Predicted Mass Loss Research Articles

X-Shooting ULLYSES: Massive stars at low metallicity

Context. Mass loss through a stellar wind is an important physical process that steers the evolution of massive stars and controls the properties of their end-of-life products, such as the supernova type and the mass of compact remnants. To probe its role in stellar evolution over cosmic time, mass loss needs to be studied as function of metallicity. For mass loss to be accurately quantified, the wind structure needs to be established jointly with the characteristics of small-scale inhomogeneities in the outflow, which are known as wind clumping. Aims. We aim to improve empirical estimates of mass loss and wind clumping for hot main-sequence massive stars, study the dependence of both properties on the metal content, and compare the theoretical predictions of mass loss as a function of metallicity to our findings. Methods. Using the model atmosphere code FASTWIND and the genetic algorithm fitting method KIWI-GA, we analyzed the optical and ultraviolet spectra of 13 O-type giant to supergiant stars in the Small Magellanic Cloud galaxy, which has a metallicity of approximately one-fifth of that of the Sun. We quantified the stellar global outflow properties, such as the mass-loss rate and terminal wind velocity, and the wind clumping properties. To probe the role of metallicity, our findings were compared to studies of Galactic and Large Magellanic Cloud samples that were analyzed with similar methods, including the description of clumping. Results. We find significant variations in the wind clumping properties of the target stars, with clumping starting at flow velocities 0.01–0.23 of the terminal wind velocity and reaching clumping factors fcl = 2–30. In the luminosity (log L/L⊙ = 5.0–6.0) and metallicity (Z/Z⊙ = 0.2–1) range we considered, we find that the scaling of the mass loss M˙ with metallicity Z varies with luminosity. At log L/L⊙ = 5.75, we find M˙ ∝ Zm with m = 1.02 ± 0.30, in agreement with pioneering work in the field within the uncertainties. For lower luminosities, however, we obtain a significantly steeper scaling of m > 2. Conclusions. The monotonically decreasing m(L) behavior adds a complexity to the functional description of the mass-loss rate of hot massive stars. Although the trend is present in the predictions, it is much weaker than we found here. However, the luminosity range for which m is significantly larger than previously assumed (at log L/L⊙ ≲ 5.4) is still poorly explored, and more studies are needed to thoroughly map the empirical behavior, in particular, at Galactic metallicity.

Co-pyrolysis of corn stalk and high-density polyethylene with emphasis on the fibrous tissue difference on thermal behavior and kinetics

The thermal properties of various corn stalk tissues (including stem, husk, ear, cob, and leaf), high-density polyethylene (HDPE), and their blends were investigated using thermogravimetric analysis under a nitrogen atmosphere. The results indicate that the thermal decomposition process of corn stalk tissue/HDPE mixtures is delayed with an increasing heating rate, regardless of the tissue type. Besides, the structural differences among various corn stalk tissues significantly influence their thermal behavior, product distribution, and co-pyrolysis kinetics with HDPE. Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) was conducted to analyze the pyrolytic products of the blends with different corn stalk tissues, revealing that corn cob/HDPE blends produce a higher yield of valuable chemicals, such as the furan derivates and aromatic hydrocarbons. Kinetic analysis was further performed using Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods to determine the activation energy for the reactions occurring during co-pyrolysis. The co-pyrolysis of corn cob/HDPE blend requires the least activation energy (149.3 kJ/mol) among five blends, which was ascribed to the high hemicellulose content in corn cob. Moreover, machine learning algorithms, including random forest (RF) and gradient boost regression tree (GBRT), were applied to predict mass loss in the corn fiber/HDPE blends, which showed RF possessed superior accuracy over GBRT. These findings suggest that isolating plant tissues during the feedstock pre-management could enhance the valorization performance of lignocellulose-waste plastic co-pyrolysis.

