Inaccurate Predictions Research Articles

The Main Controlling Factors of Coalbed Methane Productivity Based on Reservoir Structure—A Case Study of the Jiaozuo Block

In the process of CBM development, the fracturing effect has always been a major controlling factor for CBM productivity. The coal fragmentation degree is a special geological feature in the process of CBM development and research, and other types of reservoirs are not involved in this study. This paper addresses the problem of the inaccurate prediction of the reservoir fragmentation degree by studying the influence of the reservoir type and depth plane curvature on the reservoir fragmentation degree based on the coalbed characteristics of a block. It also studies the influence of faults on the reservoir fragmentation degree based on the reservoir geological characteristics and seismic inversion results. Combined with dynamic data on coalbed methane production, the influence of different geological characteristics on the productivity of coalbed methane wells is studied. The research results show that the reservoir fragmentation degree is mainly affected by the reservoir type. In the coal-forming period or after coal forming, the stronger the tectonic movement is, the higher the reservoir fragmentation degree is. Another manifestation of tectonic movement is faults. The effect of the reservoir fragmentation degree on production is negative. The better the reservoir fragmentation degree is, the worse the reconstruction effect of the coalbed methane well is, and the worse the later production effect is. At the same time, the faults generated by tectonic movement affect not only the reservoir fragmentation degree but also the water production of coalbed methane wells. The closer a well is to a fault, the greater the risk is of high water production and low gas production. Therefore, in the process of selecting a desert area, a complex reservoir fragmentation degree and areas with strong tectonic movement should be avoided. This study takes a structural control block as the research object to study the main controlling factors of coalbed methane reservoir productivity in complex structures. At present, there is no relevant research on this structure in terms of controlling productivity at home or abroad. The research in this paper can provide technical support for the development of similar CBM reservoirs. This method can guide the development of coalbed methane fields and lay a foundation for the selection of favorable coalbed methane reservoir areas.

A dual decomposition integration and error correction model for carbon price prediction.

Accurately predicting carbon prices is crucial for effective government decision-making and maintenance the stable operation of carbon markets. However, the instability and nonlinearity of carbon prices, driven by the complex interaction between economic, environmental, and political factors, often result in inaccurate predictions. To confront this challenge, this paper proposed a carbon price prediction model that integrates dual decomposition integration and error correction. Firstly, the variational mode decomposition optimized by the sparrow search algorithm (SVMD) is used to decompose carbon price series into intrinsic mode functions (IMFs). Secondly, a classification-prediction module is constructed to classify IMFs on complexity using fuzzy entropy. The long short-term memory networks optimized by the whale optimization algorithm (WLSTM) is employed to capture temporal dynamics and long-term dependencies within data. Conversely, lower complexity IMFs characterized by smoother trends and less erratic behavior are predicted using computationally efficient extreme learning machines (ELM). To further refine the prediction accuracy, ensemble empirical mode decomposition (EEMD) is introduced to decompose the initially predicted error series into IMFs and then predicted by classification-prediction module. Reconstruct the initial prediction IMFs and the error prediction IMFs to obtain the final prediction results. Finally, the proposed model was validated using real carbon price data from three Chinese carbon exchanges. Compared with the 15 comparison models, the performance indicators RMSE, MAE, MAPE, and R2 of the proposed model have promoted at least 19.89%, 25.11%, 25.01%, and 0.79% on average. These results underscore the effectiveness and superiority in predicting carbon prices, providing a robust tool for carbon market stakeholders and climate change policymakers.

Paramedics and emergency medical technicians’ perceptions of geriatric trauma care in Saudi Arabia

