Current Simulation Research Articles

An efficient q-procedures to solve q-generalized quintic complex Ginzburg-Landau equations

Abstract In this work, q-Residual power series method (q-RPSM) is extended for the first time to solve q-generalized quintic complex Ginzburg-Landau (q-GCGL) equations. In 2019, O. A. Arqub [39], has investigated the solutions of time fractional Schrödinger equations without q-derivative by using RPSM. However, in this article, the RPSM procedure is extended to non-linear q-fractional partial differential
equations and thus some useful results are obtained under the definition of q-derivative, q-gamma func tion, q-Caputo derivative and q-factorial function. The solutions to few numerical examples under the q-Caputo operator are presented. In the current procedure, the targeted q-problems are first converted into system of two non-linear q-fractional partial differential equations and then solved by using q-RPSM. In particular, we considered two illustrative examples of fractional q-GCGL for analytical solutions using q-RPSM. The numerical simulations for the suggested numerical problems are done successfully. Various graphs such as Figures 1-12 are provided to compare the q-RPSM results with the exact solutions of the proposed problems. In this connection, Figures 1 and 4 show the 3D plot comparison between q-RPSM and exact solution of the 1st numerical example for œ and q=1. Figures 2 and 5 illustrate the q-graph
for various values of q and τ . In Figures 3 and 6, we discussed the fractional-solution graphs for different values of œ = 1, 0.7 and 0.5 at fix values of q=0.5, τ = 1 and 0.5. It is also investigated that higher terms series solution have the higher degree of accuracy. In the current q-RPSM simulations, the higher accuracy is achieved by taking sixth terms series solution. The fractional-order solutions are convergent toward integer-order solution as the fractional-order approaches to integer-order. The solutions curve at different q-values is also calculated and obtain the useful dynamics of the targeted problems. The q-analogy can be used effectively in the field of quantum physics and thus keep the higher norm for researchers working in the field.

Hydrodynamic performance study of floating photovoltaic arrays with multiple floating bodies

In the context of the global transition to renewable energy, floating photovoltaic systems have garnered significant attention as an innovative method of energy utilization. To investigate the impact of different arrangements of floating photovoltaic (FPV) systems on hydrodynamic performance, fully coupled numerical simulations of wind, wave and current were conducted in ANSYS-AQWA for three arrangements: rectangular array, linear array, and honeycomb-like array. The analysis focused on the six degrees of freedom motions and maximum mooring force under operational and survival conditions. It can be found that the rectangular array exhibits the strongest system stability, effectively reducing the mooring force and showing insensitivity to variations in load incident angles. These findings provide a theoretical foundation and engineering guidance for the design of FPV systems, offering substantial practical application value.

Eliminating Inrush Current in Three-Phase Transformer using Artificial Neural Network

Transformers are important parts of an electrical power system. When a power transformer is connected to the grid, usually inrush current increases substantially with a high value of harmonic components with a duration up to many cycles. The amount of flux in the core increases causing the magnetic circuit to saturate due to the increasing in the load. This paper describes a technique to accurately predict the inrush current and third harmonic of three phase transformer. A shallow neural network was created. The input parameters of the artificial neural network were the magnetization resistance Rm, the initial flux of phase A and the switching angle q. The number of neurons has been changed in the code to see the best performance value. The best validation performance was at epoch 71 with a value of 5.3641e-05. A good prediction results were obtained using this ANN. The simulation of the inrush current was done using the MATLAB Simulink software.

