Temperature Variation Rate Research Articles

A simplified methodology to determine the latent heat of macroencapsulated phase change materials for building envelope applications

Incorporating macroencapsulated phase change materials (PCMs) in building envelopes is a strategy to reduce energy consumption. Current characterization methods focus on small quantities of PCM alone, even if it was demonstrated that they are not representative of the thermal behavior of macroencapsulated PCMs, and rely on highly specific equipment. This work proposes a novel methodology to determine the latent heat of whole macroencapsulated PCMs for building envelope applications using only temperature measurements. This method takes into account the greater quantity of PCM and the capsule material, unlike previous works, while it simplifies the necessary equipment. To develop the predictive model, a wide set of PCM panels was subjected to controlled temperature variations of 0.5 °C/h and 1 °C/h employing an insulated hotbox connected to a climatic module. Transient surface temperature and heat flux were measured. Experimental results demonstrated that not only heat flux measurements, but also temperature can be used to identify the beginning and end of the phase change process. The experimental results were then divided into a training and validation dataset. The training dataset was used to build the predictive model that correlates the area under the heat flux curve to that enclosed by the temperature curve during phase transitions. The model was validated with the remaining experimental data and optimized to assess the influence of temperature variation rate and phase change processes (melting and solidification). We found that the quotient of the heat flux area over the temperature area is 20.5 W/m2·K. Based on this, we identified that the latent heat can be accurately estimated using only temperature data, with an average error of less than 6 %. This methodology is simple but efficient requiring only thermocouples, and no other sophisticated instrumentation. The proposed model predicts the thermal response of macroencapsulated PCM with robustness, versatility, and accuracy (R2 value above 0.95). The trained empirical model applies to other macroencapsulated PCMs, contributing to the effective deployment of PCM-based thermal storage in buildings. Numerical models of envelopes with macroencapsulated PCMs will benefit from the results presented in this work, since the presented model determines the latent heat of whole PCM macrocapsules, unlike previous studies where only small quantities of PCM alone were analyzed.

Experimental and numerical study of dissimilar fiber laser welding of martensitic AISI 1060 carbon steel with different configuration with austenitic 304 and ferritic 420 stainless steel

Welding with fiber laser for two distinct stainless steel types was performed to join with the martensitic carbon steel (AISI 1060) in order to assess the effect of laser welding operating parameters for two dissimilar materials on the weld characterization thorough experimental design method and numerical investigation. A full central composite design (CCD) matrix in accordance with the response surface methodology (RSM) was developed to represent the responses variations by equations including quadratic and nonlinear interaction terms. Different laser welding parameters were taken into account, such as laser power, linear speed of welding, focal distance of the beam, and beam deviation. The responses considered were the temperature field next to the melt pool border line, the maximum penetration depth of the fusion zone and the ultimate tensile strength of the dissimilar weld joint. Additionally, the variation of the microstructure fusion zone and adjacent areas and failure mechanism of the weld joint were evaluated. The experimental results indicate that the temperature field measured at the vicinity of the melt pool fusion line with both 304 and 420 stainless steel which primarily influenced by the incident power of the laser and linear velocity of beam, respectively. Additionally, the numerical simulation results are in good agreement with temperature experimental results at vicinity of fusion line where the temperature measured, experimentally. Apart from this, at the center of the fusion zone, the temperature clearly predicts via numerical simulation results which is not possible to assess experimentally. A clear comparison between the temperature distribution with different joint configuration illustrates that distinct relation between the temperature variation rate and different thermal conductivity coefficient of AISI 1060 with different AISI 304 and 420 joint configuration resulted different temperature distribution. The weld joint microstructure for 420 stainless steel–AISI 1060 steel joint at fusion boundary zone consists of columnar dendrites and skeletal columnar ferrite. At the center of fusion zone, a cellular-type structure is seen. For 304 stainless steel joint, the fusion zone has composed of a remarkable part of skeletal delta-ferrite and dendritic microstructure at austenite matrix microstructure. The fracture section of 304 steel has a ductile fracture and the depth of fracture dimples and cavities toward 304 stainless steel are higher than AISI 1060 steel.