BackgroundSaudi ambulance clinicians face unique challenges in providing prehospital care to older trauma patients. Limited geriatric-specific training and complex needs of this population hinder effective management, leading to adverse outcomes. This study explores the perceptions of Saudi ambulance clinicians regarding geriatric trauma care and identify facilitators and barriers to improved care.MethodsA qualitative study was conducted using a purposive sample of Saudi paramedics and ambulance technicians from Riyadh and Makkah using online semi-structured interviews and analysed using the framework method.ResultsThe qualitative study recruited twenty participants and identified that they reported age-related challenges including physiological changes, polypharmacy, and communication difficulties. They all wanted training and guidelines to improve their knowledge. They reported struggling with communication difficulties, inaccurate adverse outcomes predictions, difficult intravenous cannulations, and cultural restrictions affecting care provision for female patients. We identified organisational barriers (e.g. lack of shared patient records and lack of guidelines) and cultural barriers (e.g. barriers to assessing women, attitudes towards older people, and attitudes towards paramedics) that influenced implementation of knowledge.ConclusionAmbulance clinicians in Saudi Arabia want guidelines and training in managing older trauma patients but these need to take into account the organisational and cultural barriers that we identified to facilitate implementing knowledge and changing practice to providing improved care.

Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer.

Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

The Utilization of Model Output Statistic (MOS) in Improving Weather Prediction Model Accuracy of Integrated Forecasting System (IFS)

Introduction/Main Objectives: Integrated Forecasting System (IFS) is one of the most accurate numerical weather prediction (NWP) model for Indonesia region. Background Problems: However, in fact, each model always has bias potential against observation which causes inaccuracy in weather prediction. Novelty: This research intends to overcome this problem by building a weather prediction model based on Model Output Statistic (MOS) to minimize bias and improve NWP accuracy. Research Methods: Provide an outline of the research method(s) and data used in this paper. Explain how did you go about doing this research. Again, avoid unnecessary content and do not make any speculation(s). Finding/Results: Analysis result states that compared to IFS, MOS fluctuation pattern is more relevant to observation. MOS has higher correlation to observation and lower error. However, the variance of observation value tends to be better represented by IFS. The test result of heavy rain cases prove that the application of MOS is able to provide fairly accurate prediction. This weather prediction will be able to be the basis for decision-making and preventive measure in dealing with extreme condition that may occur.

Internet Finance Non-stationary Time Series Prediction Algorithm Based on Deep Learning

Inaccurate prediction results of financial time series will lead to wrong investment decisions. Therefore, a prediction algorithm for Internet financial non-stationary time series based on deep learning is proposed. EMD (empirical mode decomposition) method is used to divide the collected historical Internet financial non-stationary time series information into high-frequency and low-frequency parts, and remove the noise in the decomposed high-frequency components to obtain the financial non-stationary time series without noise. The knowledge map method is used to mine the transaction characteristics and market characteristics of Internet finance from the financial non-stationary time series without noise, and the two are fused as the input of the improved CNN (convolutional neural network) prediction model. The prediction results of Internet financial time series are obtained through CNN. The experimental results show that after setting the CNN parameters, the predicted results are consistent with the actual market trends. The highest RSE of the predicted result is 0.551, The highest RAE is 0.443, which is relatively low, the CORR value is 0.864, which is relatively high, indicating that the relative square root error, relative absolute error, and relevant empirical coefficients of the prediction results are all good, making it a highly applicable algorithm.

Creation of Aerial Arecanut Dataset and Aerial Arecanut Ripeness Level Detection using Deep Neural Networks

Background: Arecanut, a commercially significant crop grown across various regions of the country and contributes significantly to India’s global ranking as the second-largest producer. Harvesting arecanut at precise stages is paramount to achieve optimal yields. Typically, this task requires minimum of two skilled individuals: a professional tree climber proficient in nut plucking and another adept at assessing ripeness. Before proceeding to harvest all the arecanuts, the climber picks one or two nuts and indicates the other person to check it for ripeness assessment. The assessor verifies ripeness by peeling the nut with teeth and uses roti/dhoti (an equipment used to harvest arecanut) to harvest arecanut if it is ripened. Inaccurate predictions can lead to significant crop losses. Manual harvesting, while effective, presents challenges such as labour shortages, time consumption, and potential for life-threatening circumstances. Methods: To address these challenges this paper presents the creation of aerial arecanut dataset and accurate ripeness detection of arecanut using Deep Neural Networks (DNN) such as AlexNet and VGG-16. Images of Arecanut bunch are fed as input to the DNN. The features like colour, shape, texture, etc., will be extracted and given as input features to the classifier. The classifier will categorize the arecanut into three classes: unripened, intermediate and ripened. Result: The accuracy of classification of arecanut ripeness level is 94.58% and 96.87% using AlexNet and VGG-16 respectively. If this model is integrated in roti/dhoti, the life of the human being can be saved. Or if this model is integrated with the climber unit and cutter unit, it will transform the arecanut harvesting system into a fully automated harvesting system and further it reduces the man power required to harvest the arecanut, overcomes the life threats during harvesting and arecanut harvesting process becomes faster compared to the conventional method.

