This work is devoted to the detailed investigation of heat transfer enhancement caused by an oscillatory flow of a viscoelastic fluid within a tube that is partially filled with a porous medium, which results in two distinct concentric regions: a central core of free fluid and an annular layer of porous material saturated with the same fluid. Heat transfer along the tube is driven by the oscillating flow of a Maxwell fluid induced by an oscillatory pressure gradient. For the analysis, the Darcy–Brinkman and Cauchy–Euler momentum equations are employed to describe the behavior of the viscoelastic fluid flow in the porous and non-porous regions, respectively. The governing equations are presented in its dimensionless version as they are solved analytically, enabling a more precise and comprehensive analysis of key parameters, such as the Darcy, Deborah, and Womersley numbers on heat transfer, and the effective thermal diffusivity is calculated and the conditions that increase its value are analyzed, revealing that it occurs at a specific porous layer thickness. The results show that heat transfer can be enhanced compared to viscoelastic flow in a tube without a porous medium, provided that the porous insert does not reduce the permeability. For a purely viscous fluid, an increase in effective thermal diffusivity also occurs for the same dimensionless thickness of the porous medium. Overall, the combination of an oscillatory viscoelastic fluid flow through a tube that is partially filled with a porous medium improves heat transfer within the tube for a given porous layer thickness.

En este artículo se utiliza un modelo de combustión de 6 reacciones químicas (6R) para simular 2 pruebas de rendimiento en condiciones controladas y un ciclo de cocinado para la validación de dichos resultados numéricos con mediciones experimentales. Se utiliza el software Ansys Fluent para realizar las simulaciones numéricas de dinámica de fluidos computacional (CFD) de la estufa de biomasa Patsari. Los resultados muestran que en la comparación de los datos experimentales y casos numéricos del ciclo de cocinado no se reportaron diferencias significativas en emisiones de CO2, productos de la combustión incompleta (PIC’s), eficiencias y emisiones de CO2e lo cual representa uno de los resultados más relevantes de este trabajo dado que el ciclo representa el uso real de la estufa Patsari. Por otra parte, la comparación del coeficiente de exceso de aire (λ) de 3 vs. 2 en el ciclo de cocinado, muestra una clara diferencia en el efecto del exceso de aire sobre el rendimiento de una estufa de biomasa. En el caso de la eficiencia de combustión (95.02 ± 1.03% vs 90.54 ± 2.23%, 5%, α = 0.05), mientras que para la térmica (19.33 ± 1.78% vs 17.51 ± 0.12%, 9%, p = 0.22) lo cual muestra la influencia de λ en la optimización de la eficiencia global de la estufa Patsari. La innovación de este estudio radica en el uso del modelo 6R para simular la fase gaseosa y sólida del proceso de combustión en comparación con estudios que implementan un modelo de 1 reacción.

Sustainable hydroponic strawberry cultivation requires precise, real-time management of nutrient solutions to reduce waste and improve resource efficiency for home growers. This work presents an artificial neural network (ANN)-based system that estimates and approximates optimal nutrient formulations for strawberry hydroponics, with a primary focus on nutrient and water savings. The ANN-based system is implemented on hardware for real-time operation and was compared against a traditional PID controller to evaluate performance in nutrient solution management. The ANN receives temperature, humidity, water pH, nutrient concentrations, and lighting as inputs and outputs recommended nutrient solution parameters. Training used gradient descent and reached convergence after an average of 2500 epochs, achieving a mean error of 0.001 and an operating frequency of 84 MHz. Mechanical design was optimized for compact 3D-printable assembly to facilitate adoption by small-scale and domestic producers. The validation with 15 min sampling demonstrated the ability of the system to maintain target nutrient conditions, producing measurable increases in seedling and fruit weight and size while substantially reducing water and nutrient waste. These results indicate that ANN-driven, real-time nutrient estimation can make precision hydroponics more accessible and sustainable for home growers. In the comparative analysis, the ANN-based system demonstrated superior performance compared to the PID controller in maintaining precise nutrient concentrations and reducing resource waste, achieving more accurate tracking of target nutrient levels.