Insights of Dynamic Forcing Effects of MJO on ENSO from a Shallow Water Model

Abstract The Madden–Julian oscillation (MJO) is believed to play a significant role in triggering El Niño–Southern Oscillation (ENSO) events and affect the dynamics of ENSO. In this study, the dynamic forcing effects of MJO on the equatorial oceanic dynamic fields and the onsets of different types of ENSO events are investigated through sensitive experiments using spatiotemporally filtered forcing based on an anomalous shallow water model. The comparisons between observations and model responses provide meaningful insights into the extent of MJO’s impacts on sea surface dynamics relative to other atmospheric variabilities. The following conclusions are made. First, the MJO-forced perturbations on zonal currents are stronger and more significant than those on sea surface heights. Second, MJO is essential for improving zonal current simulation in the western-central Pacific and generating activity centers of zonal currents in the eastern Pacific in the model. Third, MJO tends to contribute to the onset of El Niño events rather than La Niña events. Strong intraseasonal oceanic Kelvin waves forced by MJO are confirmed in simulations during the onset stages of the 1997/98 and 2004/05 events. The 120-day running standard deviations of zonal current and sea surface height anomaly series forced by MJO exhibit positive skewness similar to those of the 20–100-day band-passed observational series. Yet, not all the onsets of historical ENSO events are in company with strong MJO-related perturbations. Additionally, the wind stress formula can amplify the responses of zonal current and sea surface height anomalies to synoptic forcings with periods shorter than 20 days through entraining lower-frequency variabilities. Significance Statement The Madden–Julian oscillation (MJO) is believed to be able to trigger El Niño–Southern Oscillation (ENSO) events and influence our understanding of the fundamental nature of ENSO. In this study, spatiotemporally filtered forcing experiments are implemented on an anomalous shallow water model. The results show that MJO is more important for improving the simulation of surface zonal currents rather than the sea surface heights and tends to contribute to the onset of El Niño events rather than La Niña events through triggering strong intraseasonal oceanic Kelvin waves.

Rheology of polyacrylamide-based fluids and its impact on proppant transport in hydraulic fractures

Rheological experiments and measurements of particle settling velocity under static conditions were conducted for two types of polyacrylamide (PAA-based) fluids. The flow behavior of the viscoelastic fluids is described by a simplified, three-parameter model that integrates a constant viscosity at low shear rates with a power-law viscosity dependency at higher shear rates. The estimated dependencies of the rheological parameters and particle settling velocity on PAA concentration align with the classical power-law scaling for semi-dilute polymer solutions. The identified scaling offers valuable insight for optimizing the chemical composition of fracturing fluids, thereby improving the efficiency of hydraulic fracturing technology. By applying the lubrication approximation to the Navier–Stokes equations, a set of equations for the suspension flow in a vertical plane channel, characterized by the three-parameter rheology model, was derived. In typical fracturing conditions, the conventional power-law viscosity model used in current fracturing simulators significantly overestimates the gap-averaged apparent fluid viscosity compared to the proposed model. The novel model of the suspension flow aims to enhance the accuracy of proppant transport models applied in hydraulic fracturing simulators. Based on the three-parameter rheology model, a new model of particle settling in the studied PAA-based fluids is developed. It is based on smart Stokes' law, where viscosity is calculated according to either the plateau or power-law region, depending on the self-induced shear rate. The new model more accurately approximates experimentally determined settling velocity of proppant under static conditions across a broad range of PAA concentrations than existing models.

Process-Based Evaluation of Intraseasonal Oceanic Kelvin Waves in the Pacific Ocean in CMIP6 Models

Abstract Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models. Significance Statement Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.

Hydrodynamic characteristics of cavity fluctuation behind a cone-rod assembly entering water