Automatic identification of streamlined subglacial bedforms using machine learning: an open‐source Python approach

Subglacial processes exert a major control on ice streaming. Constraining subglacial conditions thus allows for more accurate predictions of ice mass loss. Due to the difficulty in observing large‐scale conditions of the modern subglacial environment, we turn to geological records of ice streaming in deglaciated environments. Morphometric values of streamlined subglacial bedforms provide valuable information about the relative speed, direction, and maturity of past ice streams and the relationship between ice streaming and subglacial erosion and deposition. However, manually identifying streamlined subglacial bedforms across deglaciated landscapes, sometimes in clusters of several thousand, is an arduous task with difficult‐to‐control sources of variability and human‐biased errors. This paper presents a new tool that utilizes a machine learning approach to automatically identify glacially derived streamlined features. Slope variations across a landscape, identified by topographic position index, undergo analysis from a series of supervised machine learning models trained from over 600 000 data points identified across the deglaciated Northern Hemisphere. A filtered data set produced through the combination of scientifically driven preprocessing and statistical downsampling improved the robustness of our approach. After cross‐validation, we found that Random Forest detected the most true positives, up to 94.5% on a withheld test set, and an ensemble average of machine learning models provided the highest stability when applied within the range of applicable data sets, performing at up to 79% identification of true positives on an out of distribution area of interest. We build these models into an open‐source Python package, bedfinder, and apply it to new data in the Green Bay Lobe region, USA, finding the general ice‐flow direction and average streamlined subglacial bedform elongation with minimal effort. This type of open, reproducible machine learning analysis is at the leading edge of glacial geomorphology research and will continue to improve with integration of newly acquired and previously collected data.

Labyrinth seal design for space applications

Labyrinth seals, extensively used in space applications, serve to prevent the loss of liquid lubricants and shield satellite subsystems from contamination. These seals are essential for the reliable functioning of bearings and for protecting satellite subsystems from contamination. This study compares analytical predictions of lubricant loss against experimental measurements and computer simulations to optimize labyrinth seal configurations. Analytical models tend to overestimate mass loss by 5–8 times compared to experimental data, indicating limited reliability for complex seal geometries. Simulations using MolFlow+ and COMSOL Multiphysics align closely with experimental results, providing accurate mass loss predictions. Key findings highlight that labyrinth length, width, and surface roughness are critical factors in minimizing evaporative mass loss. Notably, stepped labyrinth seals with relief grooves and optimized step positioning effectively reduce molecular beaming effects and improve sealing performance compared to straight geometries. Effective sealing not only reduces mission failures but also helps to minimize space debris, thereby promoting safer satellite missions.

Regional ice flow piracy following the collapse of Midgaard Glacier in Southeast Greenland

Southeast Greenland contributes significantly to global sea level rise, with mass loss having increased by about 600% over the past 30 years due to enhanced melt and dynamic instabilities of marine-terminating glaciers. Accurate modelling of glacier dynamics is crucial to minimise uncertainties in predictions of future sea level rise, necessitating detailed reconstructions of long-term glacial histories. One key complexity in these models that is not well understood or documented is ice flow piracy, where ice is redirected between catchment basins, significantly influencing regional glacier dynamics and mass balance. Here, we document and characterise the collapse of Midgaard Glacier in Southeast Greenland using a multi-data approach, providing a 90-year record of the area’s complex glacial history. Initiated over 80 years ago, this collapse triggered catchment-scale dynamic changes in several neighbouring glaciers, impacting local glacial stability throughout the 20th century and into the present. Our analysis reveals that catchment-scale ice flow piracy can cause substantial disturbances in mass balance evolution and catchment reconfigurations, independent of climatic conditions. These findings underscore the importance of understanding long-term changes in complex glacier systems to make accurate predictions of future glacial mass loss and associated sea-level rise.

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

Numerical evaluation of wear parameters using meta-models

AbstractWear parameters identified by linear friction tester (LFT) experiments overestimate the wear mass loss when applied on a laboratory abrasion & skid tester 100 (LAT100) simulation. On the other hand, the identification of wear parameters directly from the LAT100 experiment can be challenging since variables such as the contact area and the slip velocity are not measured experimentally and need to be assumed. To improve the identification process, numerical calibration is used as a more suitable approach. This approach relies on meta-models as a target function for minimization. The meta-models are generated using a sample of wear parameters applied to an LAT100 finite element modeling (FEM) to calculate the corresponding wear mass.. In this model, a transport velocity is defined for the rolling simulation, and an arbitrary lagrangian–eulerian (ALE) adaptive meshing approach is adopted for the wear modeling. For the wear model, a combination of archard’s wear model and schallamach’s abrasion law is used. This model contains a pair of wear parameters to be identified. The ALE adaptive meshing technique moves the nodes independently of the material. Since the mesh topology remains the same, failure of the simulation occurs if the wear volume loss exceeds that of the element. Meta-models are created to extend wear modeling beyond this failure. Once the meta-models are created, they are used as a target function for the minimization algorithm. The minimization algorithm aims to find the optimal wear parameters by minimizing the difference between experimentally observed and numerically produced wear mass loss. The minimization algorithm inputs a set of wear parameters into the meta-models which in turn yield a prediction of the wear mass loss. The process is carried out until an optimum parameter set is identified. Such an approach has a lower accuracy if the parameters are identified directly from the experiment using assumptions regarding the contact shear stress and the sliding velocity. Nonetheless, the main advantage of parameters identified using the meta-model approach is the usability of these parameters in an LAT100 model.

