Leonard M. Lye

Regional flood estimation for ungauged basins in Sarawak, Malaysia

Abstract Design flood estimation is an important task that is required in the planning and design of many civil engineering projects. In this study, the flood records of more than 23 gauged river basins in Sarawak, Malaysia, are examined using an index-flood estimation procedure based on L-moments. Two homogeneous regions were identified and the Generalized Extreme Value and the Generalized Logistic distributions are found to describe the distribution of extreme flood events appropriately within the respective regions. A regional growth curve is subsequently developed for each of the regions. These curves can be used for the estimation of design floods in ungauged basins in Sarawak within the limitations identified for the method. The results presented herein are useful for practicing engineers in Sarawak while the general methodology may be used in any other regions, provided flood records are available.

Long-term dependence in annual peak flows of Canadian rivers

Abstract Statistical flood frequency analysis assumes that the flood record to be analysed is a reliable set of measurements of independent random events from a stationary population. The validity of this assumption can be verified using statistical tests of independence. However, most statistical tests of independence are designed to show up only short-term serial correlation. They are insensitive to the long-term serial correlation structure of the flood record which can be far more important. To demonstrate this issue, the serial correlation structure of the peak flow series from 90 Canadian rivers was analysed for both short- and long-term dependence. The results show that although short-term dependence is practically absent for most of the peak flow series, significant long-term dependence is present for a large number of peak flow series tested.

Bayes estimation of the extreme-value reliability function

The authors obtain Bayes estimates of the reliability function of the extreme value distribution by using two Bayes approximation procedures: Lindley (1980), and Tierney and Kadane (1986). These estimates were compared to maximum-likelihood estimates (MLE) based on a Monte Carlo simulation study. Jeffreys invariant prior was used in the comparison for both Bayes procedures. The MLE are superior to either of the Bayes estimates, except for small values of t. The simpler Lindley Bayes procedure gives estimates with smaller root-mean-square error than estimates obtained by the Tierney and Kadane procedure except for large values of t. From a practical standpoint, the ML method is easiest to use and more accurate for the extreme value distribution than the two Bayes approximation procedures. Both Bayes procedures seem to perform equally. However, the Lindley method is easier to use with little loss of accuracy. >

Risk-based Winterization for Vessels Operations in Arctic Environments

Because the oil and gas industry has an increasing interest in the hydrocarbon exploration and development in the Arctic regions, it becomes important to design exploration and production facilities that suit the cold and harsh operating conditions. In addition to well-established minimum class requirements for hull strengthening, winterization should be considered as a priority measure early in the design spiral for vessels operating in the Arctic environments. The development of winterization strategies is a challenging task, which requires a robust decision support approach. This article proposes a risk-based approach for the selection of winterization technologies and determination of winterization levels or requirements on a case-by-case basis. Temperature data are collected from climatology stations located in the Arctic regions. Loading scenarios are defined by statistical analysis of the temperature data to obtain probabilistic distributions for the loadings. Risk values are calculated under different loading scenarios. Based on the risk values, appropriate winterization strategies can be determined. A case study is used to demonstrate how the proposed approach can be applied to the identification of heating requirements for gangways.

Performance of locally available bulking agents in Newfoundland and Labrador during bench-scale municipal solid waste composting

BackgroundNewfoundland and Labrador (NL) has one of the highest waste disposal rates in Canada and it has 200 small communities without access to central composting facilities. During Municipal solid waste (MSW) composting, the selection of bulking agents is critical. Bench-scale composting systems plus locally available bulking agents are thus desired for economic and effective MSW management in NL communities. This study evaluated the performance of locally available bulking agents (i.e., NL sawdust and peat) during MSW composting in a bench-scale system. Physiochemical (temperature, oxygen uptake rate, pH, electrical conductivity, moisture and ash content, and C/N ratio) and biological (enzyme activities and germination index) parameters were monitored to evaluate compost maturity and stability.ResultsIn peat composting, higher temperature for a longer duration was observed, indicating more effective pathogen removal and sterilization. High enzyme activities of dehydrogenase, β-glucosidase, and phosphodiesterase in the third week of composting imply high microbial activity and high decomposition rate. The low C/N ratio for compost product implies acceptable stability states. In sawdust composting, higher temperature and oxygen uptake rate (OUR) were observed in the third week of composting, and higher enzyme activities in the second week. Sawdust composting generated a higher germination index, indicating higher maturity.ConclusionsBoth sawdust and peat are effective bulking agents for the bench-scale composting. The choice of a bulking agent for a particular community depends on the availability of the agent and land in the region, convenience of transportation, price, and the expected quality of the compost product.

