Explore the vast range of titles available.

Purpose: The purpose of this study was to adapt the \"Student Satisfaction and Self-Confidence in Learning Scale\" (SCLS), \"Simulation Design Scale\" (SDS), and \"Educational Practices Questionnaire\" (EPQ) developed by Jeffries and Rizzolo into Turkish and establish the reliability and the validity of these translated scales. Methods: A sample of 87 nursing students participated in this study. These scales were cross-culturally adapted through a process including translation, comparison with original version, back translation, and pretesting. Construct validity was evaluated by factor analysis, and criterion validity was evaluated using the Perceived Learning Scale, Patient Intervention Self-confidence/Competency Scale, and Educational Belief Scale. Findings: Cronbach's alpha values were found as 0.77-0.85 for SCLS, 0.73-0.86 for SDS, and 0.61-0.86 for EPQ. Conclusions: The results of this study show that the Turkish versions of all scales are validated and reliable measurement tools.

This study aimed to examine the relationships between student satisfaction and self-confidence in learning (SCLS), the simulation design scale (SDS), and educational practices in simulation (EPSS) and to identify the influencing factors on SCLS in nursing students undergoing simulation learning. Of the fourth-year nursing students, 71 who were taking a medical–surgical nursing simulation course and voluntarily provided informed consent to participate in the study were enrolled. Data on SCLS, SDS, and EPSS were collected via an online survey after the simulation, from 1 October 2019 to 11 October 2019. The mean SCLS score was 56.31 ± 7.26, the mean SDS score was 86.82 ± 10.19 (range: 64~100), and the mean EPSS score was 70.87 ± 7.66 (range: 53~80). SCLS was positively correlated with SDS (r = 0.74, p < 0.001) and EPSS (r = 0.75, p < 0.001). The regression model for SCLS in nursing students revealed that SCLS increased with increasing EPSS and SDS, and that SDS and EPSS explained 58.7% of the variance in SCLS (F = 50.83, p < 0.001). Therefore, to improve the learning satisfaction and learning confidence of nursing students in simulation classes, it is necessary to consider simulation design and practice considering educational factors.

Literature reporting the evaluation of simulations for design characteristics is practically nonexistent. The Simulation Design Scale was used to evaluate the effect of time in simulation with 81 associate degree nursing students. Students completed the scale after participation in either a two-hour or four-hour nursing process simulation. As time in simulation increased, independent t -test results for the importance of elements were significant ( p = .03): Objectives and Information ( p = .002) and Fidelity ( p = .017). Scenarios written by nurse educators need to be evaluated for design characteristics, and results need to be reported.

We present the design and implementation of a spectral code, called SpectralPlasmaSolver (SPS), for the solution of the multi-dimensional Vlasov-Maxwell equations. The method is based on a Hermite-Fourier decomposition of the particle distribution function. The code is written in Fortran and uses the PETSc library for solving the non-linear equations and preconditioning and the FFTW library for the convolutions. SPS is parallelized for shared- memory machines using OpenMP. As a verification example, we discuss simulations of the two-dimensional Orszag-Tang vortex problem and successfully compare them against a fully kinetic Particle-In-Cell simulation. An assessment of the performance of the code is presented, showing a significant improvement in the code running-time achieved by preconditioning, while strong scaling tests show a factor of 10 speed-up using 16 threads.

This chapter contains sections titled: Introduction Philosophy: Evolution of Computational Experiments Application: U.S. Army Unmanned Aerial Vehicle (UAV) Mix Study Parting Thoughts References

A wall model for large-eddy simulation (LES) is proposed by devising the flow as a combination of building blocks. The core assumption of the model is that a finite set of simple canonical flows contains the essential physics to predict the wall shear stress in more complex scenarios. The model is constructed to predict zero/favourable/adverse mean pressure gradient wall turbulence, separation, statistically unsteady turbulence with mean flow three-dimensionality, and laminar flow. The approach is implemented using two types of artificial neural networks: a classifier, which identifies the contribution of each building block in the flow, and a predictor, which estimates the wall shear stress via a combination of the building-block flows. The training data are obtained directly from wall-modelled LES (WMLES) optimised to reproduce the correct mean quantities. This approach guarantees the consistency of the training data with the numerical discretisation and the gridding strategy of the flow solver. The output of the model is accompanied by a confidence score in the prediction that aids the detection of regions where the model underperforms. The model is validated in canonical flows (e.g. laminar/turbulent boundary layers, turbulent channels, turbulent Poiseuille–Couette flow, turbulent pipe) and two realistic aircraft configurations: the NASA Common Research Model High-lift and NASA Juncture Flow experiment. It is shown that the building-block-flow wall model outperforms (or matches) the predictions by an equilibrium wall model. It is also concluded that further improvements in WMLES should incorporate advances in subgrid-scale modelling to minimise error propagation to the wall model.

