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Accurate modeling and prediction of the long-term extreme response of offshore structures is of importance in their design. To ensure the operational safety of offshore systems over their service life, it must be verified that extreme load levels do not exceed the design resistance or capacity. A rational approach to formulate this problem is based on probabilistic design, which must account for uncertainties in the system and the environment in a direct manner. The most straightforward way to address this problem is by means of Monte Carlo Simulation (MCS) wherein all the sources of uncertainty are accounted for random sampling. However, due to the slow convergence rate of MCS and cost associated with response computations for each sample for dynamic systems, this method is not preferred in practice. This is especially true when dealing with low target probabilities of failure. To address noted challenges associated with the use of MCS, this study adopts the use of less expensive surrogate models that are representative of the original “truth” system that must be developed by means of a limited number of samples. Once established, MCS can be used with the surrogate model to assess any Quantity of Interest (QoI), such as a T-year return period load (for critical offshore systems, 50-100 year return periods are often of interest). For the surrogate models in this work, we apply the widely used Polynomial Chaos Expansion (PCE) method that seeks to represent the QoI original system, M(x), in terms of orthogonal polynomials that depend on a weight function f(x), defined by the probability distribution of all the underlying random variables. There are two key steps involved in building an accurate PCE surrogate model—first, one needs appropriate samples of the true system response for specified inputs and, then, one must estimate the polynomial expansion coefficients of the surrogate model using these sampled values. The accuracy of the PCE surrogate model developed in this manner depends not only on the number of evaluated samples but also on the specific locations of samples in the selected set. For engineering problems that are complex and for which the true response is expensive to evaluate, the number of samples used should be minimized. To this end, we explore advanced sampling approaches to extract as much information as possible regarding the true system by means of a limited number of samples. Specifically, instead of using simple MCS random samples, we will use optimal designs (D- and S-optimal designs, specifically) that offer an ‘optimal’ sample of small size from the domain of the input variables. Using these samples, then, Least-Angle Regression (LAR) is employed to build efficient “sparse” PCE models that improve the stability and accuracy of model prediction relative to ordinary PCE. To address the reliability problem where one is interested in rare events associated with very low probabilities of exceedance (or occurrence), a sequential sampling scheme is used with “exploration” samples that first span the entire domain of the variables; this is then followed by additional “exploitation” samples that seek to improve the accuracy of the surrogate model over-identified local domains of interest. Finally, a novel Christoffel Least Squares (CLS) approach is formulated and used to further reduce the required number of samples for accurate and stable PCE surrogate models. CLS relies on alternative but consistent distributions and associated orthogonal polynomial families for sampling that when used with appropriate weight functions lead to theoretically more efficient samples and are easily adapted in the PCE model building.

Over the past decades, wind energy has emerged as an alternative to conventional power generation that is economical, environmentally friendly, and importantly renewable. Specifically, offshore wind energy is being con- sidered by a number of countries to harness the stronger and more consistent wind resource compared to that over land. To meet the projected “20% energy from wind by 2030” scenario that was announced in 2006, 54 GW of added wind energy capacity needs to come from offshore according to a National Renewable Energy Laboratory (NREL) study. In this study, we discuss the development of a semi-submersible floating offshore platform with a catenary mooring system to support a very large vi 13.2 MW wind turbine with 100 m blades. An iterative design process is applied to baseline models with Froude scaling in order to achieve preliminary static stability. Structural dynamic analyses are performed to investigate the performance of the new model using a finite element method approach for the tower and a boundary integral equation (panel) method for the platform. The steady-state response of the system under uniform wind and regular waves is first studied to evaluate the performance of the integrated system. Response amplitude operators (RAOs) are computed in the time domain using white- noise wave excitation; this serves to highlight nonlinear as well as dynamic characteristics of the system. Finally, the stochastic dynamic response of the system is studied to assess the global performance for sea states defined by wind fields with turbulence and long-crested irregular waves.

The gene for phoshoenolpyruvate carboxykinase (GTP) (PEPCK) encodes a rate limiting enzyme in the gluconeogenic pathway. In this study, the 563 base pair promoter from -490 to +73 of the PEPCK gene was ligated to the structural gene for bacterial chloramphenicol acetyltransferase (CAT). The hormonal regulation of PEPCK gene transcription was studied by transient transfection into hepatoma cells in culture. This region of the PEPCK promoter can be regulated by cAMP, the cAMP-dependent kinase (PKA), glucocorticoids, thyroid hormones, and insulin. The ten protein binding domains previously detected by footprinting analysis were changed by site-directed mutagenesis. These replacements disrupted the binding of proteins from rat liver nuclei at each specific domain. The changes in function caused by the introduction of specific mutations were examined by transfecting these mutant plasmids into HepG2 cells in the presence of 8-Br-cAMP or by co-transfection with an expression vector for the catalytic subunit of cAMP-dependent kinase A (C subunit of PKA). Two primary regions, the cAMP responsive element at -96 to -77 (cAMP responsive element 1, CRE-1), and another at -242 to -248 (P3(I)), were found to cooperatively medi ate the induction of transcription by cAMP. Another two regions, -249 to -259 (P3(II)) and -270 to -285 (P4) also contributed to such cooperativity. Furthermore, induction of transcription is mediated through the TATA box. Mutation of the TATA box resulted in a change in the start site of transcription-24 in the PEPCK promoter and a significant decrease in the extent of induction of transcription by 8-Br-cAMP. CRE-1 and P3(I) bind the CCAAT/Enhancer Binding Protein (C/EBP) which is present in the liver. Interestingly, both the CRE-1 and P3(I) sites are required for the induction of transcription by C/EBP. P3(I) is uniquely a C/EBP binding site, while CRE-1 can bind to several transcription factors including C/EBP, Fos/Jun, and CREB (cAMP Responsive Element Binding Protein). The CRE-1 site was changed to an optimal C/EBP binding site. This modified promoter maintained C/EBP binding but had a significantly decreased binding affinity for CREB. The induction of transcription by 8-Br-cAMP or the catalytic subunit of PKA from this modified promoter was the same as from the native PEPCK promoter. These results suggest that C/EBP like protein(s) is involved in mediating the effect of cAMP on transcription of the PEPCK gene