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Bahaloo, H., Gren, P., Casselgren, J., Forsberg, F. & Sjödahl, M. (2024). Capillary Bridge in Contact with Ice Particles Can Be Related to the Thin Liquid Film on Ice. Journal of cold regions engineering, 38(1), Article ID 04023021.
Open this publication in new window or tab >>Capillary Bridge in Contact with Ice Particles Can Be Related to the Thin Liquid Film on Ice
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2024 (English)In: Journal of cold regions engineering, ISSN 0887-381X, E-ISSN 1943-5495, Vol. 38, no 1, article id 04023021Article in journal (Refereed) Published
Abstract [en]

We experimentally demonstrate the presence of a capillary bridge in the contact between an ice particle and a smooth aluminum surface at a relative humidity of approximately 50% and temperatures below the melting point. We conduct the experiments in a freezer with a controlled temperature and consider the mechanical instability of the bridge upon separation of the ice particle from the aluminum surface at a constant speed. We observe that a liquid bridge forms, and this formation becomes more pronounced as the temperature approaches the melting point. We also show that the separation distance is proportional to the cube root of the volume of the bridge. We hypothesize that the volume of the liquid bridge can be used to provide a rough estimate of the thickness of the liquid layer on the ice particle since in the absence of other driving mechanisms, some of the liquid on the surface must have been pulled to the bridge area. We show that the estimated value lies within the range previously reported in the literature. With these assumptions, the estimated thickness of the liquid layer decreases from nearly 56 nm at T = −1.7°C to 0.2 nm at T = −12.7°C. The dependence can be approximated with a power law, proportional to (TM − T)−β, where β < 2.6 and TM is the melting temperature. We further observe that for a rough surface, the capillary bridge formation in the considered experimental conditions vanishes.

Place, publisher, year, edition, pages
American Society of Civil Engineers (ASCE), 2024
National Category
Infrastructure Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-102441 (URN)10.1061/JCRGEI.CRENG-738 (DOI)2-s2.0-85175442634 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-11-15 (sofila);

Full text license: CC BY

Available from: 2023-11-13 Created: 2023-11-13 Last updated: 2023-11-15Bibliographically approved
Bahaloo, H., Forsberg, F., Casselgren, J., Lycksam, H. & Sjödahl, M. (2024). Mapping of density-dependent material properties of dry manufactured snow using μCT. Applied Physics A: Materials Science & Processing, 130, Article ID 16.
Open this publication in new window or tab >>Mapping of density-dependent material properties of dry manufactured snow using μCT
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2024 (English)In: Applied Physics A: Materials Science & Processing, ISSN 0947-8396, E-ISSN 1432-0630, Vol. 130, article id 16Article in journal (Refereed) Published
Abstract [en]

Despite the significance of snow in various cryospheric, polar, and construction contexts, more comprehensive studies are required on its mechanical properties. In recent years, the utilization of μ CT has yielded valuable insights into snow analysis. Our objective is to establish a methodology for mapping density-dependent material properties for dry manufactured snow within the density range of 400–600 kg/m 3 utilizing μ CT imaging and step-wise, quasi-static, mechanical loading. We also aim to investigate the variations in the structural parameters of snow during loading. The three-dimensional (3D) structure of snow is captured using μ CT with 801 projections at the beginning of the experiments and at the end of each loading step. The sample is compressed at a temperature of − 18 o C using a constant rate of deformation (0.2 mm/min) in multiple steps. The relative density of the snow is determined at each load step using binary image segmentation. It varies from 0.44 in the beginning to nearly 0.65 at the end of the loading, which corresponds to a density range of 400–600 kg/m 3 . The estimated modulus and viscosity terms, obtained from the Burger’s model, show an increasing trend with density. The values of the Maxwell and Kelvin–Voigt moduli were found to range from 60 to 320 MPa and from 6 to 40 MPa, respectively. Meanwhile, the viscosity values for the Maxwell and Kelvin–Voigt models varied from 0.4 to 3.5 GPa-s, and 0.3–3.2 GPa-s, respectively, within the considered density range. In addition, Digital Volume Correlation (DVC) was used to calculate the full-field strain distribution in the specimen at each load step. The image analysis results show that, the particle size and specific surface area (SSA) do not change significantly within the studied range of loading and densities, while the sphericity of the particles is increased. The grain diameter ranges from approximately 100 μ m to nearly 400 μ m, with a mode of nearly 200 μ m. The methodology presented in this study opens up a path for an extensive statistical analysis of the material properties by experimenting more snow samples.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Micro tomography, Material modeling, Stress-strain response, Digital volume correlation, Image analysis, Snow
National Category
Other Materials Engineering
Research subject
Experimental Mechanics; Fluid Mechanics
Identifiers
urn:nbn:se:ltu:diva-103511 (URN)10.1007/s00339-023-07167-y (DOI)001123446400001 ()2-s2.0-85179360802 (Scopus ID)
Funder
Luleå University of Technology
Note

