Temperature Prediction Research Articles

Прогноз сильной жары в Красноярске с использованием региональной модели WRF-ARW

The reasons for the occurrence of a severe heatwave in Krasnoyarsk have been studied, and the accuracy of the forecasts of the WRF-ARW regional model has been assessed. Four episodes of hot weather in 2020, 2021, and 2023 have been analyzed. The heatwave in June 2023 was especially intense and met the criteria for a severe weather event. All examined cases were associated with the intense advection of warm tropical air in the leading edge of the upper-air trough. The formation of the urban heat island has been investigated, and its intensity in the nighttime was found to be 6.5°C. During daytime hours, the heat island was weaker than at nighttime. It has been determined that on the second day of forecasting, the WRF-ARW model accurately reproduces the urban heat island and the impact of the Yenisei River, although it underestimates the predicted amplitude of daily temperature variations. The absolute error in air temperature predictions for the time moments close to the time of daily maximum temperatures was 2.6°C (33 hours of model time).

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). The present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

Noninvasive Temperature Measurements in Tissue-Simulating Phantoms Using a Solid-State Near-Infrared Sensor.

The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature measurements are made at the skin surface, a measurement that is impacted by the ambient environment of the individual. The use of near-infrared spectroscopy provides the potential for a measurement below the epidermal layer of skin, thereby having the potential advantage of being more reflective of physiological conditions. The feasibility of noninvasive temperature measurements is demonstrated by using an in vitro model designed to mimic the near-infrared spectra of skin. A miniaturizable solid-state laser-diode-based near-infrared spectrometer was used to collect diffuse reflectance spectra for a set of seven tissue phantoms composed of different amounts of water, gelatin, and Intralipid. Temperatures were varied between 20-24 °C while collecting these spectra. Two types of partial least squares (PLS) calibration models were developed to evaluate the analytical utility of this approach. In both cases, the collected spectra were used without pre-processing and the number of latent variables was the only optimized parameter. The first approach involved splitting the whole dataset into separate calibration and prediction subsets for which a single optimized PLS model was developed. For this first case, the coefficient of determination (R2) is 0.95 and the standard error of prediction (SEP) is 0.22 °C for temperature predictions. The second strategy used a leave-one-phantom-out methodology that resulted in seven PLS models, each predicting the temperatures for all spectra in the held-out phantom. For this set of phantom-specific predicted temperatures, R2 and SEP values range from 0.67-0.99 and 0.19-0.65 °C, respectively. The stability and reproducibility of the sample-to-spectrometer interface are identified as major sources of spectral variance within and between phantoms. Overall, results from this in vitro study justify the development of future in vivo measurement technologies for applications as wearables for continuous, real-time monitoring of body temperature for both healthy and ill individuals.

Lumped Parameter Thermal Network Modeling and Thermal Optimization Design of an Aerial Camera.

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera's unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras.

An adaptive control system based on spatial–temporal graph convolutional and disentangled baseline-volatility prediction of bellows temperature for iron ore sintering process

The temperature within the sintering furnace is a decisive factor influencing the quality of the sintered ore in the iron ore sintering process. In practical operations, the temperature at the bellows directly linked to the bed layer indirectly signifies the internal furnace temperature. Maintaining a stable temperature at the bellows, particularly at the burn-through point, is vital for minimizing gas emissions, improving carbon efficiency, and enhancing the quality of the sintered ore. This paper proposes an intelligent temperature control system based on Spatial–Temporal Graph Convolutional and Disentangled Baseline-Volatility (STGCDBV) prediction. The STGCDBV network comprises three parallel modules: Adaptive Graph Convolution Network (AGCN), Baseline and Volatility Disentangler (BVD), and a residual link, along with a Temporal–Nodal Encoder–Decoder (TNED) module. The AGCN constructs a graph reflecting the characteristics of bellows temperature, effectively merging static spatial data with dynamic thermal information. The BVD module captures the nonlinear trend data inherent in the sintering process. In contrast, the TNED synergizes the insights from the parallel modules using cross encoding and decoding functionalities. For controlling the sintering gas flow rate, a Model Reference Adaptive Control (MRAC) system is implemented, which utilizes a control scheme founded on a temperature reference model and iterative parameter adjustments. Extensive experiments using actual time-series data from a steel plant have been conducted. Moreover, comparisons between the performance of pre- and post-control interventions demonstrate that the STGCDBV-MRAC system can stabilize temperature fluctuations and exhibit exemplary control proficiency.

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction.

