Automated Design Optimization of a Small-Scale High-Swirl Cavity-Stabilized Combustor

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Alejandro M. Briones ◽  
David L. Burrus ◽  
Joshua P. Sykes ◽  
Brent A. Rankin ◽  
Andrew W. Caswell
Keyword(s):  
Total Pressure ◽  
Cfd Simulation ◽  
Fluid Dynamic ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Baseline Design ◽  
Design Point ◽  
Swirl Angle ◽  
Input Parameters ◽  
Exit Temperature

A numerical optimization study is performed on a small-scale high-swirl cavity-stabilized combustor. A parametric geometry is created in cad software that is coupled with meshing software. The latter automatically transfers meshes and boundary conditions to the solver, which is coupled with a postprocessing tool. Steady, incompressible three-dimensional simulations are performed using a multiphase Realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach with a nonadiabatic flamelet progress variable (FPV) model. There are nine geometrical input parameters. There are five output parameters, viz., pattern factor (PF), RMS of the profile factor deviation, averaged exit temperature, averaged exit swirl angle, and total pressure loss. An iterative design of experiments (DOE) with a recursive Latin hypercube sampling (LHS) is performed to filter the most important input parameters. The five major input parameters are found with Spearman's order-rank correlation and R2 coefficient of determination. The five input parameters are used for the adaptive multiple objective (AMO) optimization. This provided a candidate design point with the lowest weighted objective function, which was verified through computational fluid dynamic (CFD) simulation. The combined filtering and optimization procedures improve the baseline design point in terms of pattern and profile factor. The former halved from that of the baseline design point, whereas the latter turned from an outer peak to a center peak profile, closely mimicking an ideal profile. The exit swirl angle favorably increased 25%. The averaged exit temperature and the total pressure losses remained nearly unchanged from the baseline design point.

Automated Design Optimization of a Small-Scale High-Swirl Cavity-Stabilized Combustor

2018 ◽  
Author(s):  
Alejandro M. Briones ◽  
David L. Burrus ◽  
Joshua P. Sykes ◽  
Brent A. Rankin ◽  
Andrew W. Caswell
Keyword(s):  
Total Pressure ◽  
Cfd Simulation ◽  
Rank Correlation ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Baseline Design ◽  
Design Point ◽  
Swirl Angle ◽  
Input Parameters ◽  
Exit Temperature

A numerical optimization study is performed on a small-scale high-swirl cavity-stabilized combustor. A parametric geometry is created in CAD software that is coupled with meshing software. The latter automatically transfers meshes and boundary conditions to the solver, which is coupled with a post-processing tool. Steady, incompressible three-dimensional simulations are performed using a multi-phase Realizable k-ϵ Reynolds-averaged Navier-Stokes (RANS) approach with the non-adiabatic flamelet progress variable (FPV). There are nine input parameters based on geometrical control variables. There are five output parameters, viz., pattern factor (PF), RMS of the profile factor deviation, averaged exit temperature, averaged exit swirl angle, and total pressure loss. An iterative design of experiments (DOE) with a recursive Latin Hypercube Sampling (LHS) is performed to filter the most important input parameters. The five major input parameters are found with Spearman’s order-rank correlation and R2 coefficient of determination. The five input parameters are used for the adaptive multiple objective (AMO) optimization. The AMO algorithm provided a candidate design point with the lowest weighted objective function. This design point was verified through CFD simulation. The combined filtering and optimization procedures improve the baseline design point in terms of pattern and profile factor. The former halved from that of the baseline design point whereas the latter turned from an outer peak to a center peak profile, closely mimicking an ideal profile. The exit swirl angle favorably increased 25%. The averaged exit temperature and the total pressure losses remained nearly unchanged from the baseline design point.

