Cold Regions Science and Technology最新文献

During the design phase of the Confederation Bridge in Canada, the Canadian government Public Works and Government Services Canada asked the National Research Council (NRC) of Canada to provide information on ice loads from large ridges using a wide range of predictive technologies. The NRC put together a team that looked at loads from several sources including analytical models, physical model tests, finite element models, discrete particle models, and full-scale data. The ice loading scenario was an extreme first-year ridge loading one of the bridge piers. A large number of analytical models were used and the load components were separated into those from the consolidated layer, the sail, and the keel. An upper bound prediction from this approach gave a value of 16 MN on a pier, but the assumptions that were used to arrive at this value did not match observed behavior in the physical and numerical studies of the program. Physical model tests indicated that the loads could be 10.5 MN with a load of 7.3 MN from the keel and 3.2 MN from the consolidated layer and sail. A finite element analysis indicted a range of predicted values of 10 MN to 12 MN depending upon the assumptions used. A discrete particle analysis predicted load values from 2.2 MN to 9.5 MN depending upon the assumptions used in describing the stiffness of the ridge. A review of full-scale measurements on lighthouses and ships suggested that the loads could range from 7.3 MN to 10.4 MN. These predicted values compare to the highest load measured on the Confederation Bridge over a twenty-year span of just over 8 MN. This paper outlines the approaches used for this prediction study and their resulting predictions. It shows the value of using multiple approaches for load predictions for offshore structures in ice-covered waters.

Lined canals in cold regions often experience severe frost damage, posing a significant threat to the water supply safety. This paper proposes a modified analytical solution for the response of canal lining under soil frost heave, which is based on a Timoshenko beam on a Pasternak foundation considering tangential contact between the lining and frozen soil. This analytical solution can provide any solution using Timoshenko or Euler–Bernoulli beams on Pasternak or Winkler foundations with or without tangential contact. Then, the modified analytical solution, along with the traditional analytical solutions using Euler–Bernoulli beams on Winkle foundations, is compared against both model test and numerical simulation results. The modified analytical solution performs better than the traditional solution. Finally, the effects of foundation model, beam model, and tangential contact in simulating canal frost heave were discussed, and some measures to mitigate canal frost heave are proposed. The results show that solutions based on Winkler foundation overestimate frost heaves and tensile stresses of canal linings, and solutions without considering tangential contact only obtains overestimated frost heave. In addition, solutions based on Euler–Bernoulli beams underestimate frost heaves slightly and overestimate tensile stresses slightly. Therefore, the Pasternak foundation, Euler–Bernoulli beam, and tangential contact model can be used to simulate canal frost heave. The modified analytical solution directly uses field measurements of soil free frost heave to calculate canal frost heave, thereby enhancing result reliability. This analytical solution provides a simple method for canal frost heave design, and can be applied to frost heave analyses of flat and inclined structures.

Under depressurization, the hydrate reservoirs undergo complex mechanical behaviors such as consolidation or shear. To deeply understand the evolution in the mechanical properties of soil, triaxial equipment with an ultrasonic system was used to detect the shear wave velocity in hydrate-bearing sediments. The effects of hydrate saturation, water, and stress state on shear-wave velocity are studied. The experimental results show that: The hydrate saturation significantly affects the wave velocity in the hydrate formation stage, while the water content has little influence on it. During the consolidation period, the notable increase in shear wave velocity indicates a decrease in soil void ratio, which means the soil has significant settlement deformation. With continuous shearing, the soil elastic modulus is damaged, and finally, the maximum damage can reach 35%. The conclusions can deepen understanding of the physical and mechanical properties of hydrate-bearing sediments in permafrost, and provide a reference for predicting the stratum stability during hydrate exploitation.

The unfrozen water content (UWC) plays a crucial role in frozen soil engineering, however, traditional unfrozen water measurement or calculation methods are time-consuming and costly, and it is difficult to express the non-linearity among the influencing factors. Currently, LF-NMR is widely recognized as an effective tool for measuring unfrozen water. In this study, a calibration curve that can describe the relationship between the NMR signal and water content was derived. Moreover, the effects of temperature, initial water content, and soil height on UWC are explored along one-dimensional large-diameter columns based on LF-NMR data. Combined with the ML algorithm and the UWC test data under different factors based on LF-NMR, an intelligent prediction method that can consider the nonlinear characteristics of multiple factors is proposed. The results showed that the NMR accuracy is 176.64 semaphore per 1% water content, the error between the water content calculated from the calibration curve and that obtained from the drying method is small (average error is 0.97%), and the linearity degree of the calibration curve is good (R² = 0.999). Moreover, due to the strong correlation between unfrozen water and temperature, initial water content, and other factors, the results indicated that the GPR model can better describe this correlation and has the best prediction effect, which was evaluated with the quantitative indicators: R² = 0.96, RMSE = 1.07, MAE = 1.00, and again verifies the superiority of this model in combination with other literature data. Overall, this paper offers a technical basis for controlling and preventing freezing and thawing disasters in cold regions and artificial freezing engineering projects.