Validerad;2024;Nivå 2;2024-02-26 (signyg);

Full text license: CC BY

Available from: 2024-01-08 Created: 2024-01-08 Last updated: 2024-02-26Bibliographically approved
Bahaloohoreh, H., Forsberg, F., Lycksam, H., Casselgren, J. & Sjödahl, M. (2024). Material mapping strategy to identify the density-dependent properties of dry natural snow. Applied Physics A: Materials Science & Processing, 130(2), Article ID 141.
Open this publication in new window or tab >>Material mapping strategy to identify the density-dependent properties of dry natural snow
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2024 (English)In: Applied Physics A: Materials Science & Processing, ISSN 0947-8396, E-ISSN 1432-0630, Vol. 130, no 2, article id 141Article in journal (Refereed) Published
Abstract [en]

The mechanical properties of natural snow play a crucial role in understanding glaciers, avalanches, polar regions, and snow-related constructions. Research has concentrated on how the mechanical properties of snow vary, primarily with its density; the integration of cutting-edge techniques like micro-tomography with traditional loading methods can enhance our comprehension of these properties in natural snow. This study employs CT imaging and uniaxial compression tests, along with the Digital Volume Correlation (DVC) to investigate the density-dependent material properties of natural snow. The data from two snow samples, one initially non-compressed (test 1) and the other initially compressed (test 2), were fed into Burger’s viscoelastic model to estimate the material properties. CT imaging with 801 projections captures the three-dimensional structure of the snow initially and after each loading step at -18C, using a constant deformation rate (0.2 mm/min). The relative density of the snow, ranging from 0.175 to 0.39 (equivalent to 160–360 kg/m), is determined at each load step through binary image segmentation. Modulus and viscosity terms, estimated from Burger’s model, exhibit a density-dependent increase. Maxwell and Kelvin–Voigt moduli range from 0.5 to 14 MPa and 0.1 to 0.8 MPa, respectively. Viscosity values for the Maxwell and Kelvin–Voigt models vary from 0.2 to 2.9 GPa-s and 0.2 to 2.3 GPa-s within the considered density range, showing an exponent between 3 and 4 when represented as power functions. Initial grain characteristics for tests 1 and 2, obtained through image segmentation, reveal an average Specific Surface Area (SSA) of around 55 1/mm and 40 1/mm, respectively. The full-field strain distribution in the specimen at each load step is calculated using the DVC, highlighting strong strain localization indicative of non-homogeneous behavior in natural snow. These findings not only contribute to our understanding of natural snow mechanics but also hold implications for applications in fields such as glacier dynamics and avalanche prediction.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Material mapping, Micro tomography, Compression test, Digital volume correlation, Snow and ice
National Category
Other Materials Engineering
Research subject
Experimental Mechanics; Fluid Mechanics
Identifiers
urn:nbn:se:ltu:diva-104236 (URN)10.1007/s00339-024-07288-y (DOI)2-s2.0-85183678465 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-02-12 (joosat);

