This paper presents a framework to support decision-making in aircraft conceptual design optimisation under uncertainty. Emphasis is given to graphical visualisation methods capable of providing holistic yet intuitive relationships between design, objectives, feasibility and uncertainty spaces. Two concepts are introduced to allow interactive exploration of the effects of (1) target probability of constraint satisfaction (price of feasibility robustness) and (2) uncertainty reduction through increased state-of-knowledge (cost of uncertainty) on design and objective spaces. These processes are tailored to handle multi-objective optimisation problems and leverage visualisation techniques for dynamic inter-space mapping. An information reuse strategy is presented to enable obtaining multiple robust Pareto sets at an affordable computational cost. A case study demonstrates how the presented framework addresses some of the challenges and opportunities regarding the adoption of Uncertainty-based Multidisciplinary Design Optimisation (UMDO) in the aerospace industry, such as design margins policy, systematic and conscious definition of target robustness and uncertainty reduction experiments selection and prioritisation.

Vessel behaviour analysis plays an important role in maritime situational awareness. However, available technology still provides only limited approaches to vessel behaviour analysis. In this paper, we propose a visual analytics framework to interactively explore the characteristics of vessel behaviour by means of integrating visualisation with data mining and a human-computer interaction controlling model, which combines human insight with the enormous storage and processing capacities of computers to gain insight into vessel behaviour. In addition, we provide multiple views for visually analysing vessel trajectories, densities and speeds. Case studies with 15 days' AIS data collected from the middle Hankou channel to Yangluo channel in the Yangtze River demonstrate the effectiveness of our approach.

Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.