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Discusses a new approach to solving optimal designing and control problems in aircraft gas‐turbine engine components. This approach is a combination of optimal designing problems with optimal control problems, allowing the formation of a single problem of optimal designing of controllable systems. The solving of this problem would involve simultaneous optimization of both design parameters and control laws. Allows the making of technically correct and substantiated decisions, taking into consideration several efficiency criteria for gas‐turbine engine components; a specific feature being the determination of a set of competitive optimal solutions in terms of different efficiency criteria values. Demonstrates the effectiveness of this approach by an example of multicriteria design optimization of a controllable axial flow compressor. Presents the results of a search of compressor blade rows geometrical parameters sets and of compressor stator blades control laws which are Edgeworth‐Pareto optimal for four operating modes. Shows a possibility of increasing compressor efficiency considerably by choosing the most preferable design parameters set and implementing in airborne digital control system a number of control laws optimal for different operating modes.

In general, the existing compressor design methods require abundant knowledge and inspiration. The purpose of this study is to identify an intellectual design optimization method that enables a machine to learn how to design it.

The airfoil design process was solved using the reinforcement learning (RL) method. An intellectual method based on a modified deep deterministic policy gradient (DDPG) algorithm was implemented. The new method was applied to agents to learn the design policy under dynamic constraints. The agents explored the design space with the help of a surrogate model and airfoil parameterization.

The agents successfully learned to design the airfoils. The loss coefficients of a controlled diffusion airfoil improved by 1.25% and 3.23% in the two- and four-dimensional design spaces, respectively. The agents successfully learned to design under various constraints. Additionally, the modified DDPG method was compared with a genetic algorithm optimizer, verifying that the former was one to two orders of magnitude faster in policy searching. The NACA65 airfoil was redesigned to verify the generalization.

It is feasible to consider the compressor design as an RL problem. Trained agents can determine and record the design policy and adapt it to different initiations and dynamic constraints. More intelligence is demonstrated than when traditional optimization methods are used. This methodology represents a new, small step toward the intelligent design of compressors.

This study aims to decrease the aerodynamic drag of the body of revolution at supersonic speeds. Supersonic area rule is widely used in modern supersonic aircraft design. Further reduction of the aerodynamic drag is possible in the framework of Euler and Reynolds averaged Navier–Stokes (RANS) equations. Sears–Haack body of revolution shape variation, which decreased its aerodynamic drag in compressible inviscid and viscous gas flow at Mach number of 1.8 under constraint of the volume with lower bound equal to volume of initial body, was numerically investigated.

Calculations were carried out in two-dimensional axisymmetric mode in the framework of Euler and RANS with SST model with compressibility correction equations at structured multiblock meshes. Variation of the radius as function of the longitudinal coordinate was given as a polynomial third-order spline through five uniformly distributed points. Varied parameters were increments of the radius of the body at points that defined spline. Drag coefficient was selected as an objective function. Parameter combinations corresponding to the objective function minimum under volume constraint were obtained by mixed-integer sequential quadratic programming at second-order polynomial response surface and IOSO algorithm.

Improving variations make front part of the body become slightly blunted, transfer part of volume from front part of the body to back part and generate significant back face. In the framework of RANS, the best variation decreases aerodynamic drag by approximately 20 per cent in comparison with Sears–Haack body.

The results can be applied for the aerodynamic design of the bullets and projectiles. The second important application is knowledge of the significance of the difference between linearized slender body theory optimization results and optimization results obtained by modern computational fluid dynamics (CFD) optimization techniques.

Knowledge about the magnitude of the difference between linearized slender body theory optimization results and optimization results obtained by modern CFD optimization techniques can stimulate further research in related areas.

The optimization procedure and optimal shapes obtained in the present work are directly applicable to the design of small aerodynamic drag bodies.

The purpose of this paper is to present original methods related to the modeling of material deposit and associated heat sources for finite element simulation of gas metal arc welding (GMAW).

The filler deposition results from high-frequency impingements of melted droplets. The present modeling approach consists of a time-averaged source term in the mass equation for selected finite elements in the fusion zone. The associated expansion of the mesh is controlled by means of adaptive remeshing. The heat input includes a volume source corresponding to the droplets energy, for which a model from the literature is expressed in coherency with mass supply. Finally, an inverse technique has been developed to identify different model parameters. The objective function includes the differences between calculations and experiments in terms of temperature, but also shape of the fusion zone.

The proposed approach for the modeling of metal deposition results in a direct calculation of the formation of the weld bead, without any a priori definition of its shape. Application is shown on GMAW of steel 316LN, for which parameters of the model have been identified by the inverse method. They are in agreement with literature and simulation results are found quite close to experimental measurements.

