Pressure Flow Research Articles

Oxygen-Based Autoregulation Indices Associated with Clinical Outcomes and Spreading Depolarization in Aneurysmal Subarachnoid Hemorrhage.

Impairment in cerebral autoregulation has been proposed as a potentially targetable factor in patients with aneurysmal subarachnoid hemorrhage (aSAH); however, there are different continuous measures that can be used to calculate the state of autoregulation. In addition, it has previously been proposed that there may be an association of impaired autoregulation with the occurrence of spreading depolarization (SD) events. Study participants with invasive multimodal monitoring and aSAH were enrolled in an observational study. Autoregulation indices were prospectively calculated from this database as a 10s moving correlation coefficient between various cerebral blood flow (CBF) surrogates and mean arterial pressure (MAP). In study participants with subdural electrocorticography (ECoG) monitoring, SD was also scored. Associations between clinical outcomes using the modified Rankin scale and occurrence of either isolated or clustered SD were assessed. A total of 320 study participants were included, 47 of whom also had ECoG SD monitoring. As expected, baseline severity factors, such as modified Fisher scale score and World Federation of Neurosurgical Societies scale grade, were strongly associated with the clinical outcome. SD probability was related to blood pressure in a triphasic pattern, with a linear increase in probability below MAP of ~ 100 mm Hg. Multiple autoregulation indices were available for review based on moving correlations between mean arterial pressure (MAP) and various surrogates of cerebral blood flow (CBF). We calculated the pressure reactivity (PRx) using two different sources for intracranial pressure (ICP). We calculated the oxygen reactivity (ORx) using the partial pressure of brain tissue oxygen (PbtO2) from the Licox probe. We calculated the cerebral blood flow reactivity (CBFRx) using perfusion measurements from the Bowman perfusion probe. Finally, we calculated the cerebral oxygen saturation reactivity (OSRx) using regional cerebral oxygen saturation measured by near-infrared spectroscopy from the INVOS sensors. Only worse ORx and OSRx were associated with worse clinical outcomes. Both ORx and OSRx also were found to increase in the hour prior to SD for both sporadic and clustered SD. Impairment in autoregulation in aSAH is associated with worse clinical outcomes and occurrence of SD when using ORx and OSRx. Impaired autoregulation precedes SD occurrence. Targeting the optimal MAP or cerebral perfusion pressure in patients with aSAH should use ORx and/or OSRx as the input function rather than intracranial pressure.

Effects of turning vane on flow control and heat transfer in ribbed two-pass channel with varied rib patterns

Faced with the escalating cooling requirements of turbine blades, two-pass channels with ribs on the internal surfaces have been extensively employed to enhance internal wall heat transfer. Considering the potential to mitigate flow separation and pressure losses generated by channel curvature, in this study, a computational fluid dynamics approach based on the realizable k-ε turbulence model is employed to explore the effects of turning vane introduction on the flow and heat transfer characteristics of two-pass channels with varying rib configurations. The rib configurations are identified as NN, NP, PP and PN. “N” denotes the ribs rotated 45° clockwise relative to the flow direction, while “P” denotes the ribs rotated 45° counterclockwise. The results indicate that the introduction of turning vanes improves the flow uniformity in the downstream channel of the two-pass channel with N-patterned upstream ribs, enhancing inter-rib heat transfer in the downstream channel. It also strengthens the heat transfer performance at the bend region of the two-pass channel with P-patterned upstream ribs. Furthermore, the introduction of turning vanes results in a decrease in friction loss factor for two-pass channels with various rib arrangements at varying levels of Reynolds numbers. The reduction is more pronounced in the two-pass channel featuring N-patterned upstream ribs, with a maximum decrease of 18.6 %. In contrast, the decrease is only 5.2 % in the PP-patterned rib channel. Additionally, the thermal performance factor of NP-patterned and NN-patterned rib channels increases by 8.5 % and 8.7 % respectively after the insertion of turning vanes. However, the enhancement is limited in the PP-patterned and PN-patterned rib channels.

