Steam boiler mixing channel optimization with a surrogate based multi objective genetic algorithm

Multidisciplinary design optimization techniques become more and more applied in the field of aerodynamics due to the rapid development of computers high-performance, numerical methods and optimization algorithms. These techniques coupled with Computational Fluid Dynamics (CFD), which aims to incorporate mathematical relations and algorithms to analyze and solve fluid flow problems, involve the use of those numerical methods and algorithms to improve the fluid flow solutions. CFD analysis of an airfoil determines its ability by producing results such as lift and drag forces, and the application of an optimization algorithm involves improving the shape of this airfoil in order to manipulate the lift and drag coefficients according to the requirements. In this work, a numerical investigation, using ANSYS/Fluent, of two-dimensional transonic flow over a NACA 0012 airfoil was conducted at various Mach numbers and compared with the provided experimental data. The flow to be considered is compressible and turbulent and the solver used is the density based implicit solver, which gives good results for high speed compressible flows. Then a shape optimization algorithm, based on a Multi-Objective Genetic Algorithm, was used in order to obtain an improved performance control of the aerodynamic coefficients of the optimized airfoil.

Recently, energy saving problem attracts increasing attention from researchers. This study aims to determine the optimal arrangement of a tube bundle to achieve the best overall performance. The multi-objective genetic algorithm (MOGA) is employed to determine the best configuration, where two objective functions, the average heat flux q and the pressure drop Δp, are selected to evaluate the performance and the consumption, respectively. Subsequently, a decision maker method, technique for order preference by similarity to an ideal solution (TOPSIS), is applied to determine the best compromise solution from noninferior solutions (Pareto solutions). In the optimization procedure, all the two-dimensional (2D) symmetric models are solved by the computational fluid dynamics (CFD) method. Results show that performances alter significantly as geometries of the tube bundle changes along the Pareto front. For the case 1 (using staggered arrangement as initial), the optimal q varies from 2708.27 W/m2 to 3641.25 W/m2 and the optimal Δp varies from 380.32 Pa to 1117.74 Pa, respectively. For the case 2 (using in-line arrangement as initial), the optimal q varies from 2047.56 W/m2 to 3217.22 W/m2 and the optimal Δp varies from 181.13 Pa to 674.21 Pa, respectively. Meanwhile, the comparison between the optimal solution with maximum q and the one selected by TOPSIS indicates that TOPSIS could reduce the pressure drop of the tube bundle without sacrificing too much heat transfer performance.

In this paper, a method for predicting the extreme value distribution of the vertical bending moment (VBM) in a ship under a given short-term sea state is presented. To predict the extreme value distribution of the VBM, the first-order reliability method (FORM), by which the most probable wave episodes (MPWEs) leading to given VBMs are identified, is introduced. The coupled computational fluid dynamics (CFD) and finite-element analysis (FEA) are used to provide the high-fidelity numerical solutions for the wave-induced and whipping components of the VBM. Then, a Reduced-Order Model (ROM) which can yield the predictions equivalent to the coupled CFD–FEA results in a relatively short time is developed. The ROM is incorporated into FORM to identify the MPWEs, in lieu of the coupled CFD–FEA. The accuracy of the ROM is verified by comparing with the coupled CFD–FEA results under identified MPWEs, in terms of both the wave-induced and whipping VBM. Then, a series of tank tests using a scaled container ship is conducted. In the first series of the tests, the VBM measurements under the MPWEs identified from the FORM-based approach using the ROM are made, to validate the accuracy of the ROM. The extreme value distribution of the combined wave-induced and whipping VBM is also measured by performing the second series of the tests, in random waves. The validity of the FORM-based extreme value prediction using the ROM is investigated by comparing with the second series of the tests.

This paper deals with the coupling of Computational Fluid Dynamics (CFD) with 'home made' software for the prediction of pyrolysis within a rotary kiln. The 'home made' software describes the phenomena that occur within a bed of solid particles submitted to pyrolysis. It requires the net specific heat flux originating from the surroundings while it yields the specific mass flow of volatiles released from the bed. Used as a boundary profile condition into a CFD software, this specific mass flow rate of volatiles allows for prediction of oxidation phenomena in the gaseous phase.

