Coupled alkali feldspar dissolution and secondary mineral precipitation in batch systems: 4. Numerical modeling of kinetic reaction paths

This paper explores how dissolution and precipitation reactions are coupled in batch reactor experimental systems at elevated temperatures. This is the fourth paper in our series of “Coupled Alkali Feldspar Dissolution and Secondary Mineral Precipitation in Batch Systems”. In our third paper, we demonstrated via speciation–solubility modeling that partial equilibrium between secondary minerals and aqueous solutions was not attained in feldspar hydrolysis batch reactors at 90–300 C and that a strong coupling between dissolution and precipitation reactions follows as a consequence of the slower precipitation of secondary minerals (Zhu and Lu, 2009). Here, we develop this concept further by using numerical reaction path models to elucidate how the dissolution and precipitation reactions are coupled. Modeling results show that a quasi-steady state was reached. At the quasi-steady state, dissolution reactions proceeded at rates that are orders of magnitude slower than the rates measured at far from equilibrium. The quasi-steady state is determined by the relative rate constants, and strongly influenced by the function of Gibbs free energy of reaction (DGr) in the rate laws. To explore the potential effects of fluid flow rates on the coupling of reactions, we extrapolate a batch system (Ganor et al., 2007) to open systems and simulated one-dimensional reactive mass transport for oligoclase dissolution and kaolinite precipitation in homogeneous porous media. Different steady states were achieved at different locations along the one-dimensional domain. The time-space distribution and saturation indices (SI) at the steady states were a function of flow rates for a given kinetic model. Regardless of the differences in SI, the ratio between oligoclase dissolution rates and kaolinite precipitation rates remained 1.626, as in the batch system case (Ganor et al., 2007). Therefore, our simulation results demonstrated coupling among dissolution, precipitation, and flow rates. Results reported in this communication lend support to our hypothesis that slow secondary mineral precipitation explains part of the well-known apparent discrepancy between lab measured and field estimated feldspar dissolution rates (Zhu et al., 2004). Here we show how the slow secondary mineral precipitation provides a regulator to explain why the systems are held close to equilibrium and show how the most often-quoted “near equilibrium” explanation for an apparent field-lab discrepancy can work quantitatively. The substantiated hypothesis now offers the promise of reconciling part of the apparent fieldlab discrepancy

fatigue reliability analysis of a turbine disc under multi source uncertainties

Abstract Life and reliability analysis of hot section components like high pressure turbine (HPT) discs plays an important role for ensuring the engine structural integrity. HPT disc operates under high temperatures to withstand complex loadings, its basic parameters, including the applied loads, material properties and working environments, have shown multi-source uncertainties. The influence of these uncertainties on the structural response of the turbine disc cannot be ignored. According to this, the variations of applied loads and material properties are quantified for fatigue reliability analysis of turbine disc. In particular, material response variability is modeled by using the Chaboche model and Fatemi-Socie damage criterion. Moreover, the inhomogeneity of its constituent material is also considered through combining FE simulation with Latin hypercube sampling. Finally, fatigue reliability analysis of a HPT disc under multi-source uncertainties is conducted for different flight missions

Neural-Symbolic Recommendation with Graph-Enhanced Information

The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc

a new energy gradient based model for lcf life prediction of turbine discs

Abstract With continuous raising of thrust-weight ratio, low cycle fatigue (LCF) at high temperature is one of main failure modes for engine hot section components. Accurate life prediction of turbine discs has been critical for ensuring the engine integrity. According to this, a new LCF model through combining the energy gradient concept with critical distance theory is proposed for fatigue life prediction of turbine discs. In this paper, assuming that the processes of crack initiation and propagation in a LCF regime can be described by the cumulative strain energy. A relationship between the total strain energy in the fatigue process zone and the LCF life is explored. In particular, the energy parameters are weighted based on the energy gradient in the fatigue process zone. Using experimental data of GH4169 alloy at 650°C, a good agreement was achieved between model predictions and experimental results

Review of Physical Based Monitoring Techniques for Condition Assessment of Corrosion in Reinforced Concrete

Monitoring the condition of steel corrosion in reinforced concrete (RC) is imperative for structural durability. In the past decades, many electrochemistry based techniques have been developed for monitoring steel corrosion. However, these electrochemistry techniques can only assess steel corrosion through monitoring the surrounding concrete medium. As alternative tools, some physical based techniques have been proposed for accurate condition assessment of steel corrosion through direct measurements on embedded steels. In this paper, some physical based monitoring techniques developed in the last decade for condition assessment of steel corrosion in RC are reviewed. In particular, techniques based on ultrasonic guided wave (UGW) and Fiber Bragg grating (FBG) are emphasized. UGW based technique is first reviewed, including important characters of UGW, corrosion monitoring mechanism and feature extraction, monitoring corrosion induced deboning, pitting, interface roughness, and influence factors. Subsequently, FBG for monitoring corrosion in RC is reviewed. The studies and application of the FBG based corrosion sensor developed by the authors are presented. Other physical techniques for monitoring corrosion in RC are also introduced. Finally, the challenges and future trends in the development of physical based monitoring techniques for condition assessment of steel corrosion in RC are put forward