Optimization of Variable Combinations for Household Electricity Consumption Prediction Using a Multivariate Time Series Machine Learning Approach

Accurate household electricity consumption prediction is vital for effective energy planning in Indonesia, a nation facing rapid economic growth and technological advancements. Inaccurate predictions can lead to inefficiencies in resource allocation and energy shortages. Traditional methods like ARIMA struggle with non-linear patterns, long-term dependencies, and multivariate relationships critical in understanding electricity consumption dynamics. To address these challenges, this study employs the Long Short-Term Memory (LSTM) algorithm with a multivariate time series approach, chosen for its ability to capture complex patterns and long-term trends. The dataset comprises monthly electricity consumption data (2004–2023) from PT PLN, enriched with macroeconomic and environmental variables like Household Consumption GDP, inflation, and average temperature. The Denton-Chollete method was used to transform quarterly GDP data into monthly intervals, and correlation analysis identified Household Consumption GDP (r=0.98) and Power Contract Additions (r=0.64) as significant predictors. Testing 63 feature combinations, the best (Power Contract Additions, Household Consumption GDP, and Household Electricity Consumption) achieved a Mean Absolute Percentage Error (MAPE) of 3.54%. These results highlight LSTM's superiority in handling dynamic and complex electricity consumption patterns and provide a robust predictive tool for PT PLN. This study underscores the importance of exploring additional variables and advanced optimisation techniques to enhance predictive accuracy further.

Variable Contact Area‐Based Compressive Properties of Orally Self‐Disintegrating Puffs

ABSTRACTMilk protein‐rich, orally self‐disintegrating puffs were designed utilizing supercritical fluid extrusion (SCFX) to address the needs of both infants and the elderly who require high protein foods and suffer from swallowing difficulties. The physio‐mechanical properties of the puffs need to be accurately evaluated as the physiological processes occurring in the mouth are quite complex. Generally, mastication is simulated through a compressive test that utilizes a constant contact area which does not well simulate the oral environment. Most snacks do not have a uniform shape and undergo a change in contact area during compressing leading to inaccurate predictions of the sample's response in the mouth. The objective of this study was to quantify the change in the contact area and its effect on compressive properties. Variable contact area was experimentally measured to provide an accurate estimation of the sample stress during testing. The sample's mean variable yield contact area was 43.1 mm2 while the constant yield contact area was significantly higher at 100.5 mm2 (p &lt; 0.05). The relationship between the variable contact area and deformation was determined by fitting the data to a polynomial model (R2 = 0.99). From this relationship, Young's modulus and Poisson's ratio were evaluated. The Young's modulus values determined with variable contact area correlate well with each puff's corresponding disintegration time. The puffs have soaked Poisson's ratio values close to 1.00 indicating that these sample are deforming more like a liquid than a solid. Overall, the results showed that the variable contact area should be utilized when analyzing compressive properties to form accurate predictions of oral disintegration for extruded puffs to meet the needs of those who have difficulty swallowing.

A novel design approach for structures with inerter systems based on non-stationary seismic excitations