LES-Verified RANS-Based Deterministic and Continuous Multiple-Objective Combustor Design Optimization

2020 ◽  
Author(s):  
Alejandro M. Briones ◽  
Timothy J. Erdmann ◽  
Brent A. Rankin
Keyword(s):  
Optimum Design ◽  
Total Pressure ◽  
Three Dimensional ◽  
Training Sample ◽  
Combustion Efficiency ◽  
Small Scale ◽  
Moderate Pressure ◽  
Baseline Design ◽  
Pressure Losses ◽  
Exit Temperature

Abstract Optimization procedures are performed using RANS models due to computational wall-clock time constraints. These trends are verified against selected LES counterpart simulations. The optimization study is performed on a subsonic small-scale cavity-stabilized combustor. Steady, compressible three-dimensional simulations are performed using a multi-phase Realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach or Dynamic Kinetic Energy Subgrid-scale LES. There are nineteen geometrical input parameters and four output parameters, viz., a pattern factor proxy (maximum exit temperature), a combustion efficiency proxy (averaged exit temperature), total pressure losses (TPL), and “melting” liner area. The RANS-based optimization with initial training sample of sixty design points leads to an optimum design point that dominates the baseline design, among other designs. The non-dominated design points release 57% of the heat inside the cavity in comparison to 43% from the baseline. Dominated combustors release as low as 23% of the heat in the cavity. The optimum, a dominated and the baseline design are compared with LES counterparts. The RANS predicted trends in terms of dominated and non-dominated design points were also confirmed by LES simulations, even though the output parameters quantitative values may differ. Discrepancies between RANS and LES appear more pronounced for the optimum design. This is attributed to the fact the LES permits pressure waves to escape the domain whereas RANS can only moderate pressure-induced disturbances. By allowing boundary-induced disturbances in the LES the flow field resembles that of RANS. Therefore, it is concluded that RANS and LES follow similar total pressure loss, pattern factor, and combustion efficiency trends as function of combustor design.

Effect of Deterministic and Continuous Design Space Resolution on Multiple-Objective Combustor Optimization

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Alejandro M. Briones ◽  
Markus P. Rumpfkeil ◽  
Nathan R. Thomas ◽  
Brent A. Rankin
Keyword(s):  
Computer Aided Design ◽  
Adaptive Mesh Refinement ◽  
Design Space ◽  
Cfd Simulation ◽  
Pareto Frontier ◽  
Wall Turbulence ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Small Scale ◽  
Input Parameters

Abstract Supervised machine learning is used to classify a continuous and deterministic design space into a nondominated Pareto frontier and dominated design points. The effect of the initial training data quantity on the Pareto frontier and output parameter sensitivity is explored. The study is performed with the optimization of a subsonic small-scale cavity-stabilized combustor. A 3D geometry is created and parameterized using computer aided design (CAD) that is combined with a software for meshing, which automatically transfers grids and boundary conditions to the solver and postprocessing tool. Steady, compressible three-dimensional simulations are conducted employing a multiphase Realizable k–ε Reynolds-averaged Navier–Stokes (RANS) physics with an adiabatic flamelet progress variable (FPV) model. The near-wall turbulence modeling is computed with scalable wall functions (SWFs). For each computational fluid dynamics (CFD) simulation, four levels of adaptive mesh refinement (AMR) are utilized on the original cut-cell grid. There are 15 geometrical input parameters and three output parameters, viz., a pattern factor proxy, a combustion efficiency proxy, and total pressure loss (TPL). Three times the number of input parameters plus one (48) is necessary to yield an optimization independent of the initial sampling. This conclusion is drawn by examining and comparing the Pareto frontiers and global sensitivities. However, the latter provides a better metric. The relative influence of the input parameters on the outputs is assessed by Spearman's order-rank correlation and an active subspace analysis. Some persistent geometric features for nondominated designs are also discussed.

scholarly journals CFD simulation of solids suspension in stirred tanks: Review

2010 ◽  
Vol 64 (5) ◽  
pp. 365-374 ◽  
Author(s):  
Aoyi Ochieng ◽  
Mrice Onyango
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Concentration Distribution ◽  
Cfd Simulation ◽  
Fluid Dynamic ◽  
Cfd Modeling ◽  
Small Scale ◽  
Stirred Tanks ◽  
Solids Concentration

