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Lee, Junho

Publications and source records attributed to Lee, Junho.

4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fatigue performance of the Deeply Embedded Ring Anchor

Existing anchor solutions are limited in their applicability to the floating offshore wind industry, which demands prolonged load endurance in extreme weather conditions and varied seabed soils. This limitation restricts project feasibility and increases costs. In order to address these challenges, the Deeply Embedded Ring Anchor (DERA) is proposed to achieve substantial reductions in anchor costs through efficiency enhancements. Ensuring the long-term safety and reliability of the DERA, especially considering a minimum operational period of 25 years, necessitates a comprehensive understanding of fatigue damage. Induced by repetitive loading below material yield strength, fatigue poses a significant threat to structures exposed to waves and currents. Anchors, vital for station keeping, are susceptible to fatigue damage, particularly in the padeye area. This paper conducts a fatigue analysis, focusing on factors such as the wall thickness of the ring anchor, inner stiffeners, and stiffener thickness, to evaluate the performance of DERA in this context. The results showed that the stiffener significantly affects the elongation of the total life of the anchor. In conclusion, this paper focuses on advancing innovations like DERA, enabling the efficient utilization of offshore wind resources, and addressing safety concerns through meticulous fatigue analysis.

17 WIND ENERGY↗
DOE OSTI2023

Surface‐modified Ag@Ru‐P25 for photocatalytic CO 2 conversion with high selectivity over CH 4 formation at the solid–gas interface

Systematic optimization of the photocatalyst and investigation of the role of each component is important to maximizing catalytic activity and comprehending the photocatalytic conversion of CO 2 reduction to solar fuels. A surface-modified Ag@Ru-P25 photocatalyst with H 2 O 2 treatment was designed in this study to convert CO 2 and H 2 O vapor into highly selective CH 4 . Ru doping followed by Ag nanoparticles (NPs) cocatalyst deposition on P25 (TiO 2 ) enhances visible light absorption and charge separation, whereas H 2 O 2 treatment modifies the surface of the photocatalyst with hydroxyl (–OH) groups and promotes CO 2 adsorption. High-resonance transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray absorption near-edge structure, and extended X-ray absorption fine structure techniques were used to analyze the surface and chemical composition of the photocatalyst, while thermogravimetric analysis, CO 2 adsorption isotherm, and temperature programmed desorption study were performed to examine the significance of H 2 O 2 treatment in increasing CO 2 reduction activity. The optimized Ag 1.0 @Ru 1.0 -P25 photocatalyst performed excellent CO 2 reduction activity into CO, CH 4 , and C 2 H 6 with a ~95% selectivity of CH 4 , where the activity was ~135 times higher than that of pristine TiO 2 (P25). For the first time, this work explored the effect of H 2 O 2 treatment on the photocatalyst that dramatically increases CO 2 reduction activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Adaptive Online Multivariate Signal Extraction With Locally Weighted Robust Polynomial Regression

High-frequency, multivariate data collected in real-time and used to control or make decisions regarding a process’ operation often contain some noise and outliers. Thus, a method to extract the signal is needed in order to reduce the number and magnitude of control-based adjustments that are implemented. Such a method must be (i) online, depending only on past and current observations; (ii) fast, producing a smooth value more quickly than the measurement frequency; (iii) robust, ignoring brief bursts of erroneously measured values; (iv) multivariate, ignoring observations that are jointly unusual; (v) adaptive, adjusting to periods of rapid fluctuation in the signal versus periods of stability; and (vi) purely data-driven, not incorporating any information about the process from which the data are collected. Most existing methods are only able to address a subset of these six features. Furthermore, we also require the method to be nonlinear, providing a local nonlinear estimate of the signal. In this work, we propose a novel, real-time signal extraction method based on a local, robust polynomial fit. We demonstrate the performance of our method compared to a state-of-the-art competitor through simulation. For illustration, the methodology is applied to data collected from a reverse osmosis water treatment process.

97 MATHEMATICS AND COMPUTING↗