Weibull Parameters Research Articles

Assessment of wind energy potential along the Egyptian Mediterranean Coast

BackgroundCurrently, there is no wind park operating along the Egyptian Mediterranean Coast. Therefore, this study aims to find suitable locations for such projects. Wind data from five coastal meteorological stations were used. These are Marsa Matruh (MM), Ras El-Tin (RE), Abu Qir (AQ), Port Said (PS), and Arish (Ar), in that order from west to east. The wind regime dataset, comprising velocity and direction measurements at a 10-m elevation, was collected from January 2007 to December 2022 (16 years), with a complete record of all data points. The Weibull distribution function, along with its different parameters, was used to characterize wind energy along the Egyptian Mediterranean Coast. The coefficient of determination (R2), root mean squared error (RMSE), and relative root mean squared error (RRMSE) for the Weibull parameters, along with the relative percentage errors (RPE) for the wind power density were calculated to assess the concordance between outcomes derived from observed data and those predicted by the Weibull function.ResultsResults revealed that the dominant wind direction along the Egyptian Mediterranean Coast was the NNW to N wind, except at Ar where the dominant wind was S. The wind velocity range of 4–6 m/s dominated RE, AQ, and PS. At MM and Ar, this was reduced to 2–4 m/s. The analysis of wind power density outlined significant insights into the potential for wind energy generation in the region. The overall analysis showed that AQ and PS were potentially the most suitable locations for wind energy projects. However, the high variability at the AQ site required robust system designs to manage the fluctuating wind conditions. PS might be more suitable for projects prioritizing stability and consistency over maximum energy output. Although Arish, characterized by its lower wind power density, may be less conducive for large-scale wind energy projects, it could still be viable for smaller installations or when integrated with other renewable energy sources.ConclusionsThe different statistical indices reflected good model fitting, displaying the reliability of the Weibull distribution as a tool for preliminary wind resource assessment along the Egyptian Mediterranean Coast and facilitating accurate predictions of wind power availability.

Reliability analysis of DDV and SPM in BOP-based Weibull model and expand fault dataset using deep learning

The hydraulic systems of deepwater Blowout Preventers (BOPs) are crucial for ensuring the reliability and safety of offshore oil and gas extraction operations. A functional failure can lead to uncontrolled blowouts, resulting in casualties and significant economic losses on the rig. The Direct Drive Valve (DDV) and the Subsea Plated Mounted (SPM) valve are key components that help maintain the proper functioning of the hydraulic system in deepwater BOPs. This study begins by utilizing the Weibull analysis method to assess the reliability of the DDV and SPM valves using limited fault data samples. To enhance the accuracy of predictions, Weibull parameters are estimated through various methods, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), and a combination of Correlation Coefficient Optimization with Support Vector Regression (CCO + SVR).Given the challenges in gathering extensive fault data for DDV and SPM valves—due to complex subsea environments, cost constraints, time limitations, and other factors—this study proposes a method employing a Back Propagation Neural Network (BPNN) model to augment the limited fault data samples. To ensure the reliable operation of the DDV and SPM valves, preventive maintenance cycles are established at 2840 and 7550 operations, respectively. At the same reliability level, as the number of operational cycles increases, the remaining service life of the valves gradually decreases, leading to a higher probability of failure over a shorter timeframe. The Mean Remaining Life (MRL) of the DDV and SPM valves, corresponding to different operational times, is analyzed, providing essential reference points for their usage and maintenance. When the extended data sample is utilized for reliability evaluation, the reliability characteristics of the small fault data samples are effectively reflected, and the parameter prediction error remains low. This indicates that the extended data sample is more suitable for reliability evaluation.

