Research

Greedy Copula Segmentation of Multivariate Non-Stationary Time Series for Climate Change Adaptation 

Non-stationary climate data are often encountered in dealing with natural hazards, climate change and disaster reduction. With drought, for instance, it is common to encounter such non-stationary data sets (time series). The objectives of this work are to formulate a rational data-driven approach that can consider non-stationary and time series on multiple random variables that can have generalized underlying probability distributions and dependence structures. The methodology proposed seeks to divide up the data into non-overlapping segments, each of which is treated as stationary with some underlying probability and dependence structure, while the long time series yields multiple such segments that are mutually independent. The Greedy Copula Segmentation (GCS) algorithm developed employs best-fit probability distributions and copula functions after data-driven time series segmentation. Validation of the proposed ethodology is demonstrated using a benchmark problem as well as a single-site realistic drought example. The proposed GCS approach has potential use in climate change adaptation (CCA) and disaster risk reduction (DRR) for any climate-related hazards involving non-stationary time series data.

https://github.com/TaeminHeo/GCS

On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model 

This study is concerned with the estimation of long-term fatigue damage for a floating offshore wind turbine. With the ultimate goal of efficient evaluation of fatigue limit states for floating offshore wind turbine systems, a detailed computational framework is introduced and used to develop a surrogate model using Gaussian process regression. The surrogate model, at first, relies only on a small subset of representative sea states and, then, is supplemented by the evaluation of additional sea states that leads to efficient convergence and accurate prediction of fatigue damage. A 5-MW offshore wind turbine supported by a semi-submersible floating platform is selected to demonstrate the proposed framework. The fore–aft bending moment at the turbine tower base and the fairlead tension in the windward mooring line are used for evaluation. Metocean data provide information on joint statistics of the wind and wave along with their relative likelihoods for the installation site in the Mediterranean Sea, near the coast of Sicily. A coupled frequency-domain model provides needed power spectra for the desired response processes. The proposed approach offers an efficient and accurate alternative to the exhaustive evaluation of a larger number of sea states and, as such, avoids excessive response simulations.

Optimizing Operations and Maintenance for Offshore Multi-Purpose Platforms in Variable Weather Conditions

In an emerging “blue economy,” the use of large multi-purpose floating platforms in the open ocean is being considered. Such platforms could possibly support a diversified range of commercial activities including energy generation, aquaculture, seabed mining, transport, tourism, and sea-based laboratories. A Markov decision process (MDP) framework is proposed to deal with operations and maintenance (O&M) issues that are inevitable; challenges arise from the complex stochastic weather conditions that need to be accounted for. Using data as well as contrasting synthetic simulations of relevant weather variables, we demonstrate the robustness/versatility of the MDP model. Two case studies—one involving constant and another involving time-dependent downtime costs—are conducted to demonstrate how the proposed MDP framework incorporates weather patterns from available data and can offer optimal policies for distinct metocean conditions (i.e., temporal variations in the weather). A realistic example that illustrates the implementation of the proposed framework for multiple O&M issues involving salmon net pens and wave energy converters demonstrates how our optimal policies can minimize O&M costs and maximize crew safety almost as if the true future were known for scheduling.

https://github.com/TaeminHeo/MppsMDP

Sustainable Reuse of Decommissioned Jacket Platforms for Offshore Wind Energy Accounting for Accumulated Fatigue Damage

An offshore energy transition, even if only a gradual one, from carbon-emitting fossil fuel extraction to cleaner sources is recommended, if we are to slow the harmful impacts of climate change. The potential for sustainable reuse of decommissioned offshore jacket platforms to support wind turbines is being considered as an attractive proposition in such a transition. To maximize the benefits of such reuse of assets, what is needed is a rational optimization strategy that considers the remaining life of a repurposed platform, associated retrofit and construction costs, and a future period of gross renewable energy generation following installation of the wind turbine. We outline a study that employs a fatigue reliability-based framework, based on the global fatigue approach and Palmgren–Miner’s rule, to aid in such sustainable reuse planning and optimization. The framework proposed identifies an optimized reuse plan that incorporates metocean data analysis, structural analysis, life cycle evaluation, and revenue optimization. We employ a case study and sustainable reuse scenario for a site in the vicinity of Porto (Leixões), Portugal.

SOS WindEnergy 

Sample Efficient Learning of Factored Embeddings of Tensor Fields

Data tensors of orders 2 and greater are now routinely being generated. These data collections are increasingly huge and growing. Many scientific and medical data tensors are tensor fields (e.g., images, videos, geographic data) in which the spatial neighborhood contains important information. Directly accessing such large data tensor collections for information has become increasingly prohibitive. We learn approximate full-rank and compact tensor sketches with decompositive representations providing compact space, time and spectral embeddings of tensor fields. All information querying and post-processing on the original tensor field can now be achieved more efficiently and with customizable accuracy as they are performed on these compact factored sketches in latent generative space. We produce optimal rank-r sketchy Tucker decomposition of arbitrary order data tensors by building compact factor matrices from a sample-efficient sub-sampling of tensor slices. Our sample efficient policy is learned via an adaptable stochastic Thompson sampling using Dirichlet distributions with conjugate priors.