Thermal Model Research Articles

Field measurement and numerical simulation on vertical temperature gradient of steel box girders

The vertical temperature gradient (VTG) is a critical factor contributing to thermal stresses or deformations in bridge structures. However, the current provisions concerning the VTG of steel box girders are inadequate. Accordingly, this paper systematically investigated the VTG of a steel box girder in Wuhan using field measurement and numerical simulations. The temperature distributions of the steel box girder were first characterised based on measured temperature data. A thermal model of the steel box girder was then established using measured time-varying meteorological data, with the convective heat transfer coefficient obtained through an inversion method to update the model. The numerical findings align well with the observed temperature, and the fitted unfavourable VTG values exceed the current normative threshold. Furthermore, parametric investigations were carried out to reveal the impacts of meteorological and structural parameters on the VTG. The results indicate that solar radiation and pavement thickness have a considerable impact on the VTG. Finally, this research recommends that the present VTG guidelines should be modified by introducing appropriate adjustment factors related to the level of solar radiation intensity in different regions to strengthen their capacity for safeguarding.

Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms

The race towards performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with the slowdown of technology-driven power reduction, implies that power and thermal management become increasingly relevant. Unfortunately, existing research lacks a detailed analysis and modeling of thermal, power, and electrical coupling effects and how they have to be jointly considered to perform dynamic control of complex and heterogeneous mpsoc. To close the gap, in this work, we first provide a detailed thermal and power model targeting a modern hpc mpsoc. We consider real-world coupling effects such as actuators’ non-idealities and the exponential relation between the dissipated power, the temperature state, and the voltage level in a single processing element. We analyze how these factors affect the control algorithm behavior and the type of challenges that they pose. Based on the analysis, we propose a thermal capping strategy inspired by Fuzzy control theory to replace the state-of-the-art PID controller, as well as a root-finding iterative method to optimally choose the shared voltage value among cores grouped in the same voltage domain. We evaluate the proposed controller with model-in-the-loop and hardware-in-the-loop co-simulations. We show an improvement over state-of-the-art methods of up to \(5\times\) the maximum exceeded temperature while providing an average of \(3.56\%\) faster application execution runtime across all the evaluation scenarios.

Influence of degree of saturation on the thermal conductivity of soils: Experimental and comparative model analysis

The thermal conductivity can characterize thermal behaviour of soils in different engineering studies: environmental, geothermal, geotechnical and buildings construction. However, accurately measuring and predicting this parameter poses a challenging task. Measuring this parameter can be very complex, considering several factors, such as soil heterogeneity and external climatic conditions. In addition, predictions models may not capture all the nuances of real-world soil conditions, leading to less accurate predictions of ?. The objectives of this paper encompassed two primary aspects: (i) Evaluation of laboratory tests of thermal conductivity of unsaturated soil. (ii) Assessment of five highly recommended soil thermal conductivity models to determine their validity and strengthen their trustworthiness. The studied material consisted of calcareous tufa locally available in Beni-Saf region (Algeria). The tested samples were compacted to the Standard and Modified Proctor Optimum (SPO, MPO) followed by drying periods under laboratory conditions. The thermal conductivity of samples was evaluated using transient method. The models’ predictive results of the thermal conductivity were assessed with different criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination R². Furthermore, this work also attempts to deliver an in-depth discussion of the effect of degree of saturation Sr on the thermal conductivity of soils.

Kinetics study on inhibiting battery thermal runaway using an inorganic phase change material with a super high thermochemical storage capacity

Lithium-ion batteries are susceptible to fires and explosions due to thermal runaway, a serious safety hazard. This study explores the potential of using hydrated inorganic salt (TCM40) composite phase change materials to prevent thermal runaway in battery packs. TCM40 composites stand out due to their exceptional thermochemical heat storage capacity, which allows them to effectively absorb excess heat during runaway events. The research investigates how thermal conductivity, thermal storage capacity, and cell spacing influence the propagation of thermal runaway. The findings demonstrate that TCM40 composites, with a thermal storage density exceeding 1000 kJ/kg, are significantly more effective in preventing thermal runaway compared to traditional latent heat storage phase change materials with lower capacities. To gain a comprehensive understanding of thermal runaway mitigation, a combined thermal management model was developed. This model integrates a battery thermal runaway model with a kinetic model describing the decomposition of TCM40 composites. The analysis reveals that the high heat absorption capability of TCM40 composites minimizes heat transfer to neighboring cells during thermal runaway. Furthermore, the model provides valuable insights into the synergistic effects of thermal conductivity and heat storage capacity on runaway propagation. This knowledge can be directly applied to design safer battery packs, even for compact configurations where cell spacing is less than 2 mm. This study offers significant advancements in both thermal protection materials and design strategies for lithium-ion battery packs. These advancements have the potential to significantly improve battery system safety and minimize the risk of explosions.