Existing research predominantly focuses on developing design methods for the structural configuration of inerter systems. However, the authors find that these optimization methods often overlook the non-stationary characteristics associated with seismic response. Additionally, traditional approximation algorithms yield significant errors in non-stationary processes, resulting in inaccurate predictions of the structural responses with inerter systems, which compromises the effectiveness of the optimization designs. This paper proposes a novel approach that combines the parameters of the inerter system with a modified Vanmarcke approximation to obtain unified calculation formulas for inerter parameters. The accuracy and computational efficiency in solving spectral moments are crucial for the feasibility of this method. The study presents an efficient calculation method to accurately determine non-stationary response spectral moments of structures with inerter systems. Using the complex modal method (CMM) and pseudo-excitation method (PEM), a unified solution for structural responses under seismic actions is obtained. The quadratic decomposition of the power spectral density function (QD-PSDF) is employed to derive analytical solutions for response spectral moments and non-geometric spectral characteristics (NGSC). Substituting the obtained moment values into the parameter calculation formulas allows effective determination of the inerter system parameters. Finally, Finite Element Method (FEM) modeling verifies the validity and general applicability of this approach. The results demonstrate that the proposed method accounts for non-stationary characteristics of structures with inerter systems under seismic excitations, enhancing seismic design reliability and structural safety. Moreover, the obtained inerter system parameters at each floor effectively achieve the target performance while providing a broader range of design options for enhanced flexibility in system configuration.

Assessing Reynolds and Jakobsson–Floberg–Olsson models for infinitely long water-lubricated partial journal bearings: Static and dynamic performance predictions compared to computational fluid dynamics analysis

This study evaluates the validity of two numerical models in predicting the performance of infinitely long water-lubricated partial journal bearings (IWLPJB): the non-mass-conserving Reynolds model (RBC) and the mass-conserving Jakobsson–Floberg–Olsson model (JFO). Building upon previous research on inadequately lubricated partial, long water-lubricated bearings, this work employs an efficient modeling approach (EMA) to implement these models, with results compared against computational fluid dynamics (CFD) analysis. A comprehensive evaluation of numerical models under realistic operating conditions is provided, specifically addressing the negative pressure cavitation phenomena of water-lubricated systems. The analysis focuses on conformal surfaces of IWLPJB, examining both static and dynamic performance predictions at the high eccentricity ratios typically encountered in industrial applications. The study examines both untextured and textured cases, assessing bearing performance parameters including maximum hydrodynamic pressure, attitude angle, load-carrying capacity, coefficient of friction (COF), and eight dynamic coefficients. The results indicate that for untextured and partially textured cases, both models provide reliable predictions of the performance parameter values at the studied high eccentricity ratios (0.9–0.98), despite RBC's inaccurate prediction of the cavitation region. Additionally, the differences between the two models are not sensitive to the studied texture depths (7.8–52 μm). For the fully textured case, although prediction errors increased, both models remain applicable for a wide range of performance parameters except for the COF. Notably, this study reveals that the predictive capability of the RBC model for both static and dynamic performance, particularly in textured cases, has been somewhat underestimated in previous research.

Digital-twin-based active input refinement for insertion loss estimation and QoT optimization in C and C + L networks

Quality of transmission (QoT) prediction is a fundamental function in optical networks. It is typically embedded within a digital twin and used for operational tasks, including service establishment, service rerouting, and (per-channel or per-amplifier) power management to optimize the working point of services and hence to maximize their capacity. Inaccuracy in QoT prediction results in additional, unwanted design margins. A key contributor to QoT inaccuracy is the uncertain knowledge of fiber insertion loss, e.g., the attenuation due to connector losses at the beginning or at the end of each fiber span, as such loss cannot be directly monitored. Indeed, insertion losses drive the choice of the launch power in fiber spans, which in turn drive key physical effects, including the Kerr and stimulated Raman scattering (SRS) effects, which affect services’ QoT. It is thus important to estimate (and detect possibly anomalous) fiber insertion losses at each span. We thereby propose a novel active input refinement (AIR) technique using active probing to estimate insertion losses in C and C + L systems. Here, active probing consists of adjusting amplifier gains span by span to slightly alter SRS. The amount of adjustment must be sufficient to be measurable (such that insertion losses can be inferred from the measures) but small enough to have a negligible impact on running services in a live network. The method is validated by simulations on a European network with 30 optical multiplex sections (OMSs) in C and C + L configurations and by lab experiments on a C-band network, demonstrating that AIR significantly improves insertion loss estimation, network QoT optimization, and QoT prediction compared with other state-of-the-art monitoring techniques. This work underscores the critical role of accurate estimation of QoT inputs in enhancing optical network performance.

A Mixture Fraction Approach to Predict Polymer Burning.