The hazard of using the Poisson model to cope with immortal time bias in the case of time-varying hazard

BackgroundA time-dependent analysis, usually by means of Poisson and Cox regression models, can be applied to prevent immortal time bias. However, the use of the Poisson model requires the assumption that the event rate is constant over time. This study aims to assess the potential consequences of using the Poisson model to cope with immortal time bias on estimating the exposure-outcome relationship in the case of time-varying risks.MethodsA simulation study was carried out. Survival times were assumed to follow a Weibull distribution, and the Weibull parameters were chosen to identify three different scenarios: the hazard of the event is constant, decreases, or increases over time. A dichotomous time-varying exposure in which patients can change at most once from unexposed to exposed was considered. The Poisson model was fitted to estimate the exposure-outcome association.ResultsSmall changes in the outcome risk over time (as denoted by the shape parameter of the Weibull distribution) strongly affected the exposure-outcome association estimate. The estimated effect of exposure was always lower and greater than the true exposure effect when the event risk decreases or increases over time, and this was the case irrespective of the true exposure effect. The bias magnitude was positively associated with the prevalence of and time to exposure.ConclusionsBiased estimates were obtained from the Poisson model to cope with immortal time. In settings with a time-varying outcome risk, the model should adjust for the trend in outcome risk. Otherwise, other models should be considered.

Experimental and Statistical Investigations for Tensile Properties of Hemp Fibers

This study investigated the tensile behaviors of hemp fiber bundles and examined how properties including tensile strength and Young’s modulus vary with the bundle diameter. Hemp fibers were extracted, degummed, and separated into bundles of different diameters ranging from less than 50 μm to over 150 μm. Tensile tests were conducted on these fiber bundles using a rheometer-based tensile testing machine. The results showed that hemp fibers exhibited a tensile strength of 97.33 MPa and a Young’s modulus of 3.77 GPa at a 50% survival probability. However, the scale parameters for breaking stress and Young’s modulus were determined to be 620.57 MPa and 29.88 GPa, respectively. As the fiber bundle diameter increased, the tensile strength decreased significantly. This was attributed to the higher probability of defects and irregularities acting as weakness points in larger fiber bundles. In contrast, Young’s modulus (stiffness) increased with increasing bundle diameter, likely due to improved fiber–fiber interactions. To further understand the variability and reliability of the tensile properties, statistical models were developed. The Weibull distribution analysis was applied, revealing critical insights into the variability of diameter, stress at break, Young’s modulus, and strain at break. The Weibull parameters provided a comprehensive understanding of the fibers’ mechanical reliability. Additionally, the Griffith model was employed to predict the strength and Young’s modulus based on fiber diameters, supporting the observation that thinner fibers generally exhibited higher tensile strength due to fewer defects. Overall, this work highlights the importance of understanding structure–property relationships in natural fibers like hemp for optimizing their performance in composites.

Synergetic engineering of oxidizable, redox, and inert metal decorated copper oxide for non-volatile memory and neuromorphic computing applications

Abstract In the quest for efficient resistive switching (RS) materials for both non-volatile memory and neuromorphic computing applications, a variety of functional materials have been researched in the last few years. Herein, we systematically synthesized Ni, Ag, and Au decorated copper oxide (CuxO) by using an electrochemical approach and investigated their RS performance for both non-volatile memory storage and neuromorphic computing applications. By tuning various electrochemical parameters, we optimized Ni, Ag, and Au decoration over the CuxO switching layers to understand the effect of oxidizable, redox, and inert metal decoration, respectively. Fabricated Ni-CuxO/FTO, Ag-CuxO/FTO, and Au-CuxO/FTO devices show forming-free bipolar and analog properties of RS behavior. The electrical measurements asserted that the electrodeposited Ni-CuxO/FTO RS device shows excellent RS, non-volatile memory, and synaptic learning properties compared to the Ag-CuxO/FTO, and Au-CuxO/FTO devices. Moreover, the statistical and Weibull distribution parameters suggested that the Ni-CuxO/FTO RS device has lower switching variation than the other two devices. The conduction mechanisms of all devices are investigated by fitting the appropriate physics-oriented models. It was found that Ohmic and Child's square law were dominated during the charge transport and the RS process occurred due to the filamentary switching effect. Results suggested the electro-decorated/-deposited Ni-CuxO is a suitable switching layer for memory as well as neuromorphic computing applications.

