Mean-variance Vs. full-scale optimization: broad evidence for the UK

This article investigates the performance of a model called Full-Scale Optimisation, which was presented recently and is used for financial investment advice. The investor’s preferences of expected risk and return are entered into the model, and a recommended portfolio is produced. This model is theoretically more accurate than the mainstream investment advice model, called Mean-Variance Optimization, as there are fewer assumptions made. Our investigation of the model’s performance is broader when it comes to investor preferences, and more general when it comes to investment type, as compared to previous studies. Our investigation shows that Full-Scale Optimisation is more widely applicable than earlier known.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions.

Uniform design for the optimization of Al2O3 nanofilms produced by electrophoretic deposition

Surface modification by means of nanostructures is of interest to enhance boiling heat transfer in various applications including the organic Rankine cycle (ORC). With the goal of obtaining rough and dense aluminum oxide (Al2O3) nanofilms, the optimal combination of process parameters for electrophoretic deposition (EPD) based on the uniform design (UD) method is explored in this paper. The detailed procedures for the EPD process and UD method are presented. Four main influencing conditions controlling the EPD process were identified as nanofluid concentration, deposition time, applied voltage and suspension pH. A series of tests were carried out based on the UD experimental design. A regression model and statistical analysis were applied to the results. Sensitivity analyses of the effect of the four main parameters on the roughness and deposited mass of Al2O3 films were also carried out. The results showed that Al2O3 nanofilms were deposited compactly and uniformly on the substrate. Within the range of the experiments, the preferred combination of process parameters was determined to be nanofluid concentration of 2 wt.%, deposition time of 15 min, applied voltage of 23 V and suspension pH of 3, yielding roughness and deposited mass of 520.9 nm and 161.6 × 10− 4 g/cm2, respectively. A verification experiment was carried out at these conditions and gave values of roughness and deposited mass within 8% error of the expected ones as determined from the UD approach. It is concluded that uniform design is useful for the optimization of electrophoretic deposition requiring only 7 tests compared to 49 using the orthogonal design method.