Warm, moderate, or cool-liker? A benchmarking framework to characterize occupant overall thermal preferences based on large-scale thermostat data

Humans could exhibit distinct overall thermal preferences when exposed to identical indoor thermal environments, leading to preference groups such as "warm-likers" or "cool-likers", who consistently prefer warmer or cooler conditions than the average population, respectively. Currently, most thermal comfort modelling studies focus on capturing the instantaneous comfort states/preferences of occupants, while overlooking their overall thermal preferences. This paper proposes a benchmarking framework to identify and characterize overall thermal preferences based on the relationship between preferred setpoints and outdoor air temperatures, derived from the ECOBEE Donate Your Data program. Using descriptive statistics, we establish three temporally consistent overall preference groups, including warm-liker, moderate and cool-liker, along with a temporally chaotic preference group termed random. Our results demonstrate that warm-likers prefer temperature setpoints above 21.5 °C on heating days and 24–25 °C on cooling days, while cool-likers prefer setpoints below 19.6 °C on heating days and 22 °C on cooling days, indicating a significant impact of the seasonal transitions. We observed that around 50 % of users exhibit secondary overall preferences, implying that overall thermal preference could change over time. On average, overall thermal preference can be established in 10–16 setpoint adjustments. The study reveals varied responses to outdoor temperature changes among users: many maintain constant indoor temperature preferences, while a significant number adjust their indoor temperatures upwards by 0.1 °C–0.4 °C for each 1 °C rise in outdoor temperature. A smaller group prefers cooler indoor temperatures as it gets warmer outside, showing a unique negative adjustment trend of −0.1. We also found that climate interacts with the overall preference group, with warmer climates having more warm-likers and vice versa.

Combining thermal model and kinetics: Implications in dynamic simulation of anaerobic digesters

In this study, ADM1-based kinetics were combined with a thermal model that accounts for various heat transfers inside and through the reactor’s boundaries. Computing the energy of bioreactions based on kinetic rates prevented an overestimation of approximately 20% in the heat demand of the heat exchanger, compared to calculations using feedstock degradation heat. This also improved the representation of the digester’s thermal and reactional inertia. Simulations across different climates demonstrated the necessity to tailor digester construction. In the North West United Kingdom, biogas auto-consumption was 23% higher than that of the same reactor in South West France. Enhancing the thermal insulation of the digester reduced heat losses by 37% in the United Kingdom. Therefore, coupling the kinetic and thermal models expands the insights that can be extracted from simulations, which can be valuable in optimizing the operation and design of biogas digesters.

Review and Thermal Calculation of a Shell and Tube Heat Exchanger with Baffles in Cross - Counter Flow Arrangement

In one pass shell and tube heat exchanger with baffles one fluid flows inside of parallel tubes and other fluid flows outside of tubes in perpendicular to tube axis changing alternately its direction. Passing the window of the baffles the shell side fluid deviates its flow direction rapidly. This creates just behind the window of each baffle a so-called “dead volume” which renders a stagnation of flow. This dead volume assumed to be triangular in shape causing the flow behind the window to be conical.In this thermal model the “dead volume” taken into account and change of temperature of the shell side fluid during its flow from tube to tube is considered. The thermal analysis of the cross- counter flow arrangement for any number of tube in bundle with progressive changes in tube length has been carried out. The numerical computations with different tube and baffle numbers are performed. It is found that at the beginning the effectiveness of the heat exchanger increases distinctly with increasing tube number and baffle number. But after certain number of tube and baffles the increasing rate weakens and it becomes very small. Due to the presence of “dead volume” there is reduction in the thermally active heat transfer surface area which leads to a smaller value of Number of Transfer Units (NTU). Hence, the results of computation obtained in this paper with the consideration of the “dead volume” will provide lower value of the effectiveness than that of without the consideration of it.