Numerical Study on the Variable-Temperature Drying and Rehydration of Shiitake

Variable-temperature convective drying (VTCD) is a promising technology for obtaining high-quality dried mushrooms, particularly when considering rehydration capacity. However, accurate numerical models for variable-temperature convective drying and rehydration of shiitake mushrooms are lacking. This study addresses this gap by employing a model with thermo–hydro and mechanical bidirectional coupling to investigate five dehydration characteristics (moisture ratio, drying rate, temperature, evaporation rate, and volume shrinkage ratio) and a drying load characteristic (enthalpy difference) during VTCD. Additionally, a mathematical model combining drying and rehydration is proposed to analyze the effect of VTCD processes on the rehydration performance of shiitake mushrooms. The results demonstrate that, compared to constant-temperature drying, VTCD-dried mushrooms exhibit moderate shrinkage rates and drying time (16.89 h), along with reduced temperature variation and evaporation rate gradient (Max. 1.50 mol/(m3·s)). VTCD also improves enthalpy stability, reducing the maximum drying load by 58.84% compared to 338.15 K constant-temperature drying. Furthermore, drying time at medium temperatures (318.15–328.15 K) greatly influences rehydration performance. This study quantitatively highlights the superiority of variable-temperature convective drying, offering valuable insights for optimizing the shiitake mushroom drying processes.

Study of thermal stress deformation in hybrid 3D printing and milling process of PEEK material

Hybrid manufacturing can enable rapid production of personalized artificial bones that are made of Polyether-ether-ketone (PEEK) by integrating 3D printing and milling. A main challenge, however, is the thermal stress deformation that arises from the fused deposition process, leading to warping and shrinkage. As a result, the fabrication accuracy is significantly diminished. This work aims to address this challenge by investigating the thermal stress deformation behavior of PEEK in hybrid manufacturing. The main contributions of this work, therefore, include the development of a model for this thermal deformation behavior, and the identification of key parameters such as cross-section length ( Ls), printed layer thickness ( Lh), and current workpiece height ( He). Further, experiments are conducted based on response surface methodology (RSM) to explore the relationships between temperature variation rate (Δ T), end surface height difference (Δ S), and length shrinkage rate (Δ L) with critical hybrid processing parameters. Optimal parameter combinations are then identified. Our experiments demonstrate that the proposed methods effectively reduce the effects of warping and shrinkage.

Optimization of solid oxide electrolysis cells using concentrated solar-thermal energy storage: A hybrid deep learning approach

The Solid Oxide Electrolysis Cell (SOEC) represents a cutting-edge solution for the conversion of CO2 and H2O into syngas, offering significant economic and environmental benefits. However, the process requires substantial high-temperature heat inputs, traditionally supplied by electricity. This study introduces a novel approach leveraging concentrated solar radiation as a renewable heat source for SOEC, addressing the challenge of its inherent fluctuations through the integration of Thermal Energy Storage (TES) systems. We propose a hybrid model that combines multi-physics simulation with a deep learning algorithm, enabling rapid optimization of the electrolysis process under real-time direct normal irradiance conditions. Our findings demonstrate that the inclusion of TES within the system architecture results in a remarkable 53 % reduction in temperature variation rate at the SOEC inlet, ensuring operational stability and efficiency. Furthermore, by fine-tuning capacity parameters, we have developed a control strategy that harmonizes efficiency with safety performance. The robustness of our system is underscored by its resilience to step changes, achieving a 75 % reduction in temperature fluctuations. This research contributes a pioneering method for the real-time optimization and control of SOEC systems, harnessing the power of TES to drive sustainable energy conversion with enhanced reliability and economic viability, facilitating precise and swift predictive capabilities even under dynamic operating conditions.