This study explores the phenomenon of cavity fluctuation occurring behind a cone entering water at a constant velocity. The current simulations reveal that cavity fluctuations arise following deep pinch-off, leading to pronounced pressure oscillations in both the water and air regions. Concurrently, ripples form along the cavity surface, extending from the nose to the tail, resulting in a wavy cylindrical cavity. Notably, when the water entry Froude number is below 10, the load on the cone is predominantly due to pressure oscillations induced by cavity fluctuations, which exceed the slamming load experienced during initial water impact. The study also identifies a significant impact of an attached rod on cavity evolution. Specifically, the frequency of cavity rippling increases with the rod's radius; however, when the rod-to-cone radius ratio is less than 20%, the rod's impact on the cavity dynamics becomes negligible. A theoretical analysis, modeling the cavity as a hollow cylindrical structure, is developed to elucidate the relationship between rippling frequency and rod size. The research results demonstrate that the cavity fluctuation frequency is inversely proportional to the difference in the squared radii of the cone and rod. Furthermore, when the scaling length of the cavity at the pinch-off moment exceeds a ratio of Lp/Rc > 6, the water entry cavity can be accurately modeled as a long cylindrical cavity. The numerical results confirm that the proposed theoretical model provides reliable predictions of the impact of a solid rod on the fluctuation characteristics of the cavity.

Enzyme kinetics simulation at the scale of individual particles.

Enzyme-catalyzed reactions involve two distinct timescales: a short timescale on which enzymes bind to substrate molecules to produce bound complexes and a comparatively long timescale on which the molecules of the complex are transformed into products. The uptake of the substrate in these reactions is the rate at which the product is made on the long timescale. Models often only consider the uptake to reduce the number of chemical species that need to be modeled and to avoid explicitly treating multiple timescales. Typically, the uptake rates cannot be described by mass action kinetics and are traditionally derived by applying singular perturbation theory to the system's governing differential equations. This analysis ignores short timescales by assuming that a pseudo-equilibrium between the enzyme and the enzyme-bound complex is maintained at all times. This assumption cannot be incorporated into current particle-based simulations of reaction-diffusion systems because they utilize proximity-based conditions to govern the instances of reactions that cannot maintain this pseudo-equilibrium for infinitely fast reactions. Instead, these methods must directly simulate the dynamics on the short timescale to accurately model the system. Due to the disparate timescales, such simulations require excessive amounts of computational time before the behavior on the long timescale can be observed. To resolve this problem, we use singular perturbation theory to develop a proximity-based reaction condition that enables us to ignore all fast reactions and directly reproduce non-mass action kinetics at long timescales. To demonstrate our approach, we implement simulations of a specific third order reaction with kinetics reminiscent of the prototypical Michaelis-Menten system.

A Fast Simulation Method for Wind Turbine Blade Icing Integrating Physical Simulation and Statistical Analysis

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future.

Improved numerical modeling of photovoltaic double skin façades with spectral considerations: Methods and investigations

Photovoltaic double skin façades are crucial tools for mitigating the escalating energy consumption in buildings. However, current simulation studies often neglect the variation in solar spectra and focus on only limited operational modes, resulting in incomplete and less accurate modeling. In response to these limitations, this study proposes an improved numerical model incorporating temporal spectral variations and non-uniform surface temperatures, which comprehensively encompasses all operational modes of photovoltaic double skin facades. Real-time solar spectra are acquired using specialized software and remote sensing data, while the transportation and conversion of radiation energy will be solely solved at individual wavelength steps, providing the model with spectrum resolution. Built on the fundamental principles of optics, thermodynamics, and hydromechanics, the proposed two-dimensional numerical model consistently demonstrates desirable accuracy across various airflow paths and mechanical ventilation conditions. Based on the proposed model, the feasibility of the previously proposed parameter-based control strategy is proved, which offers potential energy savings of 25.9–341.6 MJ compared to the radical strategy and 67.5–170.7 MJ compared to the conservative strategy. The photovoltaic efficiency drop due to spectral mismatch is also quantified as about 15–35 %. These results highlight the potential of the proposed model as an efficient tool for future research.

