Luís Lages Martins

Biofilm is known to negatively affect the hydraulic performance of open channels by increasing friction and energy losses and, consequently, economic costs. This study proposes a method to accurately quantify the effect of flow reduction caused by biofouling in open channels. The approach is based on Manning’s equation, using the Monte Carlo Method for the propagation of input uncertainties. A case study concerning a trapezoidal concrete open channel of an agricultural irrigation network is presented, showing a relative expanded uncertainty (95% confidence level) of 10% for flow rates ranging from 1 m³·s⁻¹ to 50 m³·s⁻¹. Manning’s roughness coefficient was identified as the dominant contribution to the overall dispersion of flow rate values, being probabilistically modeled by a beta distribution using minimum, expected and maximum reference values. This information was used for conformity assessment of the studied open channel, considering flow reductions due to moderate and severe biofilm effects of 12.5% and 31.3%, respectively. The corresponding standard uncertainties (4.8% and 7.8%), the adopted confidence level (95%), tolerance upper limit (33%) and decision rule led to different conformity assessment outcomes. These results highlight the importance of accurate assessment of flow reduction due to biofouling for management entities.

Biofilm is known to negatively affect the hydraulic performance of open channels by increasing friction and energy losses and, consequently, economic costs. This study proposes a method to accurately quantify the effect of flow reduction caused by biofouling in open channels. The approach is based on Manning’s equation, using the Monte Carlo Method for the propagation of input uncertainties. A case study concerning a trapezoidal concrete open channel of an agricultural irrigation network is presented, showing a relative expanded uncertainty (95 % confidence level) of 10 % for flow rates ranging from 1 m3·s-1 and 50 m3·s-1. Manning’s roughness coefficient was identified as the dominant contribution to the overall dispersion of flow rate values, being probabilistically modelled by a beta distribution using minimum, expected and maximum reference values. This information was used for conformity assessment of the studied open channel, considering flow reductions due to moderate and severe biofilm effect of 12.5 % and 31.3 %, respectively. The corresponding standard uncertainties (4.8 % and 7.8 %), the adopted confidence level (95 %), tolerance upper limit (33 %) and decision rule, led to different conformity assessment outcomes. These results highlight the importance of accurate assessment of flow reduction due to biofouling for management entities.

Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become costly for such long-term experimental studies. This article proposes an innovative cost-effective solution combining optical microscopy imaging, 3D printed sliding rulers, and Python-based artificial vision to overcome these limitations. The 3D printed rulers establish a local physical reference frame, while the artificial vision system uses contour detection and point tracking of optical targets to quantify displacements. Unlike continuous monitoring systems, the proposed solution utilises a discontinuous point-tracking approach, allowing a single USB microscope to monitor an unlimited number of specimens while maintaining the possibility for moisture exchange between the material surface and the environment. The system was metrologically validated against a laser interferometer, achieving an expanded instrumental uncertainty of 0.0042 mm (4.2 µm), determined through strict calibration. These results demonstrate that the proposed solution delivers accuracy comparable to conventional sensors but with significantly higher scalability and lower cost, making it highly suitable for extensive long-term experimental programmes.

This paper describes the application of photogrammetric digital reconstruction within the context of structural condition assessment of sewers. The acquisition of multiple images of the observation scenario and application of the bundle adjustment technique, supported by computational algorithms dedicated to image parallel computation and clustering, is the base for the approach adopted. An orthographic camera model with uniform scaling was adopted, supported by scale coefficients determined by the relation between digital and actual (SI traceable) dimensions of reference objects visible in the generated model. The paper details experimental and computational methodologies and presents several examples of digital sewer models. Finally, an evaluation of measurement data retrieved from the models is also shown, including identifying uncertainty components related to the input quantities and their propagation by the GUM method to the output quantity, considering the mathematical models related to the adopted camera model.

This paper addresses the quantification of the dispersion of equivalent roughness values obtained from the experimental study of hydraulic pipes used, for example, in water supply networks. This quantity is mainly used in the determination of the friction factor related to pipe fluid flow. In this context, non-linear and complex mathematical models, such as the Colebrook-White equation, are applied to characterize the equivalent roughness of hydraulic pipes composed of different types of materials. However, knowledge about the measurement uncertainty of the obtained estimates is still reduced, having a direct impact in the conformity assessment of this type of hydraulic component and in the technical comparison between different types of pipes (materials and manufacturers). The paper describes the application of a Monte Carlo method (MCM) in the measurement uncertainty evaluation of equivalent roughness. In addition to presenting the theoretical and experimental background, the paper describes the measurement uncertainty propagation, from the probabilistic formulation of the input quantities up to the output quantity. A numerical example, based on experimental data retrieved from field testing of hydraulic pipes integrated in a large-scale agricultural irrigation network, is shown in the paper, illustrating the suitability, advantages, and limitations of the proposed approach.

Dimensional measurements are fundamental in microfluidic device manufacturing and performance. The main focus of this study is the measurement of the connection port sizes in microfluidic devices and components and, accordingly, the possible existence of fluid leaks determined using the flow rate error. The sizes associated with three different microfluidic systems were determined using laser interferometry and through an optical measuring instrument, with metrological traceability to national length standards. It was possible to infer the method with the greatest accuracy and lowest measurement uncertainty for characterizing this kind of system. In conclusion, the results of this work directly address the current lack of dimensions measuring methods of microfluidic components by providing a comprehensive comparison of different protocols, ultimately suggesting a preferred option for immediate application within the microfluidic industry.

This paper describes a video strain measurement solution developed for application in mortar destructive compression testing. Knowledge about the mechanical behavior of this type of construction material, namely up to its fracture, is still superficial due to the less common use of non-contact measurement methods. The performed research was focused on the determination of image coordinate accuracy, based on the experimental quantification of the identified main uncertainty components, using traceable reference patterns, and validated computational toolboxes dedicated to camera parameterization and digital image processing. The obtained results show the following uncertainty contributions: lens distortion - negligible; re-projection errors - 0.21 pixel; 0.10 pixel - spatial resolution; and digital image processing operations - 0.28 pixel. The combination of these uncertainty components resulted in an image coordinate standard uncertainty equal to 0.36 pixel, which was propagated (in addition to the scale coefficient measurement uncertainty) to the selected camera model – orthographic projection with uniform scaling – which supports the video strain measurement.