinear Hall effect in a quantum Hall system.

In two-dimensional systems, perpendicular magnetic fields can induce a bulk band gap and chiral edge states, which gives rise to the quantum Hall effect. The quantum Hall effect is characterized by zero longitudinal resistance (Rxx) and Hall resistance (Rxy) plateaus quantized to h/(υe2) in the linear response regime, where υ is the Landau level filling factor, e is the elementary charge and h is Planck's constant. Here we explore the nonlinear response of monolayer graphene when tuned to a quantum Hall state. We observe a third-order Hall effect that exhibits a nonzero voltage plateau scaling cubically with the probe current. By contrast, the third-order longitudinal voltage remains zero. The magnitude of the third-order response is insensitive to variations in magnetic field (down to ~5 T) and in temperature (up to ~60 K). Moreover, the third-order response emerges in graphene devices with a variety of geometries, different substrates and stacking configurations. We term the effect third-order nonlinear response of the quantum Hall state and propose that electron-electron interaction between the quantum Hall edge states is the origin of the nonlinear response of the quantum Hall state.

A novel method for aero-engine map calibration using adaptation factor surface

High-fidelity component-level models (CLMs) are essential for aero-engine performance modeling, monitoring, and diagnosis, relying heavily on precise component maps. Inaccuracies in these maps can cause significant deviations between predicted and actual measurements. This study introduces a novel wide-range performance adaptation method (WPAM) utilizing adaptation factor surfaces, enabling precise adjustment of speed line operating points. The adaptation factors for the component maps are calculated based on both steady-state and transient measurements using the Levenberg-Marquardt method, with transient adaptation calculations converted into a steady-state solution. Adaptation factor surfaces are constructed using the moving least squares method to modify component maps in both spool speed and β-value directions. Validated on two engines with distinct maps, WPAM significantly enhances CLM accuracy under steady-state and transient conditions, considering multidimensional engine and CLM map discrepancies. It is applicable to degraded and variable geometry engines, exhibiting robustness in handling noisy measurement data.

Spatial and Surface Correspondence Field for Interaction Transfer

In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

Experimental COP optimization procedure in an air-based reverse Brayton cycle for cryogenic applications

Sustainable and efficient refrigerants are essential due to increasing regulatory constraints on traditional high-GWP refrigerants. This study investigates the potential of natural refrigerants, specifically air, in Reverse Brayton Cycles (RBC) for low-temperature applications. Unlike carbon dioxide and ammonia, which pose limitations and safety concerns below -40°C, air offers a safer and more versatile solution due to its excellent thermodynamic properties and availability. An experimental RBC was constructed using automotive components like centrifugal compressors, a radial turbine, and inter-coolers. These cost-effective and accessible components shift the refrigeration paradigm. The RBC was tested to optimize the Coefficient of Performance (COP) at -100°C while dissipating 2 kW, a typical scenario for whole-body cryotherapy (WBC). Key control parameters included the pressure ratio of the centrifugal compressors and the position of the stator vanes in the variable geometry turbine (VGT). The optimization process resulted in a COP increase of up to 28%. Additionally, a 1D gas-dynamic model validated these results, suggesting that different component selections could enhance performance by 17% compared to the experimental optimum point. Air-based RBC systems using automotive components can effectively achieve temperatures below -40°C, offering a viable, eco-friendly alternative to traditional refrigerants. This advancement addresses regulatory challenges and contributes to the scientific community by providing a sustainable refrigeration solution using commercially available components and demonstrating improvements through experimental data.

Comparison of egg-shape equations using relative curvature measures of nonlinearity

A 2-dimensional (2D) egg-shape equation can be used to construct a 3D egg geometry based on the hypothesis that an egg is a solid of revolution, which helps to calculate egg volume and surface area. The parameters in the 2D egg-shape equation are potentially valuable for providing a clue to the ecology and evolution of avian eggs. In this study, the 5-parameter Preston equation (PE), the 4-parameter Troscianko equation (TE), and another 2 egg-shape equations, were compared in describing real 2D egg-shape data of 300 Gallus gallus domesticus eggs and additional 50 eggs that represented the variation in avian egg geometries. Adjusted root-mean-square error was used to quantify each equation's prediction error. Given that the 4 equations are nonlinear, relative curvature measures of nonlinearity were used to assess the extent of nonlinearity in each equation. PE was found to be the best among the 4 equations in terms of adjusted root-mean-square error and minimizing nonlinearity. The empirically determined egg volumes using a graduated cylinder were compared with the predicted egg volumes using the formula for a solid of revolution based on 2D predictions from the 4 egg-shape equations. There were negligible differences in the predicted egg volumes and surface areas among the 4 equations, indicating that these equations are all valid in calculating egg volume and surface area. In addition, we proposed a 5-parameter TE and found that it outperformed the above 4 equations in describing the 2D egg shape of G. gallus, but was less general than PE for other egg shapes. This work provides statistical evidence to show which equation is the best for describing the geometry of avian eggs and nondestructively calculating their volume and surface area, helping to classify poultry eggs into different grades according to the morphological characteristics of the eggs.