An approach to Molten Salt Reactor operation and control and its application to the ARAMIS actinide burner

Molten salt reactors (MSRs) are Generation IV nuclear reactor concepts gaining significant research interest worldwide. The present work proposes a methodology for the definition and optimization of the MSR operating conditions and control strategy. The proposed methodology employs a novel 0D steady-state model, the multi-physics system-scale MOSAICS (MOlten SAlt Incompressible Calculation System) model and a Global Sensitivity Analysis (GSA) method based on Hilbert-Schmidt Independence Criterion indices. The methodology was then applied to the ARAMIS (Advanced Reactor for Actinides Management in Salt) fast-spectrum chloride-salt burner reactor. Two alternative control strategies are proposed in order to achieve target margins to the salt freezing and structure material limit temperatures during normal operation, plus a 20 % power variation per minute load-following objective. The performance of the control strategies was assessed through comparison with the natural behavior during a load-increasing transient, as well as during Unprotected Transient Over-Power (UTOP) and Station Blackout (SBO) accidents. Controlled load variation transients are observed to reduce overall temperature variation rates throughout the salt circuits, while the inclusion of a variable fuel flow from the reactor commands can further limit these fluctuations. However, the selected operating conditions exhibited insufficient margins to freezing or the materials limit temperature under unprotected accident conditions. GSA enabled the derivation of correlations to characterize the dynamic response of MSRs to the accidents. Based on the observed reactor response to the various transients, potential modifications to the ARAMIS design and control strategies are proposed.

Thermodynamic analysis and equilibration response time prediction of recuperator in the SCO2 Brayton cycle

The recuperator of supercritical carbon dioxide (SCO2) Brayton cycle significantly affects the system's flexible control due to its thermal inertia. In this study, the structural parameters and operating parameters of recuperator are considered to quantitatively analyze the law of their influence on equilibration response time. Chebyshev spectrum method has been innovatively applied to the one-dimensional dynamic response model of recuperator. The dynamic response time of recuperator under the condition of temperature variation on the hot side is analyzed. The results show that reducing the thermal inertia of solid wall (by increasing the specific surface area of recuperator) can effectively decrease the equilibrium time. Additionally, the rate of inlet temperature variation should not be too large to reduce the thermal inertia of fluid. The zigzag channel demonstrates better dynamic response characteristic than straight channel. A novel dimensionless formula, based on structure and operating parameters, is proposed to quantitatively characterize and predict the equilibrium time for both straight and zigzag channel, the error is within ±20 %. Moreover, a high-precision neural network is trained to predict the equilibrium time directly. The results provide significant theoretical and application support for alleviating the thermal inertia of PCHE and improving the flexibility of SCO2 system.

Phase change energy storage using boron nitride/carbonized loofah sponge

Phase change thermal energy storage is a novel option for industrial waste heat recovery with the characteristics of high safety performance and low-cost. This work explores the potential novel composite phase change materials by encapsulating polyethylene glycol with boron nitride, carbonized loofah sponge fragments and boron nitride-carbonized loofah sponge fragments mixture using melt impregnation method. The results reveal that there is no chemical reaction between polyethylene glycol and the additives of boron nitride and carbonized loofah sponge fragments. The maximum thermal conductivity of the polyethylene glycol-based composites is 1.09 times of the pure polyethylene glycol. The thermal energy storage efficiency of polyethylene glycol/carbonized loofah sponge fragments composites with 2.5 wt% additives can be up to 80.13 %. The maximum temperature variation rates in the heating and cooling processes are attributed to polyethylene glycol/carbonized loofah sponge fragments, which are 0.096 °C/s and 0.057 °C /s correspondingly. Based on the numerical simulation considering utilization of waste heat from flue gases, the total melting time of composites with boron nitride, carbonized loofah sponge fragments, and boron nitride-carbonized loofah sponge fragments are 82.61 %, 85.25 % and 81.86 % of pure polyethylene glycol. These results imply the multiple usage of additives could not have superimposed effect on decrease of latent heat, and carbonized loofah sponge fragments is a cheap alternative to boron nitride.

