Wall Pressures In a Channel Flow Obstructed By a Line of Inclined Rods: Invariant Groups and Semi-Empirical Correlations

An experiment is conducted in a rectangular channel obstructed by a transverse line of four inclined cylindrical rods. The wall pressure around the perimeter of a central rod and the pressure drop through the channel are measured varying the inclination angle of the rods. Three assemblies of rods with different diameters are tested. The measurements were analyzed applying momentum conservation principles and semi-empirical considerations. Several invariant dimensionless groups of parameters relating the pressure at key locations of the system with characteristic dimensions of the rods are produced. It was found that the independence principle holds for most of the Euler numbers characterizing the pressure at different locations, that is, the group is independent of the inclination angle provided that the inlet velocity projection normal to the rods is used to non-dimensionalize the pressure. The resulting semi-empirical correlations can be useful for designing similar hydraulic units.

An alternative approach to estimate any subdaily extreme of rainfall and wind from usually available records

A wide variety of engineering applications requires the use of maximum values of rainfall intensity and wind speed related to short recording intervals, which can often only be estimated from available less exhaustive records. Given that many locations lack exhaustive climatic records that would allow accurate empirical correlations between different recording intervals to be identified, generic equations are often used to estimate these extreme values. The accuracy of these generic estimates is especially important in fields such as the study of wind-driven rain, in which both climatic variables are combined to characterise the phenomenon. This work assesses the reliability and functionality of some of these most widespread generic equations, analysing climatic datasets gathered since 2008 in 109 weather stations in Spain and the Netherlands. Considering multiple recording intervals at each location, it is verified that most of these generic estimations, used especially in the study of wind-driven rain, have functional limitations and can cause significant errors when characterising both variables for subdaily intervals and extreme conditions. Finally, an alternative approach is proposed to accurately extrapolate extreme values of both variables related to any subdaily recording interval in a functional manner and from any available records.

Predicting drag on rough surfaces by transfer learning of empirical correlations

Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity dataset to train neural networks is deterring their use in practical applications. In this study, we propose a transfer learning framework to model the drag on irregular rough surfaces even with a limited amount of direct numerical simulations. We show that transfer learning of empirical correlations, reported in the literature, can significantly improve the performance of neural networks for drag prediction. This is because empirical correlations include ‘approximate knowledge’ of the drag dependency in high-fidelity physics. The ‘approximate knowledge’ allows neural networks to learn the surface statistics known to affect drag more efficiently. The developed framework can be applied to applications where acquiring a large dataset is difficult but empirical correlations have been reported.

Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content

This paper presents the application of two artificial intelligence (AI) approaches in the prediction of total organic carbon content (TOC) in Devonian Duvernay shale. To develop and test the models, around 1250 data points from three wells were used. Each point comprises TOC value with corresponding spectral and conventional well logs. The tested AI techniques are adaptive neuro-fuzzy interference system (ANFIS) and functional network (FN) which their predictions are compared to existing empirical correlations. Out of these two methods, ANFIS yielded the best outcomes with 0.98, 0.90, and 0.95 correlation coefficients (R) in training, testing, and validation respectively, and the average errors ranged between 7 and 18%. In contrast, the empirical correlations resulted in R values less than 0.85 and average errors greater than 20%. Out of eight inputs, gamma ray was found to have the most significant impact on TOC prediction. In comparison to the experimental procedures, AI-based models produces continuous TOC profiles with good prediction accuracy. The intelligent models are developed from preexisting data which saves time and costs. Article highlights In contrast to existing empirical correlation, the AI-based models yielded more accurate TOC predictions. Out of the two AI methods used in this article, ANFIS generated the best estimations in all datasets that have been tested. The reported outcomes show the reliability of the presented models to determine TOC for Devonian shale.

An improved Wilson equation for phase equilibrium K values estimation

Phase equilibrium K values are either estimated with empirical correlations or rigorously calculated based on fugacity values determined from an equation of state. There have been several empirical analytical equations such as Raoult’s Law, the Hoffman Equations (Hoffman A, Crump J, Hocott C. Equilibrium constants for a gas condensate system. J Petrol Technol 1953;5:1–10) and their modifications and the well-known Wilson Equation (Wilson G. A modified Redlich–Kwong equation of state applicable to general physical data calculations. In: AIChE National Meeting Paper15C, May 4–7, Cleveland, OH; 1969). along with several modifications. This work presents a new modification of the Wilson Equation for estimating phase equilibrium K values, predominantly for light hydrocarbon mixtures. The modification is based on correlating a subset of a database of K values, established from convergence pressure data. Results show the method to accurately correlate and predict the K value data, within 10% on average. Moreover, the predicted K factors provide remarkable results for such a simple model when used in a variety of phase equilibrium calculations. The results also show that the new model compares favorably with existing empirical analytical methods. Such a model would provide excellent initial estimates for rigorous thermodynamic calculations.