Multi-split configuration design for fluid-based thermal management systems

Abstract High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow splits are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The architectures discussed here can be used for various applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include three categories of problems and are discussed in detail.

Thermal modeling and Machine learning for optimizing heat transfer in smart city infrastructure balancing energy efficiency and Climate Impact

The paper proposes a framework based on deep learning, transfer learning, and multi-objective optimisation to model and optimise heat transfer in smart city infrastructure to make them energy efficient and thermally comfortable. The framework in the paper contains a building thermal dynamics prediction model developed using hybrid CNN-LSTM on an extensive dataset (12.56 metric tonnes) of Indian buildings covering various characteristics, which is then fine-tuned with data from five major Indian cities. This predictive framework has a high generalisation capability of energy consumption and predicting indoor temperature profiles with the mean absolute errors (MAE) of building energy consumption ranging from 8.7 to 12.3 kWh and indoor temperature as 0.6 to 1.1 °C, respectively. Transfer learning is considerably improving the performance of the proposed model in newly added cities, which improved the MAE in the training cities (New Delhi and Mumbai) by 3.6 % and reduced the R^2 to 10.7 %. The multi-objective optimisation involving decision-making processes resulted in energy savings of 15.7 % to 22.3 % and improved comfort levels by 21.8 % to 28.5 % in the evaluated cities. The paper significantly contributes to developing a data-driven, generalisable, and interpretable framework, which can usher how to optimise heat transfer using deep learning to make smart city infrastructure resilient and comfortable. It also provides a novel solution to addressing the problems posed by energy efficiency and climate change in Indian cities. Policymakers and urban planners can utilise these key policy recommendations suggested in the paper to design new, liveable and self-sustaining urban environments in India.

Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition

Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in input spaces. To address this challenge, this paper introduces a novel approach for constructing an MF surrogate for predicting melt pool geometry by integrating models of varying complexity, that operate on heterogeneous input spaces. The first thermal model incorporates five input parameters i.e., laser power, scan velocity, powder flow rate, carrier gas flow rate, and nozzle height. In contrast, the second thermal model can only handle laser power and scan velocity. A mapping is established between the heterogeneous input spaces so that the five-dimensional space can be morphed into a pseudo two-dimensional space. Predictions are then blended using a Gaussian process-based co-kriging method. The resulting heterogeneous multi-fidelity Gaussian process (Het-MFGP) surrogate not only improves predictive accuracy but also offers computational efficiency by reducing evaluations required from the high-dimensional, high-fidelity thermal model. The tested Het-MFGP yields an R2 of 0.975 for predicting melt pool depth. This surpasses the comparatively modest R2 of 0.592 achieved by a GP trained exclusively on high-dimensional, high-fidelity data. Similarly, in the prediction of melt pool width, the Het-MFGP excels with an R2of 0.943, outshining the GP's performance, which registers a lower R2 of 0.588. The results underscore the benefits of employing Het-MFGP for modeling melt pool behavior in L-DED. The framework successfully demonstrates how to leverage multimodal data and handle scenarios where certain input parameters may be difficult to model or measure.

Personalized low-cost thermal comfort monitoring using IoT technologies

Thermal comfort plays an essential role in the well-being and productivity of occupants. Typically, thermal comfort is assessed either through surveys completed by building occupants or through sensor data that is analyzed using thermal comfort models. Automating comfort surveys and data collection processes reduce the risk of information loss, providing more accurate and personalized thermal comfort assessments over longer periods of time. To this end, this paper presents the design and implementation of a thermal comfort monitoring system consisting of low-cost hardware components and using IoT technologies. The system consists of intelligent wireless sensor nodes that collect and process environmental data, a portable main station that integrates and stores data, and a digital survey that provides feedback from building occupants. To ensure accuracy, the low-cost hardware components of the intelligent sensor nodes are calibrated in a climate chamber, using high-precision sensors for reference. After calibration, the system is deployed in a field test where several intelligent sensor nodes collect environmental data in an office, while occupants complete the digital thermal comfort survey. In addition, thermal comfort indexes are computed by the intelligent sensor nodes and compared with the feedback of each building occupant. The results indicate that the low-cost thermal comfort monitoring system successfully collects and integrates thermal comfort data from the intelligent sensor nodes and the digital survey, being able to create personalized thermal comfort profiles. In future work, the system can be used in large-scale thermal comfort surveys, to develop personalized thermal comfort models and to control personalized comfort systems.