Many chemical reactions are carried out using stirred tanks, and the efficiency of such systems depends on the quality of mixing, which has been a subject of research for many years. For solid-liquid mixing, traditionally the research efforts were geared towards determining mixing features such as off-bottom solid suspension using experimental techniques. In a few studies that focused on the determination of solids concentration distribution, some methods that have been used have not been accurate enough to account for some small scale flow mal-distribution such as the existence of dead zones. The present review shows that computational fluid dynamic (CFD) techniques can be used to simulate mixing features such as solids off-bottom suspension, solids concentration and particle size distribution and cloud height. Information on the effects of particle size and particle size distribution on the solids concentration distribution is still scarce. Advancement of the CFD modeling is towards coupling the physical and kinetic data to capture mixing and reaction at meso- and micro-scales. Solids residence time distribution is important for the design; however, the current CFD models do not predict this parameter. Some advances have been made in recent years to apply CFD simulation to systems that involve fermentation and anaerobic processes. In these systems, complex interaction between the biochemical process and the hydrodynamics is still not well understood. This is one of the areas that still need more attention.

Effect of Deterministic and Continuous Design Space Resolution on Multiple-Objective Combustor Optimization

2019 ◽  
Author(s):  
Alejandro M. Briones ◽  
Markus P. Rumpfkeil ◽  
Nathan R. Thomas ◽  
Brent A. Rankin
Keyword(s):  
Design Space ◽  
Pareto Frontier ◽  
Output Parameter ◽  
Supervised Machine Learning ◽  
Small Scale ◽  
Multiple Objective ◽  
Fuel Injector ◽  
Progress Variable ◽  
Input Parameters ◽  
Exit Temperature

Abstract A supervised machine learning technique namely an Adaptive Multiple Objective (AMO) optimization algorithm is used to divide a continuous and deterministic design space into non-dominated Pareto frontier and dominated design points. The effect of the initial training data quantity, i.e., computational fluid dynamics (CFD) results, on the Pareto frontier and output parameter sensitivity is explored. The optimization study is performed on a subsonic small-scale cavity-stabilized combustor. A parametric geometry is created using CAD that is coupled with a meshing software. The latter automatically transfers meshes and boundary conditions to the solver, which is coupled with a post-processing tool. Steady, incompressible three-dimensional simulations are performed using a multi-phase realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach with an adiabatic flamelet progress variable (FPV). Scalable wall functions are used for modeling turbulence near the wall. For each CFD simulation four levels of adaptive mesh refinement (AMR) are utilized on the original cut-cell grid. The mesh is refined where the flow exhibits large progress variable curvature. There are fifteen geometrical input parameters and three output parameters, viz., a pattern factor proxy (maximum exit temperature), a combustion efficiency proxy (averaged exit temperature), and total pressure loss (TPL). The Pareto frontier and the input-to-output parameter sensitivities are reported for each meta-model simulation. For the investigated design space, three times the number of input parameters plus one (48) yields an optimization independent of the initial sampling. This conclusion is drawn by comparing the Pareto frontiers and global sensitivities. However, the latter provides a better metric. The relative influence of the input parameters on the outputs is assessed by using both a Spearman’s order-rank correlation approach as well as an active subspace analysis. In general, non-dominated design points exhibit persistent geometrical features such as offset opposed cavity forward and aft driver jet alignment. Larger cavities necessitate larger chutes and smaller outer liner jet diameters, whereas smaller cavities require smaller chutes and larger outer liner jet diameters. The fuel injector radial location varies, but can be located either radially inward or outward with respect to the forward dilution jet radial locations. For these non-dominated designs there is substantial burning inside and outside of the cavity. The downstream dilution jets quench the upstream hot gases.

