Quantifying hydraulic and water quality uncertainty to inform sampling of drinking water distribution systems.

Sampling of drinking water distribution systems is performed to ensure good water quality and protect public health. Sampling also satisfies regulatory requirements and is done to respond to customer complaints or emergency situations. Water distribution system modeling techniques can be used to plan and inform sampling strategies. However, a high degree of accuracy and confidence in the hydraulic and water quality models is required to support real-time response. One source of error in these models is related to uncertainty in model input parameters. Effective characterization of these uncertainties and their effect on contaminant transport during a contamination incident is critical for providing confidence estimates in model-based design and evaluation of different sampling strategies. In this paper, the effects of uncertainty in customer demand, isolation valve status, bulk reaction rate coefficient, contaminant injection location, start time, duration and rate on the size and location of the contaminant plume are quantified for two example water distribution systems. Results show that the most important parameter was the injection location. The size of the plume was also affected by the reaction rate coefficient, injection rate and the injection duration, while the the exact location of the plume was additionally affected by the isolation valve status. Uncertainty quantification provides a more complete picture of how contaminants move within a water distribution system and provides more information when using modeling results to select sampling locations.

Theory and simulation of the dynamic heat capacity of the east Ising model.

A recently developed methodology for the calculation of the dynamic heat capacity from simulation is applied to the east Ising model. Results show stretched exponential relaxation with the stretching exponent, beta, decreasing with decreasing temperature. For low temperatures, the logarithm of the relaxation time is approximately proportional to the inverse of the temperature squared, which is the theoretical limiting behavior predicted by theories of facilitated dynamics. In addition, an analytical approach is employed where the overall relaxation is a composite of relaxation processes of subdomains, each with their own characteristic time. Using a Markov chain method, these times are computed both numerically and in closed form. The Markov chain results are seen to match the simulations at low temperatures and high frequencies. The dynamics of the east model are tracked very well by this analytic procedure, and it is possible to associate features of the spectrum of the dynamic heat capacity with specific domain relaxation events.