A novel control strategy to neutralize internal heat source within solid oxide electrolysis cell (SOEC) under variable solar power conditions

The integration of solid oxide electrolysis cells (SOECs) with a photovoltaic (PV) system presents a viable method of storing variable solar energy through the production of green hydrogen. To ensure the safety and longevity of SOEC amidst dramatic fluctuations in solar power, control strategies are needed to limit temperature gradients and rates of temperature change within the cell. Recognizing that the supply of the reactant influences the current, a novel control strategy is developed to modulate internal heat source in the SOEC by adjusting the steam flow rate. The effectiveness of this strategy is assessed through numerical simulations conducted on a coupled PV-SOEC system using actual solar irradiance data. The irradiance data are recorded at two-second intervals to account for rapid changes in solar exposure. The results indicate that conventional control strategies, which increase airflow rates, are inadequate in effectively suppressing the rate of temperature variation in scenarios of drastic changes in solar power. In contrast, our proposed strategy demonstrates precise management of SOEC internal heat generation, thus reducing the temperature gradient and variation within the cell to less than 5 K cm−1 and 1 K min−1, respectively, and maintaining a high electricity-to-hydrogen conversion efficiency of 94.9%.

Heat and Mass Transfer of Water During Activated Carbon Adsorption

AbstractIn order to investigate the dynamic process of the heat and mass transfer phenomenon of water vapor on activated carbon, a fixed‐column adsorption unit and two kinds of activated carbon were used as adsorbent for water vapor adsorption under the adsorption temperature 293.15 K, 303.15 K and 313.15 K, respectively. A coupled model was developed by mass and energy balance, mass transfer and simplified DO adsorption equilibrium equation. The effects of physical parameters on heat and mass transfer were theoretically investigated. Results show that the numerical model match well with the experimental data (R2>0.993). The axial diffusion coefficient (DL) increases from 1.226×10−6 m2 s−1 to 4.062×10−6 m2 s−1 for RAC and 1.425×10−6 m2 s−1 to 4.030×10−6 m2 s−1 for MAC, mass transfer coefficient (k) increases from 8.171 s−1 to 27.083 s−1 for RAC and 9.499 s−1 to 26.864 s−1 for MAC. Concurrently, the breakthrough time of adsorption water vapor gradually shorten from 1000 s to 780 s with temperature increasing from 293.15 K to 313.15 K. The effect of axial diffusion coefficient (DL) on mass transfer is not obvious. Furthermore, the influence of the mass transfer coefficient (k) on temperature variation rate surpasses that of the internal heat transfer coefficient (h).

Effects of channel structure at inlet region on temperature distribution of proton exchange membrane fuel cells

Enhancing the temperature distribution uniformity in membrane electrodes is crucial for improving the durability and performance stability of proton exchange membrane fuel cells. In this study, we discovered an entrance region in the cathode membrane electrode of proton exchange membrane fuel cells characterized by a rapid rise in temperature. This inlet region shows a notably higher temperature variation rate compared to other regions. This phenomenon is attributed to the temperature difference between the constant temperature fluid supplied by the channel inlet and the heat generation in the cathode catalyst layer. Thus, the impact of channel structure at the entrance region on the temperature distribution of the cathode catalyst layer is investigated, based on a three-dimensional, non-isothermal, two-phase model. Results show that boosting heat transfer in the inlet region is more effective for reducing the average temperature than in other regions. We proposed five kinds of step arrangement schemes and discovered that the step arrangement in the entrance region of the cathode flow channel can increase the temperature uniformity of the cathode catalyst layer by 9.71% without net power loss. Furthermore, this study examines the impact of length, height of entrance steps, and outlet channel height on heat transfer and identifies the optimum value. When severe water flooding occurs, the gas purge coupled with the step-fronted scheme is essential for improving temperature uniformity.