Temperature field analysis and prediction of winter construction warm shed method based on hot air heating

The warm shed method is commonly employed in concrete construction during winter as an effective technique. This study aims to investigate the impact of different sizes and heating methods on the temperature inside the shed and the concrete through scale model testing. The hot air heating method is simulated using finite element software, and a prediction model is developed. The findings reveal an inverse relationship between the size of the warm shed and its heat preservation effect. When utilizing hot air, both the maximum temperature inside the warm shed and at the center of the concrete increase by 15.5 °C and 25.2 °C respectively compared to when employing a point heat source, resulting in earlier stabilization times of 390 min and 1000 min respectively, as well as a more uniform temperature distribution across all parts. Based on finite element simulation results, an optimal wind speed μ is proposed along with a corresponding prediction model.

Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism

The rapid advancement of electric vehicles (EVs) accentuates the criticality of efficient thermal management systems for electric motors, which are pivotal for performance, reliability, and longevity. Traditional thermal modeling techniques often struggle with the dynamic and complex nature of EV operations, leading to inaccuracies in temperature prediction and management. This study introduces a novel thermal modeling approach that utilizes a multihead attention mechanism, aiming to significantly enhance the prediction accuracy of motor temperature under varying operational conditions. Through meticulous feature engineering and the deployment of advanced data handling techniques, we developed a model that adeptly navigates the intricacies of temperature fluctuations, thereby contributing to the optimization of EV performance and reliability. Our evaluation using a comprehensive dataset encompassing temperature data from 100 electric vehicles illustrates our model’s superior predictive performance, notably improving temperature prediction accuracy.

A SUPERPOSITION TECHNIQUE FOR PREDICTING OVERALL EFFECTIVENESS WITH MULTIPLE SOURCES OF INDIVIDUALLY CHARACTERIZED INTERNAL AND EXTERNAL COOLANT FLOWS

Abstract Gas turbine engines rely on intricate cooling schemes to protect turbine components from the high temperature freestream gas. Determining the optimal placement of film cooling holes while remaining mindful of the consumption of coolant air is therefore a design challenge. For several decades a method of superposition has been in use that provides a reasonable prediction of the adiabatic effectiveness in a region influenced by multiple rows of film cooling holes acting together if the adiabatic effectiveness distributions resulting from individual rows are already known. This classical superposition technique has been used to provide a first cut at determining where coolant holes might be placed. A shortcoming of the method is that it strictly applies only to prediction of adiabatic effectiveness which is indicative of the adiabatic wall temperature, not the actual surface temperature. The overall effectiveness, which is indicative of the actual surface temperature is somewhat more complicated as it is influenced not only by the external cooling, but also internal cooling. In the present work, a method of superposition of overall effectiveness data is proposed, allowing prediction of the actual surface temperature distribution on a component. Experimental results show that it is possible to use knowledge of the overall effectiveness distributions resulting from individual internal cooling and associated film cooling holes acting alone to determine how they are likely going to act when combined. This technique shows promise for a turbine designer to use superposition for actual surface temperature prediction and therefore higher quality initial cooling designs.

Utilizing a CNN-RNN machine learning approach for forecasting time-series outlet fluid temperature monitoring by long-term operation of BHEs system

The Borehole Heat Exchanger (BHE) plays a pivotal role in enhancing heat exchange efficiency within Ground Source Heat Pump (GSHP) systems. The accurate prediction of the BHE's outlet fluid temperature is crucial for optimizing GSHP performance, energy storage, and resource conservation. However, conventional machine learning methods encounter challenges in manual feature extraction, learning complex nonlinear relationships, and adapting to real-world scenarios. To address these limitations, this research proposes a crossbreed model integrating Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures to forecast long-term outlet fluid temperature in BHE systems. The model framework encompasses data preprocessing, utilizing refined data in the CNN module for temporal feature extraction, subsequently passed to the RNN module to capture sequential and temporal patterns from each dataset. Specifically, the advanced CNN-RNN architecture is designed to establish a comprehensive input-output mapping, leveraging essential input features such as inlet fluid, ambient air, and subsurface temperatures at varying depths (0, 10, and 20 m). Performance evaluation metrics, including R2, RMSE, MAE, and AARE, are employed to compare and assess prediction accuracy across various models, including LSTM, CNN, and SimpleRNN. The obtained results demonstrate the superior performance of the proposed model, achieving an RSME of 0.818, MAE of 0.642, AARE of 0.0305, and an R2 value of 98.75 %. This surpasses the performance of traditional prediction models (LSTM, CNN, and SimpleRNN) by 3.01 %, 5.80 %, and 19.52 %, respectively. Notably, the remarkably low MAE of 0.642 exhibited by a CNN-RNN model underscores its capability to outperform traditional approaches, especially when handling large datasets. These findings emphasize the significance of the developed model in facilitating efficient operation, positioning it as a valuable tool for advancing the long-term sustainability of BHE systems.

