Efficient climate data analyses in decision making for the design and operation of land-based and ocean infrastructure systems
Creators: name: Heo, Taemin
Publisher: name: The University of Texas at Austin
Year: 2022
Subjects: Time series segmentation
Greedy algorithm
Non-stationary stochastic process
Marine heatwaves
Sustainable reuse
Metocean data analysis
Aero-hydro-servo-elastic simulations
Fatigue damage assessment
Reliability-based life cycle estimation
Economic revenue evaluation
Offshore jacket platform
Markov decision process
Offshore floating multipurpose platform
Operation and maintenance
Optimized scheduling

description: 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&amp;M) activities for MPPs. By following the provided policy, the overall loss of revenue and costs of O&amp;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.