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To replicate and extend research on psychopathy and intrinsic interpersonal preferences under the broader umbrella of affiliation, intimacy and antagonism, this paper aims to examine motivational correlates of psychopathy in a nonclinical sample (N = 125).

We used a multimethod design, including self-reports, a behavioral task and a physiological assessment of motive dispositions (automatic affective reactions to stimuli of interpersonal transactions measured with facial electromyography).

Results showed that self-reported psychopathy was negatively associated with self-reported intimacy motive. In the same vein, via the social discounting task, this paper found a negative association between psychopathy and a tendency to share hypothetical monetary amounts with very close others. Finally, regarding fEMG findings, multilevel analyses revealed that although individuals with low levels of psychopathy reacted more positively to affiliative stimuli, individuals with high levels of psychopathy reacted equally positively to both affiliative and antagonistic stimuli, and these results were robust across psychopathy measures. Results remained mostly unchanged on the subscale level.

These findings highlight the contribution of multimethod assessments in capturing nuances of motivation. Implicit physiological measures might be particularly sensitive in capturing motive dispositions in relation to psychopathy. Identifying mechanisms that foster positive connections between psychopathic traits and nonprosocial tendencies may be theoretically and clinically informative, with implications for forensic and penal practices.

The present numerical investigation examines the magnetohydrodynamic (MHD) double diffusion natural convection of power-law non-Newtonian nano-encapsulated phase change materials (NEPCMs) in a trapezoidal cavity.

The governing Navier-Stokes, energy and concentration equations based on the Cartesian curvilinear coordinates are solved using the collocated grid arrangement’s finite volume method. The in-house FORTRAN code is validated with the different benchmark problems. The NEPCM nanoparticles consist of a core-shell structure with Phase Change Material (PCM) at the core. The enclosure, shaped as a trapezoidal hollow, features a warmed (T_h) left wall and a cold (T_c) right wall. Various parameters are considered, including the power law index (0.6 ≤ n ≤ 1.4), Hartmann number (0 ≤ Ha ≤ 30), Rayleigh number (10⁴ ≤ Ra ≤ 10⁵) and fixed variables such as buoyancy ratio (Br = 0.8), Prandtl number (Pr = 6.2), Lewis number (Le = 5), fusion temperature (Θ_f = 0.5) and volume fraction (ϕ = 0.04).

The findings indicate a decrease in local Nusselt (Nu) and Sherwood (Sh) numbers with increasing Hartmann numbers (Ha). Additionally, for a shear-thinning fluid (n = 0.6) results in the maximum local Nu and Sh values. As the Rayleigh number (Ra) increases from 10⁴ to 10⁵, the structured vortex in the streamline pattern is disturbed. Furthermore, for different Ra values, an increase in n from 0.6 to 1.4 leads to a 67.43% to 76.88% decrease in average Nu and a 70% to 77% decrease in average Sh.

This research is for two-dimensioal laminar flow only.

PCMs represent a class of practical substances that behave as a function of temperature and have the innate ability to absorb, release and store heated energy in the form of hidden fusion enthalpy, or heat. They are valuable in these systems as they can store significant energy at a relatively constant temperature through their latent heat phase change.

As per the literature review and the authors’ understanding, an examination has never been conducted on MHD double diffusion natural convection of power-law non-Newtonian NEPCMs within a trapezoidal enclosure. The current work is innovative since it combines NEPCMs with the effect of magnetic field Double diffusion Natural Convection of power-law non-Newtonian NEPCMs in a Trapezoidal enclosure. This outcome can be used to improve thermal management in energy storage systems, increasing safety and effectiveness.

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

This paper aims to prepare a new donor–π–acceptor (D–π–A) and acceptor–π– D–π–A (A–π–D–π–A) phenothiazine (PTZ) in conjugation with vinyl isophorone (PTZ-1 and PTZ-2) were designed and their molecular shape, electrical structures and characteristics have been explored using the density functional theory (DFT). The results satisfactorily explain that the higher conjugative effect resulted in a smaller high occupied molecular orbital–lowest unoccupied molecular orbital gap (Eg). Both compounds show intramolecular charge transfer (ICT) transitions in the ultraviolet (UV)–visible range, with a bathochromic shift and higher absorption oscillator strength, as determined by DFT calculations.