Evaluation and selection of the best artificial lift method for optimal production using PIPESIM software

A well can be produced and exploited when it has production power, in other words, if the well does not have enough pressure, it will not be able to flow. Artificial production is a method to increase the lifespan of well production. The well studied in this article has a significant annual lower pressure drop; So now with the current pressure of the existing well, it is not possible to send the oil of this well to the separator of the first stage of the exploitation unit. Among the existing solutions to maintain or increase production and increase the flow pressure of the well, is the use of artificial extraction. In this article, an attempt has been made to simulate the effect of using a core pipe, gas pumping and installing an electric submersible pump (ESP) on the production flow rate and flow pressure of the well by using the well data and the static and current pressure test. The current production data with the PIPESIM software was checked, and then the best extraction method for the studied well. Finally, the installation of an ESP was determined and selected as the best method of artificial extraction.

Heat and Mass Transfer Study of a High-Pressure Membrane Dehumidifier Under Non-Isothermal and Humid Sweep Psychrometric Conditions

Abstract High-pressure membrane dehumidification is of interest for aircraft environmental control system applications. Typically, membrane dehumidifier modules are characterized by manufacturers over a range of mass flow rates, temperatures, and pressures at saturation conditions. However, to evaluate the suitability of high-pressure membrane dehumidification for aircraft applications, the performance must be characterized over the broader range of psychometric conditions that could be encountered. Moreover, the practical integration of a membrane dehumidifier into an environmental control system necessitates reconsideration of the traditional product sweep approach, where some of the dry air produced is removed and supplied to the other side of the membrane to enhance mass transfer. In an environmental control system, using product sweep would require excessive bleed air from the engine and cause unbalanced flow in the turbomachinery. To avoid this, alternate sources of sweep must be considered and their effects on dehumidification must be evaluated. This work conducts an experimental investigation of a membrane dehumidifier module using product sweep under a range of psychometric conditions, then compares product and humid sweep modes to understand the effects of sweep conditions on dehumidification performance. Concurrently, a numerical model of a membrane dehumidifier is implemented and validated - first against the module specification sheet, then against the empirical results that represent moderate altitude aircraft conditions. Ultimately, the feasibility of a hot and humid sweep source to maintain sufficient dehumidification performance under realistic altitude conditions is demonstrated.

Numerical Investigation of the Flow Structure of Cryogenic Hydrogen in Corrugated Metal Flexhoses Due to Fluid Structure Interactions

Abstract In this paper, details of flow field characteristics and pressure drop of cryogenic hydrogen in corrugated metal flexhoses will be numerically evaluated using multiphase flow physics coupled with fluid structure interactions. Multiphase flow regime is often influenced by concentration ratio, shape factor, and orientation of the piping. Corrugated metal flexhoses are commonly used as part of cryogenic commodity transportation for their ability to operate in a large range of temperatures and pressures with a high degree of geometric flexibility. These sharp changes in the geometry of the flexhose convolutions are one of the leading factors of liquid and gas phase entrainment which can significantly reduce the efficiency of piping systems and fuel delivery systems to the engine components. The entrained gas phase hydrogen can eventually propagate in successive convolutes leading into the permanent entrapment of the gas phase hydrogen in the corrugations. The volume fraction up to 5% gas phase with various convolute heights and pitch will be evaluated, with a hose length to diameter ratio (L/D) range between 20 to 60 and a Reynolds number range between 5,000 to 350,000 based on the hose hydraulic diameter. The findings of the study contribute to a better understanding of multiphase flow physics of hydrogen in corrugated metal flexhoses.

Numerical Investigation of Flow Structure and Pressure Drop Prediction for Radial Inflow between Co-Rotating Discs with Negative Effective Inlet Swirl Ratio

Abstract This paper presents a numerical simulation of the flow structure of radial inflow between co-rotating discs with a negative ceff (effective inlet swirl ratio), which may occur in a vortex reducer equipped with deswirl nozzles. When the value of ceff approaches zero, asymmetric flow structure is observed in the cavity, possibly as a result of the ‘Coanda effect’. Besides this, the flow structure inside the disc cavity at ceff < 0 can be divided into a source region, a sink region, an interior core region, and two Ekman layers, which is identical to the situation when 0 < ceff = 1. However, there exist two distinct patterns: the stagnation point on the disc and on the peripheral. According to a theoretical analysis, ceff = -1/8 is used to distinguish between these two patterns. Based on flow structure partitioning, a theoretical model for predicting the swirl ratio radial distribution and pressure drop in a disc cavity with ceff < 0 was established. The model employs the turbulent boundary layer integral method, and von Karman's assumption of velocity profile and wall shear stress for a free disc. The calculation results of the swirl ratio in the cavity are in good agreement with the CFD results except when the negative ceff approaches zero because of the deviation of the radial velocity profile from the ‘1/7’ power law. Furthermore, pressure drop prediction across the cavity by the model has been verified through comparison with public experimental results.

