Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Context: Wind tunnels are essential devices in the study of flow properties through objects and scaled prototypes. This work presents a numerical study to characterize an existing wind tunnel, proposing modifications with the aim to improve the quality of the flow in the test chamber. Method: Experimental measurements of the inlet velocity and pressure distribution of a wind tunnel are nperformed. These empirical values are used as parameters to define boundary conditions in simulations. The Finite Element Method (FEM) at low speeds is implemented to determine the stream function by using a standard Galerkin method. Polynomial interpolations are employed to modify the contraction section design, and numerical simulations are performed in order to compare the numerical results of the flow for the existing and the modified wind tunnels. Results: Experimental measurements of the flow at the wind tunnel entrance are presented. The velocity field and distribution of thermodynamic variables inside the tunnel are numerically determined. This computations are useful since it is experimentally difficult to make measurements inside the channel. Additionally, numerical calculations of these variables are presented under modifications in the tunnel geometry. Conclusions: A comparison between these simulations show that laminar flow at low velocities can be modeled as incompressible and irrotational fluid under a bidimensional approximation along its longitudinal section. It is observed that modifications in the geometry of the tunnel can improve the flow in the test section of the wind tunnel in the laminar regime.

Context: Currently, companies are facing a problem associated with inventory control since there is a high number of references that they must manage to reduce lost sales and minimize low-turnover inventory. Method: Demand Driven MRP (DDMRP) is an innovative inventory control strategy that incorporates elements of Lean systems and theory of constraints which lead to the introduction of dynamic buffers with the purpose of controlling inventory levels while responding to the demand. Petri nets are used to simulate and validate the application of the DDMRP methodology in a finished product inventory. Results: From the conducted simulation, it is possible to verify the effectiveness of the DDMRP methodology for the case under study, where the overstock is reduced while the possibility of out-of-stock is minimized. Conclusions: The simulation of the DDMRP methodology through Petri nets allowed evaluating the performance of the policy before its implementation with the purpose of validating the effectiveness of the strategy on company performance indicators such as the level of service and costs storage.

Context: Solid waste management during the Covid-19 pandemic has caused environmental effects that have not been sufficiently studied, especially in Latin American cities. This study presents a characterization of waste in Honduras during the Covid-19 lockdown, which uses a novel methodology that allows collecting information without the need for fieldwork. Method: This study was implemented in three phases (data recording, training, and data generation). All activities were carried out with the support of virtual tools such as forms and group calls. The participants received four training sessions and characterized their residues for seven days. Results: The waste generation per capita was 0,475 kg/inhab/day at the national level and 0,549 kg/inhab/day in Distrito Central [the central district]. There is an increase in the proportion of non-usable inorganic waste and a decrease in organic and recyclable waste. It is estimated that each household generates 3,7 masks nationwide and 4 masks in Distrito Central on a weekly basis. Conclusions: The per-capita generation of household waste decreased during the lockdown, which may be due to a decline in families’ purchasing power given the reduction in the economic activities. The appearance of a new residue is recorded: disposable masks and gloves.

Context: Bifacial photovoltaic solar panels can generate more energy than monofacial ones since, in the case of the former, both sides of the panel are available to capture the solar irradiance and transform it into electrical energy. The additional power that can be generated by bifacial panels depends, among other factors, on the nature and the type of the reflective surface, as well as on the orientation of the photovoltaic module. Method: In this work, several reflective surfaces are evaluated in order to determine the optimal configuration of a bifacial panel. To this effect, and based on the response surface methodology, the characteristic curves of the photovoltaic panel (current-voltage) for different elevation levels with respect to four reflective surfaces are experimentally represented. Results: The results of this study reveal that, by installing a bifacial panel on reflective mirror surfaces at an elevation of 98,66% of the panel width, a 6,6% increase in the electrical energy generation is achieved in comparison with monofacial photovoltaic modules. Conclusions: Through the applied methodology, the mirror surface was identified to be the best among the four surfaces evaluated for the use of reflected radiation, followed by concrete, water, and soil.

Context: Research has shown the efficiency of various treatments for sludge. However, new alternatives have been presented which merit an updated review. That is why this work aims to carry out said update with regard to conventional and new techniques to treat sludge. Method: The article is based on a review of documents in various databases, first grouping the conventional alternatives and later the new ones, both consisting of alternatives of sludge thickening, stabilization, and dewatering techniques, and analyzing them based on economic, technical, and environmental criteria. Results: This study found that direct osmosis is one of the most promising alternatives in sludge thickening, supercritical water oxidation, and pyrolysis in stabilization and vacuum filtration during the dehydration of said byproducts. Conclusions: This article allowed an updated review of conventional and new alternatives for sludge treatment, observing that, both in thickening and stabilization, the new alternatives are highly efficient, whereas vacuum filtration, a conventional alternative, improved its performance through the use of modified flocculants, standing out in the dehydration of sludge. This is why more research is required which improve the current challenges and yields and inquire into the release of pollutants into the environment when using biosolids.