MODELING THERMAL BEHAVIOR IN HIGH-POWER SEMICONDUCTOR DEVICES USING THE MODIFIED OHM’S LAW

This paper addresses the challenge of thermal management in high-power semiconductor devices, where increasing power densities and complex operating environments demand more accurate thermal prediction methods. Traditional approaches often rely on simplified models that do not account for the crucial factor of temperature-dependent resistance variations. This limitation leads to inaccurate device temperature predictions, potentially compromising device reliability. This work proposes a novel approach for thermal management by introducing the first empirical application of a Modified Ohm’s Law. This modified law incorporates an exponential term to account for the non-linear relationship between temperature, current, and resistance. The paper demonstrates through simulations and empirical validation that the Modified Ohm’s Law offers a more accurate representation of thermal behavior compared to the standard version. This translates to more precise predictions of device temperature, especially during periods of rapid temperature changes. The validation process goes beyond simply establishing the Modified Ohm’s Law. It provides valuable insights into the thermal dynamics of the device, allowing for the refinement of simulation parameters used to assess various cooling strategies. These strategies include simulating different heat sink geometries and materials, modifying airflow rates over the device’s surface, and exploring the impact of Thermal Interface Materials (TIMs) between the device and the heat sink. By incorporating these elements, the simulations provide a more comprehensive picture of the device’s thermal behavior under various operating conditions and cooling configurations. Ultimately, this paper not only advances the theoretical understanding of thermal management but also offers practical benefits. Through enabling more accurate thermal predictions, the Modified Ohm’s Law model paves the way for informed decision-making in device design and optimization.

Sea surface temperature prediction by stacked generalization ensemble of deep learning

Accurate sea surface temperature (SST) prediction is of great significance for fishery farming, marine ecological protection, and planning of maritime activities. In this paper, the stacked generalization ensemble is demonstrated for SST prediction to improve every single deep-learning model. Long-term high-resolution satellite-derived SST is used with the sub-regions of the Taiwan Strait and East China Sea taken as the study area. We select the Multilayer Perceptron, Long short-term memory (LSTM), Convolutional Neural Networks (CNN), CNN-LSTM as individual learners, and Convolutional LSTM as the meta-learner. The individual learners are trained and validated on the retained data subset I, while the meta-learner is trained and validated by constructing the samples with the predictions of validated individual learners on the retained data subset II. The two types of models are evaluated on the same test dataset with root-mean-square error and coefficient of efficiency as the scoring criteria. We find that the meta-model outperforms any individual model and other baselines for the one-day-/three-day-ahead forecasts in the Taiwan Strait and one-day-/three-day-/five-day-ahead predictions in the East China Sea. Furthermore, when the lead time is 1 day and 3 days, the meta-model has a better spatial distribution of prediction metrics across all grid points in the Taiwan Strait sub-area. For the East China Sea sub-region, the meta-model advantage is extended to the lead time of 5 days. Probably due to the higher quality of offshore satellite data, the prediction ability enhancement of the stacked ensemble applied in the East China Sea is better than that in the Taiwan Strait. The better-performing meta-model prediction suggests that the stacked generalization ensemble is encouraging and promising for improving the short-term prediction of the daily SST field.

Enhancing solar furnace thermal stress testing using an adaptive model and nonlinear predictive control

Solar furnaces rely on renewable energy for thermal tests using concentrated solar direct irradiance. To address the complex dynamics and constraints of these systems, a novel control approach is introduced based on an adaptive model strategy. The proposed adaptive model representation with an Adaptive Practical Nonlinear Predictive Controller (A-PNMPC) strategy is employed in the solar furnace SF60 at Plataforma Solar de Almería (Spain), demonstrating enhanced model prediction that culminates in a finer control performance. A Recursive Least Squares solution for model adaptation adjusts the model dynamics to the actual behavior of the solar furnace. Then, the predictive control strategy uses the updated model to calculate optimal control actions that lead the sample temperature to the desired reference. Simulation studies demonstrated the superior performance of the A-PNMPC in temperature control and prediction compared to the constant parameter model PNMPC strategy. The proposed A-PNMPC was compared to the conventional PNMPC in simulations, showing up to a 5% improvement in control performance and an 80% in large model predictions. Additionally, the A-PNMPC was tested on the actual system for various temperature profiles, achieving a mean error of less than 10 °C in sample temperature ramp tracking

Evaluation of GlassNet for physics‐informed machine learning of glass stability and glass‐forming ability