Characteristics of Transient Flow in Rapidly Filled Closed Pipeline

In this study, a one-dimensional mathematical model based on rigid theory is developed to evaluate the maximum water filling flow rate and filling time of closed pipeline water supply systems during rapid-filling processes. Polynomial fitting is utilized for prediction, and numerical simulation results are analyzed to understand the variations in maximum water filling flow rate, filling time, and pressure with respect to opening valve time, air valve area, filling head, and segmented filling pipe length. The findings highlight the significant impact of the filling head on the maximum water filling flow rate, while the filling time is predominantly influenced by the gas discharge coefficient. Rapid changes occur only at the initial stage of rapid filling, reaching the maximum value with a very high acceleration (around t = 4 s). It is observed that pressure fluctuations in the gas–liquid two-phase flow inside the pipeline lead to velocity differences and periodic changes in gas pressure opposite to the filling head. When the gas discharge coefficient reaches approximately 0.3, pressure variation within the water supply system diminishes, and the time and flow rate required for pipeline filling become independent of the discharge coefficient. This study suggests the use of a segmented filling approach to ensure the effectiveness and stability of pipeline filling.

Improved Flow Rate and Pressure ANN Estimators for a Centrifugal Fan with Induction Motor Drive

Improved Flow Rate and Pressure ANN Estimators for a Centrifugal Fan with Induction Motor Drive

التصميم ألامثل للعملية الهيدروليكية لرأس الحفر

All drilling companies emphasize the importance of maintaining optimum drilling bit hydraulics in real-time drilling operations. The behavior of flow rate and pressure plays a crucial role in monitoring and optimizing drilling processes. By ensuring the hydraulic conditions are optimal, various drilling-related issues such as equipment failure, wellbore instability, and kicks can be minimized, resulting in significant time and cost savings. The paper's main objective is to illustrate the impact of mud pump flow rate optimization on the cutting Transport Fluid Velocity (TFV). This optimization directly influences the pressure loss inside the drill string and annular space, which, in turn, affects the selection of optimum nozzle sizes. The goal is to achieve efficient bottom hole cleaning and hole conditioning, ultimately leading to satisfactory rates of penetration (ROP). The study conducted a series of tests using different pump flow rates ranging from 100 to 500 gallons per minute (gpm) to drill two different sections. An 8 ½" bottom hole assembly (BHA) with a Tri-cone bit and a 6 1/8" BHA with a Polycrystalline Diamond bit were used. The WellPlane Software was employed for optimization. The results of the study indicate that for the 8 ½" section, a minimum pump rate of 488.3 gpm is necessary to avoid cutting accumulation and the formation of bed height. On the other hand, for the 6 1/8" section, a minimum pump rate of 193.2 gpm is required. The optimal parameters for achieving a bed height of zero in the 8 ½" section are a pump flow rate of 500 gpm and nozzle size of (316). For the 6 1/8" section, a pump flow rate of 250 gpm and nozzle size of (514) are recommended. In summary, the optimization of bit hydraulics is essential for mitigating drilling problems and reducing overall drilling costs. By maintaining proper flow rate and pressure conditions, along with appropriate nozzle sizes, efficient bottom hole cleaning can be achieved, leading to improved rates of penetration and overall drilling performance.

Assessment of blood flow parameters in a hybrid-digital model of the cardiovascular system applying recurrent neural networks

End-systolic elastance of the left ventricle along with the waveforms of pressure and volumetric blood flow in particular sectors of the circulatory system are of importance in diagnosing various problems like dilated cardiomyopathy, left-ventricular hypertrophy, pulmonary hypertension, or ischemic heart disease. The objective of the paper is to broaden the spectrum of available methods to estimate those parameters since currently accessible techniques are often costly or troublesome. Six models have been developed − three of them estimate end-systolic elastance, two perform regression of volumetric blood flow, and one predicts blood pressure. Training datasets have been collected applying the unique hybrid-digital model. The input of the designed models consists of two or three different waveforms representing pressure and volumetric blood flow in particular areas, including heart ventricles, atria, and pulmonary vessels, in addition to the heart rate value. The basis of each model comprises bidirectional Long Short-Term Memory layers along with the dropout and feed-forward layers. Models that estimate end-systolic elastance achieved various accuracy. One of them performed exceptionally well since the absolute error did not exceed 0.169 mmHgcm3 which is a negligibly small value. The root-mean-square error (RMSE) of the model predicting pressure waveform reached 0.165 mmHg in the worst case. Regression of the volumetric blood flow resulted in 6.062 cm3s worst-case RMSE for the model focusing on the pulmonary valve and 15.979 cm3s for pulmonary veins model. Computed results, especially those of the models estimating end-systolic elastance, indicate that it is possible to utilize neural networks to estimate those parameters with sufficient accuracy.