Particle crushing mechanism of recycled aggregate with different component categories: applied to pervious concrete base

This paper focuses on the particle crushing mechanism of construction solid waste recycled aggregate (CWRA), particularly recycled concrete (RC) and recycled bricks (RB), using natural gravel (NG) as a control group. Compression tests, combined with the Weibull distribution, fractal dimension theory, and relative crushing rate, were conducted to investigate the crushing behavior of these materials under different loads. Results indicate that, under the same load, the crushing degree ranks from highest to lowest as RB, RC, and NG. The Weibull distribution effectively describes the crushing state, with “fragmentation” being the primary mode for most materials, while RC also exhibits “grinding.” As the load increases, the relative crushing rate rises for all materials but slows beyond a certain load threshold, indicating greater difficulty in further crushing. The study identifies a load threshold of 400 kN, beyond which changes in void fraction and relative crushing rates become less significant, particularly in stronger aggregates. Highlights Identified that mortar attached to the RC surface is the primary cause of the increased particle crushing below 0.6 mm. Introduced Weibull distribution parameters a and b to accurately characterize the crushing state of RA particles. Revealed the errors between fractal dimension-based evaluations and the actual crushing state of RC particles. Proposed a two-stage crushing mode for RC. Determined the critical threshold value for the variation of crushing degree with load level.

New method to model the wind speed distribution: method of decile

Energy is a primary need for individuals and societies; the new era has revolutionized everything, from cell phones to daily used equipment in houses; all need electrical energy. Advanced countries have already managed energy demand according to their need. Renewable energy is better than conventional energy sources such as gas, coal, and nuclear fuel. Researchers have continuously worked on renewable energy to make it as helpful as possible. Mathematical modeling and new methods of harnessing energy are the leading fields researchers are working in. Wind energy is ubiquitous; however, it depends on a cut-off speed. In numerous places worldwide, the wind speed surpasses the cut-off speed. Such locations are good candidates for generating electricity from wind energy. Proper modeling of wind energy enables one to know available wind potential. An effort has been made to model wind speed; a new method known as the method of decile has been developed. The method works using the first and last decile of the wind distribution. The Weibull parameters, average wind speed, and errors have been calculated and compared to five other methods. Akaike information criterion (AIC) was used to see the suitability of the new method. The AIC values for the new method were calculated for 12 months of wind speed data in 2016; the minimum values occurred for the novel method. It shows that the new method is the best among the other five methods. The annual output energy is estimated using E-5 (by Ryse Energy company) horizontal axis wind turbine and evaluated as 6900 kWh at Hyderabad, the highest among the three sites.

Evaluation of wind potential and economic prospects in the southern regions of Morocco with MERRA-2 and ERA5

Meteorological reanalyses are generating interest in the modelling of wind energy production. This study provides a comparison between ERA5 and MERRA-2. Two regions in Morocco were studied: Laayoune-Sakia-El-Hamra (22 sites) and Dakhla-Oued-Eddahab (17 sites). Among the 150 wind turbines tested, the two most efficient ones were identified for each site. The results show that MERRA-2 outperforms ERA5 in several aspects, including scatter plots and R2 correlation. Wind potential and economic viability were evaluated for each of the 39 sites, incorporating a loss factor of 26.71% and applying Weibull parameters to precisely characterize each site. The analysis reveals that a few sites stand out for their high wind speeds, significant power densities and a capacity factor reaching 54.37%. Economically, the cost of electricity, ranging between $ 0.012 and $ 0.023 per kWh, is particularly favourable for wind energy production.

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

Tensile Properties of Cattail Fibres at Various Phenological Development Stages.

Cattails (Typha latifolia L.) are naturally occurring aquatic macrophytes with significant industrial potential because of their abundance, high-quality fibers, and high fiber yields. This study is the first attempt to investigate how phenological development and plant maturity impact the quality of cattail fibers as they relate to composite applications. It was observed that fibers from all five growth stages exhibited a Weibull shape parameter greater than 1.0, with a goodness-of-fit exceeding 0.8. These calculations were performed using both the Least Square Regression (LSR) and Maximum Likelihood Estimation (MLE) methods. Among the estimators, the MLE method provided the most conservative estimation of Weibull parameters. Based on the Weibull parameters obtained with all estimators, cattail fibers from all five growth stages appear suitable for composite applications. The consistency of shape parameters across all five growth stages can be attributed to the morphological and molecular developments of cattail fiber during the vegetative period. These developments were confirmed through the presence of calcium oxalate (CaOx) plates, elemental composition, and specific infrared peaks at 2360 cm-1 contributing to the strength, cellulose peaks at 1635 cm-1, 2920 cm-1, and 3430 cm-1. In conclusion, it was found that the mechanical properties of cattail fiber remain similar when harvested multiple times in a single growing season.

