Hydrodynamic Simulations Of Vacuum-Driven Storage Tanks

As a result of urbanization, stormwater runoff flow rates and volumes are significantly increased due to increasing impervious land cover and the decreased availability of depression storage. Storage tanks are the basic devices to efficiently control the flow rate in drainage systems during wet weather. Presented in the paper conception of vacuum-driven detention tanks allows to increase the storage capacity by usage of space above the free surface water elevation at the inlet channel. Partial vacuum storage makes possible to gain cost savings by reduction of both the horizontal area of the detention tank and necessary depth of foundations. Simulation model of vacuum-driven storage tank has been developed to estimate potential profits of its application in urban drainage system. Although SWMM5 has no direct options for vacuum tanks an existing functions (i.e. control rules) have been used to reflect its operation phases. Rainfall data used in simulations were recorded at raingage in Czestochowa during years 2010÷2012 with time interval of 10minutes. Simulation results gives overview to practical operation and maintenance cost (energy demand) of vacuum driven storage tanks depending of the ratio: vacuum-driven volume to total storage capacity. The following conclusion can be drawn from this investigations: vacuum-driven storage tanks are characterized by uncomplicated construction and control systems, thus can be applied in newly developed as well as in the existing urban drainage systems. the application of vacuum in underground detention facilities makes possible to increase of the storage capacity of existing reservoirs by usage the space above the maximum depth. Possible increase of storage capacity can achieve even a few dozen percent at relatively low investment costs. vacuum driven storage tanks can be included in existing simulation software (i.e. SWMM) using options intended for pumping stations (including control and action rules ).

Optimization Of Groundwater Remediation With New Efficient Parallel Algorithm For Global Optimization

Application of optimization algorithm to PDE modeling groundwater remediation can greatly reduce remediation cost. However, groundwater remediation analysis requires a computational expensive simulation, therefore, effective parallel optimization could potentially greatly reduce computational expense. The optimization algorithm used in this research is Parallel Stochastic radial basis function. This is designed for global optimization of computationally expensive functions with multiple local optima and it does not require derivatives. In each iteration of the algorithm, an RBF is updated based on all the evaluated points in order to approximate expensive function. Then the new RBF surface is used to generate the next set of points, which will be distributed to multiple processors for evaluation. The criteria of selection of next function evaluation points are estimated function value and distance from all the points known. Algorithms created for serial computing are not necessarily efficient in parallel so Parallel Stochastic RBF is different algorithm from its serial ancestor. The application for two Groundwater Superfund Remediation sites, Umatilla Chemical Depot, and Former Blaine Naval Ammunition Depot. In the study, the formulation adopted treats pumping rates as decision variables in order to remove plume of contaminated groundwater. Groundwater flow and contamination transport is simulated with MODFLOW-MT3DMS. For both problems, computation takes a large amount of CPU time, especially for Blaine problem, which requires nearly fifty minutes for a simulation for a single set of decision variables. Thus, efficient algorithm and powerful computing resource are essential in both cases. The results are discussed in terms of parallel computing metrics i.e. speedup and efficiency. We find that with use of up to 24 parallel processors, the results of the parallel Stochastic RBF algorithm are excellent with speed up efficiencies close to or exceeding 100%.