A Review of Building Physical Shapes on Heating and Cooling Energy Consumption

The shape of a building profoundly impacts its energy consumption throughout its life and is a critical consideration in early architectural design. Despite its significance, the influence of building shape on heating and air conditioning energy usage remains insufficiently understood. This study systematically analyzes the relationship between building shape and energy consumption, grounded in objective facts about building energy performance from the perspective of architects during the initial design phases. This analysis aids designers in making informed decisions. Key parameters, notably the widely used building shape coefficient, are examined. The relationship between building shape and energy consumption across various global and China’s diverse climate zones is synthesized. Current simulation tools and methodologies are assessed to guide future research. Findings reveal a predominant reliance on simulations for comparing energy use across specific building shapes. The academic understanding of the shape−energy relationship remains superficial, complicating standardization. Future research should prioritize extensive, multi-parameter simulations to enhance understanding of building performance, thereby facilitating energy-efficient design.

Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations

Multivariate bias correction (BC) models are well-known to correct more statistical attributes in climate model simulations. However, their inherent complexity and excessive parameters can introduce higher uncertainty into future climate simulations. In contrast, univariate BC models, with fewer parameters, are limited to correcting certain attributes. An issue that has not been investigated in-depth is the impact ofan increased number of variables in the multivariate BC has on the bias-corrected climate models’ stability. This study compares the performance of a multivariate BC approach, Multivariate Recursive Nested Bias Correction (MRNBC), and a univariate BC approach, Continuous Wavelet-based Bias Correction (CWBC), as the number of variables to be corrected increases, known as the “curse of dimensionality” (CoD). The analysis uses high-resolution climate model outputs for both current and future simulations of sea surface temperature and precipitation in the Niño 3.4 region. Results show both BC models effectively correct current climate biases. As the number of variables increases, CWBC remains robust and produces sensible future simulations, while MRNBC’s complexity leads to deterioration in standard deviations and spatial cross-correlation. CWBC, based on univariate correction, isrelatively unaffected by the CoD.

The Response of Cloud Precipitation Efficiency to Warming in a Rainfall Corridor Simulated by WRF

Due to model errors caused by local variations in cloud precipitation processes, there are still significant uncertainties in current predictions and simulations of short-duration heavy rainfall. To tackle this problem, the effects of warming on cloud-precipitation efficiency was analyzed utilizing a weather research and forecasting (WRF) model. The analysis focused on a rainstorm corridor event that took place in July 2020. Rainstorm events from 4–6 July formed a narrow rain belt with precipitation exceeded 300 mm in the middle and lower reaches of the Yangtze River. Temperature sensitivity tests revealed that warming intensified the potential temperature gradient between north and south, leading to stronger upward motion on the front. It also strengthened the southwest wind, which resulted in more pronounced precipitation peaks. Warming led to a stronger accumulation and release of convective instability energy. Convective available potential energy (CAPE) and convective inhibition (CIN) both increased correspondingly with the temperature. The precipitation efficiency increased sequentially with 2 °C warming to 27.4%, 31.2%, and 33.1%. Warming can affect the cloud precipitation efficiency by both promoting and suppressing convective activity, which may be one of the reasons for the enhancement of extreme precipitation under global warming. The diagnostic relationship between upward moisture flux and lower atmospheric stability during precipitation evolution was also revealed.

Magnetized wastewater treatment using Williamson’s triple hybrid nanofluid with variable viscosity and internal heat generation on a permeable surface

PurposeTry to find a way to treat wastewater and achieve its purification from suspended waste, which was removed by examining the magneto-Williamson fluid on a horizontal cylindrical tube while taking advantage of solar radiation and nanotechnology.Design/methodology/approachThe effect of Cattaneo–Christoph law of heat transfer, solar radiation, oblique magnetic field, porosity and internal heat generation on the flow was studied. The control system was solved by the numerical technique of Chebyshev pseudospectrum (CPS) with the help of the program MATHEMATICA 12. The tables comparing the published data results with the existing numerical calculation match exactly.FindingsThe tables comparing the published data results with the existing numerical calculation match exactly. The current simulation results indicate that when using variable viscosity, the Nusselt number and surface friction decrease significantly compared to their value in the case of constant viscosity, and variable viscosity has a significant effect on flow, which reduces speed. Curves and increasing temperature profiles.Originality/valueDeveloping a theoretical framework for the problem of sewage turbidity in a healthier and less costly way, by studying the flow of Williamson fluid with variable viscosity (to describe the intensity of sewage turbidity) on a horizontal cylindrical tube, and taking advantage of nanotechnology, solar radiation, Christoph’s thermal law and internal heat generation to reach water free of impurities. Inclined magnetic force and porous force were used, both of which played an effective role in the purification process.

