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This paper aims to present a numerical method based on computational fluid dynamics that allows investigating the buffet envelope of reference equivalent wings at the equivalent cost of several two-dimensional, unsteady, turbulent flow analyses. The method bridges the gap between semi-empirical relations, generally dominant in the early phases of aircraft design, and three-dimensional turbulent flow analyses, characterised by high costs in analysis setups and prohibitive computing times.

Accuracy in the predictions and efficiency in the solution are two key aspects. Accuracy is maintained by solving a specialised form of the Reynolds-averaged Navier–Stokes equations valid for infinite-swept wing flows. Efficiency of the solution is reached by a novel implementation of the flow solver, as well as by combining solutions of different fidelity spatially.

Discovering the buffet envelope of a set of reference equivalent wings is accompanied with an estimate of the uncertainties in the numerical predictions. Just over 2,000 processor hours are needed if it is admissible to deal with an uncertainty of ±1.0° in the angle of attack at which buffet onset/offset occurs. Halving the uncertainty requires significantly more computing resources, close to a factor 200 compared with the larger uncertainty case.

To permit the use of the proposed method as a practical design tool in the conceptual/preliminary aircraft design phases, the method offers the designer with the ability to gauge the sensitivity of buffet on primary design variables, such as wing sweep angle and chord to thickness ratio.

The infinite-swept wing, unsteady Reynolds-averaged Navier–Stokes equations have been successfully applied, for the first time, to identify buffeting conditions. This demonstrates the adequateness of the proposed method in the conceptual/preliminary aircraft design phases.

The purpose of this paper is to document an efficient and accurate approach to generate aerodynamic tables using computational fluid dynamics. This is demonstrated in the context of a concept transport aircraft model.

Two designs of experiment algorithms in combination with surrogate modelling are investigated. An adaptive algorithm is compared to an industry-standard algorithm used as a benchmark. Numerical experiments are obtained solving the Reynolds-averaged Navier–Stokes equations on a large computational grid.

This study demonstrates that a surrogate model built upon an adaptive design of experiments strategy achieves a higher prediction capability than that built upon a traditional strategy. This is quantified in terms of the sum of the squared error between the surrogate model predictions and the computational fluid dynamics results. The error metric is reduced by about one order of magnitude compared to the traditional approach.

This work lays the ground to obtain more realistic aerodynamic predictions earlier in the aircraft design process at manageable costs, improving the design solution and reducing risks. This may be equally applied in the analysis of other complex and non-linear engineering phenomena.

This work explores the potential benefits of an adaptive design of experiment algorithm within a prototype working environment, whereby the maximum number of experiments is limited and a large parameter space is investigated.

This paper aims to develop an efficient evaluation method to more intuitively and effectively investigate the influence of the wing fuel mass variations because of fuel burn on transonic aeroelasticity.

The proposed efficient aeroelastic evaluation method is developed by extending the standard computational fluid dynamics (CFD)-based proper orthogonal decomposition (POD)/reduced order model (ROM).

The results of this paper show that the proposed aeroelastic efficient evaluation method can accurately and efficiently predict the aeroelastic response and flutter boundary when the wing fuel mass vary because of fuel burn. It also shows that the wing fuel mass variations have a significant effect on transonic aeroelasticity; the flutter speed increases as the wing fuel mass decreases. Without rebuilding an expensive, time-consuming CFD-based POD/ROM for each wing fuel mass variation, the computational cost of the proposed method is reduced obviously. It also shows that the computational efficiency improvement grows linearly with the number of model cases.

The paper presents a potentially powerful tool to more intuitively and effectively investigate the influence of the wing fuel mass variation on transonic aeroelasticity, and the results form a theoretical and methodological basis for further research.

The proposed evaluation method makes it a reality to apply the efficient standard CFD-based POD/ROM to investigate the influence of the wing fuel mass variation because of fuel burn on transonic aeroelasticity. The proposed efficient aeroelastic evaluation method, therefore, is ideally suited to deal with the investigation of the influence of wing fuel mass variations on transonic aeroelasticity and may have the potential to reduce the overall cost of aircraft design.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

The purpose of this study is to investigate the status of perceived need, seeking behavior and utilization of health services in the elderly population of Sanandaj (west of Iran).

This is a cross-sectional study conducted on 800 elderly people in Sanandaj. Subjects were selected using multistage sampling and data were collected using self-report questionnaires. A multivariate logistic model with odds ratios (ORs) was used to determine the relationship of independent variables with seeking perceived need. Also, the concentration index was used to measure the inequality in using health services.

The perceived need for outpatient (during the last 30 days) and inpatient health-care services (during the past 12 months) was 69.7% and 29.7%, respectively. Among them, the unmet need for outpatient and inpatient health-care services was 46.6% and 17%, respectively. Having health insurance (adjusted OR 12.08; 95% confidence interval [CI] 1.04–140.11), middle economic status (adjusted OR 5.18; 95% CI 1.30–20.51) and being in an age group of 65–70 years (adjusted OR 7.60; CI 1.42–40.61) increased the chance of seeking inpatient care. Also, being in an age group of 60–65 years (adjusted OR 0.41; 95% CI 0.18–0.95) reduced the chance of seeking outpatient care. There was also a pro-rich inequality in using outpatient health services.

The elderly population suffers from unmet health-care needs, especially in outpatient services. The most important reason for not seeking outpatient and inpatient services was financial barriers and self-medication, respectively. So, designing targeted policies and interventions to address barriers in the conversion of need to demand in the elderly population is essential.