How to raise the bridge barrier with a concrete base height of only 51 cm to SS level of protection is not yet studied. In order to effectively retrofit an existing concrete barrier design to meet new crash testing criteria, the structural dimensions and concrete strength of the existing bridge barrier were investigated, and finite element simulation analysis was carried out, and simulation suggested the existing barrier was insufficient. Based on the structural dimension design principles of bridge barriers, the existing structure of bridge barrier was designed after adding lightweight and high-strength B750HL material crossbeams and posts on top of the concrete base, and the bearing capacity of the bridge barrier was calculated based on the yield line theory. Then, a finite element simulation analysis model was established to study and analyze the blocking, guiding, and cushioning functions of the improved design of bridge barrier. Finally, full-scale-vehicle crash tests were conducted with the SS-level small car, bus, and tractor-van trailer for this bridge barrier design scheme. This paper is the world’s first to use B750HL steel as the material for the crossbeam and post of a bridge barrier with a concrete base height of only 51 cm. According to the research results, the B750HL bridge barrier, which was designed based on the calculation of structural dimension design and yield line theory, effectively reduces the increased constant load on the bridge deck caused by the extra crossbeams and posts. At the same time, it can reduce material costs and save engineering costs. After being verified by finite element simulation crash tests and full-scale-vehicle crash tests, the protective capacity of the B750HL bridge barrier was proven to meet the SS-level evaluation requirements of the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). The research findings of this paper is that the finite element simulation crash tests can effectively simulate full-scale-vehicle crash test, and the finite element simulation crash tests is reliable. If the safety performance of the barrier in the finite element simulation crash tests meets the requirements, the probability of passing the full-scale-vehicle crash test is higher. Therefore, a design scheme is proposed for the B750HL bridge barrier to improve hybrid bridge barriers at a height of 51 cm or more based on various design methods.

In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional models for providing closure mapping from the mean velocities to Reynolds stresses. Most data-driven models in this category need full-field Reynolds stress data for training, which not only places stringent demand on the data generation but also makes the trained model ill-conditioned and lacks robustness. This difficulty can be alleviated by incorporating the Reynolds-averaged Navier–Stokes (RANS) solver in the training process. However, this would necessitate developing adjoint solvers of the RANS model, which requires extra effort in code development and maintenance. Given this difficulty, we present an ensemble Kalman method with an adaptive step size to train a neural-network-based turbulence model by using indirect observation data. To our knowledge, this is the first such attempt in turbulence modelling. The ensemble method is first verified on the flow in a square duct, where it correctly learns the underlying turbulence models from velocity data. Then the generalizability of the learned model is evaluated on a family of separated flows over periodic hills. It is demonstrated that the turbulence model learned in one flow can predict flows in similar configurations with varying slopes.

Purpose The purpose of the paper is to propose a homogenization-based topology optimization method to optimize the design of variable-density cellular structure, in order to achieve lightweight design and overcome some of the manufacturability issues in additive manufacturing. Design/methodology/approach First, homogenization is performed to capture the effective mechanical properties of cellular structures through the scaling law as a function their relative density. Second, the scaling law is used directly in the topology optimization algorithm to compute the optimal density distribution for the part being optimized. Third, a new technique is presented to reconstruct the computer-aided design (CAD) model of the optimal variable-density cellular structure. The proposed method is validated by comparing the results obtained through homogenized model, full-scale simulation and experimentally testing the optimized parts after being additive manufactured. Findings The test examples demonstrate that the homogenization-based method is efficient, accurate and is able to produce manufacturable designs. Originality/value The optimized designs in our examples also show significant increase in stiffness and strength when compared to the original designs with identical overall weight.

Fluid flow in microporous and nanoporous media exhibits unique behaviors that deviate from classical continuum predictions due to dominant surface forces at small scales. Understanding these microscale flow mechanisms is critical for optimizing unconventional reservoir recovery and other energy applications. This review provides a comparative analysis of the existing literature, highlighting key advances in experimental techniques, theoretical models, and numerical simulations. We discuss how innovative micro/nanofluidic devices and high-resolution imaging methods now enable direct observation of confined flow phenomena, such as slip flow, phase transitions, and non-Darcy behavior. Recent theoretical models have clarified scale-dependent flow regimes by distinguishing microscale effects from macroscopic Darcy flow. Likewise, advanced numerical simulations—including molecular dynamics (MD), lattice Boltzmann methods (LBM), and hybrid multiscale frameworks—capture complex fluid–solid interactions and multiphase dynamics under realistic pressure and wettability conditions. Moreover, the integration of artificial intelligence (e.g., data-driven modeling and physics-informed neural networks) is accelerating data interpretation and multiscale modeling, offering improved predictive capabilities. Through this critical review, key phenomena, such as adsorption layers, fluid–solid interactions, and pore surface heterogeneity, are examined across studies, and persistent challenges are identified. Despite notable progress, challenges remain in replicating true reservoir conditions, bridging microscale and continuum models, and fully characterizing multiphase interface dynamics. By consolidating recent progress and perspectives, this review not only summarizes the state-of-the-art but underscores remaining knowledge gaps and future directions in micro/nanopore flow research.