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This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level.

Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate.

Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day.

The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues.

Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures.

It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures.

The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components.

The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

The purpose of this paper is to provide an empirical comparison of a high-fidelity managerial simulation, assessment center (AC) ratings, to that of a lower fidelity managerial simulation, a video situational judgment test (SJT) in the prediction of manager career success.

Archival data were collected from a large utility company. A measure of general mental ability (GMA), an SJT, and an AC were examined as predictors of career success as measured by increases in salary.

The AC and the video SJT used in this study appeared to assess different constructs, extending previous findings that ACs and written SJTs measure distinct constructs. Furthermore, the AC dimensions and the SJT remained valid predictors of salary over a six year span following the test administration. In addition, the AC explained significant incremental variance beyond GMA and SJTs in career success six years after the assessment.

The SJTs and AC used in this study are similar in psychological fidelity, yet the ACs remained a more valid predictor over time. The recommendation is that lower fidelity simulations should not be used as prerequisites for higher fidelity simulations.

The results lend general support to the value of high-fidelity instruments in predicting longitudinal success.

The paper offers a comparison of the validity of ACs and video SJTs.

This paper aims to investigate the strategy for low-cost yield optimization of miniaturized microstrip couplers using variable-fidelity electromagnetic (EM) simulations.

Usefulness of data-driven models constructed from structure frequency responses formulated in the form of suitably defined characteristic points for statistical analysis is investigated. Reformulation of the characteristics leads to a less nonlinear functional landscape and reduces the number of training samples required for accurate modeling. Further reduction of the cost associated with construction of the data-driven model, is achieved using variable-fidelity methods. Numerical case study is provided demonstrating feasibility of the feature-based modeling for low cost statistical analysis and yield optimization.

It is possible, through reformulation of the structure frequency responses in the form of suitably defined feature points, to reduce the number of training samples required for its data-driven modeling. The approximation model can be used as an accurate evaluation engine for a low-cost Monte Carlo analysis. Yield optimization can be realized through minimization of yield within the data-driven model bounds and subsequent model re-set around the optimized design.

The investigated technique exceeds capabilities of conventional Monte Carlo-based approaches for statistical analysis in terms of computational cost without compromising its accuracy with respect to the conventional EM-based Monte Carlo.

The proposed tolerance-aware design approach proved useful for rapid yield optimization of compact microstrip couplers represented using EM-simulation models, which is extremely challenging when using conventional approaches due to tremendous number of EM evaluations required for statistical analysis.

The purpose of this work is to investigate the similarity requirements for the application of multifidelity modeling (MFM) for the prediction of airfoil dynamic stall using computational fluid dynamics (CFD) simulations.

Dynamic stall is modeled using the unsteady Reynolds-averaged Navier–Stokes equations and Menter's shear stress transport turbulence model. Multifidelity models are created by varying the spatial and temporal discretizations. The effectiveness of the MFM method depends on the similarity between the high- (HF) and low-fidelity (LF) models. Their similarity is tested by computing the prediction error with respect to the HF model evaluations. The proposed approach is demonstrated on three airfoil shapes under deep dynamic stall at a Mach number 0.1 and Reynolds number 135,000.

The results show that varying the trust-region (TR) radius (λ) significantly affects the prediction accuracy of the MFM. The HF and LF simulation models hold similarity within small (λ ≤ 0.12) to medium (0.12 ≤ λ ≤ 0.23) TR radii producing a prediction error less than 5%, whereas for large TR radii (0.23 ≤ λ ≤ 0.41), the similarity is strongly affected by the time discretization and minimally by the spatial discretization.

The findings of this work present new knowledge for the construction of accurate MFMs for dynamic stall performance prediction using LF model spatial- and temporal discretization setup and the TR radius size. The approach used in this work is general and can be used for other unsteady applications involving CFD-based MFM and optimization.

The purpose of this systematic review was to explore the benefits and challenges in the implementation of simulation-based education (SBE) in the classroom and clinical settings in sub-Saharan Africa. The objectives of this systematic review were to identify the benefits of utilising SBE in the classroom and clinical practice in sub-Saharan Africa and to assess the challenges in the implementation of SBE in the classroom and clinical practice in sub-Saharan Africa.

Five databases were searched for existing English literature (Medline, CINAHL and Science Direct), including grey literature on the subject. Out of 26 eligible studies conducted in sub-Saharan Africa between 2014 and 2021, six studies that used mixed-methods design were included. Hawker et al.’s framework was used to assess the quality of the studies. Quantitative data were presented using descriptive and inferential statistics in the form of means and standard deviations while qualitative data were analysed and presented thematically.