A mixture fraction approach was applied to predict the combustion behavior of polymeric materials. In comparison to the combustion of gaseous mixtures, the presence of solid fuels complicates the description of the combustion. Accurate predictions of burning characteristics can only be achieved through the proper resolution of heat and mass transfer between the gas-phase flame and the solid fuel. We focused on a model case of flame spread over a solid fuel surface. Polymethyl methacrylate (PMMA) was selected as a polymeric material. An approach was proposed to account for heat loss from the gas phase to the solid material through calculations of counterflow diffusion flames with the flame positioned closely to the fuel supply. A combination of these solutions was applied to restore temperature and species mass fractions from tabulated chemistry. An analysis of the numerical results from previous studies on flame spread over PMMA, based on one-step combustion reaction and calculating the chemical source term at each time step, demonstrated a monotonic distribution of the mixture fraction in the flame region between the fuel and oxidizer streams. The shape of the flame tip was satisfactorily resolved using the proposed approach that employs a skeletal chemical mechanism for gas-phase combustion consisting of 29 species and 33 reactions. However, the heat flux from the flame to the solid fuel was overpredicted, resulting in higher flame spread rates compared to experimental data and previous calculations. Preliminary results show a promising opportunity for the mixture fraction approach to describe the combustion behavior of polymers. An analysis showed that oversimplifying the heat transfer process in the flame tip area is a main source of prediction inaccuracies. Multidimensional heat transfer has to be properly incorporated into a tabulated chemistry approach. Several potential directions for future work have been outlined.

Chemiresistive sensor array for quantitative prediction of CO and NO2 gas concentrations in their mixture using machine learning algorithms.

Single sensors have been developed for specific gas detection in real-time environments, but their selectivity is limited by interference from other gases when considering mixtures of gases. Consequently, accurate detection of target gases in mixed gas environments is essential. Therefore, this study develops a sensor array approach to quantitatively estimate the concentration of carbon monoxide (CO) and nitrogen dioxide (NO2) gases in their binary mixture (CO and NO2). Thesensor array consists of two different sensors, developed with zinc oxide and graphene-cobalt sulfide. The sensor array wastested in the presence of 29 different proportions of the binary mixture at room temperature. Subsequently, machine learning (ML) algorithms are applied on sensor signals to estimate the concentration of gases. The ML models unfortunately exhibited inaccurate prediction when all sensor signals were considered, therefore, to improve the prediction accuracy, the sensor signals were divided into three levels based on the mixed gas concentration regime. Interestingly, the classification and regression algorithms provided good classification accuracy (85.13 ± 3.2%) and reasonable predictions of target gas concentrations at three levels. The proposed computational framework can be extended to include additional gases in mixed gases and used in various applications, including automotive, industrial, and environmental monitoring.

Investigation of transonic flows through an idealized ORC turbine vane using Delayed Detached Eddy simulations

An idealized transonic blade configuration is studied using Delayed Detached Eddy Simulations (DDES), with focus on the loss mechanisms associated with the base pressure and turbulent wake development. The working fluid is Novec649, which is commonly used in low temperature Organic Rankine Cycle (ORC) power plants. Due to the molecular complexity of the fluid, a lower pressure ratio and exit Mach number are achieved compared to an air flow through the same geometry, a phenomenon that becomes more pronounced when considering thermodynamic operating conditions that lead to stronger non-ideal gas behavior, and which is well captured by models of standard use in ORC design and analysis, such as Reynolds-Averaged Navier–Stokes (RANS) models. Overall, as the fluid isentropic exponent and the exit Mach number decrease, the losses tend to increase. When performing a breakdown of the losses, the wake region provides the dominant contribution. Unlike RANS models, the DDES resolves the largest turbulent scales in the wake region, allowing fine-detail analysis of the unsteady wake dynamics, namely vortex shedding, and its influence on the loss coefficients. In the low supersonic Mach number range considered (M2∼1–2), the vortex shedding exhibits a flapping motion in the reattachment region behind the trailing edge. The Strouhal number, classically formed with the diameter of the trailing edge, is found to increase with increasing outlet Mach number. However, when introducing a new scaling based on the neck width of the reattachment region, directly related to the shape of the recirculation bubble and the associated reattachment shocks, the Strouhal number collapses to a relatively constant value of about 0.3 in all cases. DDES also shows that, due to the large heat capacity of Novec649, temperature fluctuations in the wake are greatly reduced, leading to the suppression of the so-called energy separation phenomenon occurring in air flows. While RANS simulations capture satisfactorily the main features and global quantities of the flow, they fail in describing the flow around the trailing edge. This results in inaccurate estimates of the back pressure, which ultimately leads to an underestimation of the losses by about 20%. Unsteady RANS simulations can predict the wake unsteadiness, but do not capture the couplings between large coherent structures, the recirculation, and the shocks, also resulting in inaccurate performance predictions. Thus, the present result showcase the importance of using advanced scale-resolving simulations for improved analysis and design of ORC turbines.