Energy consumption analysis intended for real office space with energy recovery ventilator by integrating BES and CFD approaches

In this study, a floating offshore wind turbine with a foundation constructed from steel–UHPC (ultrahigh performance concrete) is investigated. The structural design and strength performance of the floating foundation are evaluated through a combination of numerical simulation and wave tank experiments. Load assessment and feasibility analyses, which reveal that the proposed steel–UHPC foundation satisfies structural safety requirements while exhibiting significant potential for material cost reduction, are conducted. With respect to the numerical simulation, a coupling of computational fluid dynamics (CFD) and finite element analysis (FEA) solvers is employed to address the fluid–structure interaction (FSI) problem. External hydrodynamic pressure obtained from the CFD solver is used to derive the structural response in the FEA solver. Given that the deformation of the steel–UHPC structure has a negligible effect on the surrounding flow field, one-way CFD–FEA coupling, in which fluid loads are transferred to the structural model without feedback of structural deformation to the fluid solver, is used. Wave tank experiments are conducted to validate the accuracy and reliability of the proposed one-way coupling methodology. Furthermore, an equivalent constitutive model for steel–UHPC is implemented within the FEA solver. The corresponding physical and mechanical properties are derived, and key structural design parameters of the floating foundation are determined accordingly.

ABSTRACTThe present work deals with the investigation of the exhaust manifold of a four-cylinder diesel engine. Geometry creation and finite element method of the exhaust manifold was done using Computer Aided Design and CAE software. To simulate the thermal boundary coefficients of the exhaust manifold wall under steady-state loading conditions, the internal flow field structure was obtained using Computational Fluid Dynamics (CFD) software. The thermal boundary conditions were mapped to structural element surfaces of the exhaust manifold based on commercial finite element code ABAQUS. Thermal analysis was conducted to find out the high thermal stress areas. Design modifications were done on the high stress area and the process was repeated and stresses were reduced to meet the target. Numerical results and contour plots were reported on. The analysis of such a system is critical due to the complexity of meeting the structural and flow requirements. The use of coupled CFD and FEA simulation gives a better prediction of thermal stresses and flow behaviour in the exhaust manifold. The simulation captures the stress pattern and stress concentration at the failure location. The modification has reduced the stress level by 13% and eliminated the stress concentration.

The use of available and cheap industrial producer gases as alternative fuels for the steel reheating furnaces is an attractive topic for steel industry. The application of producer gases for such furnaces introduces not only the complicated combustion system of Low Calorific Value (LCV) gases, but also several impurities that could be problematic for the quality of final steel products. The quality of steel can be highly affected by the interaction of impurities with iron-oxides at hot slab surfaces. In this research, the combustion of producer gases and the behavior of impurities at the steel slab surface are studied by aid of a novel coupled computational fluid dynamics (CFD) and thermodynamics approach. The impurities are introduced as mineral ash particles with the particle size distributions of 15–100 μm. The CFD predicted data regarding the accumulation of ash particles are extracted from an interface layer at the flaring gas media around the steel slab surface. Later on, these predicted data are used for the thermo-chemical calculations regarding the formation of sticky solutions and stable phases at the steel slab surface. The results show that the particles are more likely follow the flow due to the high injection velocity of fuel (70 m/s) and the dominant inertial forces. More than 90 percent of particles have been evacuated through the exhaust pipes. The only 10 percent of remaining particles due to the high recirculation zones at the middle of furnace and the impinging effect of front walls tend to stick to the side wall of slab in the heating zone more than the soaking zone.

A computational fluid dynamics and finite element analysis design of a microtubular solid oxide fuel cell stack for fixed wing mini unmanned aerial vehicles

Water treatment systems have been implemented by urbanizing societies for millennia to facilitate water management goals. Common models of surface overflow rate (SOR), plug flow reactor (PFR), and continuously stirred-tank reactor (CSTR) were developed through conceptual, empirical, and analytical tools; implemented based on idealized hydrodynamics and geometrics. More recently, computational fluid dynamics (CFD) and artificial intelligence (AI), from evolutionary optimization to machine learning (ML) methods, have been introduced. AI methods can be effectively coupled with CFD simulations to optimize water treatment. In this study, CFD coupled with physical models and selected ML and optimization tools, including DeepXtorm, are examined with respect to design, treatment analysis, and retrofits, providing significant economic and treatment benefits.