Diffusion-induced stress in crystals: Implications for timescales of mountain building

Intracrystalline chemical diffusion offers valuable insights into the durations of metamorphic and igneous processes. However, it can yield timescale estimates for orogenic and subduction zone events that are considerably shorter than those obtained via isotopic geochronology. One potential explanation that has been offered for the discrepancy is that the interdiffusion of species with different atomic or ionic radii may generate intracrystalline, compositional stresses that alter or limit diffusional relaxation. In this study we test this idea by developing and applying the compositional stress theory of materials scientists F. Larché and J. Cahn to garnet from the Barrovian sillimanite zone, Scotland. Relaxed contacts from the garnet, independent diffusion chronometers, and thermal modeling all indicate a > 100 kyr duration for peak temperature metamorphism. Nonetheless, the garnet records sharp, μm-scale variations in calcium and iron contents that standard diffusion treatments predict should relax in 1–10 kyr at peak temperature conditions. Our results show that the development of compositional stress during diffusional relaxation can explain the preservation of the observed short wavelength compositional oscillations at a > 100 kyr timescale. Thus, it may be necessary to account for compositional stress when modeling diffusion in solid solutions with appreciable differences in their endmember molar volumes. This will be particularly relevant when considering sharp, μm-scale chemical gradients involving grossular, the garnet endmember with the largest molar volume relative to pyrope, almandine, and spessartine. Neglecting compositional stress in such cases could result in the underestimation of the timescales of lithospheric processes by potentially orders of magnitude. The effects of compositional stress in garnet are predicted to be the most pronounced under amphibolite and blueschist–eclogite facies conditions. At lower temperatures diffusion is limited, and at higher temperatures both plastic deformation and more ideal solid solution behavior will act to diminish the impact of stress.

An efficient 3D tube-level flux-thermal model of external tubular receivers in solar power tower systems

Accurate and efficient radiative flux simulation and thermal analysis of liquid tubular receivers are essential for safe and stable operations of solar power tower systems. However, the complicated structure and photothermal processes on these receivers pose significant challenges for simulation. Although existing models have made great simplifications, the simulation results are still inefficient and inaccurate. To address these issues, a novel and universal 3D flux-thermal model is proposed. Firstly, a flux model is proposed via a tube-level Monte Carlo ray tracing method based on GPU parallel computing, enabling real-time and accurate radiative flux density distribution simulation for each absorber tube. Secondly, a high-resolution multi-tube thermal model is proposed, providing accurate 3D thermal analysis for each absorber tube. Through pre-factorization and highly parallelized designs, the proposed thermal model achieves a 600 times acceleration in efficiency, even with tens of millions of discrete elements. Finally, the novel flux-thermal model is validated against publicly published measured data from Solar Two, indicating a mean deviation of only 0.1% and a root mean square error of 0.006, demonstrating its high accuracy. Furthermore, the proposed flux-thermal model reveals complex and asymmetrical flux and temperature distributions on the absorber tube, in contrast to the symmetrical distributions obtained by the simplified models. Such inaccuracies in the simplified simulations may cause operational misjudgments and affect system safety potentially. Therefore, the proposed 3D tube-level flux-thermal model provides an accurate and efficient solution to radiative flux simulation and thermal analysis, enhancing the safe and stable operation of solar power tower systems.