On-Design Component-Level Multiple-Objective Optimization of a Small-Scale Cavity-Stabilized Combustor

2021 ◽  
Author(s):  
Alejandro Briones ◽  
Timothy Erdmann ◽  
Brent Rankin
Keyword(s):  
Pareto Frontier ◽  
Supervised Machine Learning ◽  
Combustion Model ◽  
Small Scale ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Component Level ◽  
Baseline Design ◽  
Design Point ◽  
Design Component

Abstract This work presents an on-design component-level multiple-objective optimization of a small-scaled uncooled cavity-stabilized combustor. Optimization is performed at the maximum power condition of the engine thermodynamic cycle. The CFD simulations are managed by a supervised machine learning algorithm to divide a continuous and deterministic design space into non-dominated Pareto frontier and dominated design points. Steady, compressible three-dimensional simulations are performed using a multi-phase Realizable k-? RANS and non-adiabatic FPV combustion model. Conjugate heat transfer through the combustor liner is also considered. There are fifteen geometrical input parameters and four objective functions viz., maximization of combustion efficiency, and minimization of total pressure losses, pattern factor, and critical liner area factor. The baseline combustor design is based on engineering guidelines developed over the past two decades. The small-scale baseline design performs remarkably well. Direct optimization calculations are performed on this baseline design. In terms of Pareto optimality, the baseline design remains in the Pareto frontier throughout the optimization. However, the optimization calculations show improvement from an initial design point population to later iteration design points. The optimization calculations report other non-dominated designs in the Pareto frontier. The Euclidean distance from design points to the utopic point is used to select a "best" and "worst" design point for future fabrication and experimentation. The methodology to perform CFD optimization calculations of a small-scale uncooled combustor is expected to be useful for guiding the design and development of future gas turbine combustors.

On-Design Component-Level Multiple-Objective Optimization of a Small-Scale Cavity-Stabilized Combustor

2021 ◽  
Author(s):  
Alejandro M. Briones ◽  
Timothy J. Erdmann ◽  
Brent A. Rankin
Keyword(s):  
Pareto Frontier ◽  
Supervised Machine Learning ◽  
Combustion Model ◽  
Small Scale ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Component Level ◽  
Baseline Design ◽  
Design Point ◽  
Design Component

Abstract This work presents an on-design component-level multiple-objective optimization of a small-scaled uncooled cavity-stabilized combustor. Optimization is performed at the maximum power condition of the engine thermodynamic cycle. The CFD simulations are managed by a supervised machine learning algorithm to divide a continuous and deterministic design space into non-dominated Pareto frontier and dominated design points. Steady, compressible three-dimensional simulations are performed using a multi-phase Realizable k-ε RANS and non-adiabatic FPV combustion model. Conjugate heat transfer through the combustor liner is also considered. There are fifteen geometrical input parameters and four objective functions viz., maximization of combustion efficiency, and minimization of total pressure losses, pattern factor, and critical liner area factor. The baseline combustor design is based on engineering guidelines developed over the past two decades. The small-scale baseline design performs remarkably well. Direct optimization calculations are performed on this baseline design. In terms of Pareto optimality, the baseline design remains in the Pareto frontier throughout the optimization. However, the optimization calculations show improvement from an initial design point population to later iteration design points. The optimization calculations report other non-dominated designs in the Pareto frontier. The Euclidean distance from design points to the utopic point is used to select a “best” and “worst” design point for future fabrication and experimentation. The methodology to perform CFD optimization calculations of a small-scale uncooled combustor is expected to be useful for guiding the design and development of future gas turbine combustors.

scholarly journals Impeller Optimization in Crossflow Hydraulic Turbines

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 313
Author(s):  
Marco Sinagra ◽  
Calogero Picone ◽  
Costanza Aricò ◽  
Antonio Pantano ◽  
Tullio Tucciarelli ◽  
...  
Keyword(s):  
Cfd Simulation ◽  
Fluid Dynamic ◽  
Structural Safety ◽  
3D Fem ◽  
Good Efficiency ◽  
Standard Material ◽  
Variable Head ◽  
Hydraulic Turbines ◽  
Constructive Simplicity ◽  
Mechanical Optimization

