Variability of Extreme Riser Responses due to Wave Frequency Motions of a Weather-Vaning FPSO

2017 ◽  
Author(s):  
C. Armstrong ◽  
Y. Drobyshevski ◽  
C. Chin ◽  
I. Penesis
Keyword(s):  
Low Frequency ◽  
Extreme Value ◽  
Wave Frequency ◽  
Extreme Value Distributions ◽  
Flexible Riser ◽  
Short Term ◽  
Robust Fitting ◽  
Slow Drift ◽  
Extreme Responses ◽  
Maximum Interval

The variability of extreme responses of a flexible riser due to wave frequency motions of weather-vaning FPSO is investigated numerically. The objective of this study is to examine such variability in isolation from that caused by the low frequency (slow drift) vessel motions and vessel offsets. Investigation of the extreme value distributions of flexible risers provides the statistical foundation for flexible riser Response Based Analysis (RBA) for use in system design; the determination of the statistical properties of extreme flexible riser responses provides a method for the prediction of extreme responses of offshore systems in cyclonic conditions. A case study conducted in OrcaFlex included an FPSO vessel with a Lazy-S configured riser system. Five riser responses were selected in critical locations including tension, heave, and curvature responses. The environmental cases included two cyclonic storms consisting of multiple half-hour intervals. For each interval, time domain simulations included 40 wave realizations in order to provide a dataset for robust fitting of the extreme value distributions in the Gumbel format. Once the short term interval distributions were established, response distributions in a storm were generated by multiplying the short term distributions and the most probable maximum (MPM) response in a storm computed. A comparison of maximum interval, storm and 3-hour MPMs is presented, which indicates to what extent the MPM response in a storm exceeds the corresponding maximum interval response. Differences between the tension and heave responses are compared with those observed in the curvature responses. This study was limited to riser excitation by waves, current and wave frequency motions of a turret moored FPSO and it is expected that further inclusion of low frequency motions would contribute to the response variability. The inclusion of such variability will ultimately enable the storm-based statistical approach to be used for the development of long-term distribution of the riser responses.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

HEART RATE VARIABILITY RELATED TO N95 RESPIRATOR USE IN INTERNS DURING COVID-19 PANDEMIC

2021 ◽  
pp. 69-70
Author(s):  
Pakanati Sujana ◽  
Venkata Mahesh Gandhavalla ◽  
K. Prabhakara Rao
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Low Frequency ◽  
Protective Equipment ◽  
Short Term ◽  
Non Invasive ◽  
Critical Components ◽  
The Individual ◽  
N95 Respirators ◽  
Respiratory Droplets

Introduction: COVID19 is caused by SARS-CoV-2 which is primarily transmitted through respiratory droplets and contact routes. WHO recommended the use of personal protective equipment (PPE) for prevention and N95 respirators are critical components of PPE. Breathing through N95 respirator will impart stress in the individual and that can be assessed by heart rate variability (HRV). HRV measures the variation in time between each heartbeat controlled by autonomic nervous system (ANS), which is a non invasive reliable index to identify the ANS imbalances. Aims And Objectives: This study is aimed at assessing the HRV of Interns working in COVID19 wards using N95 respirators. Methodology: This study included 100 interns in whom short term HRV was recorded using the standard protocol. Lead II of ECG was recorded using AD instruments (ADI) 8channel polygraph and HRV was analysed using Labchart 8pro software. The recordings were taken before and 1hour after wearing N95 respirator. Results: Overall HRV (SDRR) was found to decrease signicantly after wearing N95 respirator for 1hr (p=0.000). Similarly, indices representing the parasympathetic component ( RMSSD and HF ) were also found to decrease signicantly with the use of N95 respirator. Low frequency (LF) power and LF/HF ratio increased signicantly with N95 respirator use (p=0.000). Conclusion: We conclude that using N95 respirator increased sympathetic activity reecting decreased HRV in our subjects Hence we recommend that it is better to change the duty pattern for interns.

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