Personalized ventilation has great potential to improve inhaled air quality and to accommodate individual thermal preferences. In order to quantify these perceived benefits, a numerical method has been developed. In this method, a numerical thermal manikin (NTM), with the real geometry of a human body, is obtained by employing a laser scanning technique. When placed in a virtual environment, the thermal interactions with the environment are calculated using computational fluid dynamics (CFD). By iteration, the calculated air velocity near the body surface is fed into a sophisticated thermoregulation model developed at the University of California, Berkeley, so that the local thermal comfort in a non-uniform environment created by personalized air (PA) is rigorously investigated. In this paper, the performances of three different PV systems are investigated, namely, the desk-edge-based PV, PV using a movable panel (MP), and chair-based PV. The results exhibit reasonable agreement with the experimental measurements. The three types of PV are all able to lower human exposure to ambient room pollutants and bring a “cool head” thermal condition favorable for thermal comfort. The present work illustrates that in the development of localized personal environmental control systems, an NTM coupled with a human-body thermal regulation model is a useful tool for visualizing thermal comfort and ventilation effectiveness.

Coupling DAEM and CFD for simulating biomass fast pyrolysis in fluidized beds

Summary Extended reach drilling (ERD) is an advanced technique that achieves broad reservoir coverage by significantly extending the well’s horizontal reach. However, drilling these long, high-angle sections faces a significant challenge—wellbore cleaning. Traditional wellbore cleaning strategies form the foundation of hole cleaning by optimizing parameters such as rate of penetration (ROP), drillstring rotation, and fluid rheology. In the demanding context of ERD, however, the cleaning capacity provided by these methods alone may be inadequate to manage cuttings in long horizontal sections, calling for enhanced cleaning techniques. The deployment of cuttings bed remover (CBR) to mechanically disrupt persistent accumulations has proved to be an effective solution. The CBR disrupts the structure of the cuttings bed by generating a high-intensity spiral flow field and alleviates the cuttings accumulation caused by gravitational settling in low-flow velocity zones. However, the underlying mechanisms governing the structure-performance relationships require further systematic investigation. To this end, we develop a 3D flow model by coupling computational fluid dynamics (CFD) with the discrete element method (DEM). Meanwhile, we simulate the migration and sedimentation behaviors of cuttings in the annulus by considering the coupling effect between the drilling fluid and cutting particles, thereby systematically analyzing the influence of the CBR on the morphology, thickness, and removal efficiency of the cuttings bed. Based on the simulation results, the action zone of CBR can be divided into the intense agitation zone and the intense transport zone, with swirl strength and transport intensity used as quantitative evaluation indicators, respectively. Moreover, we discuss the effects of drillpipe rotational speed, CBR structural parameters, and annular flow velocity. Compared with rectangular-type CBR configurations, the deflection-angled CBR design demonstrates marked superiority in performance of generating swirl, exhibiting enhanced generation capacity, although potentially incurring more pressure loss. With this study, we introduce an innovative method for delineating the operational zones of the CBR and quantitatively assessing its effectiveness, thereby establishing a robust theoretical framework for evaluating the impact of CBR. By analyzing swirl intensity and effective action distance in the high-agitation zone, the key parameters of CBR are optimized as blade height (25–27 mm), length (1.5 m), count (4–5), and inclination angle (30–40°). Additionally, a multiunit short-CBR configuration outperforms a single long-unit design.

Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time.

A mathematical model has been developed as a design optimization tool for the prediction of the aerodynamic behavior of a web in an industrial dryer. In particular the key modeling objective is to examine the propagation of stress waves within the web to determine its lateral motion and hence its stability. Numerical solutions were obtained through a coupling of computational fluid dynamics (CFD) calculations and structural dynamic simulations (SDS). The viscoelastic behavior of the web and the presence of turbulence were found to significantly affect the stability of the web.