Modeling and Optimization of Direct Methanol Fuel Cells using Statistical Design of Experiment Methodology

Direct methanol fuel cells (DMFC) are difficult to model due to their complex nonlinear multivariate nature. This paper describes the development of a DMFC system model, which incorporates an optimization approach, based on statistical design of experiment (DoE) methodology. It is demonstrated through experimentation with a DMFC setup that DoE provides a very efficient methodology to obtain a model for the studied multivariable system with only a few experiments. A description of a useful procedure to model DMFC systems that can be extended to other fuel cell technologies is presented in this work. Central composite design (CCD) and the steepest ascent method (SAM), both parts of DoE methodology, are used to build up a DMFC model and to obtain the maximum power point respectively. The results obtained can be used for feasibility study, optimization, and control of the FC system

Resistance and Static Yaw Experiments on the Underwater Vehicle "Phoenix"; Modeling and Analysis, Utilizing Statistical Design of Experiments Methodology

This research is focused on the hydrodynamic behaviour of a series of hull forms for an underwater vehicle, which includes a range of length-to-diameter ratios. Experimental data were gathered for several standard manoeuvring experiments and a reverse Design Of Experiment (DOE) was applied to the available data. From the DOE point of view, the effects of the main factors in each type of experiment were studied and an appropriate Response Surface Model (RSM) was fitted to the data. The developed empirical model is very useful in predicting the non-dimensional hydrodynamic force and moment coefficients, which is a major step in simulating the motion of an underwater vehicle

Estimating design temperatures in Arctic environments: A new approach

Over the past eight decades the ice-infested Northern Sea Route (NSR) has been steadily developed although historically it has been of little interest to commercial shipping companies. However, the shorter routes, more open waters, and the increasing demand for shipping have caused shipping companies to reconsider shipping along these routes. The NSR is now part of an overall world transportation system. However, a key problem of shipping in Arctic environments is the extreme low temperatures encountered and sea ice during most of the year. The American Bureau of Shipping (ABS) has provided some guidance for shipping along the Arctic sea routes with winterization of the Arctic going vessel as a suggested requirement. The technology and level of winterization is determined by the design service temperature (DST) estimated by the lowest mean daily average temperature (MDAT) over at least a 20-year period for the intended geographical area and season of operation. Systems that are more susceptible to the lower temperatures have another temperature which ABS designates as the Minimum Anticipated Temperature (MAT). However, the suitability of that value with respect to a risk based assessment on a vessel and its systems is questionable. For a modern risk-based approach to winterization, knowledge of the magnitude and frequency of occurrence for a given duration are prerequisites. This paper presents a new and more rational approach to estimate the DST or MAT. The approach is based on the set of Temperature-Duration-Frequency (TDF) curves developed for a given climate station. For this study, the Arctic was divided into four regions. Annual extreme low temperatures of durations 1, 3, 6, 12, 24, 36, 48, 72, 96, 120, 144, and 168 hours were extracted from the last 20 years of historical record from three to four representative stations in each region. Magnitudes of low temperatures were then estimated from the data for return periods ranging from 2 to 100 years. These estimates are then used to construct the TDF curves that provide estimates of the magnitude of extreme low temperatures for a given return period, for various durations of practical interest. It is proposed that the 100-year return period 1-hour duration lowest extreme temperature be used as the estimate of the MAT. The proposed approach addresses the drawbacks of the current approach and provides a practical estimate of the DST or MAT for risk-based winterization decisions.

Modelling long-term dependence based on cumulative departures of annual flow series

Abstract This paper investigates the suitability of Sens approach in modelling hydrologic time series and in particular the generation of synthetic flow series. It was found that several problems exist with the method as proposed. These include too many parameters in the model, difficulties in modelling skewed series, and finding a suitable stochastic model for the residuals between the original and the fitted cumulative departure curves. On the other hand, it has been found that Sens method is effective in preserving the Hurst phenomenon and is especially suited for modelling time series with a relatively high Hurst coefficient but low lag-one serial correlation coefficient. Also, with some modifications to the suggested procedure some of the stated problems can be somewhat overcome.

Risk-based Winterization on a North Atlantic-based Ferry Design

The Arctic is a recent focal point of the marine and offshore industries. Winterization is required for safe and efficient operations in these harsh cold environments. A riskbased approach to winterization was recently proposed to provide a quantitative way of determining the need for winterization and its appropriate level. To further validate and enhance this approach, it has been applied to a new ice-class passenger ferry design, which will operate in a particular area of the North Atlantic. This location is ideal for the application with low temperatures, strong wind, and high waves. To facilitate this application and eliminate some limitations of the proposed approach, this article proposes a generic framework of risk-based winterization. Results from this article validated the effectiveness and feasibility of using risk-based winterization on vessel designs.