Influence of particle lithology, size and angularity on rates and products of bedload wear: An experimental study

AbstractPhysical wear during bedload transport influences downstream changes in the size distributions and shapes of riverbed sediment, which in turn affect myriad fluvial processes ranging from incision into bedrock to provision of aquatic habitat. Here we use laboratory tumbling experiments to address several remaining knowledge gaps, including the roles of lithologic susceptibility to fragmentation, particle size distributions and particle angularity in controlling wear rates and the size distribution of wear products. To focus on the dynamics of particle wear in headwater channels, we used initially angular sediment and ran the experiments until 10% of the initial bedload particle mass had been lost to fine‐grained wear products. We measured individual particle mass and diameter by hand and used photo analysis to quantify particle shape and angularity. We find strikingly different wear patterns between limestone and welded tuff. Although wear rates in the tuff were an order of magnitude slower than the limestone, tuff wear was primarily by fragmentation while limestone wear was dominantly by attrition. Fragmentation of the tuff resulted in a widening of the bedload size distribution, a lack of consistent particle rounding and a fine‐wear product dominated by sand. In contrast, limestone particles rounded substantially and produced silt‐sized wear products. Differences in wear product size and susceptibility to fragmentation may reflect contrasts in the size distribution of mineral crystals in each lithology. Wear rates in both rock types declined substantially with increasing cumulative mass loss, due to rounding in the limestone and reduced fragmentation in the tuff. These results suggest that applications of the conventional exponential model to predict mass loss with travel distance need to account for lithologic influence on susceptibility to fragmentation and the influence of rounding on particle wear rates.

Thermodynamic assessment of evaporation during molten steel testing onboard the International Space Station

Evaporation control is a critical facility resource during solidification experiments that limits processing time and must be tracked to ensure facility health. A thermodynamic analysis was performed on a ternary FeCrNi sample processed onboard the International Space Station (ISS) using ESA Electromagnetic Levitation (EML) facility in a microgravity environment. A non-ideal solution-based mathematical model was applied for the overall sample mass loss prediction during this study. The overall sample mass loss prediction is consistent with the post-flight mass loss measurements. The species-specific findings from this study were validated using post-mission SEM-EDX surface evaluations by three different facilities. The bulk composition prediction was validated using SEM-EDX and wet chemical analysis. The non-ideal solution model was then applied to predict the composition of the dust generated during EML testing. The thicknesses of the deposited layer on the EML coil at various locations were also calculated using the geometry of the facility and results were validated with near-real-time dust layer predictions from toxicity tracking software developed by the German Space Center (DLR) Microgravity User Support Center (MUSC).

An electromechanical impedance-based sensor for monitoring the pitting corrosion of steel: Simulation with experimental validation

Quantitative monitoring of steel corrosion plays a crucial role in evaluating structural safety and predicting structural performance. The electromechanical impedance (EMI) technique has already emerged for uniform corrosion monitoring. This study explores its potential for pitting corrosion monitoring. Specifically, two EMI-based sensor designs were proposed, namely the cylinder and the tube structures. Finite element models were developed to analyze the influence of the pit’s location, morphology parameters, as well as the random distribution of multiple pits, on the conductance peak frequency of sensors. The computer numerical controlled processes were employed to create random pits on sensors. Experimental results revealed that different distributions of pits correspond to distinct conductance peak frequencies, even with the same mass loss. Additionally, the tubular sensor exhibited reduced sensitivity to pits distribution compared to the cylindrical sensor. This is beneficial for accurately predicting mass loss induced by random pitting. This paper enhances the capability of EMI-based sensors for quantitatively monitoring pitting corrosion by focusing on structural design aspects.