Tectonic controls on ore deposit exhumation and preservation: A case study of the Handan-Xingtai iron-skarn district

Despite the growing concern regarding post-mineralization thermo-tectonic processes in recent years, the relative roles in exhuming and preserving ore deposits remain highly controversial. This study presents new apatite fission track and (U-Th)/He data from the Xishimen iron skarn deposit in the Handan-Xingtai district, central North China Craton. Apatite fission track dating yielded central ages ranging from 87.9 ± 18.0 Ma to 124.8 ± 8.8 Ma, with mean confined track lengths varying between 11.9 ± 0.4 μm and 13.3 ± 0.2 μm. Integrated apatite (U-Th)/He dating provided ages of 42.5 ± 0.8 Ma to 48.1 ± 3.3 Ma. Our new data, combined with previous zircon U-Pb and potassium-bearing mineral 40Ar/39Ar ages, revealed three cooling episodes: very rapid cooling (100–140 °C/Ma) at ca. 130–120 Ma, a protracted slow cooling period (0.2–0.4 °C/Ma) at ca. 120–50 Ma, and moderate cooling (0.8–1.0 °C/Ma) since ca. 50 Ma. The initial rapid cooling phase was primarily attributed to post-magmatic thermal equilibration following the shallow emplacement of the Xishimen deposit. The subsequent cooling phases were controlled by uplift and exhumation processes. Our thermal models indicate an estimated total unroofing thickness of < 3 km, which is shallower than the emplacement depth of the ore deposit (3–5 km). This suggests significant potential for mineral exploration. Furthermore, a comprehensive review of preservation mechanisms for various ore deposits underscores the significant role of tectonics in both exhuming and preserving ore bodies.

Application and optimization of residual connection neural network in spacecraft thermal design

In thermal analysis modeling, the finite element method (FEM) is commonly used; however, it incurs high computational costs and complicates the global optimization of thermal parameters. To address these challenges, developing simplified surrogate models is crucial for enhancing analysis efficiency. Yet, constructing such models demands exceptional predictive accuracy, making conventional parameter adjustment methods inadequate for design needs. This paper introduces a novel Residual connection Neural Network model, called Res-NN, designed to approximate the CMOS finite element model. By employing residual connections, the Res-NN model significantly reduces fitting errors between networks, achieving a predictive accuracy of 94.6 %, which is 6.6 % higher than that of comparable Multi-Layer Perceptron (MLP) models. Moreover, Res-NN is over 100 times faster than traditional FEM in prediction speed, effectively circumventing the difficulties associated with parameter adjustments. To optimize the temperature fluctuations of the CMOS model, we utilized the Res-NN model as an iterative object within an optimization algorithm. Through experimental comparisons, we identified the PSO algorithm as the most effective option. The PSO optimization results demonstrated a chip temperature difference of 0.045 °C, with a simulation error of only 0.0649 °C, meeting design specifications and achieving a 61.7 % reduction in temperature variance compared to traditional thermal design method. This validates the superiority of the entire optimization process.

Exploring the Natural Ventilation Potential of Urban Climate for High-rise Buildings Across Different Climatic Zones

The natural cooling capacity of the environment can effectively reduce the building energy consumption by natural ventilation (NV). For architects, developing NV strategies requires a comprehensive understanding of the distribution characteristics of urban meteorology. This study investigates the vertical characteristics of urban local-scale meteorological parameters across climatic zones. It aims to assess the climatic potential for NV of outdoor air in high-rise buildings. Firstly, using Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) data and sounding data, the vertical distribution of meteorological parameters are explored in typical urban areas across different climatic zones. Then, based on an adaptive thermal comfort model, the climatic potential is assessed for NV in high-rise buildings via the indicator of annual natural ventilation hour (NVH). Finally, the characteristics of urban meteorology is further is analyzed in terms of its impact on the natural ventilation potential (NVP) in each climatic zone. Results indicate that air temperature decreases with height from 0 to 500 meters, with more pronounced temperature gradients in compact building areas. Near the ground level, Kunming (Temperate Zone) has the most NVH, reaching 4,840 hours annually. However, when building height exceeds 100 meters, Guangzhou (Hot Summer and Warm Winter, HSWW) becomes more suitable for NV. In cold regions, low temperatures limit NVP during winter, while high humidity constrains it in Temperate and HSWW zones. Overall, the annual NVP in high-rise building areas exceeds that of low-rise building areas. These distribution characteristics of NVP are anticipated to promote the utilization of natural resources for sustainable urban development.