Experimental study on flow and heat transfer of High-Pressure helium flow in compact helical tube heat exchanger in HTGR

In this study, experiments were conducted on helium flow within a compact helical tube heat exchanger. High-temperature helium and deionized cooling water, flowing in opposite directions, were allocated to the shell side and tube side, respectively. The Reynolds number of helium ranged from 3 × 103 to 1.6 × 104 under an operational pressure of 2.1 MPa. Measurements were taken for helium temperature, tube outer-surface temperature distribution, pressure, and mass flow rate to illustrate the flow and heat transfer characteristics. Sensitivity analysis of thermal–hydraulic parameters, including heat transfer power and efficiency under various operational conditions, was performed. The convective heat transfer coefficient and pressure drop were calculated. The transition point (Re = 9000) between laminar and turbulent flow of helium on the shell side was identified, and empirical correlations for the Nusselt number and friction coefficient were proposed. The experimental results indicate that the heat transfer performance of helium in the compact helical tube heat exchanger exceeds that of water in large-scale helical tube heat exchanger by approximately 20.75 % under fully developed turbulence, while the flow resistance characteristics remain essentially consistent.

Numerical simulation of depressurization exploitation in class 1 hydrate reservoirs under different development factors in Shenhu area, South China sea

Most of the implemented marine gas hydrate test exploitation in the world adopt the depressurization method to break down the hydrate in the reservoir into natural gas and then extract it, but because the gas production results are still a certain distance away from the commercial exploitation, and it mainly stays in the stage of theoretical research and trial exploitation. Based on two trial productions in the Shenhu area of the South China Sea, this study established a model for hydrate exploitation and investigated the impact of different well types on the recovery rates of hydrates and free gas in different development layers during depressurization. For the Class 1 hydrate reservoirs, horizontal wells are the optimal solution to extract hydrate and free gas simultaneously when exploiting the hydrate three-phase layer. Meanwhile, the effect of different well spacing in vertical wells on the recovery rate of hydrate and free gas was studied. It is found that the best recovery efficiency is achieved when the spacing between two wells is 80 m. The lower the bottom flow pressure of the well, the higher the production capacity, but its influence is limited.

Improving large angle flow measurement accuracy of five-hole probe using novel calibration coefficient and accuracy progressive neural network

Novel calibration coefficients and data-fitting techniques based on a two-stage accuracy progressive neural network were developed to improve the accuracy of a five-hole probe in measuring large-angle flows. By modifying the denominator and numerator of traditional calibration coefficients, the novel coefficients can solve the singularity and multi-value problems of large-angle flow measurement. By training the neural network using both global calibration data and the calibration data of large measurement error points, the two-stage accuracy progressive neural network can effectively improve the measurement accuracy of a five-hole probe when flow separation occurs around the probe head at large flow angles. The experimental results demonstrate that applying the novel calibration coefficients and accuracy progressive neural network ensures that the calibration error of the flow angle is less than 0.8°, and the flow pressure error is less than 0.1 % when the flow angle reaches 50° at low subsonic speeds.

Evaluation of Key Development Factors of a Buried Hill Reservoir in the Eastern South China Sea: Nonlinear Component Seepage Model Coupled with EDFM

The HZ 26-B buried hill reservoir is located in the eastern part of the South China Sea. This reservoir is characterized by the development of natural fractures, a high density, and a complex geological structure, featuring an upper condensate gas layer and a lower volatile oil layer. These characteristics present significant challenges for oilfield exploration. To address these challenges, this study employed advanced embedded discrete fracture methods to conduct comprehensive numerical simulations of the fractured buried hill reservoirs. By meticulously characterizing the flow mechanisms within these reservoirs, the study not only reveals their unique characteristics but also establishes an embedded discrete fracture numerical model at the oilfield scale. Furthermore, a combination of single-factor sensitivity analysis and the Pearson correlation coefficient method was used to identify the primary controlling factors affecting the development of complex condensate reservoirs in ancient buried hills. The results indicate that the main factors influencing the production capacity are the matrix permeability, geomechanical effects, and natural fracture length. In contrast, the impact of the threshold pressure gradient and bottomhole flow pressure is relatively weak. This study’s findings provide a scientific basis for the efficient development of the HZ 26-B oilfield and offer valuable references and insights for the exploration and development of similar fractured buried hill reservoirs.