Slurry Erosion Resistance, Morphology, and Machine Learning Modeling of Plasma-Sprayed Si3N4+TiC+VC and CrNi based Ceramic Coatings

This study presents a comprehensive analysis of the morphology, mechanical properties, erosion resistance, and machine learning modeling of Si3N4+TiC+VC and CrNi based ceramic coatings deposited via plasma spraying. The cross-sectional morphology analysis revealed good mechanical interlocking between the coatings and the substrate, with pores predominantly observed in the Si3N4+TiC+VC coating. The coating thicknesses were measured as follows: 260 μm for Si3N4+TiC+VC, 243 μm for Si3N4+TiC+VC blended with CrNi, and 232 μm for pure CrNi coating. Mechanical characterization showed that microhardness values ranged from 1933 HV for Si3N4+TiC+VC coating to 499 HV for pure CrNi coatings. Slurry erosion tests demonstrated that pure Si3N4+TiC+VC coating exhibited superior erosion resistance at 90º impingement angle compared to other coatings. Additionally, a rigorous evaluation of over 20 machine learning regression models revealed that Gaussian Process Regressors and Artificial Neural Network (ANN) with a layer size of 20, displayed satisfactory performance in modeling mass loss prediction. The erosion mechanisms observed in the coatings provided valuable insights into their response to slurry erosion, highlighting the influence of coating composition and impingement angles. The findings underscore the potential of Si3N4+TiC+VC ceramic coatings to enhance the erosion resistance of stainless steel, offering promising applications in various industries.

Enhancing cavitation erosion resistance of VC+TiC coatings with PTFE in marine environments via lasso regression optimization

This research explores the mechanical properties and erosion mechanisms of SS316, VC+TiC, and PTFE coatings to study cavitation erosion resistance. Microhardness analysis revealed that VC+TiC showed the highest resilience due to its hardness, while PTFE, despite lower hardness, displayed potential with formulation adjustments. Lasso Regression models effectively predicted mass loss under various testing conditions, indicating strong relationships between independent variables and erosion responses. Scanning Electron Microscopy (SEM) analysis uncovered distinct erosion mechanisms for each coating, with SS316 showing plastic deformation, VC+TiC exhibiting crater formation, and PTFE displaying torn splats. The development of superhydrophobic surfaces from VC+TiC and PTFE coatings offers promising applications in mitigating cavitation erosion, providing valuable material-specific insights into this phenomenon.

Theoretical modeling of concrete friction-impact deterioration using water-borne sand abrasion experiment

To explore the influence of impact angle on concrete abrasion deterioration under water-borne sand, a theoretical model for friction-impact deterioration was developed. This model innovatively proposes the inhibition effect of the normal impact action on the tangential friction mass loss and the facilitation effect of the tangential friction action on the normal impact mass loss to rationally explain the effect of impact angle on concrete mass loss. Simultaneously, a water-borne sand accelerated abrasion experiment was conducted to initiate a comprehensive discussion on surface abrasion deterioration indicators, including mass loss, number of holes, aperture, hole area, and hole volume. In addition, the concrete abrasion mass loss rate was evaluated based on the friction-impact deterioration model combined with experimental data. Applying this model facilitates a reliable prediction of concrete's mass loss caused by abrasion. Consequently, this allows for a more accurate assurance of the safety of concrete structures exposed to abrasive conditions throughout their operational life. The results show that the mass loss per unit area, maximum aperture, hole area ratio, and hole volume exhibit a trend of first decreasing, then increasing and finally decreasing with the increase of impact angle. Under 15°, 60°, and 90°, 70 % of the mass loss per unit area of the concrete specimens occurred within the first 6 d of abrasion. When the angles were 30° and 45°, 50 % of the mass loss per unit area of the concrete specimens occurred in the 9–12 d abrasion. It has been revealed that 69° results in the most severe abrasion mass loss. The abrasion deterioration process of concrete can be divided into three primary stages, including surface mortar abrasion, prime aggregate-mortar abrasion, and continuous aggregate-mortar abrasion. Additionally, the friction-impact theoretical model was employed to simulate the abrasion of both single and multiple pier columns, and the simulations provided insights into the non-uniform mass loss rate distribution around the pier columns.