Symbiotic stars in X-rays

White dwarf symbiotic binaries are detected in X-rays with luminosities in the range of 1030–1034 ergs s−1. Their X-ray emission arises either from the accretion disk boundary layer, from a region where the winds from both components collide, or from nuclear burning on the surface of the white dwarf (WD). In our continuous effort to identify X-ray-emitting symbiotic stars, we studied four systems using observations from the Neil Gehrels Swift Observatory and XMM-Newton satellites in X-rays and from Transiting Exoplanet Survey Satellite (TESS) in the optical. The X-ray spectra were fit with absorbed optically thin thermal plasma models that are either single- or multitemperature with kT &lt; 8 keV for all targets. Based on the characteristics of their X-ray spectra, we classified BD Cam as possible β-type, V1261 Ori and CD −27 8661 as δ-type, and confirmed NQ Gem as β/δ-type. The δ-type X-ray emission most likely arises from the boundary layer of the accretion disk, while in the case of BD Cam, its mostly soft emission originates from shocks, possibly between the red giant and WD and disk winds. In general, we find that the observed X-ray emission is powered by accretion at a low accretion rate of about 10−11 M⊙ yr−1. The low ratio of X-ray to optical luminosities, however indicates that the accretion-disk boundary layer is mostly optically thick and tends to emit in the far or extreme UV. The detection of flickering in optical data provides evidence of the existence of an accretion disk.

Measurement of slagging thickness on the heating surfaces of a 330 MW subcritical boiler based on mid-infrared imaging technology

In this paper, a mid-infrared thermal imager and a Complementary Metal Oxide Semiconductor (CMOS) imaging device were employed to measure the slagging thickness on the heating surface in a 330 MW subcritical drum boiler. A way to establish the relationship between the slagging temperature and emissivity was proposed based on the Boom emissivity model and the Hadley thermal conductivity model, and then the slagging thickness was calculated by the Newtonian iterative method. In the superheater area, the average slagging thickness of the lower section was 8.6 mm, and it was 1.71 mm higher than that of the upper section. In the water-cooled wall area, the average slagging thickness of the right wall was 10.8 % larger than that of the left wall. To verify the accuracy of the calculated slagging thickness, the slagging image was measured by the direct visual probe, and the relative error was 7.69 %.

Human thermal comfort model and evaluation on building thermal environment

A healthy and comfortable thermal environment has become the core focus on building environment evaluation. The key is to realize room temperature regulation based on the demand for human thermal comfort. The study aims to build the core of building thermal environment regulation, building thermal environment evaluation system, to achieve accurate evaluation and prediction of thermal comfort temperature for different type buildings and populations. Considering the differences between traditional and smart buildings in building intelligence, whose thermal environment evaluation frameworks are proposed respectively. Taking the traditional buildings in a typical city from the severe cold region as an example, the thermal comfort models of different type buildings, heating periods, and populations are established. Furthermore, the corresponding correction factors are proposed for age, gender, BMI and metabolic rate. This study provides theoretical support for the evaluation of building thermal environments.

Revealing the ablative behavior and mechanism of a 2D C/SiC Ti3SiC2 modified composite through a multi-scale laser ablation model

Ti3SiC2 MAX phase ceramic has effectively enhanced the oxidation resistance of C/SiC composites. However, there is still a need for a numerical ablation model that can analyze the ablative behavior of 2D C/SiC composites. To address this, an efficient, phenomenological multi-scale ablation model, including the thermal property theoretical model, oxidation, decomposition, and sublimation ablation models, is established for revealing the effect of Ti3SiC2 on the ablation resistance of the 2D C/SiC composite. The ablative behavior is evaluated using the continuous-wave laser with different laser power densities as the heat source and used as a basis for numerical model verification. The results show that the Ti3SiC2 MAX phase ceramic can improve the ablation resistance of the 2D C/SiC composite under different laser power densities. The ablation roughness is reconstructed through the mesoscopic geometry structure and maximum/minimum ablation depth. The numerical model can analyze the effect of the mesoscopic geometry structure parameters on the ablation behavior. The model can provide a predicting method for the quantitative ablative calculation of 2D C/SiC and matrix-modified composites.