Experimental and numerical investigation of water flow in different media models at multiple scales

ABSTRACT The study of the flow behavior in pores, fractures, and pore-fracture dual media plays a crucial role in understanding the mechanisms of sudden water inrush and sand flow. In this study, laboratory experiments were conducted using a custom-made transparent porous model to investigate the flow characteristics of fluids in complex media. Additionally, finite element simulations were employed to explore the flow features at multiple scales. A detailed analysis at the micrometer scale was conducted to examine the variations in the water velocity, volumetric flow rate, Reynolds number, flow field, and permeability under different porosity and pressure drop conditions. The results indicate that within the studied range of flow velocities and pressures, the fluid flow in all the media models adhered to Darcy’s law. The improvement in structural connectivity in the random pore model has made the flow channels of fluids in the random pore network more diverse, resulting in an increase in the peak water flow velocity from 4.89E–5 m/s to 4.19E–4 m/s. The fluid accelerated while bypassing the protruding solid particles, resulting in the formation of high-velocity zones in the vicinity. As the pressure drop increases, the volume flow rate of the random dual-medium model with a porosity of 0.6 can increase to 1.01E–9 m3/s, with the highest growth rate. We also simulate the fluid flow of a regular dual-medium model at different porosities and find that the permeability rapidly increases from 5.65E–13 m2 to 1.01E–11 m2, demonstrating that the presence of fractures can significantly improve the permeability performance of the medium. The research findings deepen our understanding of the migration behavior of water in complex geological environments and provide theoretical guidance for groundwater resource protection and underground engineering.

Energy absorption and frequency analysis of the nanocomposites reinforced car’s hood under airflow pressure

This study focuses on the energy absorption and frequency analysis of a car’s hood reinforced with graphene platelets nanocomposites under airflow pressure. The integration of nanocomposites aims to enhance the mechanical properties and energy absorption capacity of the hood, crucial for improving vehicle safety and performance. We investigate the dynamic response of the hood structure subjected to varying airflow pressures, utilizing advanced computational models to simulate real-world conditions. The analysis employs numerical method to evaluate the frequency characteristics and energy absorption efficiency of the nanocomposite-reinforced hood. Results indicate significant improvements in both energy absorption and frequency response, highlighting the potential of nanocomposite materials to optimize the dynamic performance and structural resilience of automotive components. This research provides valuable insights into the design and application of nanocomposite materials in the automotive industry, promoting the development of safer and more efficient vehicles. The findings suggest that incorporating nanocomposites in car hoods can lead to superior impact resistance and vibration dampening, which are critical for maintaining the integrity and functionality of the vehicle under operational conditions.

Enhancing mechanical properties of additively manufactured voronoi-based architected metamaterials via a lattice-inspired design strategy

Voronoi-based architected metamaterials have gained significant recognition as promising candidates for bone defect repair implants. However, the demanding requirements for reliable and adjustable load-bearing capacity pose challenges in applying irregular Voronoi-based architected metamaterials in implant applications. In this study, we propose a lattice-inspired design methodology for these metamaterials, enabling precise control over topologies and porosities to enhance their mechanical properties. We demonstrate the influence of unit cell topology on the printability, mechanical properties, and permeability of lattice-inspired Voronoi-based metamaterials (LIVMs) fabricated via laser powder bed fusion (LPBF) additive manufacturing. The LPBF-printed LIVMs exhibited yield strengths ranging from 3.35 to 17.59 MPa and specific energy absorption ranging from 3.81 to 14.29 J/g. Through finite element modeling and experimentation, we show that the deformation behavior of LIVMs with various topologies plays a crucial role in enhancing mechanical performance through mechanisms such as homogeneous load transfer between unit cells and multistage-contact strengthening within a unit cell. Additionally, we analyze the impact of unit cell type and porosity on the mass-transport behavior of LIVMs using computational fluid dynamics simulations. The LIVMs achieved experimental permeability values ranging from 3.88 × 10−9 to 16.83 × 10−9 m2 (consistent with trabecular bones), indicating that multiple fluid flow channels can be utilized to enhance mass transport by distributing flow pressure and increasing fluid mobility. The proposed design method effectively achieves a favorable combination of superior mechanical properties and tunable permeability in Voronoi-based architected metamaterials. These findings provide valuable theoretical guidance for the development of architected metamaterials for bone implant applications.