A rapid and low-cost platform for detection of bacterial based on microchamber PCR microfluidic chip.

Polymerase chain reaction (PCR) has been considered as the gold standard for detecting nucleic acids. The simple PCR system is of great significance for medical applications in remote areas, especially for the developing countries. Herein, we proposed a low-cost self-assembled platform for microchamber PCR. The working principle is rotating the chamber PCR microfluidic chip between two heaters with fixed temperature to solve the problem of low temperature variation rate. The system consists of two temperature controllers, a screw slide rail, a chamber array microfluidic chip and a self-built software. Such a system can be constructed at a cost of about US$60. The micro chamber PCR can be finished by rotating the microfluidic chip between two heaters with fixed temperature. Results demonstrated that the sensitivity of the temperature controller is 0.1℃. The relative error of the duration for the microfluidic chip was 0.02s. Finally, we successfully finished amplification of the target gene of Porphyromonas gingivalis in the chamber PCR microfluidic chip within 35min and on-site detection of its PCR products by fluorescence. The chip consisted of 3200 cylindrical chambers. The volume of reagent in each volume is as low as 0.628 nL. This work provides an effective method to reduce the amplification time required for micro chamber PCR.

Deep learning model for rapid temperature map prediction in transient convection process using conditional generative adversarial networks

This article presents a deep learning model for three-dimensional (3D) transient mixed convection in a horizontal channel with a heated bottom surface. Using Conditional Generative Adversarial Networks (cGAN), we successfully approximate temperature maps at arbitrary channel locations and time steps. The model is specifically designed for mixed convection at Reynolds number 100, Rayleigh number 3.9 × 106, and Richardson number 88.8. To investigate the impact of the discriminator network architecture on model accuracy, we compare Convolutional Neural Network (CNN) based classifiers with PatchGAN classifiers, both with and without strided convolutions. Remarkably, the cGAN with PatchGAN based classifier (without strided convolutions) yields the highest clarity and accuracy in inferring temperature maps. Additionally, other factors such as image contrast, spatiotemporal variation rate of temperature, and the number of channels in the temperature image significantly influence cGAN accuracy. This work highlights the potential of deep learning in efficiently modeling complex transport processes.

Enhancing the thermal storage performance of biochar/paraffin composite phase change materials: Effect of oleophobic modification of biochar

Composite phase change materials (PCMs) possess excellent temperature-regulating capabilities, which can effectively reduce building energy consumption, ultimately contribute to energy saving and carbon reduction. In this study, oleophobic modification was carried out on the Zn modified - white pine biochar. The resultant oleophobic material (Zn-WPC-A) showed excellent supporting capability in the preparation of paraffin wax (PW)/biochar composite PCMs (PW/Zn-WPC-A). The oleophobic properties of biochar could effectively prevent PW leakage, and resulted in a high PW loading rate of 84.26% and an encapsulation efficiency of 82.26%. Zn-WPC-A demonstrated good physical and chemical compatibility with PW. The inclusion of Zn-WPC-A effectively promoted the nucleation in the crystallization process of PW. PW/Zn-WPC-A exhibited a thermal conductivity 3.28 times that of pure PW, displaying excellent thermal responsiveness. The fusion enthalpy of PW/Zn-WPC-A was 107.2 J/g. Furthermore, it displayed excellent stability within the working temperature range with no leakage observed. Simulated housing application experiments illustrated that the use of PW/Zn-WPC-A as building insulation material effectively slowed down the variation rate of indoor temperature. Therefore, the composite PCM using oleophobic modified biochar as supporting material showed excellent thermal performance and strong temperature-regulating capability.

