A framework for identifying turbulence periods with the relative financial Reynolds number: case study on six construction projects

A New Approach to Correlate Heat Transfer Data for Turbulent Flow in Smooth Pipes Based on Generalization of Reynolds Number

This paper shows that the conventional Reynolds number has a clear physical meaning only for laminar, hydraulically developing or unsteady flow, while the generalized Reynolds number is physically meaningful for both laminar and turbulent regimes; the two Reynolds numbers coincide only in case of laminar flow. A review of widely used heat transfer correlations for turbulent liquid flow in smooth pipes (Re > 2000) is presented, and new correlations are developed using the generalized Reynolds number. The analysis is based on a database of 1025 experiments (including 110 in the transitional regime), covering Re = 2000-522000 and Pr = 0.18-414. The proposed correlations apply over the same ranges and show improved statistical performance, exceeding commonly cited correlations by 4.7-7.8 percentage points. It is also demonstrated that the range Re = 2000-4000 represents a hybrid region where both Reynolds and Graetz numbers simultaneously affect the Nusselt number.

Evaluation and Prediction Methods for a Steel Company Using Six Sigma Metrics, Capability Indicators, and Markov Chains

The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a quantitative, evaluative, and longitudinal three-phase design, the retrospective evaluation of nine preventive variables was employed using Six Sigma metrics (DPMO, Z, Y), along with the evaluation of overall performance through the Geometric Capability Indicator (GCI) and the Arithmetic Capability Indicator (ACI), and the stochastic modeling of the process using Markov chains. It was demonstrated that evaluating processes in isolation hides structural inefficiencies, as four variables showed an Excellent individual performance (Z≈6.0), but the comprehensive multivariate evaluation revealed a Deficient systemic state (GCI of 0.471 and ACI of 0.493). Furthermore, Markov modeling on the compliance of the process management index predicted a 100% probability of long-term stagnation in a deficient absorbing state (x1=1). It is concluded that the proposed method functions as a rational anticipation system that provides potential managerial benefits by offering early warning indicators of operational degradation, supporting corrective decision-making on unstable preventive indicators.

Correlation-Based Estimation of Condensate Yield from Downhole Fluid Gradient

Evaluation of investment opportunities in gas developments requires comprehensive subsurface data analysis and integration to quantify both value potential and associated risks. A critical element in such evaluations is the estimation of condensate yield, expressed as the Condensate–Gas Ratio (CGR), which supports the determination of condensate initially in place (CIIP) and forecasted condensate recoveries under different development scenarios. Although gas volumes typically dominate in such systems, the associated condensate liquids often provide a significant value upside, particularly under Nigeria's favorable natural gas liquids (NGL) fiscal regimes. During due diligence on a new gas asset, neutron–density log responses exhibited ballooning behavior consistent with a gas phase. This interpretation was corroborated by Repeat Formation Tester (RFT) pressure gradients of less than 0.18 psi/ft, as well as by seismic attribute analysis that also indicated gas-bearing intervals. However, the absence of bottomhole or recombined surface PVT data and Drill Stem Test (DST) results posed a challenge for estimating the fluid's condensate yield, a critical parameter for project evaluation. To address this data gap, a comprehensive corporate database of retrograde gas reservoirs with laboratory PVT analyses and RFT/MDT fluid gradient data was utilized. Empirical correlations were developed by trending measured fluid gradients against known laboratory-determined CGRs. The derived correlation was subsequently applied to the new opportunity, using its measured gradients to estimate the CGRs for the respective reservoirs. The accuracy of the developed correlation was tested against MDT and PVT data from a condensate reservoir in a recently drilled well and the resulting CGR estimate lies within <5% variance from that measured in the lab. These estimates were then integrated into PVT correlations and dynamic material balance models to compute condensate initially in place and evaluate development scenarios. The correlation-based approach provided reliable CGR estimates in the absence of direct PVT measurements and delivered a significant fiscal uplift to the project's overall economics. The study demonstrates that gradient-based empirical correlations, when supported by robust internal datasets, can effectively reduce uncertainty in condensate yield estimation and enhance investment decision-making in gas and condensate projects.

Development of Reynolds-number-based Correction Factors for Low Specific-speed Centrifugal Pumps Using Energy Loss Analysis

Accurate prediction of centrifugal-pump performance under viscous operating conditions remains challenging, particularly for low specific-speed pumps operating at low Reynolds numbers. This study develops a Reynolds-number-based correction-factor framework derived from a physically based energy-loss analysis. The method explicitly accounts for major internal loss mechanisms, including hydraulic losses, disk friction, leakage flow, recirculation, mixing and diffusion losses, slip-factor deviation, and blade blockage. The model was calibrated using water-test data from an FM-50 centrifugal pump at 1200 rpm and validated using an independent dataset at 900 rpm. The validation results showed accurate head prediction, with and . Efficiency prediction showed larger deviation, with percentage points, reflecting the sensitivity of efficiency to measurement uncertainty and combined loss mechanisms. After validation, the model was extended to viscous-flow conditions and used to derive compact analytical correction factors for head and efficiency as functions of Reynolds number. The proposed expressions showed strong cross-validation performance within the investigated range, with mean for the head correction factor and for the efficiency correction factor. Comparison with ANSI/HI, KSB, and Gülich methods shows that the proposed formulation follows the expected Reynolds-number-dependent trend while providing a more physically interpretable basis for viscous-performance correction. The proposed method offers a practical alternative to conventional correction charts for low specific-speed centrifugal pumps operating under viscous or low-Reynolds-number conditions.

Reliability Analysis of Rolling Bearings Using a Weighted Nonlinear Mixed-Effects Degradation Model

Reliability assessment of rolling element bearings is critical for the predictive maintenance of industrial rotary machinery.This study proposes a Quadratic-Exponential Weighted Model (QEWM) based on Nonlinear Mixed-Effects (NLME) to characterize the degradation process of bearings. Utilizing the IMS Bearing Dataset (Set No. 2), we define the failure threshold based on the latest ISO 20816-3:2022 vibration severity standards, setting the critical RMS limit at 0.4 mm/s for Zone D. Unlike traditional models, the proposed QEWM incorporates a weight function to address heteroscedasticity, which typically intensifies during the rapid degradation phase. Model comparison based on the Akaike Information Criterion (AIC)demonstrates that QEWM significantly outperforms linear and unweighted quadratic models. To quantify the uncertainty ofthe estimation, a parametric bootstrap method with 5,000 replications was employed. The results identify a B10 life (t0.1) of165.3 hours, supported by a precise 95% confidence interval of [162.7, 168.6] hours. This research provides a robust statistical framework for bearing life prediction that aligns with international industrial standards, ensuring high precision inprognostic assessments. 

The Evolution and Future of Machine Learning in Rock Slope Stability: A Systematic Review and Meta-Analysis