The proposed algorithm for material deposit offers an alternative to the element activation techniques that are commonly used to simulate the deposition of filler metal. The proposed inverse method for parameter identification is original in that it encompasses an efficient and convenient technique to take into account the shape of the fusion zone.

The purpose of this paper is to expand possibilities of stability computing method when performing a dynamic analysis of bar- or rod-shaped elements for actual structures.

The methodology is based on the changes of stress–strain state of the bar-shaped elements at the moment of buckling. The proposed method is based on three assumptions. Firstly, the spatial stress–strain state is determined in the bar. Secondly, technological deviations inherent in real structures are introduced into the bar. Thirdly, mechanical behaviour of the bar is investigated in the mode of real time, which makes it possible to take into account wave deformation processes in the bar. To implement the suggested method of analysis, LS-DYNA package was selected in a dynamic formulation using solid finite elements.

Validity of the proposed method is shown by an example of dynamic stability analysis of a steel flat thin bar with two types of loads: short-time and long-term axial compressions. Comparison of the results showed different nature of the mechanical behaviour of the bar: wave processes are observed under short-time loading, and continuous monotone ones are stated under long-term loads.

Research results are applicable in the rocket and space industry.

A new computer-based methodology for dynamic analysis of heterogeneous elastic-plastic bar-, rod-shaped structures under shock axial compressive loads is proposed.

Urban green space can be viewed as a preventative public health measure. Nature contributes to health through disease prevention, disease management, and well-being (physical, mental, and social) promotion. Those contributions are based on improvement in health determinants. Nature and green spaces have been related to more physical activity, restoration and less stress, more social capital, and ecosystem services such as better air quality, less traffic noise, less heat island effects, more biodiversity, among others. Nature, vegetation, and green spaces have also been associated with better mental health, immune system, metabolic system, pregnancy outcomes, reduced cardiovascular disease, and premature mortality. This chapter presents the connections between nature and health, describing how nature impacts key health determinants, how those health determinants are associated with health outcomes (i.e. diseases, injuries, deaths), and provides examples of urban nature interventions that have been related to public health.

The purpose of this paper is to study the predictability of the recently proposed length scale-based two-equation k-kL model for external aerodynamic flows such as those also encountered in the high-lift devices.

The two-equation k-kL model solves the transport equations of turbulent kinetic energy (TKE) and the product of TKE and the integral length scale to obtain the effect of turbulence on the mean flow field. In theory, the use of governing equation for length scale (kL) along with the TKE promises applicability in a wide range of applications in both free-shear and wall-bounded flows with eddy-resolving capability.

The model is implemented in the in-house unstructured grid computational fluid dynamics solver to investigate its performance for airfoils in difficult-to-predict situations, including stalling and separation. The numerical findings show the good capability of the model in handling the complex flow physics in the external aerodynamic computations.

The model performance is studied for stationary turbulent external aerodynamic flows, using five different airfoils, including two multi-element airfoils in high-lift configurations which, in the knowledge of the authors, have not been simulated with k-kL model until now.

The purpose of this study is to present and demonstrate a numerical method for solving chemically reacting flows. These are important for energy conversion devices, which rely on chemical reactions as their operational mechanism, with heat generated from the combustion of the fuel, often gases, being converted to work.

The numerical study of such flows requires the set of Navier-Stokes equations to be extended to include multiple species and the chemical reactions between them. The numerical method implemented in this study also accounts for changes in the material properties because of temperature variations and the process to handle steep spatial fronts and stiff source terms without incurring any numerical instabilities. An all-speed numerical framework is used through simple low-dissipation advection upwind splitting (SLAU) convective scheme, and it has been extended in a multi-component species framework on the in-house density-based flow solver. The capability of solving turbulent combustion is also implemented using the Eddy Dissipation Concept (EDC) framework and the recent k-kl turbulence model.

The numerical implementation has been demonstrated for several stiff problems in laminar and turbulent combustion. The laminar combustion results are compared from the corresponding results from the Cantera library, and the turbulent combustion computations are found to be consistent with the experimental results.

This paper has extended the single gas density-based framework to handle multi-component gaseous mixtures. This paper has demonstrated the capability of the numerical framework for solving non-reacting/reacting laminar and turbulent flow problems. The all-speed SLAU convective scheme has been extended in the multi-component species framework, and the turbulent model k-kl is used for turbulent combustion, which has not been done previously. While the former method provides the capability of solving for low-speed flows using the density-based method, the later is a length-scale-based method that includes scale-adaptive simulation characteristics in the turbulence modeling. The SLAU scheme has proven to work well for unsteady flows while the k-kL model works well in non-stationary turbulent flows. As both these flow features are commonly found in industrially important reacting flows, the convection scheme and the turbulence model together will enhance the numerical predictions of such flows.