A 3D Thermal Field Restructuring Method for Concrete Dams Based on Real-Time Temperature Monitoring

Lin, Peng; Peng, Haoyang; Fan, Qixiang; Xiang, Yunfei; Yang, Zongli; Yang, Ning

doi:10.1007/s12205-021-1084-8

Cited by 12 publications

(7 citation statements)

References 63 publications

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“…Even at the surface of the object, an enormous amount of data is needed, generated by interpolation, to finish temperature modeling and visualization 18 . On the other hand, it is common for locations without sensors to estimate the current temperature using the interpolation algorithm 19 . Interpolation is therefore an essential part of temperature field modeling and visualization.…”

Section: Methodsmentioning

confidence: 99%

“…18 On the other hand, it is common for locations without sensors to estimate the current temperature using the interpolation algorithm. 19 Interpolation is therefore an essential part of temperature field modeling and visualization.…”

Section: Pre-processingmentioning

confidence: 99%

“…According to a World Food Programme report, more than 800 million people worldwide already face chronic malnutrition, and more than 100 million need life-saving food assistance due to increased violent conflict and the effects of climate change. 1 Especially under the influence of coronavirus disease 2019 (COVID- 19), food security has been elevated to a higher level. 2 Ensuring food security requires the improvement of food-production capacity and enhancing food reserve capacity.…”

Section: Introductionmentioning

confidence: 99%

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Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

2023

J Sci Food Agric

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BACKGROUNDPost‐harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three‐dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA). The ANCA‐based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile.RESULTSThe experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA‐based visualization method for grain pile temperatures has a shorter rendering time and better visual effects.CONCLUSIONThis research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.

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Section: Methodsmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

2023

J Sci Food Agric

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“…A unit of 10 days is used to divide the annual data into 36 periods to obtain the multiyear average temperatures (Shao et al., 2023). Then, the kriging interpolations of the temperatures of the FE nodes on the dam surface are performed and used as the boundary conditions of the temperature field simulation (P. Lin et al., 2021). Based on the information provided by the management center of the DS dam, the recommended values of the thermal parameters for the numerical simulation are shown in Table 2.…”

Section: Case Studymentioning

confidence: 99%

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

Li,

Wu,

Chen

et al. 2023

Computer aided Civil Eng

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The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single‐objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi‐objective optimization problem as a single‐objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi‐objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA‐III algorithm to establish and solve multi‐objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single‐objective optimization method.

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“…Field measurements 10 – 14 and numerical simulations 15 – 17 are two effective ways to investigate the thermal effect. Experiments are necessary for obtaining the real thermal responses of concrete structures.…”

Section: Introductionmentioning

confidence: 99%

Analytical and experimental analysis of concrete temperature and energy considering open-air environmental variations

Yang,

Li,

Zhuo

et al. 2024

Sci Rep

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Longwave radiation is an important open-air environmental factor that can significantly affect the temperature of concrete, but it has often been ignored in the temperature analysis of open-air concrete structures. In this article, an improved analytical model of concrete temperature was proposed by considering solar radiation, thermal convection, thermal conduction and especially longwave radiation. Temperature monitoring of an open-air concrete block was carried out to verify the proposed model and analyze the heat energy characteristics of open-air concrete. As demonstrated by the open-air experiment, under the influence of longwave radiation, the temperature at the top of the concrete block could decrease rapidly at night and even become lower than the minimum temperature at its bottom. Compared with the analytical model that ignores longwave radiation, the improved model that includes it better matches the measured temperature. According to the energy analysis, although solar radiation controls the transient variation in heat energy, the heat exchange caused by longwave radiation were more than that caused by convection on sunlit surfaces, which indicates the importance of considering longwave radiation.

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A 3D Thermal Field Restructuring Method for Concrete Dams Based on Real-Time Temperature Monitoring

Cited by 12 publications

References 63 publications

Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

Online temperature‐monitoring technology for grain storage: a three‐dimensional visualization method based on an adaptive neighborhood clustering algorithm

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

Analytical and experimental analysis of concrete temperature and energy considering open-air environmental variations

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