Experimental investigation of the onset of sand deposits on Hastelloy-X between 1,000°C and 1,100°C

2017 ◽  
Vol 121 (1242) ◽  
pp. 1187-1199
Author(s):  
A. Boulanger ◽  
J. Hutchinson ◽  
W.F. Ng ◽  
S.V. Ekkad ◽  
M.J. Keefe ◽  
...  
Keyword(s):  
Arid Regions ◽  
Prediction Models ◽  
Quadratic Function ◽  
Empirical Models ◽  
Turbine Engines ◽  
Near Surface ◽  
Hastelloy X ◽  
Metallic Substrates ◽  
Particle Ingestion ◽  
Health Monitor

ABSTRACTDeposit formation on turbine hardware in propulsion turbine engines can occur in many arid regions globally. Characterising crystalline deposits on metallic substrates can aid in component resilience and health monitor algorithms during particle ingestion. This study has developed two statistical empirical models for prediction from acquired experimental data for the onset of deposits. The prediction models are for crystalline particulate (Arizona Road Test Dust) deposits on a flat rectangular Hastelloy-X test coupon. Particle impingement angles varied between 20° and 80° in experimental flow temperatures of 1,000–1,100°C. Averaged deposits are methodically quantified through normalised particle deposit tallies per area and percent coverage of the surface using microscopic imaging and image processing programs. Deposit accumulation is a quadratic function of both near-surface coupon temperature and coupon angle.

scholarly journals Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 601
Author(s):  
Nelson K. Dumakor-Dupey ◽  
Sampurna Arya ◽  
Ankit Jha
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Academic Research ◽  
Empirical Models ◽  
Rock Breakage ◽  
Environmental Implications ◽  
Learning Methods ◽  
Factors Affecting ◽  
Impact Prediction ◽  
Machine Learning Methods

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

scholarly journals An Assessment of the Accuracy of MODIS Land Surface Temperature over Egypt Using Ground-Based Measurements

2019 ◽  
Vol 11 (20) ◽  
pp. 2369 ◽  
Author(s):  
Ahmed M. El Kenawy ◽  
Mohamed E. Hereher ◽  
Sayed M. Robaa
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Arid Regions ◽  
Surface Albedo ◽  
Hydrological Modeling ◽  
Hot Spot ◽  
Near Surface ◽  
Lower Accuracy ◽  
Semi Arid ◽  
Minimum Air Temperature

Space-based data have provided important advances in understanding climate systems and processes in arid and semi-arid regions, which are hot-spot regions in terms of climate change and variability. This study assessed the performance of land surface temperatures (LSTs), retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua platform, over Egypt. Eight-day composites of daytime and nighttime LST data were aggregated and validated against near-surface seasonal and annual observational maximum and minimum air temperatures using data from 34 meteorological stations spanning the period from July 2002 to June 2015. A variety of accuracy metrics were employed to evaluate the performance of LST, including the bias, normalized root-mean-square error (nRMSE), Yule–Kendall (YK) skewness measure, and Spearman’s rho coefficient. The ability of LST to reproduce the seasonal cycle, anomalies, temporal variability, and the distribution of warm and cold tails of observational temperatures was also evaluated. Overall, the results indicate better performance of the nighttime LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of −1.3, −1.2, and −1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Importantly, the results indicate that the performance of LST over Egypt varies considerably as a function of season, lithology, and land use. LST performs better during transitional seasons (i.e., spring and autumn) compared to solstices (i.e., winter and summer). The varying interactions and feedbacks between the land surface and the atmosphere, especially the differences between sensible and latent heat fluxes, contribute largely to these seasonal variations. Spatially, LST performs better in areas with sandstone formations and quaternary sediments and, conversely, shows lower accuracy in regions with limestone, igneous, and metamorphic rocks. This behavior can be expected in hybrid arid and semi-arid regions like Egypt, where bare rocks contribute to the majority of the Egyptian territory, with a lack of vegetation cover. The low surface albedo of igneous and limestone rocks may explain the remarkable overestimation of daytime temperature in these regions, compared to the bright formations of higher surface albedo (i.e., sandy deserts and quaternary rocks). Overall, recalling the limited coverage of meteorological stations in Egypt, this study demonstrates that LST obtained from the MODIS product can be trustworthily employed as a surrogate for or a supplementary source to near-surface measurements, particularly for minimum air temperature. On the other hand, some bias correction techniques should be applied to daytime LSTs. In general, the fine space-based climatic information provided by MODIS LST can be used for a detailed spatial assessment of climate variability in Egypt, with important applications in several disciplines such as water resource management, hydrological modeling, agricultural management and planning, urban climate, biodiversity, and energy consumption, amongst others. Also, this study can contribute to a better understanding of the applications of remote sensing technology in assessing climatic feedbacks and interactions in arid and semi-arid regions, opening new avenues for developing innovative algorithms and applications specifically addressing issues related to these regions.

scholarly journals Influence of Precipitation Assimilation on a Regional Climate Model’s Surface Water and Energy Budgets

2007 ◽  
Vol 8 (4) ◽  
pp. 642-664 ◽  
Author(s):  
Ana M. B. Nunes ◽  
John O. Roads
Keyword(s):  
Surface Water ◽  
Energy Budget ◽  
Prediction Models ◽  
Regional Climate ◽  
Longwave Radiation ◽  
Specific Humidity ◽  
Climate Simulation ◽  
Energy Budgets ◽  
Near Surface ◽  
Regional Reanalysis

