Chemical composition of Lake George (New York) iron-manganese nodules and underlying sediment

Lake George, New York, is the site of a new discovery of iron-manganese nodules. These nodules occur at a water depth between 21 and 36 m along a stretch of lake extending for about 5 mi north and south of the Narrows, a constricted island-dotted area which separates the north and south Lake George basins. Nodules occur on or within the uppermost 5 cm of a varved glacial clay. Some areas are solidly floored with a carpet of nodules in areas where active currents keep the nodules exposed. The nodules form around nuclei which consist of clay and less commonly of spore capsules, detrital particles, or bark. By their shape we recognize three types of nodules: spherical, discoidal, and lumps. On X-ray examination all nodules show small goethite peaks; in one nodule the manganese mineral birnessite was identified. Manganese and part of the iron appears to be in X-ray amorphous ferromanganese compounds. The Lake George nodules are enriched in iron with respect to marine nodules but are lower in manganese. They have a higher trace element concentration than nodules from other known freshwater lake occurrences, but a lower concentration than marine nodules.

EPANET Input Files of New York tunnels and Pacific City used in a metamodel-based optimization study

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.