Imaging flow focusing and isolation of aqueous fluids in synthetic quartzite: implications for permeability and retained fluid fraction in deep-seated rocks

The microstructure of realistic fluid–rock systems evolves to minimize the overall interfacial energy, enabling local variations in fluid geometry beyond ideal models. Consequently, the permeability–porosity relationship and fluid distribution in these systems may deviate from theoretical expectations. Here, we aimed to better understand the permeability development and fluid retention in deep-seated rocks at low fluid fractions by using a combined approach of high-resolution synchrotron radiation X-ray computed microtomography imaging of synthesized rocks and numerical permeability computation. We first synthesized quartzite using a piston-cylinder apparatus at different fluid fractions and wetting properties (wetting and non-wetting systems with dihedral angles of 52° and 61°–71°, respectively) under conditions of efficient grain growth. Although all fluids should be connected along grain edges and tubules in the homogeneous isotropic wetting fluid–rock system enabling segregation by gravitational compaction in natural settings, the fluid connectivity rapidly decreased to ~ 0 when the total fluid fraction decreased to 0.030–0.037, as the non-ideality of quartzite, including the interfacial energy anisotropy (i.e., grain faceting), became critical. In non-wetting systems, where the minimum energy fluid fraction based solely on the dihedral angle is ~ 0.015–0.035, the isolated (disconnected) fractions was 0.048–0.062. A streamline computation in the non-wetting system revealed that with decreasing total porosity, flow focusing into fewer channels maintained permeability, allowing the effective segregation of the connected fluids. These results provide insight into how non-wetting fluids segregate from rocks and exemplify the fraction of retained fluids in non-wetting systems. Thus, the findings suggest a potential way for wetting system fluids to be transported into the deep Earth's interior, and the amount of fluids dragged down to the Earth’s interior could be higher than what was previously estimated.

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 the Performance of PCM Thermal Storage Systems

In this article, we present some optimised geometries for a thermal storage system previously proposed exploiting Phase-changing materials (PCMs). The optimization has been carried out by using a genetic algorithm. We demonstrate that a simple single-parental, mutation-based, single-objective genetic algorithm can be conveniently employed to optimize the geometry of the proposed PCM thermal energy storage system. Optimization I was the one with the least restrictive conditions and, therefore, with the greatest possibility of variation in the channel geometry. While the one with the worst results is Optimization II because of the most restrictive conditions, primarily constant solid/liquid volume. A metal frame increases the surface area by 6.

Quick Estimation of Seepage Discharge and Water Level Height Out Of the Central Core in the Zoned Earth-fill Dam

This paper relates to the investigation of the amount of seepage discharge and the water level height out of the central core in the zoned earth-fill dam using the finite element procedure by Geo Studio-SEEP/W software. The investigation models were carried out in two shapes of the central core, the rectangular and the wedge shapes, by 200 miscellaneous models. For each model, seepage discharge and height of water level out were determined. Investigation results were examined using the artificial neural network technique (ANN) to demonstrate the influence weight of each independent variable (geometry and characteristics) on the output-dependent variables results using the statistical software SPSS. Core material hydraulic conductivity had the highest impact on seepage discharge and water level height while dam upstream slope had the least impact on them. Two statistical empirical equations were developed using multiple nonlinear regression for both the seepage discharge and the height of water level out using the SPSS software. The recommended equations were also verified by comparing their results with the results of 25% of the models analyzed by Geo Studio software. They showed great agreement that the correlation coefficient (r2) was 97.3% for the seepage discharge equation and 96.6% for the height of the water level-out equation.

