Stability margins of nonlinear optimal regulators with nonquadratic performance criteria involving cross-weighting terms

In this paper we derive guaranteed gain, sector, and disk margins for nonlinear optimal and inverse optimal regulators that minimize a nonlinear-nonquadratic performance criterion involving cross-weighting terms. Specifically, sufficient conditions that guarantee gain, sector, and disk margins are given in terms of the state, control, and cross-weighting nonlinear-nonquadratic weighting functions. The proposed results provide a generalization of the “meaningful” inverse optimal nonlinear regulator stability margins as well as the classical linear-quadratic optimal regulator gain and phase margins.     

Towards a continuous microfluidic rheometer

In a previous paper we presented a way to measure the rheological properties of complex fluids on a microfluidic chip (Guillot et al., Langmuir 22:6438, 2006). The principle of our method is to use parallel flows between two immiscible fluids as a pressure sensor. In fact, in a such flow, both fluids flow side by side and the size occupied by each fluid stream depends only on both flow rates and on both viscosities. We use this property to measure the viscosity of one fluid knowing the viscosity of the other one, both flow rates and the relative size of both streams in a cross-section. We showed that using a less viscous fluid as a reference fluid allows to define a mean shear rate with a low standard deviation in the other fluid. This method allows us to measure the flow curve of a fluid with less than 250 μL of fluid. In this paper we implement this principle in a fully automated set up which controls the flow rate, analyzes the picture and calculates the mean shear rate and the viscosity of the studied fluid. We present results obtained for Newtonian fluids and complex fluids using this set up and we compare our data with cone and plate rheometer measurements. By adding a mixing stage in the fluidic network we show how this set up can be used to characterize in a continuous way the evolution of the rheological properties as a function of the formulation composition. We illustrate this by measuring the rheological curve of four formulations of polyethylene oxide solution with only 1.3 mL of concentrated polyethylene oxide solution. This method could be very useful in screening processes where the viscosity range and the behavior of the fluid to an applied stress must be evaluated.     

Valence-Driven Connectivity Encoding for 3D Meshes

In this paper, we propose a valence-driven, single-resolution encoding technique for lossless compression of triangle mesh connectivity. Building upon a valence-based approach pioneered by Touma and Gotsman²² , we design a new valence-driven conquest for arbitrary meshes that always guarantees smaller compression rates than the original method. Furthermore, we provide a novel theoretical entropy study of our technique, hinting the optimality of the valence-driven approach. Finally, we demonstrate the practical efficiency of this approach (in agreement with the theoretical prediction) on a series of test meshes, resulting in the lowest compression ratios published so far, for both irregular and regular meshes, small or large.     

Optimising Reliability: Portfolio Modeling of Contract Types for Retail Water Providers

This paper considers the retail water provider’s purchasing decision of a portfolio of permanent contracts from wholesalers with multiple volatile water sources. We consider the reliability of two contract types: (1) fixed annual quantities, and (2) an inflow harvest function with storage. Our four-reservoir case in Sydney (Australia) has cross-correlated inflow data. To accommodate multi-reservoir cross-correlation we adapt Portfolio Theory from finance to lognormal reservoir inflows, re-framing traditional storage theory from the wholesaler’s optimal operating policy to the retailer’s optimal purchasing policy. We find that Reliability improves with access to a source pool (cf. fixed quantities from separate sources), demonstrating the ‘insurance effect’, and the portfolio that minimises lognormal variance also minimises harvest (and thus environmental impact). Reform direction in Australian (and other international) water markets is towards multi-provider vertical disintegration, which may reduce pool opportunities and negate the insurance effect. We consider diminishing reliability returns as reservoir harvesting increases, and conclude a retail portfolio of permanent contracts from reservoirs, plus short-term contracts from alternative sources (either independently or negatively cross-correlated) efficiently secures high reliability. The challenge in incomplete water markets remains in encouraging and sustaining supply diversification that may only be needed aperiodically during extreme droughts.     

Groundwater Model Calibration by Meta-Heuristic Algorithms

Groundwater models are computer models that simulate or predict aquifer conditions by using input data sets and hydraulic parameters. Commonly, hydraulic parameters are extracted by calibration, using observed and simulated aquifer conditions. The accuracy of calibration affects other modeling processes, especially the hydraulic head simulation. Meta-heuristic algorithms are good candidates to determine optimal/near-optimal parameters in groundwater models. In this paper, two meta-heuristic algorithms: (1) particle swarm optimization (PSO) and (2) pattern search (PS) are applied and compared in the Ghaen aquifer, by considering the sum of the squared deviation (SSD) between observed and simulated hydraulic heads and the sum of the absolute value of deviation (SAD) between observed and simulated hydraulic heads as the objective functions. Results show that obtained values of the objective function are enhanced significantly by using the PS algorithm. Accordingly, PS improves (decreases) the SSD and SAD by 0.20 and 2.36 percent, respectively, compared to results reported by using the PSO algorithm. Results also indicate that the proposed PS optimization tool is effective in the calibration of aquifer parameters.     

Discussion of “Assessments of Impacts of Climate Change and Human Activities on Runoff with SWAT for the Huifa River Basin, Northeast China” by Aijing Zhang; Chi Zhang; Guobin Fu; Bende Wang; Zhenxin Bao; and Hongxing Zheng

Zhang et al. (Water Resour Manag doi:10.1007/s11269-012-0182-2, 2012) studied the impacts of climate change and human activities on the runoff for the Huifa River basin. They employed a soil and water assessment tool (SWAT), which was calibrated for the baseline period 1956–1964, and then used to reconstruct the natural runoff for the climate change period 1965–2000. Results showed that both climate change and human activities decrease the observed runoff. Climate change impacts on annual runoff were ?37.7, ?59.5, +36.9, and ?45.2 mm/a for 1965–1975, 1976–1985, 1986–1995, and 1996–2005 respectively, compared with the baseline period 1956–1964. Human activities decreased runoff and caused a relatively larger magnitude impact than those of climate change after 1985. Human activities decreased the annual runoff by ?32.9, ?46.8, ?67.8, and ?54.9 mm/a for 1965–1975, 1976–1985, 1986–1995, and 1996–2005 respectively. Human activities further decreased runoff in wet years due to regulation and storage of water projects.     

Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems.     

Robust H∞ stabilization via parametrized Lyapunovbounds

The parametrized Lyapunov bounding technique of Haddad and Bernstein (1991, 1993, 1995) is extended to include an H_∞ -disturbance attenuation constraint. The results presented in this paper provide a framework for designing fixed-order (i.e., full- and reduced-order) controllers that guarantee robust H₂ and H_∞ performance in the presence of structured constant real parameter variations in the state space model     

Robust controller synthesis via shifted parameter-dependentquadratic cost bounds

Parameterized Lyapunov bounds and shifted quadratic guaranteed cost bounds are merged to develop shifted parameter-dependent quadratic cost bounds for robust stability and robust performance. Robust fixed-order (i.e., full- and reduced-order) controllers are developed based on new shifted parameter-dependent bounding functions. A numerical example is presented to demonstrate the effectiveness of the proposed approach