Effects of Induction-Furnace Slag on Strength Properties of Self-Compacting Concrete

Indiscriminate waste disposal poses a severe environmental challenge globally. Recycling of industrial wastes for concrete production is currently the utmost effective way of managing wastes for a cleaner environment and sustainable products. This study investigates the strength characteristics of self-compacting concrete (SCC) containing induction furnace slag (IFS) as a supplementary cementitious material (SCM). The materials utilized include 42.5R Portland cement, induction furnace slag as an SCM ranging from 0 to 50 % by cement weight at 10 % interval, river sand, granite, water and superplasticizer. The fresh properties were tested for filling ability, passing ability and segregation resistance, the strength characteristics measured include compressive strength, splitting tensile strength, flexural strength and Schmidt/rebound number. The oxide compositions and microstructural analysis of SCC were investigated using x-ray fluorescence analyser (XRF) and scanning electron microscopy equipped with energy-dispersive x-ray spectroscopy (SEM-EDS), respectively. Empirical correlations were statistically analyzed using MS-Excel tool. The filling ability characteristic was determined via both the slump flow test and the T50cm slump flow time test. Moreover, the passing ability characteristic was determined using L-Box test. The segregation resistance characteristic was determined using V-funnel at T5minutes test. The results of the fresh properties showed a reduction in the slump flow with increasing IFS content. On the other hand, the T50cm slump flow increased with increasing IFS content. Furthermore, the L-Box decreased with higher IFS content. On the contrary, the V-funnel at T5minutes increased considerably with greater IFS content. The strength test results revealed that the strength properties increased to 20 % IFS, with a value of 66.79 N/mm2 compressive strength at 56 days, giving a rise of 12.61 % over the control. The SCC microstructural examinations revealed the amorphous and better interface structures with increasing IFS content in the mix. The empirical correlations revealed that linear relationships exist among the measured responses (fresh and strength properties). Ultimately, IFS could be utilized as a sustainable material in producing self-compacting concrete.

Evaluates A PVT Correlation to Estimate Dead Oil Viscosity for Libyan Crudes Using 104 Samples from Different Reservoirs

Viscosity is defined as the resistance of the fluid to flow. It plays a very significant role in most oil and gas engineering applications. The measured viscosity for any crude oil at surface condition is called by the dead oil viscosity, where the dead oil viscosity is a function in any correlation to calculate the viscosity of the crude oil. Thus, the dead oil viscosity is important in most applications related to the petroleum engineering. Accordingly, a new mathematical and artificial neural network (ANN) dead oil viscosity correlations were developed for Libyan crudes and compared with renowned dead oil viscosity correlations using 104 samples from different reservoirs. The evaluation in this study has been done by statistical and graphical error analysis. The results shown that the ANN model has proven to be a useful tool for predicting where the ANN model has given the best result with low error AAD was 14.40509 % and R^2 was 95.91%. The ANN model and mathematical model gave the lowest error when they compared with different empirical correlations.

Empirical Correlations of Spectral Input Energy with Peak Amplitude, Cumulative, and Duration Intensity Measures

ABSTRACT In earthquake engineering, it is acknowledged that a vector of intensity measures (IMs) can better predict seismic structural responses than a single measure. Hence, a vector of IMs is widely applied in a number of applications, such as probabilistic seismic hazard analysis, probabilistic seismic risk analysis, and ground-motion selections. Spectral input energy (EI) has been demonstrated as a promising IM in earthquake engineering, especially in the energy-based seismic design of structures. However, this important measure has not been included in the vector of IMs. Therefore, it is worthwhile to incorporate EI with other important IMs by examining correlations. This study analyzes the empirical correlations of spectral EI with peak amplitude-based IMs, cumulative-based IMs, and duration-related IMs. It is found that spectral absolute EI has strong correlations with peak ground velocity at all investigated periods. However, spectral EI is negatively correlated with duration-based IMs. To demonstrate the applicability of the examined correlations, a simple example is finally presented by employing EI for the ground-motion selections and seismic hazard assessment based on the generalized conditional intensity measure approach.