CC BY Full text license

Available from: 2024-02-12 Created: 2024-02-12 Last updated: 2024-02-12Bibliographically approved
Bahaloohoreh, H., Gren, P., Casselgren, J., Forsberg, F. & Sjödahl, M. (2023). Capillary bridge in contact of ice particles reveals the thin liquid film on ice.
Open this publication in new window or tab >>Capillary bridge in contact of ice particles reveals the thin liquid film on ice
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2023 (English)Manuscript (preprint) (Other academic)
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-94783 (URN)
Available from: 2022-12-08 Created: 2022-12-08 Last updated: 2022-12-09
Sollén, S. & Casselgren, J. (2023). Comparing floating car data regarding tire-to-road friction for different-sized operational areas during winter- and summertime in Sweden. In: Pre-proceedings Prague 2023: . Paper presented at XXVIIth World Road Congress (WRC 2023), Prague, Czech Republic, October 2-6, 2023.
Open this publication in new window or tab >>Comparing floating car data regarding tire-to-road friction for different-sized operational areas during winter- and summertime in Sweden
2023 (English)In: Pre-proceedings Prague 2023, 2023Conference paper, Published paper (Refereed)
National Category
Transport Systems and Logistics Infrastructure Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-102302 (URN)
Conference
XXVIIth World Road Congress (WRC 2023), Prague, Czech Republic, October 2-6, 2023
Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-11-06
Sollén, S. & Casselgren, J. (2023). Comparison of methods for winter road friction estimation using systems implemented for floating car data. International Journal of Vehicle Systems Modelling and Testing, 17(2), 101-111
Open this publication in new window or tab >>Comparison of methods for winter road friction estimation using systems implemented for floating car data
2023 (English)In: International Journal of Vehicle Systems Modelling and Testing, ISSN 1745-6436, E-ISSN 1745-6444, Vol. 17, no 2, p. 101-111Article in journal (Refereed) Published
Abstract [en]

Winter road maintenance is important for preventing accidents and enabling mobility. If the road friction gets low, there is a higher risk of road accidents. Therefore, it is vital to have information about road friction levels. Traditionally this is done by dedicated vehicles; however, using friction information from floating car data (FCD) would be more beneficial, as the coverage both in time and space increases. In this investigation, road friction data from three FCD suppliers, using only one test vehicle each, has been compared with a continuous method of road friction measurement. The test has been conducted on proving grounds covered with ice and snow, and on public roads covered with water, ice, snow, and slush; thereby both high friction and low friction surfaces have been evaluated. The investigation shows that the FCD provides a continuous method of friction measurement and is closer to the reality of road friction experienced by road users.

Place, publisher, year, edition, pages
InderScience Publishers, 2023
Keywords
road friction, friction estimation, winter road maintenance, vehicle data, optical sensor, floating car data, FCD, big data, experimental validation, vehicle testing
National Category
Applied Mechanics
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-93418 (URN)10.1504/IJVSMT.2023.132935 (DOI)2-s2.0-85170229688 (Scopus ID)
Note

Validerad;2023;Nivå 1;2023-09-04 (joosat);

This article has previously appeared as a manuscript in a thesis.

Available from: 2022-10-05 Created: 2022-10-05 Last updated: 2023-10-11Bibliographically approved
Sollén, S. & Casselgren, J. (2023). Correlation between floating car data and road weather information implemented for winter road maintenance follow-up by monitoring theroad friction. In: : . Paper presented at International Conference on Road Weather and Winter Maintenance, Washington D.C., USA, May 9-10, 2023.
Open this publication in new window or tab >>Correlation between floating car data and road weather information implemented for winter road maintenance follow-up by monitoring theroad friction
2023 (English)Conference paper, Oral presentation only (Refereed)
National Category
Infrastructure Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-97594 (URN)
Conference
International Conference on Road Weather and Winter Maintenance, Washington D.C., USA, May 9-10, 2023
Projects
Digital Vinter
Funder
Swedish Transport Administration
Available from: 2023-05-25 Created: 2023-05-25 Last updated: 2023-09-05Bibliographically approved
Hatamzad, M., Polanco, G. & Casselgren, J. (2022). A Semiquantitative Approach to Assess Uncertainty for Predicting Road Surface Temperature if a Sensor Fails at a Station. In: Proceedings of the International Conference on Electrical, Computer, and Energy Technologies (ICECET 2022): . Paper presented at International Conference on Electrical, Computer and Energy Technologies (ICECET 2022), Prague, Czech Republic, July 20-22, 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Semiquantitative Approach to Assess Uncertainty for Predicting Road Surface Temperature if a Sensor Fails at a Station
2022 (English)In: Proceedings of the International Conference on Electrical, Computer, and Energy Technologies (ICECET 2022), Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