A Comparison of Parameters of Weibull Distribution on Nigeria Stock Prices

This paper compared 2-parameter and 3-parameter Weibull Distribution using monthly average stock price returns of five different bottling companies enlisted in the Nigerian Stock market comprising Nigerian Breweries PLC, Coca-Cola Bottling Company, Guinness Nigeria PLC, Seven Up Bottling Company and International Breweries from 2012 to 2016 obtained from www.market.ft.com/data/equities/tearsheet/historical. The parameters were obtained using the Method of Moments (MOM) and were compared by the means of minimum Mean Squared Error; the result shows that Coca-Cola bottling company stock price return performed better under 2-parameter Weibull estimate while International Breweries was a choice under 3-parameter Weibull estimate. Also, the goodness-of-fit test was conducted using Kolmogorov-Smirnov Test and the result reviewed that the price of the stock returns does not follow a Weibull Distribution (P-value<0.05).

Fracture failure analysis of plastic fiber based on thermodynamic strength theory and experiment

In order to analyze the mechanical behavior of plastic fiber bundle, a theoretical model considering elastic and plastic behavior of single fiber is established. Based on uniaxial tensile tests of polythene single fiber, elastoplastic mechanical properties, such as elastic modulus and plastic modulus, are obtained. Moreover, distributions of yield strength and breaking strength of single fiber are determined. The stress–strain curve of polythene fiber bundle is predicted by theoretical model. The correctness of this theoretical model is verified by uniaxial tensile test of polythene fiber bundle. All experimental data points are within the range predicted by theoretical model. Some key parameters which regulate mechanical behavior of single fiber are investigated theoretically. Results show that Weibull parameters of yield strength and breaking strength of single fiber have great effect on mechanical performance of fiber bundle. The concentration of distribution area of breaking strength can improve the overall fracture strength of fiber bundle. Results and conclusions in this investigation can extend and perfect fiber bundle model in terms of plastic behavior.

Optimal power density estimation in Sub-Saharan Africa regions based on Weibull distribution parameters

The production of wind energy requires knowledge of certain wind speeds and directions. Several tools are used for this purpose to characterize the wind power, including the Weibull distribution function with two parameters, shape factor k, and the scale factor c. In this paper six methods Graphical Method (GP), Empirical Method of Justus (EMJ), Empirical Method of Lysen (EML), Energy Pattern Factor (EPF), Maximum Likelihood (ML), and Moroccan Method (MMa) are used to estimate Weibull distribution parameters to evaluate the wind potential an its power density. Twelve sites in West African sub-region such as Abuja, Accra Kotoka, Bamako Senou, Conakry Gbessia, Cotonou Cadjehoun, Kano Mallam Aminu, Lagos Ikeja, Lome Tokoin, Niamey, Niamtougou, Ouagadougou, Tambacounda were selected as case studies. For each selected site, hourly wind speed data collected at 10 m height for the twelve years from January 2011 to December 2023 are used. The evaluations of each method were carried out every month and statistical criteria to provide a more complete analysis. The results show that the EPF, EMJ, EML, and ML provide highly desirable better performance while the GP and MMa showed poor performance for all stations. For all sites, the EPF was recognized as the most appropriate method except for the Lagos site where the EMJ ranks first on the others. The methods that ranked second after EPF varied among the sites.