Abstract Initialization of the moisture profiles has been used to overcome the imbalance between analysis schemes and prediction models that generates the so-called spinup problem seen in the hydrological fields. Here precipitation assimilation through moisture adjustment has been proposed as a technique to reduce this problem in regional climate simulations by adjusting the specific humidity according to 3-hourly North American Regional Reanalysis rain rates during two simulated years: 1988 and 1993. A control regional simulation provided the initial condition fields for both simulations. The precipitation assimilation simulation was then compared to the control regional climate simulation, reanalyses, and observations to determine whether assimilation of precipitation had a positive influence on modeled surface water and energy budget terms. In general, rainfall assimilation improved the regional model surface water and energy budget terms over the conterminous United States. Precipitation and runoff correlated better than the control and the global reanalysis fields to the regional reanalysis and available observations. Upward shortwave and downward short- and longwave radiation fluxes had regional seasonal cycles closer to the observed values than the control, and the near-surface temperature anomalies were also improved.

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

2020 ◽  
Author(s):  
Jun Meng ◽  
Jingfang Fan ◽  
Josef Ludescher ◽  
Ankit Agarwala ◽  
Xiaosong Chen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Statistical Prediction ◽  
Analysis Tool ◽  
Strong Positive Correlation ◽  
Near Surface ◽  
Spring Predictability Barrier ◽  
Prediction Horizon ◽  
The Individual

<p>The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows to forecast the magnitude of an El Niño with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error = 0.23°C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasts a weak El Niño with a magnitude of 1.11±0.23°C.  Our framework presented here not only facilitates a long–term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.</p>

scholarly journals Models for Prediction of Industrial Insolvency of Manufacturing Companies in India

2018 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Nisarg A Joshi ◽  
Jay M Desai
Keyword(s):  
Financial Institutions ◽  
Emerging Economies ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Empirical Models ◽  
Manufacturing Companies ◽  
Z Score ◽  
The World ◽  
New Models ◽  
Score Model

Investors, activists and corporations across the world are emphasizing on prediction of insolvency well in advance so that corrective actions can be taken and erosion of funds can be prevented. For this purpose, this study attempts to construct models for forecasting industrial sickness and to validate the performance of these models. This paper proposes new models for prediction of industrial sickness or leading bankruptcy using three different techniques i.e. a model using Multi Discriminant Analysis (MDA), a model using MDA + PCA (Principle Component Analysis), and a model using ANN (Artificial Neural Network).This paper focuses to propose prediction models for bankruptcy which contribute more to this impact in emerging economies like India.The results show that the forecasting ability of the models is higher than the empirical models already available such Altman’s Z score model, Ohlson’s model and model developed by Odom and Sharda to name a few. The prediction accuracy of MDA model is highest among proposed models for prediction of industrial sickness. These results are recommended to financial institutions, banks and executives. This study may also be used for evaluating the repayment behavior of a borrower. These models may be used by the potential investors for screening out undesirable investments. Since the models are of predictive nature, the investors may use it for portfolio selection.

scholarly journals A Research on Modification Method for NSM FRP-Concrete Bonded Joints Strength Models

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jing Li ◽  
Liwen Zhang ◽  
Yanggui Deng ◽  
Junping Zhang
Keyword(s):  
Model Uncertainty ◽  
Prediction Models ◽  
Regression Function ◽  
Practical Significance ◽  
Design Parameters ◽  
Strength Prediction ◽  
Bonded Joints ◽  
Near Surface ◽  
Uncertainty Factors ◽  
Modification Method

A modification method was proposed for near-surface mounted (NSM) fiber-reinforced polymer (FRP)-concrete bonded joints strength prediction models considering model uncertainty. A database consisting of 246 test records was involved. Three bonded joints strength prediction models for NSM FRP reinforcement system were selected for modification. All the three selected models have model uncertainty factors associated with input design parameters. Spearman correlation analysis was used to prove the systematic correlation of the model uncertainty factors. For each model, a regression function f was established to eliminate the systematic nonrandom part of the model uncertainty factor. Then, the model uncertainty factors could be described by random variables obeying logarithmic normal distribution. A reliability analysis using the JC method was carried out to validate the practical significance and value of model modification. This study improves the predictability of FRP NSM reinforcement systems and provides valuable references for model calibration in practical engineering.

Global Air–Sea Fluxes of Heat, Fresh Water, and Momentum: Energy Budget Closure and Unanswered Questions

2019 ◽  
Vol 11 (1) ◽  
pp. 227-248 ◽  
Author(s):  
Lisan Yu
Keyword(s):  
Surface Energy ◽  
Fresh Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ocean Surface ◽  
Surface Radiation ◽  
Global Ocean ◽  
Personal Perspective ◽  
Near Surface ◽  
Water Budgets

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air–sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air–sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints—are the community-standard precipitation and surface radiation products pairwise compatible?

scholarly journals Understanding the performance of the FLake model over two African Great Lakes

2014 ◽  
Vol 7 (1) ◽  
pp. 317-337 ◽  
Author(s):  
W. Thiery ◽  
A. Martynov ◽  
F. Darchambeau ◽  
J.-P. Descy ◽  
P.-D. Plisnier ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Prediction Models ◽  
Mixed Layer Depth ◽  
Wave Radiation ◽  
Contrast Model ◽  
Near Surface ◽  
Lake Kivu ◽  
Tropical Conditions ◽  
Meteorological Observations ◽  
The One

Abstract. The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from ~ 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60 m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

scholarly journals Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5268
Author(s):  
Praveena Krishnan ◽  
Tilden P. Meyers ◽  
Simon J. Hook ◽  
Mark Heuer ◽  
David Senn ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Oak Ridge

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

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