This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative.

Wind power, especially offshore, is considered one of the most promising sources of 'clean' energy towards meeting the EU and UK targets for 2020 and 2050.Deployment of wind turbines in constantly increasing water depths has raised the issue of the appropriate selection of the most suitable support structures' options.Based on experience and technology from the offshore oil and gas industry, several different configurations have been proposed for different operational conditions. This paper presents a methodology for the systematic assessment of the selection of the most preferable, among the different configurations, support structures for offshore wind turbines, taking into consideration several attributes through the widely used multi-criteria decision making method TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for the benchmarking of those candidate options. An application comparing a monopile, a tripod and a jacket, for a reference 5.5 MW wind turbine and a reference depth of 40 m, considering multiple engineering, economical and environmental attributes, will illustrate the effectiveness of the proposed methodology.

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

In this paper, the design methodology of composite ballistic helmets has been enhanced considering biomechanical requirements by means of finite element analysis. Modern combat helmets lead to a new type of non-penetrating injury, the Behind Helmet Blunt Trauma (BHBT), generated by the deformation of the inner face of the helmet, the so-called backface deformation (BFD). Current standard testing methodologies use BFD as the main measure in ballistic testing. Nonetheless, this work discusses the relationship between this mechanical parameter and the head trauma (BHBT) by studying different head injury criteria. A numerical model consisting of a helmet and a human head is developed and validated with experimental data from literature. The consequences of non-penetrating high-speed ballistic impacts upon the human head protected by an aramid combat helmet are analysed, concluding that the existing testing methodologies fail to predict many types of head injuries. The influence of other parameters like bullet velocity or head dimensions is analysed. Usually, a single-sized helmet shell is manufactured and the different sizes are adjusted by varying the foam pad thickness. However, one of the conclusions of this work is that pad thickness is critical to avoid BHBT and must be considered in the design process.

Many discrepancies are found in the literature regarding the damage and constitutive models for head tissues as well as the values of the constants involved in the constitutive equations. Their proper definition is required for consistent numerical model performance when predicting human head behaviour, and hence skull fracture and brain damage. The objective of this research is to perform a critical review of constitutive models and damage indicators describing human head tissue response under impact loading. A 3D finite element human head model has been generated by using computed tomography images, which has been validated through the comparison to experimental data in the literature. The threshold values of the skull and the scalp that lead to fracture have been analysed. We conclude that (1) compact bone properties are critical in skull fracture, (2) the elastic constants of the cerebrospinal fluid affect the intracranial pressure distribution, and (3) the consideration of brain tissue as a nearly incompressible solid with a high (but not complete) water content offers pressure responses consistent with the experimental data.