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Abstract Groundwater is the primary source of water in the Bengal Delta but contamination threatens this vital resource. In deltaic environments, heterogeneous sedimentary architecture controls groundwater flow; therefore, characterizing subsurface structure is a critical step in predicting groundwater contamination. Here, we show that surface information can improve the characterization of the nature and geometry of subsurface features, thus improving the predictions of groundwater flow. We selected three locations in the Bengal Delta with distinct surface river network characteristics—the lower delta with straighter tidal channels, the mid‐delta with meandering and braided channels, and the inactive delta with transitional sinuous channels. We used surface information, including channel widths, depths, and sinuosity, to create models of the subsurface with object‐based geostatistical simulations. We collected an extensive set of lithologic data and filled in gaps with newly drilled boreholes. Our results show that densely distributed lithologic data from active lower and mid‐delta are consistent with the object‐based models generated from surface information. In the inactive delta, metrics from object‐based models derived from surface geometries are not consistent with subsurface data. We further simulated groundwater flow and solute transport through the object‐based models and compared these with simulated flow through lithologic models based only on variograms. Substantial differences in flow and transport through the different geologic models show that geometric structure derived from surface information strongly influences groundwater flow and solute transport. Land surface features in active deltas are therefore a valuable source of information for improving the evaluation of groundwater vulnerability to contamination.
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Abstract The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatial distribution of heat, called the heat flux or thermal history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the heat flux in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the heat flux in the part. For instance, constrained heat flux because of ill-considered part design leads to defects, such as warping and thermal stress-induced cracking. Existing non-proprietary approaches to predict the heat flux in AM at the part-level predominantly use mesh-based finite element analyses that are computationally tortuous — the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational thermal models to predict the heat flux, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared to finite element analysis techniques, the proposed mesh-free graph theory-based approach facilitates layer-by-layer simulation of the heat flux within a few minutes on a desktop computer. To explore these assertions we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach, with finite element analysis and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume, and (2) simulating the layer-by-layer deposition of three part geometries in a laser powder bed fusion metal AM process with: (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the heat flux predictions from the last two approaches with a commercial solution. From the first study we report that the heat flux trend approximated by the graph theory approach is found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the heat flux trends predicted for the AM parts using graph theory approach agrees with finite element analysis with error less than 15%. More pertinently, the computational time for predicting the heat flux was significantly reduced with graph theory, for instance, in one of the AM case studies the time taken to predict the heat flux in a part was less than 3 minutes using the graph theory approach compared to over 3 hours with finite element analysis. While this paper is restricted to theoretical development and verification of the graph theory approach for heat flux prediction, our forthcoming research will focus on experimental validation through in-process sensor-based heat flux measurements.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/2014WR016577