Moss removal facilitates decomposition and net nitrogen loss of monospecific and mixed-species litter in a boreal peatland

AbstractLitter decomposition plays an important role in biogeochemical cycling in boreal peatlands, where mosses, especially Sphagnum species, are a determinant. In recent decades, these peatlands have experienced a decline in moss cover due to abrupt climate warming and atmospheric nitrogen (N) deposition. To reveal the effect of the reduction in moss cover on litter decomposition, we adopted a field living moss removal experiment (with the senesced tissues remaining) in a Sphagnum-dominated boreal peatland, and investigated litter mass loss and net N loss of three deciduous woody species decomposing in monocultures and mixtures over 3 years. Based on the observed and predicted mass loss and net N loss of litter mixtures, we divided litter mixing effects into additive (no significant difference), synergistic (observed value greater than predicted value), and antagonistic (observed value lower than predicted value) effects. Across 3 years of decomposition, moss removal increased litter mass loss and net N loss, irrespective of single- or mixed-species compositions. Moss removal generally changed litter mixing effects on mass loss from antagonistic to additive effects in the initial 2 years, but from synergistic to additive effects after 3 years of decomposition. Regarding net N loss of litter mixtures, moss removal often resulted in a shift from additive to synergistic effects or from antagonistic to additive effects after 2 and 3 years of decomposition. Our observations suggest that the declines in living moss cover can accelerate litter decomposition and nutrient release, and highlight that living moss loss makes litter mixture decomposition predictable by reducing non-additive effects in boreal peatlands. Given the widespread occurrence of reduced moss cover in boreal peatlands, the mechanisms explaining living moss controls on litter decomposition and N cycling should receive significant attention in further studies.

Numerical erosion prediction of aluminum induced by cavitating jet using an Eulerian–Lagrangian method

Cavitation-induced erosion in pump machinery is a significant issue that leads to material loss and increased operating costs. This study develops a numerical method based on an energy balance approach to address the risk of cavitation erosion. Compared with other published methods, three improvements are made. Firstly, it assumes that the local instantaneous pressure is the driving force behind cavity collapse, rather than the far-away ambient pressure field. Secondly, it counts erosion only at the moment that the released shock wave is strong enough to cause surface damage. Thirdly, an Eulerian–Lagrangian method is introduced to simulate multiscale cavitation, in which the volume of fluid (VOF) method and a discrete bubble model (DBM) are combined to reproduce resolvable water-vapor interfaces and unresolvable discrete bubbles, respectively. The developed numerical modeling framework is validated by predicting the erosion of pure aluminum surface caused by a cavitating jet, and it is shown that the simulated erosion region fits well against the experimental results. Additionally, appropriate model coefficients of the erosion prediction method are introduced to achieve quantitative prediction of material mass loss, and the detailed erosion behavior is discussed.

Tribological and mechanical properties of nanofilled glass fiber reinforced composites and analyzing the tribological behavior using artificial neural networks

AbstractThis study investigates the effects of filler content and type on the mechanical (tensile strength and impact resistance) and tribological properties of woven glass fiber‐reinforced composites (WGFC) produced using three different nanofillers (MgO, CuO, and Al2O3) at various loading levels (0.7, 1.4, and 2.8 wt%). Additionally, analyses of the artificial neural network algorithm were reported for the predictions of wear rate, mass loss, and coefficient of friction (COF) by given applied load, sliding distance, filler ratio, and filler type. The composites were fabricated using the hand lay‐up method. The filled composites were characterized using Fourier Transform Infrared (FTIR) spectroscopy, and the worn surfaces of the samples after the wear test were analyzed using Scanning Electron Microscopy (SEM). The wear resistance of the composite samples improved at 0.7 wt% CuO and 1.4 wt% MgO filler contents. At 200 m sliding distance and 1.4 wt% filler ratio, the highest wear ratio was determined as 55 mm3/m × 10−3 in the Al2O3‐filled composite, and the lowest was determined as 31 mm3/m × 10−3 in the MgO‐filled composite. At 1.4% filler ratio and 15 N load, the lowest friction coefficient was determined as 0.45 μ in MgO‐filled composite, and the highest was 0.56 μ in Al2O3‐filled composite. Al2O3 filler increased the mass loss and wear rate of the composite material. In Charpy impact tests, the maximum impact energy of 13.9 J/m2 was obtained with a 2.8 wt% Al2O3 filler content. Tensile tests showed an increase in tensile strength for all filler contents compared to unfilled composites. The highest tensile strength of 242.1 MPa was achieved with 0.7 wt% CuO reinforcement.Highlights In all applied wear parameters and filler ratios, the Al2O3 filler adversely affected the wear resistance of the composite, while the MgO filler showed the best performance in terms of wear resistance. All types and ratios of fillers increased the tensile strength compared to the unfilled composite. Contrary to its negative effect on wear, the Al2O3‐filled composites exhibited the highest impact resistance. The prediction analysis of the tribological behavior of composite material using ANN reveals that the Levenberg–Marquardt algorithm fits very well.