AbstractGlassy materials form the basis of many modern applications, including nuclear waste immobilization, touch‐screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass‐forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, we explore the application of an open‐source pre‐trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics‐informed machine learning (PIML)‐predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top‐performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

A deep learning network for improving predictions of maximum and minimum temperatures over complex terrain

A deep learning network for improving predictions of maximum and minimum temperatures over complex terrain

Effective attention model for global sea surface temperature prediction

Sea Surface Temperature (SST) is an essential parameter of the ocean–atmosphere system, so accurately predicting the future SST is of great significance. To address the low prediction accuracy due to insufficient consideration of spatiotemporal correlations in existing machine learning models, this paper proposes an Effective Attention Model (EAM) to achieve daily global SST prediction (predicting the SST of the next 15 days using those of the past 15 days) and monthly global SST prediction (predicting the SST of the next 12 months using those of the past 12 months). The EAM adopts an attention mechanism to avoid information redundancy and improve prediction accuracy. This is the first work to realize high-accuracy global SST prediction with a high spatial resolution of 0.25° on two time scales (daily and monthly) based on deep learning. Experiments show that (1) the prediction results have a consistent global spatiotemporal distribution with the ground truth, with the Root Mean Squared Difference (RMSD) of daily prediction ranging from 0.212 to 0.932 ℃ and the RMSD of monthly prediction ranging from 0.666 to 0.782 ℃. The EAM is superior to the deep learning models (ConvLSTM, 3D-CNN, and LSTM) and the numerical model (HYCOM); (2) Besides global prediction, the EAM also achieves good performance in a climate change sensitive area (Niño 3.4 sea area), with the lowest RMSD of 0.231 ℃. This study may not only benefit the research on climate events (El Niño and La Niña) but also serve to predict other marine disasters (such as heat waves and storms). In the future, the proposed attention module can also be adapted to other prediction scenarios such as meteorology.

Experimental and modeling investigations on CH4 hydrate phase equilibria in multi-ion “Haima” cold seep environment

CH4 hydrate formation represents a primary pathway for CH4 transformation within the deep-sea CH4 seepage environment, holding utmost significance for global carbon budget and marine hydrate resources distribution. As a typical active CH4 seeping, however, the influence characteristics of various ions in in-situ “Haima” cold seep on CH4 hydrate stability remains unclear. In this study, founded on the multiple ion components from the seawater of the “Haima” cold seep, the effects of salt ion categories and concentrations on CH4 hydrate phase equilibrium were investigated, and a thermodynamic model suitable for high-salinity deep-sea environment was proposed. Results indicated that, in higher concentration salt solutions (>3.45 wt%), hydrate stability was no longer solely governed by salinity, and significant differences among ion types were observed. The inhibitory strength of the ions on CH4 hydrate stability followed the order of Mg2+>Na+>Ca2+>Mn2+≈K+>Sr2+>Ba2+. The predicted CH4 hydrate phase equilibrium conditions from the proposed model exhibited strong agreement with both experimental results and data reported in the literature. The average absolute deviation of pressure (AADP) from the model prediction was 2.7% and 4.3% compared to the experimental and published literature data, respectively, which outperformed those of the CSMHYD model, Chen-Guo model and Hu-Lee-Sum correlation (4.7%–6.2%). In light of the marine temperature gradient and prediction results, it is estimated that CH4 hydrate could maintain stably below 600 m depth of the seawater in the cold seep area.

Rotor Temperature Prediction of PMSM Based on LSTM Neural Networks

Rotor Temperature Prediction of PMSM Based on LSTM Neural Networks

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever.

Three-dimensional heat transfer analysis of Hot Gas Torch (HGT)-assisted Automated Fiber Placement (AFP) for thermoplastic composites

Automated Fiber Placement (AFP) is a key additive manufacturing process for the production of complex composite structures. This study focuses on the in-situ AFP process for thermoplastic composites with Hot Gas Torch (HGT), which includes heating, consolidation, and solidification steps. Temperature control is critical to achieving high quality parts as it affects bond quality, crystallization, and solidification. However, previous studies have oversimplified convective heat transfer by assuming a constant coefficient, resulting in discrepancies between simulations and experiments. This paper introduces a novel distribution function to model the convective heat transfer coefficient, thereby improving temperature predictions. An optimization loop is used to determine the parameters of the function, which ensures agreement with experimental data. The proposed approach accurately predicts the temperature distribution, which is validated against unseen experimental results. By incorporating the distribution of the convective heat transfer coefficient, this study improves the understanding of heat transfer mechanisms in AFP for thermoplastic composites, leading to improved manufacturing processes and part quality.