Numerical model for determining the effective heat capacity of macroencapsulated PCM for building applications

This paper presents a finite difference model of macroencapsulated PCM panels coupled with the genetic algorithm for the determination of effective heat capacity of whole panels via inverse method. This provides an accurate characterization of the thermal properties of macroencapsulated PCMs for building envelope applications. A novel definition of the effective heat capacity is proposed based on the superimposition of two Gaussian curves, applicable to any PCM whose phase transition is characterized by a single peak. Three PCMs were tested, subjected to temperature variation rates typically experienced in building envelopes: 0.5 °C/h and 1 °C/h. Surface temperature and heat flux were measured and used in the inverse method procedure. The developed model is accurate, as numerical results greatly agree with the experiments: the root mean square difference between the experimental and numerical heat fluxes ranged between 0.543 and 1.246 W/m2. Significant differences in the effective heat capacity were found between the whole macrocapsule and small quantities of PCM (specified in the datasheets). The effective heat capacity specified in the datasheets is sensibly greater than that of the whole macrocapsules determined through the inverse method: the specific heat in the solid phase was up to 107.39 % higher in the datasheet values, the specific heat in the liquid phase up to 184.04 %, and the peak effective heat capacity, between 18.28 % and 164.11 %. The same happened to the enthalpy: datasheet values were 61.24 % – 175.55 % greater than inverse method results. This proves that latent heat is overestimated if small quantities of PCM are analyzed, and not the whole panels. The scale effect was assessed by comparing two capsules with the same material, but with different quantities of PCM: 0.5 kg and 1 kg. A greater mass of PCM over the total mass of the capsule implies a different relationship between the effective heat capacity and temperature, with higher peak heat capacity. The capsule with 1 kg of PCM showed a peak effective heat capacity up to 30.65 % greater than that of the panel with 0.5 kg of PCM. Thus, adequate modeling in building applications requires characterization of whole macroencapsulated PCMs. The determination of the relationship between temperature and effective heat capacity of macroencapsulated PCMs presented in this work could easily be incorporated into other simulation software, facilitating the assessment of adaptive envelopes with PCM macrocapsules.

Investigation of the interaction mechanism between power and heat of a reversible solid oxide cell during a mode switching cycle

Chemical energy and electrical power can be converted bi-directionally with unrivaled efficiency by the mode switching of reversible solid oxide cell (RSOC) stacks. Mode switching can lead to temperature fluctuations in RSOC stacks, which will significantly affect the mechanical stability of stacks. To investigate the transient performance of the RSOC, a 1D dynamic model of a single repeating unit (SRU) is established and validated against experimental data. The interaction mechanisms between the thermodynamic and electrochemical parameters of the RSOC during transients are elucidated elaborately. Two indicators, i.e., temperature variation rate and temperature gradient, are used for the quantification analysis. The effects of current density, hydrogen fraction, and inlet gas temperatures on the temperature distribution along the SRU are investigated. In the simulation scenarios, the maximal temperature variation rate can achieve 1.49 and 1.495 K min−1 in co- and counter-flow configuration, and the maximal temperature gradient can achieve 5.6 and 4.6 K cm−1, respectively. The hydrogen fraction has a significant influence on the temperature. Increasing the hydrogen fraction from 0.4 to 0.7, the maximal temperature variation rate decreases by 12.8 % and 12.1 % in the co- and counter-flow SRU, and the maximal temperature gradient reduces by 10.6 % and 14.2 %, respectively.

Pontryagin’s Minimum Principle-Based Power Management of Plug-In Hybrid Electric Vehicles to Enhance the Battery Durability and Thermal Safety

Temperature affects the battery aging and stability, bringing about significant effects on the electric vehicle’s economy, reliability, and safety. Therefore, it is necessary to consider the battery thermal condition when designing the power management scheme. In this paper, an optimal power management strategy for plug-in hybrid electric vehicles is proposed based on Pontryagin’s Minimum Principle (PMP). Two situations, normal operation and cooling system failure, are considered. The impacts of battery aging and temperature rise are characterized based on durability analysis model and thermal model. The temperature variation rate of the battery is incorporated into the Hamiltonian function, and the correlation between the cost optimization and temperature rise is disclosed by shooting method. Then the optimal co-state variables in different cases are determined. Based on above efforts, the PMP-based multi-objective optimal control strategy is proposed and evaluated based on the temperature statistical data in Shenyang, China. The results show that the proposed method can improve the durability of the battery and inhibit the excessive temperature rise. In case of the cooling system failure, it can realize the trade-off between safety and economy to reduce the risk of battery thermal runaway.

