Drying of a porous bed of iron ore pellets is here considered by modeling a discrete two-dimensional system of round pellets. As a complement to the two-dimensional model, a continuous one-dimensional model enabling fast calculations is developed. Results from the discrete model show that the temperature front advances faster in areas with large distances between the pellets. In areas with low flow speed, the temperature of the pellets increases with a relatively slow rate. The water inside these pellets will therefore remain for a long time. The continuous model fits the discrete model very well for a regular distribution of equal-sized particles. A discrete model with irregular packing will, compared to the continuous model, show a larger variation in the distribution of temperature and moisture content in the final phase of drying.

In today’s industry, functional provision is becoming more and more important, necessitating increased simulation support. In this paper, the objective is to present a modeling and simulation approach for simulation-driven design (SDD) to support function development. The scope of this paper is simulation support for developing hardware equipment used in processing industry. The research is founded on industrial needs identified through two parallel interview-based studies in the Swedish process industry. Both companies explore doing business with functional products rather than hardware, in scenarios where the responsibility for and availability of the functions may remain with the service provider. One as-is and one future (to-be) scenario are presented. A decomposition of a general processing function (applicable to both companies) describes how the companies transfer machine input to output specifications. The decomposition includes customer and provider value and the paper demonstrates, as part of the results and based on the SDD approach, how that value may be increased through evaluation and prioritization. Additionally, the SDD approach shows that it is possible to identify a set of solutions which meet the specified requirements, supporting evaluation and prioritization of business offers and activities.

A multiobjective surrogate-based inverse modeling technique to predict the spatial and temporal pressure distribution numerically during the fabrication of sheet moulding compounds (SMCs) is introduced. Specifically, an isotropic temperature-dependent Newtonian viscosity model of a SMC charge is fitted to experimental measurements via numerical simulations in order to mimic the temporal pressure distribution at two spatial locations simultaneously. The simulations are performed by using the commercial computational fluid dynamics (CFD) code ANSYS CFX-10.0, and the multiobjective surrogate-based fitting procedure proposed is carried out with a hybrid formulation of the NSGA-IIa evolutionary algorithm and the response surface methodology in Matlab. The outcome of the analysis shows the ability of the optimization framework to efficiently reduce the total computational load of the problem. Furthermore, the viscosity model assumed seems to be able to re solve the temporal pressure distribution and the advancing flow front accurately, which can not be said of the spatial pressure distribution. Hence, it is recommended to improve the CFD model proposed in order to better capture the true behaviour of the mould flow.

A multiple surrogate-based optimization strategy in conjunction with an evolutionary algorithm has been employed to optimize the shape of a simplified hydraulic turbine diffuser utilizing three-dimensional Reynolds-averaged Navier-Stokes computational fluid dynamics solutions. Specifically, the diffuser performance is optimized by changing five geometric design variables to maximize the average pressure recovery factor for two inlet boundary conditions with different swirl, corresponding to different operating modes of the hydraulic turbine. Polynomial response surfaces and radial basis neural networks are used as surrogates, while a hybrid formulation of the NSGA-IIa evolutionary algorithm and a ε-constraint strategy is applied to construct the Pareto front from the two surrogates. The proposed optimization framework drastically reduces the computational load of the problem, compared to solely utilizing an evolutionary algorithm. For the present problem, the radial basis neural networks are more accurate near the Pareto front while the response surface performs better in regions away from it. By using a local resampling updating scheme the fidelity of both surrogates is improved, especially near the Pareto front. The optimal design yields larger wall angles, nonaxisymmetrical shapes, and delay in wall separation, resulting in 14.4% and 8.9% improvement, respectively, for the two inlet boundary conditions.

The design of hydraulic turbine draft tubes has traditionally been based on simplified analytic methods, model and full-scale experiments. In the recent years the use of numerical methods such as computational fluid dynamics (CFD) has increased in the design process, due to the rapid escalation in computer performance. Today with parallel computer architectures, new aspects of flow can be considered. However, several problems have still to be solved before CFD can routinely be applied in product development. This paper aims to investigate the parallel performance of commercial CFD software on homogeneous computer networks, as common solutions encountered in the industry today. In addition, the efficiency improvements obtained in earlier experiments by modifying the shape of the draft tube will be considered, to deduce if the improvements can be captured with aid of CFD. Results from both the steady and the unsteady CFD simulations show that almost full scalability is obtained with the commercial CFD software CFX-5.7.1. Furthermore, no noticeable improvement in the pressure recovery factor or the flow field is noticed in the CFD simulations as compared to experiments. The discrepancy may be to the applied inlet boundary conditions and/or the turbulence model.

Purpose - This paper aims to develop an efficient and accurate numerical method that can be used in the design process of the waterways in a hydropower plant. Design/methodology/approach - A range of recently published (2002-2006) works, which aim to form the basis of a shape optimization tool for flow design and to increase the knowledge within the field of computational fluid dynamics (CFD) and surrogate-based optimization techniques. Findings - Provides information about how crude the optimization method can be regarding, for example, the design variables, the numerical noise and the multi objectives, etc. Research limitations/implications - It does not give a detailed interpretation of the flow behaviour due to the lack of validation data. Practical implications - A very useful flow design methodology that can be used in both academy and industry. Originality/value - Shape optimization of hydraulic turbine draft tubes with aid of CFD and numerical optimization techniques has not been performed until recently due to the high CPU requirements on CFD simulations. The paper investigates the possibilities of using the global optimization algorithm response surface methodology in the design process of a full scale hydraulic turbine draft tube.