Simplification of solvation shell with water clusters in the simulation of electrochemical nitrogen reduction reaction.

Electrochemical methods for nitrogen reduction have received extensive attention due to the mild reaction conditions. In order to gain an insight into the mechanism of the electrochemical nitrogen reduction process, theoretical simulations are necessary. However, current simulation studies contain many imprecise approximations that may hinder the real recognition of the reaction process. Although solvation methods have recently been developed to provide efficient descriptions, their further applications are hindered by controversy over modeling of solvents and the enormous computational effort required. In this work, we simplify the solvation conditions by using water clusters and compare them with an accurate water layer model. The results demonstrate that the simplified water clusters can effectively capture protons, simulate the surface electric environment, and enable the calculation of the activation energy of the reaction. This method offers an affordable approach for simulating the surface potentials and solvents and provides a new reference for the theoretical study of the electrochemical nitrogen reduction reaction.

A Set of Models for Device Positioning in Sixth Generation Networks. Part 2. Review of Algorithms and Accuracy Assessment

Relevance. This work is the second part of a series devoted to the study of a set of positioning models in sixth-generation terahertz networks and solves the problems of systematizing algorithms and assessing the accuracy of determining the location of a user device depending on the configuration and size of the antenna array at the base station.Purpose. Within the framework of the scientific problem of searching for means of achieving decimeter accuracy of coordinate estimates, outlined in the first part of the cycle, the analysis of accuracy assessment models, a review of algorithms and ways of their optimization, as well as a numerical experiment, performed in this study serve the purpose of justifying the configuration and dimensions of the antenna array used at the base station.The research method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Solution / results. The paper presents models for assessing the accuracy of positioning in 6G terahertz networks, formalizes the relationship between primary measurements and coordinate estimates for multi-position and single-position positioning in the near and far zones. It provides an overview of algorithms for geometric positioning and positioning with training for cases of one-stage and two-stage processing; analyzes the specifics of implementing algorithms for simultaneous tracking and map construction. It provides an analysis of the features of optimizing algorithms in offline and online modes. Simulation modeling is used to assess the accuracy for a scenario of territorial distribution with direct visibility and ideal synchronization.Novelty. Using simulation modeling tools, the achievement of decimeter accuracy of coordinate and orientation estimates of 1° in the terahertz range for a far-field model using a 1 GHz band and a composite antenna array of more than half a thousand elements has been scientifically substantiated.The theoretical significance lies in establishing the dependence of the accuracy of coordinate and orientation estimates of the device on the configuration and dimensions of the antenna array at the base station.The practical significance of the developed simulation model lies in the numerical justification of the limits of device positioning accuracy in sixth-generation networks depending on the antenna array used at the base station for a given scenario.

Characterization of heat-treated bearing rings via measurement of electromagnetic properties for pulsed eddy current evaluation

Nondestructive evaluation of heat-treated bearing rings is a critical technique for quality control in industries. However, there are few reports addressing the complex mapping between hardness and electromagnetic properties due to the intricate changes in the microstructure. This paper proposes a dual-method approach, combining magnetic saturation eddy current techniques and Barkhausen noise reconstruction hysteresis loop techniques, to independently establish the relationship between hardness and electromagnetic properties. The results show that the electrical properties of unqualified specimens are significantly higher than those of other specimens, with qualified specimens have slightly higher properties than untreated ones. Additionally, an inverse relationship between hardness and magnetic properties is observed. Based on the obtained electromagnetic parameters, a pulsed eddy current hardness detection simulation model is established, which has the potential to improve purely data-driven methods for hardness detection in deep learning.