The produced PTZ-1 and PTZ-2 sensors were characterized using various spectroscopic methods, including Fourier-transform infrared spectroscopy and nuclear magnetic resonance spectroscopy (¹H/¹³CNMR). UV–visible absorbance spectra of the generated D–π–A PTZ-1 and A–π–D–π–A PTZ-2 dyes were explored in different solvents of changeable polarities to illustrate positive solvatochromism correlated to intramolecular charge transfer.

The emission spectra of PTZ-1 and PTZ-2 showed strong solvent-dependent band intensity and wavelength. Stokes shifts were monitored to increase with the increase of the solvent polarity up to 4122 cm⁻¹ for the most polar solvent. Linear energy-solvation relationship was applied to inspect solvent-dependent Stokes shifting. Quantum yield (ф) of PTZ-1 and PTZ-2 was also explored. The maximum UV–visible absorbance wavelengths were detected at 417 and 419 nm, whereas the fluorescence intensity was monitored at 586 and 588 nm.

The PTZ-1 and PTZ-2 dyes leading to colorimetric and emission spectral changes together with a color shift from yellow to red.

Aerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.

The nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.

Ratios of low- and high-fidelity training samples to degrees of freedom of n_LF/n_DOFs = 50 and n_HF/n_DOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.

The application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.

Deep-learning techniques are recently gaining a lot of importance in the field of turbulence. This study focuses on addressing the problem of data imbalance to improve the performance of an existing deep learning neural network to infer the Reynolds-averaged Navier–Stokes solution, proposed by Thuerey et al. (2020), in the cases of airfoils with high wake formation behind them. The model is based on a U-Net architecture, which calculates pressure and velocity solutions for fluid flow around an airfoil.

In this work, we propose various methods for training the model on selectively generated data with different distributions, which would be representative of the under-performing test samples. The property we chose for selectively generating data was the fraction of negative x-velocity in the domain. We have used Grad-CAM to compare the layer activations of different models trained using the proposed methods.

We observed that using our methods, the average performance on the samples with high wake formation (i.e. flow over airfoils at high angle of attack) has improved. Using one of the proposed methods, an average performance improvement of 15.65% was observed for samples of unknown airfoils compared to a similar model trained using the original method.

This work demonstrates the use of imbalanced learning in the field of fluid mechanics. The performance of the model is improved by giving significance to the distribution of the training data without changes to the model architecture.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data.

Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade’s AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data.

To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics.

This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory.

Assessment of the reliability of AoA and relative velocity estimation methods for VAWT’ blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions.

The purpose of this study aims to synthesize a novel donor–acceptor dye based on phenothiazine as a donor (D) and nonconjugated spacer was devised and synthesized by condensing of 2,2'-(1H-indene-1,3(2H)-diylidene) dimalononitrile with aldehyde and the practical synthesis methodology as given in Scheme 1.

The prepared phenothiazine dye was systematically experimentally and theoretically examined and characterized using nuclear magnetic resonance spectroscopy (1H,13C NMR), Fourier-transform infrared spectroscopy (IR) and high-resolution mass spectrometry. Density functional theory (DFT) and time-dependent density functional theory DT-DFT calculations were implemented to determine the electronic properties of the new dye

The UV-Vis absorption and fluorescence spectroscopy of the synthesized dye was investigated in a variety of solvents with varying polarities to demonstrate positive solvatochromism correlated with intramolecular charge transfer (ICT). The probe’s quantum yields (Фf) are experimentally measured in ethanol, and the Stokes shifts are found to be in the 4846–9430 cm⁻¹ range.

The findings depicted that the novel (D-π-A) chromophores may act as a significant factor in the organic optoelectronics.