Glacier Retreat Leads to the Expansion of Alpine Lake Karakul Observed Via Remote Sensing Water Volume Time Series Reconstruction

Due to high altitudes, Central Asian alpine lakes can serve as indicators of localized climate change. This article monitored the water volume time series trends of the ungauged alpine Lake Karakul, which is typical because of the abundance of glaciers in the basin, from 1990 to 2020 via multiple source remote sensing data. The “Global-Local” multi-scale lake extraction method is used to delineate the boundary of Lake Karakul. Consistency analysis was performed on the altimetry data of CryoSat-2, ICESat-1 and ICESat-2, assuming that the lake surface was flat; a threshold value was set to remove gross error, and then 3σ was used to remove the surface elevation anomaly. Based on the pyramid volume model, the lake area and surface elevation information were used to reconstruct the water volume time series of Lake Karakul. The influencing factors of water volume temporal variation were discussed. The results show that Lake Karakul has been on an expansionary trend in recent years: The lake area increased from 394.9 km2 in 1988 to 411.4 km2 in 2020; the rate of increase is 0.74 m/year. The surface elevation increased from 3886.6 m in 2003 to 3888.6 m in 2020; the rate of increase is 0.11 km2/year. The lake water volume accumulated was 0.817 km3 in 2003–2020, with an accumulation rate of 0.059 km3/year. The Lake Karakul basin is developing towards dry heat, with a cumulative temperature variation rate of +0.38 °C/year; the average rate of variation in annual cumulative precipitation is −3.37 mm/year; the average evapotranspiration in the watershed is on a fluctuating increasing trend, with a rate of variation of +0.43 mm/year; glaciers in the lake basin have a retreating trend, with an average annual rate of variation of −0.22 km2/year from 1992 to 2020. Lake Karakul is more sensitive to temperature variations, and the runoff from retreating glaciers in the basin is an important contribution to the expansion of Lake Karakul.

FEM Modelling of the Heating Behaviour in Vibrothermography Based on Thermoelastic Damping on Crack Location

The paper aims to simulate the heat generation in crack location based on vibrothermography inspection. For this study, the thermoelastic damping effect was considered as the source of heat generation and the value of thermoelastic damping was determined. In simulation, the model of the specimen with crack was AL7075-T6. The vibrational frequencies were given at 20kHz, 24kHz and 28kHz, while the maximum amplitude was constant of 4.8nm. The results of temperature variation, stress, energy loss and heat flow rate on the crack location were obtained from the simulations which were performed by using ANSYS software. The ratio between power loss and heat flow rate was calculated and presented as the index for selecting the most suitable vibrational frequency in term of heat accumulation. The case study 20kHz was considered as the most suitable vibrational frequency for this study because the index ratio was higher than the case studies 24kHz, 28kHz at 6.29% and 9.14% respectively.

A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures

The identification of thermal load/thermal shock of aircraft during service is beneficial for collecting information of the service environment and avoiding risks. In the paper, a method based on multivariate information fusion and physics-guided neural network is developed for the inverse problem of thermal load identification of honeycomb sandwich structures. Two thermal feature parameters: temperature gradient and temperature variation rate are used to build the dataset. A 16-layers physics-guided neural network is presented to achieve the predicted results consistent with physical knowledge. In the work, laser irradiation is used as the thermal load, and two laser parameters are to be identified, i.e. spot diameter, power. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The effects of physics-guided loss function and multivariate information fusion are discussed, and it is found that the results based on the proposed method are much better than the results based on the method without physical model. Besides, results based on multivariate information fusion are better than results based on single temperature response. Then, the effects of network models and hyper parameters on the proposed method are also discussed.