Crossflow turbines represent a valuable choice for energy recovery in aqueducts, due to their constructive simplicity and good efficiency under variable head jump conditions. Several experimental and numerical studies concerning the optimal design of crossflow hydraulic turbines have already been proposed, but all of them assume that structural safety is fully compatible with the sought after geometry. We show first, with reference to a specific study case, that the geometry of the most efficient impeller would lead shortly, using blades with a traditional circular profile made with standard material, to their mechanical failure. A methodology for fully coupled fluid dynamic and mechanical optimization of the blade cross-section is then proposed. The methodology assumes a linear variation of the curvature of the blade external surface, along with an iterative use of two-dimensional (2D) computational fluid dynamic (CFD) and 3D structural finite element method (FEM) simulations. The proposed methodology was applied to the design of a power recovery system (PRS) turbine already installed in an operating water transport network and was finally validated with a fully 3D CFD simulation coupled with a 3D FEM structural analysis of the entire impeller.

scholarly journals Computational fluid dynamics (CFD) simulation analysis on retinal gas cover rates using computational eye models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Gozawa ◽  
Yoshihiro Takamura ◽  
Tomoe Aoki ◽  
Kentaro Iwasaki ◽  
Masaru Inatani
Keyword(s):  
Cfd Simulation ◽  
Simulation Analysis ◽  
Fluid Dynamic ◽  
Fluid Analysis ◽  
Intraocular Gas ◽  
Pars Plana ◽  
Computational Fluid Dynamics Cfd ◽  
Multiple Breaks ◽  
Gas Volume ◽  
Wall Wettability

AbstractWe investigated the change in the retinal gas cover rates due to intraocular gas volume and positions using computational eye models and demonstrated the appropriate position after pars plana vitrectomy (PPV) with gas tamponade for rhegmatogenous retinal detachments (RRDs). Computational fluid dynamic (CFD) software was used to calculate the retinal wall wettability of a computational pseudophakic eye models using fluid analysis. The model utilized different gas volumes from 10 to 90%, in increments of 10% to the vitreous cavity in the supine, sitting, lateral, prone with closed eyes, and prone positions. Then, the gas cover rates of the retina were measured in each quadrant. When breaks are limited to the inferior retina anterior to the equator or multiple breaks are observed in two or more quadrants anterior to the equator, supine position maintained 100% gas cover rates in all breaks for the longest duration compared with other positions. When breaks are limited to either superior, nasal, or temporal retina, sitting, lower temporal, and lower nasal position were maintained at 100% gas cover rates for the longest duration, respectively. Our results may contribute to better surgical outcomes of RRDs and a reduction in the duration of the postoperative prone position.

scholarly journals Simulation of Hydraulic Cylinder Cushioning

2021 ◽  
Vol 13 (2) ◽  
pp. 494
Author(s):  
Antonio Algar ◽  
Javier Freire ◽  
Robert Castilla ◽  
Esteban Codina
Keyword(s):  
Dynamic Model ◽  
Cfd Simulation ◽  
Fluid Dynamic ◽  
Computational Fluid Dynamic Simulation ◽  
Hydraulic Cylinder ◽  
Outlet Port ◽  
Optimal Behavior ◽  
Radial Movement ◽  
Mechanical Shocks ◽  
Radial Gap

The internal cushioning systems of hydraulic linear actuators avoid mechanical shocks at the end of their stroke. The design where the piston with perimeter grooves regulates the flow by standing in front of the outlet port has been investigated. First, a bond graph dynamic model has been developed, including the flow throughout the internal cushion design, characterized in detail by computational fluid-dynamic simulation. Following this, the radial movement of the piston and the fluid-dynamic coefficients, experimentally validated, are integrated into the dynamic model. The registered radial movement is in coherence with the significant drag force estimated in the CFD simulation, generated by the flow through the grooves, where the laminar flow regime predominates. Ultimately, the model aims to predict the behavior of the cushioning during the movement of the arm of an excavator. The analytical model developed predicts the performance of the cushioning system, in coherence with empirical results. There is an optimal behavior, highly influenced by the mechanical stress conditions of the system, subject to a compromise between an increasing section of the grooves and an optimization of the radial gap.

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