Evaluation of wind energy-based electricity potential at 40m height in 3 districts of Bihar

In this study, three northern districts of Bihar, namely, Bettiah, Madhubani, and Muzaffarpur, were carefully chosen to understand the wind energy resource availability for energy conversion. The wind speed data set was gathered from local meteorological station for a period of 12 years from April 2008 to March 2020. For evaluating wind resources, the widely used two-parameter Weibull distribution was combined with other statistical techniques. The obtained results exhibited that the wind speed in the three districts of Bihar fluctuated from 3.2 m/s to 4.7 m/s while the scale and shape parameters (also known as the Weibull parameters) are discovered to be in the ranges of 10.247 to 11.432 and 3.5718 to 4.0477, respectively. Further, wind power density was 22.729, 26.288, and 28.021 W/m$^2$ at 40 m above the ground for the three districts considered in the study. These findings suggested that the places of the study are not suitable for widespread wind power extraction at a hub height of 40 m but suitable for small-scale wind turbine setup.

Assessment of wind power potential and economic viability at Al-Hodeidah in Yemen: Supplying local communities with electricity using wind energy

Wind energy is a promising sector in the power generation industry because it is renewable and globally available. This study assessed the wind power potential and the economic viability of utilizing wind turbines at the chosen location to produce electricity. This work uses wind speed data collected during (2012–2014) at 10 m height. This research presents the maximum and mean values of measured wind speeds. We have analyzed annual, seasonal, and monthly variations of wind speed. The wind characteristics and wind power potential of the study site have been investigated using Weibull and Rayleigh distribution functions. The annual mean wind speed and Weibull parameters (shape and scale) have been determined to be 4.477 m/s, 2.494, and 5.046 m/s, respectively, according to the actual data collected over the three years. The annual mean power density and energy density have been computed to be 86.39 W/m2 and 757.226 kWh/m2 according to the actual data collected over the three years, and the predicted annual mean power density and energy density have been computed to be 86.492 W/m2 and 758.358 kWh/m2 using Weibull parameters and 164.372 W/m2 and 1441.2 kWh/m2 using Rayleigh distribution, respectively. Accordingly, the power density computed using the Weibull parameters was similar to the power density computed using real wind data, whereas the power density calculated by the Rayleigh function was higher. An economic analysis has been done to choose a suitable turbine for the study site. The results indicate that the wind turbine ZEFIR D14-Px-Ty is indeed a viable option for wind power generation in Al-Hodeidah. It has a relatively low cost of energy of $0.03/kWh and a high capacity factor of 0.428, indicating its efficiency in converting wind energy into electricity. Therefore, the selected location is recommended for some small stand-alone systems as well as small wind farms.

Experimental Research on Breakage Characteristics of Feed Pellets under Different Loading Methods

Particle breakage is a common phenomenon during the processes of production, storage, and transportation. Because of the requirements for pellet integrity in poultry farming, research on the breakage characteristics of feed pellets is necessary. In this paper, repeated compression tests under different loading forces and repeated impact tests under different air pressures were carried out with feed pellets as the research object. The breakage behaviors were described, and the particle size distribution of feed pellets was analyzed quantitatively. The results revealed a positive correlation between crack density in feed particle beds and loading force. The compression process was divided into three stages based on force–displacement curves. The size of the feed pellets during repeated impacts decreased continuously and was negatively correlated with air pressure. The Weibull function accurately described the particle size distribution, with R2 values exceeding 0.97 and 0.96. The Weibull parameters showed a steady breakage degree in compression tests and a growing breakage degree in impact tests. The variation in energy and pulverization rate under different loading conditions was examined as the number of loading cycles increased. The relationship between energy and pulverization rates was fitted, showing that both parameters increased with loading cycles in different loading methods. The model of Vogel and Peukert could describe the relationship between energy and pulverization rate well, with R2 values exceeding 0.94. The minimum energy required for pellet breakage was higher in compression than in impact due to the compaction of the feed particle bed during repeated compression. The results can provide basic theory and data support for breakage characteristics and quality evaluation of feed pellets.