Charge Diffusion and Repulsion in Semiconductor Detectors.

Semiconductor detectors for high-energy sensing (X/γ-rays) play a critical role in fields such as astronomy, particle physics, spectroscopy, medical imaging, and homeland security. The increasing need for precise detector characterization highlights the importance of developing advanced digital twins, which help optimize the design and performance of imaging systems. Current simulation frameworks primarily focus on modeling electron-hole pair dynamics within the semiconductor bulk after the photon absorption, leading to the current signals at the nearby electrodes. However, most simulations neglect charge diffusion and Coulomb repulsion, which spatially expand the charge cloud during propagation due to the high complexity they add to the physical models. Although these effects are relatively weak, their inclusion is essential for achieving a high-fidelity replication of real detector behavior. There are some existing methods that successfully incorporate these two phenomena with minimal computational cost, including those developed by Gatti in 1987 and by Benoit and Hamel in 2009. The present work evaluates these two approaches and proposes a novel Monte Carlo technique that offers higher accuracy in exchange for increased computational time. Our new method enables more realistic performance predictions while remaining within practical computational limits.

Evaluation method of carbon-fiber-reinforced polymer material anti-radiation performance based on synergistic effect model

Aiming at the high computational costs of the current multi-source X-ray radiation numerical simulations, a multi-source X-ray evaluation method based on a synergistic effect model is studied. First, based on the advantage of the low computational cost of single-source X-ray radiation numerical simulations, a hierarchical Gaussian process model is used to construct a superimposed single-source numerical simulation data fusion model. Second, by constructing composite correlation functions, the data fusion ability of the Gaussian process model is improved. The unknown parameters in the data fusion evaluation model are solved with the help of the Markov chain Monte Carlo method and a nonlinear optimization algorithm. Based on the obtained sampling values of the unknown parameters, an approximate solution method for the estimated value of the superimposed single-source X-ray radiation numerical simulations with unknown input conditions is given. Then a synergistic effect model is constructed based on the established linear regression surrogate model. The synergistic evaluation of multi-source X-ray radiation tests based on single-source X-ray radiation numerical simulation superimposed test data is studied. Finally, carbon-fiber-reinforced polymer experiments show the proposed method in better than existing Kriging methods, for prediction of multi-source X-ray radiation data.

Cold Gas Subgrid Model (CGSM): a two-fluid framework for modelling unresolved cold gas in galaxy simulations

ABSTRACT The cold ($\sim 10^{4}\, {\rm K}$) component of the circumgalactic medium (CGM) accounts for a significant fraction of all galactic baryons. However, using current galaxy-scale simulations to determine the origin and evolution of cold CGM gas poses a significant challenge, since it is computationally infeasible to directly simulate a galactic halo alongside the sub-pc scales that are crucial for understanding the interactions between cold CGM gas and the surrounding ‘hot’ medium. In this work, we introduce a new approach: the Cold Gas Subgrid Model (CGSM), which models unresolved cold gas as a second fluid in addition to the standard ‘normal’ gas fluid. The CGSM tracks the total mass density and bulk momentum of unresolved cold gas, deriving the properties of its unresolved cloudlets from the resolved gas phase. The interactions between the subgrid cold fluid and the resolved fluid are modelled by prescriptions from high-resolution simulations of ‘cloud crushing’ and thermal instability. Through a series of idealized tests, we demonstrate the CGSM’s ability to overcome the resolution limitations of traditional hydrodynamics simulations, successfully capturing the correct cold gas mass, its spatial distribution, and the time-scales for cloud destruction and growth. We discuss the implications of using this model in cosmological simulations to more accurately represent the microphysics that govern the galactic baryon cycle.