Beyond simulation: designing for uncertainty and robust solutions

Simulation is an increasingly essential tool in the design of our environment, but any model is only as good as the initial assumptions on which it is built. This paper aims to outline some of the limits and potential dangers of reliance on simulation, and suggests how to make our models, and our buildings, more robust with respect to the uncertainty we face in design. It argues that the single analyses provided by most simulations display too precise and too narrow a result to be maximally useful in design, and instead a broader description is required, as might be provided by many differing simulations. Increased computing power now allows this in many areas. Suggestions are made for the further development of simulation tools for design, in that these increased resources should be dedicated not simply to the accuracy of single solutions, but to a bigger picture that takes account of a design’s robustness to change, multiple phenomena that cannot be predicted, and the wider range of possible solutions. Methods for doing so, including statistical methods, adaptive modelling, machine learning and pattern recognition algorithms for identifying persistent structures in models, will be identified. We propose a number of avenues for future research and how these fit into design process, particularly in the case of the design of very large buildings

Robust fixed order dynamic compensation for large space structure control

A simple formulation for designing fixed order dynamic compensators which are robust to both uncertainty at the plant input and structured uncertainty in the plant dynamics is presented. The emphasis is on designing low order compensators for systems of high order. The formulation is done in an output feedback setting which exploits an observer canonical form to represent the compensator dynamics. The formulation also precludes the use of direct feedback of the plant output. The main contribution lies in defining a method for penalizing the states of the plant and of the compensator, and for choosing the distribution on initial conditions so that the loop transfer matrix approximates that of a full state design. To improve robustness to parameter uncertainty, the formulation avoids the introduction of sensitivity states, which has led to complex formulations in earlier studies where only structured uncertainty has been considered

Capture of manufacturing uncertainty in turbine blades through probabilistic techniques

Efficient designing of the turbine blades is critical to the performance of an aircraft engine. An area of significant research interest is the capture of manufacturing uncertainty in the shapes of these turbine blades. The available data used for estimation of this manufacturing uncertainty inevitably contains the effects of measurement error/noise. In the present work, we propose the application of Principal Component Analysis (PCA) for de-noising the measurement data and quantifying the underlying manufacturing uncertainty. Once the PCA is performed, a method for dimensionality reduction has been proposed which utilizes prior information available on the variance of measurement error for different measurement types. Numerical studies indicate that approximately 82% of the variation in the measurements from their design values is accounted for by the manufacturing uncertainty, while the remaining 18% variation is filtered out as measurement error

Bayesian fan charts for U.K. inflation: forecasting and sources of uncertainty in an evolving monetary system

We estimate a Bayesian vector autoregression for the U.K. with drifting coefficients and stochastic volatilities. We use it to characterize posterior densities for several objects that are useful for designing and evaluating monetary policy, including local approximations to the mean, persistence, and volatility of inflation. We present diverse sources of uncertainty that impinge on the posterior predictive density for inflation, including model uncertainty, policy drift, structural shifts and other shocks. We use a recently developed minimum entropy method to bring outside information to bear on inflation forecasts. We compare our predictive densities with the Bank of England's fan charts

Enhanced LFR-toolbox for MATLAB and LFT-based gain scheduling

We describe recent developments and enhancements of the LFR-Toolbox for MATLAB for building LFT-based uncertainty models and for LFT-based gain scheduling. A major development is the new LFT-object definition supporting a large class of uncertainty descriptions: continuous- and discrete-time uncertain models, regular and singular parametric expressions, more general uncertainty blocks (nonlinear, time-varying, etc.). By associating names to uncertainty blocks the reusability of generated LFT-models and the user friendliness of manipulation of LFR-descriptions have been highly increased. Significant enhancements of the computational efficiency and of numerical accuracy have been achieved by employing efficient and numerically robust Fortran implementations of order reduction tools via mex-function interfaces. The new enhancements in conjunction with improved symbolical preprocessing lead generally to a faster generation of LFT-models with significantly lower orders. Scheduled gains can be viewed as LFT-objects. Two techniques for designing such gains are presented. Analysis tools are also considered

Uncertainty and Cooperation: Analytical Results and a Simulated Agent Society

Uncertainty is an important factor that influences social evolution in natural and artificial environments. Here we distinguish between three aspects of uncertainty. Environmental uncertainty is the variance of resources in the environment, perceived uncertainty is the variance of the resource distribution as perceived by the organism and effective uncertainty is the variance of resources effectively enjoyed by individuals. We show analytically that perceived uncertainty is larger than environmental uncertainty and that effective uncertainty is smaller than perceived uncertainty, when cooperation is present. We use an agent society simulation in a two dimensional world for the generation of simulation data as one realisation of the analytical results. Together with our earlier theoretical work, results here show that cooperation can buffer the detrimental effects of uncertainty on the organism. The proposed conceptualisation of uncertainty can help in understanding its effects on social evolution and in designing artificial social environments.Agent-Based Modelling, Cooperation, Social Interaction Simulation, Uncertainty

Expert opinion: uncertainties in hydraulic roughness

Water level predictions in rivers are used for a variety of purposes in water management. For example, designing flood defence measures and evaluating natural rehabilitation in flood plains, cannot be done without water level predictions. However, these water level predictions are uncertain and a major part of this uncertainty is caused by the uncertainty in the roughness coefficient (Van der Klis, 2003; Van Vuren, 2005). Hydraulic roughness in rivers results from (among others): grain roughness, form roughness and vegetation roughness. The roughness coefficient is uncertain because different elements creating the hydraulic roughness are uncertain (e.g. grain size, dune height). To quantify the influence of the uncertain roughness coefficient on water level predictions, we first need a quantification of the uncertainty in the roughness coefficient

Measuring information security breach impact and uncertainties under various information sharing scenarios

This study draws on information theory and aims to provide simulated evidence using real historical and statistical data to demonstrate how various levels of integration moderate the impact and uncertainties of information security breach on supply chain performance. We find that the supply chain behaves differently under various levels of integration when a security breach occurs. The entropy analysis revealed that the wholesaler experience the most uncertainty under system failure and data corruption. This sort of impact-uncertainty information will aid in designing and managing a resilient supply chain poised for minimal breach impact