Quantitative findings showed that participants rated SBE highly in terms of teaching (93.2%), learning (91.4%) and skill acquisition (88.6%). SBE improved the clinical skill competency from 30% at baseline to 75% at the end. On the other hand, qualitative findings yielded themes namely: improved confidence and competence; knowledge acquisition and critical thinking; motivation and supervision; independent, self-paced learning; simulation equipment and work schedules; and planning and delivery of simulation activity. Pedagogical skills, competence and confidence are some of the elements that determine the feasibility of implementing SBE in the classroom and clinical settings.

SBE could help to bridge the gap between theory and practice and improve the quality of care provided by nurses. Simulation-based training is effective in improving the clinical skills of midwives and increasing their confidence in providing care. However, SBE trainees require motivation and close supervision in classroom settings if simulation is to be successfully implemented in sub-Saharan Africa. Furthermore, careful planning of scenarios, students briefing and reading of content prior to implementation facilitate effective simulation.

While there may be a lack of literature on the use of SBE for training nurses and midwives in the developing world, there is growing evidence that it can be an effective way to improve clinical skills and quality of care. However, there are also significant challenges to implementing simulation-based training in resource-limited settings, and more research is needed to understand how best to address these challenges. This study fills this gap in the literature.

Responsible for lift generation, the helicopter rotor is an essential component to protect against ice accretion. As rotorcraft present a smaller wing cross-section and a lower available onboard power compared to aircraft, electro-thermal heating pads are favored as they conform to the blades’ slender profile and limited volume. Their optimization is carried out here taking into account, for the first time, the highly three-dimensional (3D) nature of the flow and ice accretion, in contrast to the current state-of-the-art that is limited to two-dimensional (2D) airfoils.

Conjugate heat transfer simulation results are provided by the truly 3D finite element Navier–Stokes analysis package-ICE code, embedded in a proprietary rotorcraft simulation toolkit, with reduced-order modeling providing a time-efficient evaluation of the objective and constraint functions at every iteration. The proposed methodology optimizes heating pads extent and power usage and is versatile enough to address in a computationally efficient manner a wide variety of optimization formulations.

Low-error reduced-order modeling strategies are introduced to make the tackling of complex 3D geometries feasible in todays’ computers, with the developed framework applied to four problem formulations, demonstrating marked reductions to power consumption along with improved aerodynamics.

The present paper proposes a 3D framework for the optimization of electro-thermal rotorcraft ice protection systems, in hover and forward flight. The current state-of-the-art is limited to 2D airfoils.

Higher education organisations have the need and capacity to meet the expectations of industry partners to develop practice-ready graduates. Altered social and fiscal constraints have implications for the health workforce and, as such, decisions about how best to prepare undergraduate students should be considered. The purpose of this (conceptual) paper is to present the undergraduate simulation framework that has been designed to promote a standardised delivery of simulation, enabling graduates to have a higher capacity to meet workforce needs.

Education and simulation literature were explored to inform the development of the undergraduate simulation framework. A working knowledge of accreditation standards of health professions was key in designing a framework that could be applied across disciplines.

The framework encompasses tenets of a learner-centred pedagogy as well as professional and simulation standards. Experiential learning, behaviourism and social constructivism are viewed as foundational pillars when developing and delivering a simulation and have been considered in developing the framework. Other constructs of the undergraduate simulation framework are prebriefing, debriefing, repetition in the form of simulation cycles and the roles of student and educator.

It is recommended that this framework be incorporated into undergraduate health programmes to enhance student learning and potentiate the transference of knowledge and skills to the clinical setting.

The undergraduate simulation framework was developed to contribute to education and simulation research literature specific to health programmes to enhance student learning in readiness for the clinical environment.

High-fidelity simulation has well-established educational value. However, its use in psychiatry remains underexplored. This study explores medical students’ experiences of high-fidelity simulation teaching during their psychiatry placements. A session was delivered on “psychiatric emergencies”, set in a simulated emergency department, with equal emphasis on the management of physical and psychiatric aspects of patient care. This paper aims to report on student attitudes to high-fidelity simulation teaching in psychiatry, as well as student attitudes to “integrated” teaching (i.e. covering both physical and psychiatric knowledge).

Semi-structured focus groups were conducted with medical students at a UK university. This exploratory approach generated rich qualitative data. Thematic analysis was used.

High-fidelity simulation teaching in psychiatry is well regarded by medical students, and helps students recognise that psychiatric problems can present in any clinical setting. This study has demonstrated that students value this type of “integrated” teaching, and there is potential for this approach to be more widely adopted in undergraduate health-care professional education. High-fidelity simulation could also be considered for incorporation in undergraduate examinations.

To the best of their knowledge, the authors are the first to conduct an in-depth exploration of attitudes to simulation teaching specifically in psychiatry. The authors are also the first to directly explore student attitudes to “integrated” teaching of psychiatry and physical health topics. The results will support the effective planning and delivery of simulation teaching in psychiatry, the planning of undergraduate summative assessments and will likely be of interest to health-care professionals, educational leads, simulation practitioners and students.

The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models.

The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments.

Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches.

The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.