Context:  In the context of business organizations, every process in which the product is immersed has a cost and time associated with it. The area of maintenance planning and scheduling is no exception; however, it is an aspect in which few companies specialize, tending to be outsourced. In this sense, the application of combinatorial models is a tool with a high potential to improve the overall performance of the organization through the understanding of the integral maintenance process. Method: A two-phase (maintenance and routing) dynamic algorithm is proposed which considers a set of clients distributed in a maintenance network (distance), where each of the technicians start from the same central node (depot), which, in turn, is the endpoint of each assigned route. The objective is to minimize the total cost associated with the development of preventive and corrective maintenance of all machines to be evaluated. With this purpose, the formulation of the mathematical problem for each of the phases and its interrelation method is proposed. Then, performance measures are expressed to evaluate the achieved objectives. Results: The results satisfy a consistent alternative for the resolution of problems of the NP-Hard type, which generates a high level of complexity to the model. That is, it proposes a tool for solving problems of these characteristics in low computational response times and with appealing results. Conclusions: The combined maintenance and routing model using a dynamic algorithm addresses the maintenance and routing problem satisfactorily. The model shows good results with respect to the comparison optimization model in percentage gaps of performance measures lower than 5%. As for the computational time required, a reduction of up to 98% was achieved, which makes it an ideal alternative for highly complex scenarios. Finally, achieving a higher level of characterization, employing multi-objective decision criteria and a greater number of constraints to the problem, is proposed in future research. Acknowledgements: To the High-Performance Computing Center (CECAD - Centro de computación de Alto Desempeño) of Universidad Distrital Francisco José de Caldas for their support, as well as for providing us with a virtual machine to run the proposed mathematical model, which was an essential element in the results obtained.

Context:  Software engineering allows us to approach software design and development from the practical application of scientific knowledge. In the case of this IoT solution and the machine learning approach to the monitoring and control of environmental variables in a coffee crop, it allows us to visualize certain artifacts of the system in their interaction with users and their behavior with other artifacts or devices that constitute a technological solution. Method: For this work, the application of software engineering from a conceptual approach and the behavior of the system is proposed. To meet these objectives, we decided to use the Unified Modeling Language (UML) in such a way that the most important components of the technological solution could be represented from a static perspective through the use case diagrams, as well as from a dynamic viewpoint through the sequence diagrams. Results: Through the application of the UML, it was possible to develop the conceptual and behavioral modeling of certain artifacts and components. This knowledge allowed identifying the interaction between physical components and devices (machine to machine) and human-machine interaction, that is, the relationship between users and the processes that make up the technological solution. Conclusions: Through software engineering, and more specifically the UML, we were able to establish the importance of knowing the different software artifacts that make up a system or application from a different technical and functional approach, while being able to collect valuable information about the behavior of certain system artifacts, as well as the interaction between users and processes.

Context: This article describes an educational or pre-surgical mixed-reality prototype using the Metavision Meta II headset. 3D objects of morphological structures for neurosurgery and laparoscopy were created from MRI and CT images of a real patient in order to simulate the access to the brain and conduct a reconnaissance of the abdominal area.Method: This prototype has an educational approach whose objective is to encourage the recognition of morphological schemes of organs, for which three tests were designed. These tests consisted of searching for tumors through navigation and interaction with an object displayed in the Meta 2 mixed-reality headset. Two of the three tests were dedicated to the neurological area, in which the search time was measured with five tumors, and a test was conducted for the abdominal area regarding the search of two tumors, designing various interactions in the spatial path of the generated 3D object. An immersion measurement process was conductedwhich was based on the use of a questionnaire.Results: 100% of users found the application highly interesting, while 90% expressed that they tried to focus their attention on the successful completion of the test, an indicator of the level of absorption of the application. Furthermore, 70% of users described their experience as highly immersive.Conclusions: Tests showed the prototype to be usable, absorbent, and has an acceptable level of immersion.

Context: Forensic geotechnical engineering aims to determine the most likely causes leading to geotechnical failures. Standard practice tests a set of credible hypotheses against the collected evidence using backward analysis and complex but deterministic geotechnical models. Geotechnical models involving uncertainty are not usually employed to analyze the causes of failure, even though soil parameters are uncertain, and evidence is often incomplete. Method: This paper introduces a probabilistic model approach based on Bayesian Networks to test hypotheses in light of collected evidence. Bayesian networks simulate patterns of human reasoning under uncertainty through a bidirectional inference process known as “explaining away.” In this study, Bayesian Networks are used to test several credible hypotheses about the causes of levee failures. Probability queries and the K-Most Probable Explanation algorithm (K-MPE) are used to assess the hypotheses. Results: This approach was applied to the analysis of a well-known levee failure in Breitenhagen, Germany, where previous forensic studies found a multiplicity of competing explanations for the causes of failure. The approach allows concluding that the failure was most likely caused by a combination of high phreatic levels, a conductive layer, and weak soils, thus allowing to discard a significant number of competing explanations. Conclusions: The proposed approach is expected to improve the accuracy and transparency of conclusions about the causes of failure in levee structures.

Context:  The automobile industry has included active and passive safety. Active safety incorporates elements to avoid crashes and collisions. Some elements are ABS brakes and stabilization bars, among others. On the other hand, passive safety avoids or minimizes damage to the occupants in the event of an accident. Some passive safety features include seat belts and front and curtain airbags for the driver and other occupants. Method: In this research work, we propose a new category called Extraordinary Passive Safety (XPS). A model of a sensor network was designed to inspect the conditions inside the car to detect fire, smoke, gases, and extreme temperatures. The sensors send data to a device (DXPS) capable of receiving and storing the data. Results: Each sensor collects data and sends it to the DXPS every period. The sensor sends 0s while there is no risk, and 1s when it detects a risk. When the DXPS receives a 1, the pattern is evaluated, and the risk is identified. Since there are several sensors, the reading pattern is a set of 0s (000000). When a pattern with one or more 1s (000100, 010101) is received, the DXPS can send an alert or activate a device. Conclusions: The proposed solution could save the lives of children left in the car or people trapped when the car catches fire. As future work, it is intended to define the devices to avoid or minimize damage to the occupants such as oxygen supply, gas extraction, regulating the temperature, among others.