How effective are fixed-effects models in fixing the transit supply–demand bidirectional interaction?

ABSTRACT Transit agencies use direct demand models (DDM) to allocate services. Since the service supply – a crucial predictor in DDMs – is endogenous to demand, including it in the model might yield biased estimations. A widely used methodology that is believed to handle this issue is Fixed Effects (FE). However, the underlying assumptions of FE are valid only if service adjustments take a considerable amount of time. This study investigates the performance of FE for estimating transit ridership. We collected 2013–2019 data and constructed 16 DDMs, employing four methodologies with a shared set of variables. We found that FE has significant limitations in handling endogeneity and will result in parameter estimates that significantly differ from those produced by methodologies that are specifically designed to control for endogeneity (such as FE-IV). Moreover, the use of FE leads to the omission of certain predictors and inaccurate ridership predictions, misguiding agencies as to what changes to implement and potentially impacting revenue projections.

A hysteretic water retention model incorporating the soil deformability and its application to rainfall-induced landslides

Traditional water retention models often overlook the dynamic interplay between soil structure and moisture content, leading to inaccurate predictions of unsaturated soil behaviors. In this research, based on laboratory data, van Genuchten’s soil water characteristic (van Genuchten, 1980) is modified to establish two bounding surfaces that define the permissible range of soil states in terms of the void ratio (e), suction (s), and degree of saturation (Sl). Considering a bounding surface technique, the model effectively captures hysteresis in the soil water retention behavior, encompassing main curves and scanning paths. This approach presumesthat within the permissible range of soil states, which is included between two main surfaces, the derivative of the degree of saturation by void ratio and suction relieson the soil state’s proximity to the main bounding surface. This hypothesis guarantees that the wetting–drying or compressing-swelling scanning curves transit smoothly toward their corresponding main surfaces. The derived equations for Sl are integrated into closed forms, allowing all scanning curves to be distinguished by varying values of the integration constant. Model necessitates the determination of two parameters (b and β) related to the slope and intercept of the linear line interpolating experiments in the ln(s)-ln(eSl/s) plane, which can be defined based on at least a single wetting–drying test. The model predictions are validated against various data sets, including sands and clayey soils, published in the literature. This validation demonstrates the model’s ability to reflect the behavior observed in experimental tests accurately. This new technique offers a significant advantage in the simplicity of parameter determination. Finally, this hysteretic water retention procedure is implemented into a finite element program (Code_Bright), and its performance is evaluated by simulating the behavior of a representative slope subjected to rainfall conditions.

Study on the Liquid‐Holding Rate Law of Gas–Water–Foam Three‐Phase Flow in a Wavy Pipe

