6 research outputs found
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Efficient climate data analyses in decision making for the design and operation of land-based and ocean infrastructure systems
Climate change presents significant challenges to the built environment. To deal with them, optimized adaptation and mitigation strategies are needed. Rational data-driven approaches are needed that consider non-stationary characteristics of the climate processes to assess the changing risks. The transition to sustainable clean sources is one component of mitigation. Investment in ocean-based renewable energy has received much attention in this endeavor. Still, associated costs of such energy need to be reduced significantly. Some innovative ideas are being considered—such as, for instance: (1) offshore floating multi-purpose platforms (MPPs) that offer benefits from shared use of infrastructure assets for multiple services including resource extraction activities (such as renewable energy generation), aquaculture, leisure, and transport functions; and (2) sustainable reuse of decommissioned oil and gas offshore jacket platforms for wind energy generation. Such investments have the potential to reduce costs but they are still in their early stages with many missing validated rational solutions. In this dissertation, three studies are undertaken to develop scientific frameworks that use climate and ocean data to aid in making optimized decisions for climate change adaptation and mitigation. The first study targets judicious near-future modeling of non-stationary climate processes while employing past observations optimally. A Greedy Copula Segmentation (GCS) algorithm is developed that employs best-fit multivariate probability distributions and copula functions after data-driven time series segmentation is undertaken. Predictions based on the GCS approach more closely describe the actual future than those made by a traditional model using all the available data. The second study aims to maximize the benefits of the sustainable reuse of oil and gas platform for wind energy generation by establishing an optimized plan that accounts for the remaining life of the repurposed platform, overall platform construction and retrofit costs, and an expectation of a period of clean energy generation and associated revenues after the wind turbine installation. A realistic case study and sustainable reuse scenario for a site near Porto (Leixões), Portugal, are employed to illustrate the feasibility and advantages of the model developed. The last study involves the formulation of a Markov decision process (MDP) to provide an optimized policy that guides the scheduling of operation and maintenance (O&M) activities for MPPs. By following the provided policy, the overall loss of revenue and costs of O&M are inherently minimized. The robustness of the method is validated by demonstrating that the optimized policy leads to lower accumulated costs than is possible with conventional practice and the benefits are realized for a wide range of general meteorological and oceanographic (metocean) conditions— i.e., the combined wind, wave and associated climate conditions.Civil, Architectural, and Environmental Engineerin
Operations and maintenance for multipurpose offshore platforms using statistical weather window analysis
With increasing offshore-related commerce, the choice of appropriate operations and maintenance activities must take into consideration safety, costs and performance targets. Stochastic weather conditions at each site of interest presents uncertain situations. We present an optimized decision making procedure that seeks to maximize monetary benefits while minimizing safety risks. Our proposed approach outlines and illustrates application of such a policy by incorporating traditional weather window analysis using a Markov Decision Process approach. In particular, the approach is applied in case study involving the operation of a multipurpose platform at an offshore Scotland site
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Learning optimal sampling policies for sketching of huge data matrices
This study presents methods called TSGPR- and SAC-SketchyCoreSVD to improve the protocol of subsampling data fibers for building random sketches and low rank SVD of a data matrix by formulating them as a sequential decision making problem. An agent progressively decides which data fibers to subsample next to maximize the accuracy of low rank SVD under the limited computational resources. Using the information coming from the partially observed data matrix constructed by subsampled fibers so far, methods learn the optimal policy. Thompson sampling and Gaussian process regression are used for TSGPR-SketchyCoreSVD, and Soft Actor-Critic is applied to SAC-SketchyCoreSVD. Experiments show TSGPR-SketchyCoreSVD actively learns the subsampling policy and produces higher accuracy than the original SketchyCoreSVD. SAC-SketchyCoreSVD is still developing, but its intermediate result also shows promising results. This study can be easily expanded to higher order data tensors.Computational Science, Engineering, and Mathematic
Explosive Cyclogenesis around the Korean Peninsula in May 2016 from a Potential Vorticity Perspective: Case Study and Numerical Simulations
An explosive cyclone event that occurred near the Korean Peninsula in early May 2016 is simulated using the Weather Research and Forecasting (WRF) model to examine the developmental mechanisms of the explosive cyclone. After confirming that the WRF model reproduces the synoptic environments and main features of the event well, the favorable environmental conditions for the rapid development of the cyclone are analyzed, and the explosive development mechanisms of the cyclone are investigated with perturbation potential vorticity (PV) fields. The piecewise PV inversion method is used to identify the dynamically relevant meteorological fields associated with each perturbation PV anomaly. The rapid deepening of the surface cyclone was influenced by both adiabatic (an upper tropospheric PV anomaly) and diabatic (a low-level PV anomaly associated with condensational heating) processes, while the baroclinic processes in the lower troposphere had the smallest contribution. In the explosive phase of the cyclone life cycle, the diabatically generated PV anomalies associated with condensational heating induced by the ascending air in the warm conveyor belt are the most important factors for the initial intensity of the cyclone. The upper-level forcing is the most important factor in the evolution of the cyclone's track, but it is of secondary importance for the initial strong deepening.11Nsciescopu