There are 14 northern communities in Nunavik, the Arctic region of Quebec province, Canada. Transportation infrastructure plays a vital role in the social and economic development of these localities. The thawing of permafrost compromises the stability of northern transportation infrastructure. Harsh Arctic climate conditions limit the installation of effective monitoring systems to assess infrastructure stability. In Akulivik, the access road connects the Akulivik airport and the village of Akulivik. There is no monitoring to observe the thermal condition of the permafrost foundation of the access road, hindering the capacity to perform preventive maintenance activities, especially in the context of observed climate warming in Nunavik. This paper describes a project aiming at the assessment of the stability of the access road using a new approach and proposes adaptation solutions to stabilize the road, based on design tools recently developed. Particular attention was paid to the foundation soil under the side slope where relatively rapid permafrost degradation was occurring due to accumulated snow. The results indicate a positive thermal gradient of 0.29 °C/m under the side slope and a near-zero thermal gradient under the centerline. Projected climate warming was also considered to further investigate the thermal condition, providing a safety margin for the design of promising adaptation solutions. These results assist government agencies in evaluating the thermal conditions of underlying permafrost and deploying potential adaptation solutions in Akulivik.

The macroscopic and microscopic mechanical characteristics of subgrade soil in cold regions play an important role in the stability of embankment engineering in cold regions. In this study, we conduct triaxial tests and isotropic loading-unloading tests on frozen clay in the cold region subgrade. The tests are conducted under different temperatures and confining pressures to obtain its macroscopic strength and deformation characteristics. Meanwhile, we establish a discrete element model of frozen clay based on the Discrete Element Method (DEM) to simulate conventional triaxial and isotropic loading-unloading tests, and analyze its mechanical characteristics from a microscopic perspective. The results of the study indicate that the strength and deformation of frozen clay are greatly affected by the cooling temperature and confining pressure. As the cooling temperature decreases, the cohesion of the specimen significantly increases, and the internal friction angle slightly increases, along with the elastic moduli. Under low confining pressure, the specimen exhibits significant volumetric expansion, while under high confining pressure, the specimen mainly undergoes volumetric contraction. Through discrete element numerical simulation, we obtain the microscopic mechanical characteristics of frozen clay, explain the “bulging” phenomenon of the specimen from a microscopic perspective, and verify the applicability of the flexible membrane. Meanwhile, the influence rules of various microscopic parameters on the mechanical properties of frozen clay are also obtained through a series of parameter calibration works.

Icing has been an aeronautical industry problem for safety and for energy consumption save from the beginning of aviation. It affects the safety reducing the lift, decreasing the stall angle of attack, affecting the aircraft stability and reducing the control efficiency. The European project SENS4ICE (2019–2023) introduces a new technology based on hybridization of different detection techniques, combining indirect ice sensing with direct, using atmospheric and ice accretion sensors. In the present work a study about a Fiber Optic Detector based on latent heat that uses a Fiber Bragg Grating for measuring the surface temperature. The Fiber Optic Detector (FOD) was tested in a SAFIRE Flight Testing Platform ATR42 during 40 h of Flight testing, having Liquid Water encounters in all flights. The sensor performance and its ability for measuring the icing severity is evaluated in the paper, showing results in a representative Flight test.

During Flight Test, different icing conditions were seen, adapting the detection and severity evaluations to the data seen with other reference atmospheric sensors. For ice detection Discrete Wavelet Transform (DWT) was used using different levels in order to detect all the possible events during the Flight test. The DWT ice severity assessment results were compared with a Messinger Model and with the DLR Nevzorov data in order to evaluate the precision and the sensor performance.

Ice accretion on overhead transmission line systems is a leading cause of power outages and can lead to substantial economic losses in northern regions. Therefore, accurately and rapidly predicting ice accretion on power lines is crucial for ensuring the safe operation of the power grid. This study introduces a machine learning method for predicting the ice-to-liquid ratio ($\mathrm{ILR}$), an important parameter for assessing ice accretion efficiency. While estimating $\mathrm{ILR}$ is vital for operational forecasting, many existing ice accretion models do not include this capability. A feedforward neural network (FFNN) trained with stochastic gradient descent and various metaheuristic optimizers - specifically particle swarm optimization, grey wolf optimizer, whale optimizer, and slime mold optimizer - is employed to forecast hourly $\mathrm{ILR}$. Environmental data required for training and testing the FFNN model were obtained from the Automated Surface Observing System (ASOS). A global sensitivity analysis using the Sobol index, evaluated via the coefficients of a polynomial chaos expansion, was conducted to identify the most influential input parameters. The results indicate that only four input parameters significantly contribute to the variance in the response: precipitation, temperature, dew point temperature, and wind speed. Furthermore, the FFNN model trained with metaheuristic optimizers outperformed the stochastic gradient descent approach. With the predicted $\mathrm{ILR}$, ice accumulation can be easily calculated as the product of $\mathrm{ILR}$ and the amount of liquid precipitation depth.

Ice storms are one of the most devastating natural hazards which have the potential to inflict significant damage to the built environment. The multi-hazard nature of ice events complicates the analysis of their induced risk due to their inherent nonlinear interactions. In addition, the concurrent and interacting hazards are often responsible for several aerodynamical/dynamical instabilities such as the galloping mechanism. Moreover, the existing risk approaches are usually not suited for large-scale risk evaluation over extended geographical regions due to the involved high-computational costs. Therefore, in this study, a novel data-driven multi-scale performance-based ice engineering (PBIE) framework is developed to support the design of new structures subjected to ice storms or the rehabilitation of existing ones. In addition, the proposed PBIE is capable of rapidly estimating the real-time risk over an extended region due to an ice event. Specifically, it leverages the superior capabilities of state-of-the-art data-driven techniques (e.g., machine learning) to efficiently generate the corresponding risk maps and identify the high-risk areas. The proposed PBIE framework is applied to a simplified example in which the galloping-induced risk on iced conductors, in terms of the galloping amplitude, is evaluated for both local and regional scales. The resulting PBIE framework can be readily applied for design or retrofitting purposes or integrated within an emergency response management system to inform preventive actions that can mitigate the ice storm-induced damages and save lives.