ABSTRACTAs a common method for liquid drainage in wave‐shaped pipelines, foam drainage has been used in gas‐gathering pipelines in various oil and gas fields. One of the common problems encountered in wave‐shaped foam drainage pipelines is the inaccurate prediction of the liquid retention rate. This issue makes it difficult to predict liquid accumulation points, which affects gas output efficiency, causes pipeline corrosion, and generates natural gas hydrates. To clarify the liquid‐holding rate law of wavy foam drainage pipelines is studied. In this study, through experiments to verify the accuracy of the numerical simulation method, we investigate the easiest liquid accumulation points and the liquid‐holding rate of wave‐shaped foam drainage pipelines, with factors such as inlet gas velocity, inlet liquid velocity, import and export differential pressure, undulation angle, and inlet temperature change rule. Among them, the error between the numerical simulation results and the experimental results is 4.68%. Finally, after considering the influence of the aforementioned factors on the liquid retention rate of wave‐shaped pipelines, a new liquid retention rate calculation model for wave‐shaped pipelines with foam drainage is established by introducing dimensionless numbers, such as gas‐phase velocity coefficient, liquid‐phase velocity coefficient, and angle correction coefficient. The method is compared with the results of previous research, and the error is within 15%. The model has a simple form and high calculation accuracy, providing a theoretical basis for pipeline inspectors to predict liquid accumulation conditions and reasonably adjust production schedules.

Thermodynamically consistent interfacial curvatures in real pore geometries: Implications for pore-scale modeling of two-phase displacement processes

In conventional Pore-network Modeling (PNM) approaches, fluid flow and transport are solved in a network of pore elements with idealized geometries. Such simplification can lead to inaccurate predictions when the original pore space features complex geometries. To overcome this limitation, this study introduces a novel workflow that integrates four key components: (i) an enhanced pore network extraction (PNE) platform capable of identifying and extracting pore cross-sections from high-resolution micro-computed tomography (micro-CT) images of the pore space, (ii) a computationally-efficient semi-analytical model that can faithfully predict capillary entry pressure and the corresponding fluid configuration of piston-like displacements using real two-dimensional cross-sections of pores, (iii) a PNM approach for two-phase flow modeling that utilizes the capillary entry pressure of pores predicted by the semi-analytical model, and (iv) an Artificial Intelligence (AI)-driven model that paves the way for future advancements in efficiently predicating fluid displacement properties in intricate pore structures. To validate this new workflow, we constructed various pore networks containing real pore and throat cross-sections over a diverse group of sandstone and carbonate rock samples. Subsequently, we simulate capillary pressure curves of Mercury Intrusion Capillary Pressure (MICP) and oil–water primary drainage displacements in Bentheimer and Berea sandstones, respectively, using both the conventional and enhanced PNM approaches. The latter demonstrated improved prediction accuracy compared to conventional methods. Next, primary drainage simulations are conducted for two carbonates, and the resulting capillary pressure curves from both PNM approaches are compared. In addition, we conduct an in-depth analysis of fourteen geometric features of the pore space, identifying key factors of hydraulic radius, circumradius, sphericity, and area, that significantly impact capillary entry pressure of pores. After that, we construct an Artificial Neural Network (ANN) to predict the capillary entry pressure of pores using their critical geometric features. This AI model, trained using data derived from the semi-analytical model, exhibits excellent predictive accuracy (with a R2 of 0.995 for the test data set) in estimating capillary entry pressure of pores. Overall, our newly proposed, integrated workflow represents a significant step forward in the field of digital rock technology (DRT), offering an accurate and efficient method for modeling fluid flow in rock samples with complex pore geometries.

Conformal cooling channels with composite layers

ABSTRACT Conformal cooling channels (CCCs) are a cooling method used in injection molding. According to the literature review, CCCs can reduce cooling time by 2%–70%, with the average reduction being 30%, and they can improve warpage by 5%–90%, with the average improvement being 37%. But the present study found that previous research has ignored the effect of the solidified skin layer in CCCs. This study posits that during the design of cooling channel positions, the plastic solidified layer exhibits distinct thermal properties. Heat transfer involves conduction thermal resistance, which affects the effectiveness of heat conduction. Ignoring the solidified layer can lead to inaccurate predictions of cooling channel positions. Accordingly, this study developed a novel calculation formula for determining the cooling channel positions while considering the existence of the solidified layer, leveraging the concept of steady one-dimensional conduction in composite layers. The results of this study indicated that in the uniform-thickness scenarios, optimal cooling efficiency and quality were obtained when the thickness of the solidified layer was 0.10 mm. However, in the variable-thickness scenario, the cooling efficiency decreased by approximately 2%. Regarding quality, higher deformation control in three dimensions but poorer warpage control in the z-dimension was observed.