Kinetic reaction mechanism of lignocellulosic biomass oxidative pyrolysis based on combined kinetics

The kinetics knowledge of lignocellulosic biomass decomposition is essential to develop efficient thermochemical conversion technology. However, the simplification of reaction mechanisms in existing oxidative pyrolysis studies largely compromises the application of kinetic models. To explore more exact kinetic parameters and reaction mechanism of lignocellulosic biomass oxidative pyrolysis, an updated oxidative pyrolysis kinetic model (seven-step reaction combined kinetics model) coupled with an optimization algorithm is proposed. Based on a series of thermogravimetric experiments in an air atmosphere, the extra oxidative pyrolysis kinetic parameters are obtained by the Shuffled Complex Evolution method. The proposed kinetic model is validated based on the degradation process of each component (hemicellulose, cellulose, and lignin). Furthermore, the obtained kinetic parameters are applied to predict the oxidative pyrolysis behavior, and the predicted mass loss rate is in good agreement with the experimental data. Eventually, according to the key combined kinetics parameters, it is found that the oxidative pyrolysis mechanisms of hemicellulose, cellulose, and lignin correspond to the power law, nucleation & growth, and chemical reaction order, respectively, while the combustion of char corresponds to the reaction order mechanism.

Application of wavelet transform techniques for corrosion assessment of embedded rebars in RC elements using electromechanical impedance

Corrosion of steel reinforcement embedded in concrete significantly influences the integrity and durability attributes of reinforced concrete structures. Monitoring of the early age corrosion of the reinforcements can contribute to an effective planning of structural maintenance and restoration. This paper demonstrates an innovative approach of monitoring the rate of corrosion (mass loss due to corrosion) of steel bars embedded in concrete by capturing conductance signature using Piezoelectric sensors (PZT) by Electromechanical Impedance Technique. In the recent times Wavelet Transform (WT) based different techniques have been proved to be promising techniques for different structural health monitoring assessment. This study aims to comprehensively use the WT techniques based on EMI signals to assess the corrosion levels in the reinforcement embedded in RC members. Specifically, the study focuses on capturing the progression of corrosion levels using the RMSD index derived from the DWT, CWT and WPT analysis of conductance signatures. WT techniques provides energy values of conductance signatures. Energy level of the signatures decreases with the increase of corrosion levels and deviation of energy level between current and pristine stage is measured using statistical index Root Mean Square Deviation (RMSD). Robust relationship between the RMSD index and the corrosion mass loss ratio through nonlinear curve fitting have been derived and presented using data captured from the 48 specimens of concrete cylinders with embedded reinforcement. At last, the comparison between the actual mass loss ratio and the predicted mass loss ratio derived from these techniques have been made for the validation of the proposed relation of predicted mass loss due to corrosion.

An experimental investigation on kinetic analysis of thermal degradation of shape stable composite phase change materials and adaptive neuro fuzzy inference system modeling for predicting mass loss

An experimental investigation on kinetic analysis of thermal degradation of shape stable composite phase change materials and adaptive neuro fuzzy inference system modeling for predicting mass loss

ANN SUPPORTED STUDY ON THE PERFORMANCE AND SLURRY EROSION RESISTANCE OF THERMAL SPRAYED WC20CR3C27NI COATINGS

The thermal spray coatings are commonly employed in slurry pump components and hydrodynamic turbine blades, where wear progression is an intricate phenomenon. In this research work, the performance analysis of HVOF and APS sprayed WC20Cr3C27Ni coatings for slurry erosion wear is carried out by using artificial neural networks (ANN). The influence of time, particle size, impact angle, speed, and slurry concentration on wear performance of coatings and turbine steel substrate are evaluated. Under the experimental settings, slurry erosion wear rates and mass loss for both coatings and substrate were determined. When ASTM A743 steel was coated with thermal sprayed WC20Cr3C27Ni coatings, the slurry erosion wear resistance of the steel was enhanced by 2 and 3.5 times for APS and HVOF coatings, respectively. The design of ANN made it possible to examine the interactions between the seven input variables. A robust model was formed by the two outputs that followed. This model enables the prediction of slurry erosion wear rate and mass loss of WC20Cr3C27Ni coatings and substrate.