Estimation of Friction for a Hydrodynamic Sliding Bearing for the Turbulent Regime From Single-Phase and Two-Phase Fluid Flow Behaviour

The hydrodynamic plain bearing relies on the generation of a lubricant film through the rotational movement of the journal. As the journal rotates, it creates a layer of lubricant between the bearing and journal surfaces. This lubricant film separates the two surfaces, preventing direct contact and minimizing friction and wear. The high speeds can lead to severe conditions such as cavitation due to rapid oil evaporation, which introduces a new phase into the flow. In this study, it is anticipated that a two-phase flow will occur through the shaft-bushing conjunction because of the rupture of the lubricating film near the contact outlet. Pressure disturbances above these rupture zones may induce vapor-cavity formation at a small scale. A numerical analysis was conducted by solving Navier–Stokes continuity equations and vapor-transport equations. The k-epsilon model was used to analyze friction at the fluid-bearing interface for the turbulent regime. The results indicate that higher flow velocity values and pressure were observed in the case of two-phase flow compared to one-phase flow.

Experimental study on the pressure drop of helium purge gas in particle crushing pebble beds

The solid breeder blanket is a critical component in fusion reactor, where helium purge gas flows through the solid breeder pebble bed to carry out the tritium generated during the fusion process. The flow pressure drop of helium purge gas within the breeder pebble bed is a significant parameter affecting the design of the tritium extraction system. Previous studies have indicated that the helium flow in the breeder pebble bed conforms to the theory of porous media flow. However, due to potential pebble breakage during the plasma operation, the pressure drop characteristics of the helium flow in breeder pebble bed may change as the void structure changes. The objective of this study is to measure the variation in flow pressure drop of the breeder pebble bed under different pebble crushing conditions. The flow pressure drops of intact beds (Alumina, diameter 1-1.2 mm) and four groups with different pebble breakage rates (3%, 5%, 7%, 9%) are measured using the pebble bed pressure drop test facility. The following results are obtained through experimental research: (1) The Ergun equation, Foumeny equation, and Reichelt equation can all reasonably match the experimental results of intact pebble beds; (2) The pressure drop across the pebble bed increases with the increase in pebble breakage rate, reaching approximately 1.6 times that of the intact bed at a 9% breakage rate; (3) A correlation for predicting the pressure drop of the broken pebble bed is established by introducing the pebble breakage rate (η) into the Ergun equation, which can be used to determine the pressure drop variation within a conservative range of breakage rates.

Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables

This study aims to improve the understanding of geomagnetic storms by utilizing machine learning models and analyzing several heliophysical variables, such as the interplanetary magnetic field, proton density, solar wind speed, and proton temperature. Rather than relying on traditional correlation-based methods, we employ advanced machine learning techniques to examine the complex relationships between these factors and geomagnetic storms. Our analysis covers a large dataset spanning six solar cycles, including the current 25th cycle, to provide comprehensive insights into the dynamics of these storms.Our study highlights the significance of the interplanetary magnetic field as a key predictor of geomagnetic storms, challenging previous beliefs that primarily focused on sunspot activity. By using high-resolution data, we uncover new patterns and provide a more detailed analysis of the factors influencing geomagnetic storms. We emphasize the importance of considering a range of heliophysical variables, such as proton temperature and flow pressure, which offer new insights into the complex dynamics driving these storm events.The application of machine learning models, particularly Random Forest and Gradient Boosting, demonstrated superior predictive accuracy compared to traditional methods. Our results reveal that the Dst-index MIN, scalar B, and alpha/proton ratio are among the most influential factors, accounting for a significant portion of the prediction model’s accuracy. These findings underscore the utility of machine learning in identifying critical drivers of geomagnetic activity and enhancing forecast precision.Additionally, our research underscores the need for comprehensive models that can accurately predict geomagnetic storms by integrating various data sources. This machine learning approach not only improves predictive accuracy but also enhances our understanding of the underlying mechanisms of space weather. The insights gained from this study have important implications for both scientific research and practical applications, such as improving early warning systems for geomagnetic storms and mitigating their potential impacts on Earth.