An Optimal Strategy for Estimating Weibull distribution Parameters by Using Maximum Likelihood Method

Several methods have been used to estimate the Weibull parameters such as least square method (LSM), weighted least square method (WLSM), method of moments (MOM), and maximum likelihood (MLE). The maximum likelihood method is the most popular method (MLE). Newton-Raphson method has been applied to solve the normal equations of MLE’s in order to estimate the Weibull parameters. The method was used to find the optimal values of the Weibull distribution parameters for which the log-likelihood function is maximized. We tried to find the approximation solution to the normal equations of the MLE’s because there is no close form for get analytical solution. In this work, we tried to carry out a study that show the difference between two strategies to solve the MLE equations using Newton-Raphson algorithm. Both two strategies are provided an optimal solution to estimate the Weibull distribution parameters but which one more easer and which one converges faster. Therefore, we applied both strategies to estimate the Weibull’s shape and scale parameters using two different types of data (Real and simulation). We compared between the results that we got by applying the two strategies. Two studies have been done for comparing and selecting the optimal strategy to estimate Weibull distribution parameters using maximum likelihood method. We used some measurements to compare between the results such as number of steps for convergence (convergence condition), the estimated values for AIC, BIC and the RMSE value. The results show the numerical solution that we got by applying first strategy convergence faster than the solution that we got by applying second strategy. Moreover, the MRSE estimated by applying the first strategy is lower than the MRSE estimated by applying second strategy for the simulation study with different noise levels and different samples size.

Estimation and Management of Wind Energy in Ikorodu Lagos Lagoon, Using Weibull Parametric Measurement to Electricity Production in Nigeria

This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

Temporal characteristics of a 6.2 Ma-long ash-fall history in the NW Pacific

Explosive volcanism is one of the most dangerous and far-reaching natural hazards. The largest eruptions are the rarest, so studies of their temporal patterns have to rely on long archives. In this paper, we apply fractal and spectral analyses to the 6.2 Ma-long record of ash-falls at the Detroit Seamount (DS6M), > 600 km east of the Kamchatka Peninsula, NW Pacific, combined with a terrestrial record for the last 30 ka (T30ka). These datasets are the most complete for volcanism in the North Pacific, and DS6M spans all Pleistocene glaciations. In both datasets, events are grouped (Weibull parameter k < 0.84) with no characteristic scale of grouping in the time domain of thousands to millions of years. The fractal dimension of the studied data below the unity may be intrinsic to the volcanism (e.g. as a proxy of fractal composition and topography of a subducting plate) or represent uneven deposition and recovery of tephra. Only for the last 700 ka of DS6M, an increase in the correlation dimension values suggests the applicability of spectral analysis; however, no Milankovitch frequencies have been detected in this dataset. When compared to other North Pacific data for areas repeatedly glaciated in the Middle and Late Pleistocene and to a climate proxy of 18O isotopic stack LR04, the studied data suggest that variation in timing of ash-falls among the sites predominates over hemispheric-scale climatic forcing. If so, Quaternary glaciations had a limited effect on the timing of large explosive eruptions in North Pacific, still affecting the transit and deposition of erupted material.

Data-driven prediction of fracture toughness size effect in ductile-to-brittle transition using Two-Step-Scaling procedure

The fracture properties of ferritic steels in transition temperature region are usually statistically treated by models that presuppose the existence of various forms of quenched disorder (critical cleavage triggers). In this study, the fracture toughness from the Euro fracture toughness dataset (stress intensity factor used in the master curve KJc) for reactor steel 22NiMoCr37 is used with the aim of outlining a methodology for using the recently proposed two-step scaling (2SS) method. Three widely different temperatures (−154 °C, −91 °C, and 0 °C), which cover the entire range from lower shelf to upper shelf fracture, are selected to demonstrate the accuracy of extrapolation and interpolation of the fracture toughness CDF (cumulative distribution function) and the pertinent issues related to the procedure application. The obtained predictions at the two lower temperatures are in good agreement with the experimental results and well within the inherent experimental data scatter, while 2SS method is shown not to be applicable in upper-shelf transition region. Special attention is devoted to the effect of statistical sample size on prediction accuracy/reliability along with the minimum sample size requirement. Moreover, the reduction in random sample size affects the Weibull parameters, which in turn impacts the accuracy of predictions, introducing a degree of uncertainty and highlighting limitations in the method.