11,358 research outputs found
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
Examining trade-offs between social, psychological, and energy potential of urban form
Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages
The climatic interdependence of extreme-rainfall events around the globe
The identification of regions of similar climatological behavior can be
utilized for the discovery of spatial relationships over long-range scales,
including teleconnections. In this regard, the global picture of the
interdependence patterns of extreme rainfall events (EREs) still needs to be
further explored. To this end, we propose a top-down complex-network-based
clustering workflow, with the combination of consensus clustering and mutual
correspondences. Consensus clustering provides a reliable community structure
under each dataset, while mutual correspondences build a matching relationship
between different community structures obtained from different datasets. This
approach ensures the robustness of the identified structures when multiple
datasets are available. By applying it simultaneously to two satellite-derived
precipitation datasets, we identify consistent synchronized structures of EREs
around the globe, during boreal summer. Two of them show independent
spatiotemporal characteristics, uncovering the primary compositions of
different monsoon systems. They explicitly manifest the primary intraseasonal
variability in the context of the global monsoon, in particular the `monsoon
jump' over both East Asia and West Africa and the mid-summer drought over
Central America and southern Mexico. Through a case study related to the Asian
summer monsoon (ASM), we verify that the intraseasonal changes of upper-level
atmospheric conditions are preserved by significant connections within the
global synchronization structure. Our work advances network-based clustering
methodology for (i) decoding the spatiotemporal configuration of
interdependence patterns of natural variability and for (ii) the
intercomparison of these patterns, especially regarding their spatial
distributions over different datasets
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