Seismic response of irregular RC buildings designed for gravity and seismic loads

Irregular reinforced concrete framed buildings are peculiar and their seismic response is difficult to predict using simplified approaches. The irregularity in structural configuration is characterized by cross-sectional area reduction of the columns along the height, in-elevation and in-plan irregular distribution of the masses, complex floor geometry or floor geometry variation along the height. This study analyses the seismic response of several four-storey buildings with different types of irregularities, namely in-elevation floor height and floor geometry variation. Additionally, responses of both seismically designed and gravity load designed structures are compared for each geometry considered. A numerical model accounting for non-linear flexural and shear response of the structure is developed, aimed at conducting non-linear incremental dynamic analyses. The results are discussed in terms of inter-storey drift, floor acceleration profiles, fragility functions and floor response spectra. A significant influence of the irregularity on floor accelerations and displacements was observed, as well as on the spectral acceleration at collapse, mainly caused by mass and stiffness variation along the height. On the other hand, no significant influence was detected on failure modes.

Effects of substituted chalcogen atoms on excited state proton transfer reaction for 2,5‐bis(benzoxazole‐2‐yl)‐thiophene‐3,4‐diol derivatives: A theoretical study

AbstractMotivated by the captivating allure of exquisitely regulated characteristics exhibited by 2‐(2‐hydroxyphenyl)‐benzoxazole and its derivatives in the domains of photochemistry and photophysics, our current endeavor primarily focuses on delving into the intricacies of photo‐induced excited state reactions for derivatives of 2,5‐bis(benzoxazol‐2‐yl)‐thiophene‐3,4‐diol (BTD). Given the profound impact of chalcogen element doping, our primary focus lies in investigating the excited state behaviors of BTD‐O, BTD‐S, and BTD‐Se fluorophores. Through simulations encompassing variations in geometry and vertical excitation charge reorganization, we unveil atomic‐electronegativity‐dependent hydrogen bonding interactions and photoexcitation‐induced charge recombination that can significantly augment the intramolecular double proton transfer (ESDPT) reaction in the excited state for BTD‐O, BTD‐S, and BTD‐Se fluorophores. By constructing potential energy surfaces and identifying transition state forms, we elucidate the ultrafast stepwise ESDPT mechanism facilitated by the low potential barriers. Moreover, we rigorously validate the chalcogen atomic electronegativity‐driven regulation of the stepwise ESDPT mechanism. We sincerely anticipate that manipulating solvent polarity will pave the way for groundbreaking advancements in luminescent materials.

Stress Concentration Modelling on Resistance Spot Welding Lap Joint of Steel ASS316L and Titanium Ti-6Al-4V with Variable Weld Geometries

This research is a finite element simulation on resistance spot welding (RSW) process between dissimilar sheet metals consist of Titanium alloy, Ti-6Al-4V and Austenitic Stainless Steel (ASS) 316L. The problem statement was inability to visualize the stress concentration profile over weld nugget joint when Titanium alloy and steel welded with variable electrode geometry of circle, triangle, square and hexagon. To determine the best geometry for best weld with lowest maximum stress concentration. The methodology of simulation was tensile-shear test using SOLIDWORKS software. The tensile-stress load of 664.09 N was applied across all 4 different weld geometries. The result for the lowest magnitude of maximum stress 180.6 MPa was on circle weld geometry. Triangle geometry registered highest stress concentration of 219.6 MPa. This proves that most common weld geometry used in industry was circle. Even for dissimilar material joint the result supports that circle weld geometry as the best geometry. Keywords: Resistance spot welding (RSW), stress concentration, weld nugget, weld geometry.

Development of conjugate heat- and moisture-transfer model for pineapple drying using OpenFOAM

The kinetic model of drying is important for food preservation, particularly for pineapples, which have a high moisture content and are highly perishable. This paper presents a novel approach utilizing OpenFOAM, which is an open-source computational fluid dynamics (CFD) software, to model the hot-air drying kinetics of pineapple slices. The developed conjugate heat- and moisture-transfer model integrates forced-convection phenomena with moisture-diffusion dynamics. Owing to enhancements to the original OpenFOAM solver, C++ programming facilitates the appropriate representation of heat and moisture transfer based on the finite-volume method. Experimental data are utilized to derive a moisture diffusivity model, which achieved high statistical quality with an average R2 of 0.9443. Additionally, the SST k–ω turbulence model accurately describes the airflow around the pineapple-slice model. The developed model agrees well with the experimental results, with an average error of less than 1.29 %. Further analysis using the model provides insights into the effects of variations in the pineapple-slice geometry, where increased inner diameter and reduced thickness result in enhancement indices of 1.002 and 1.005, respectively. This novel CFD solver is promising for simulating the drying processes of various agricultural products, including pineapples, thus contributing to improvements in process optimization and efficiency.

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.

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.