Road surface temperature (RST) plays an essential role in analyzing road surface conditions during winter in countries with adverse winter climates. A reduction in RST can have a negative impact on road safety due to decreasing vehicle grip on the road surface. Therefore, decision makers need to monitor low surface temperatures and plan for winter road maintenance. However, RST sensors can fail for different reasons, such as power outages. RST sensor failure will lead to lack of information about the road surface, which can be problematic, especially for critical road segments. Hence, the novelty of this study is to use a deep learning algorithm to predict RSTs in road segments if a sensor fails at a station using historical data from two other road stations. The mean absolute error in the proposed model is 0.453 and the model explains 98.6% of observations. In addition, since the adjustment of deep learning parameters (e.g., hidden layers, optimizer, activation function, etc.) is associated with epistemic uncertainty, a semiquantitative approach is developed for uncertainty assessment. With this approach, the most important and uncertain parameters in RST prediction models can be identified. The results have shown that the optimizer is the most uncertain and important parameter.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Deep learning, road safety, road surface temperature, winter road maintenance, uncertainty assessment
National Category
Computer Engineering Infrastructure Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-93576 (URN)10.1109/ICECET55527.2022.9872814 (DOI)2-s2.0-85138907912 (Scopus ID)
Conference
International Conference on Electrical, Computer and Energy Technologies (ICECET 2022), Prague, Czech Republic, July 20-22, 2022
Available from: 2022-10-12 Created: 2022-10-12 Last updated: 2022-10-12Bibliographically approved
Hatamzad, M., Pinerez, G. P. & Casselgren, J. (2022). Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance. Safety, 8(1), Article ID 14.
Open this publication in new window or tab >>Addressing Uncertainty by Designing an Intelligent Fuzzy System to Help Decision Support Systems for Winter Road Maintenance
2022 (English)In: Safety, E-ISSN 2313-576X, Vol. 8, no 1, article id 14Article in journal (Refereed) Published
Abstract [en]

One of the main challenges in developing efficient and effective winter road maintenance is to design an accurate prediction model for the road surface friction coefficient. A reliable and accurate prediction model of road surface friction coefficient can help decision support systems to significantly increase traffic safety, while saving time and cost. High dynamicity in weather and road surface conditions can lead to the presence of uncertainties in historical data extracted by sensors. To overcome this issue, this study uses an adaptive neuro-fuzzy inference system that can appropriately address uncertainty using fuzzy logic neural networks. To investigate the ability of the proposed model to predict the road surface friction coefficient, real data were measured at equal time intervals using optical sensors and road-mounted sensors. Then, the most critical features were selected based on the Pearson correlation coefficient, and the dataset was split into two independent training and test datasets. Next, the input variables were fuzzified by generating a fuzzy inference system using the fuzzy c-means clustering method. After training the model, a testing set was used to validate the trained model. The model was evaluated by means of graphical and numerical metrics. The results show that the constructed adaptive neuro-fuzzy model has an excellent ability to learn and accurately predict the road surface friction coefficient.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
adaptive neuro-fuzzy inference system (ANFIS), prediction methods, road surface friction, road transportation safety, winter road maintenance
National Category
Geotechnical Engineering Computer Sciences
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-89874 (URN)10.3390/safety8010014 (DOI)000774293600001 ()2-s2.0-85125137821 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-03-25 (hanlid);

Funder: Ministry of Education and Research, Norway (470079)

Available from: 2022-03-25 Created: 2022-03-25 Last updated: 2022-11-02Bibliographically approved
Gustavsson, T., Mostafavi, T., Bogren, V. & Casselgren, J. (2022). AHEAD – A new technology for detection of road conditions. In: : . Paper presented at PIARC XVI World Winter Service and Road Resilience Congress, Calgary, Canada, February 7-11, 2022.
Open this publication in new window or tab >>AHEAD – A new technology for detection of road conditions
2022 (English)Conference paper, Published paper (Refereed)
National Category
Vehicle Engineering Control Engineering
Research subject
Experimental Mechanics
Identifiers
urn:nbn:se:ltu:diva-89886 (URN)
Conference
PIARC XVI World Winter Service and Road Resilience Congress, Calgary, Canada, February 7-11, 2022
Available from: 2022-03-25 Created: 2022-03-25 Last updated: 2022-11-02Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8225-989x

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