El problema de Kirsch publicado en 1898, es utilizado como base para corroborar la precisión relativa de los métodos numéricos desarrollados en la mecánica de sólidos. Por esta razón se utiliza la solución de este problema para evaluar la precisión del método numérico Mfree con una función de forma utilizando los puntos radiales de interpolación, en el método numérico libre de malla. El método de puntos radiales de interpolación es una técnica de interpolación utilizada para construir funciones de forma con nodos distribuidos localmente en una formulación débil la cual permite representar el problema como un sistema de ecuaciones. El tipo de funciones más usuales son las funciones polinomiales o funciones de base radial MQ (RBF, radio basis functions), la cual fue utilizada por la estabilidad que presenta al momento de solucionar el problema numéricamente. Para hacer la comparación se usó la solución analítica dada por Kirsch y la solución numérica desarrollada en el presente trabajo, obtenido un error del 0.00899% lo que muestra que la técnica Mfree con bases radiales de interpolación MQ son precisas y confiables al momento de ser utilizadas como método numérico de análisis.

This paper discusses the design methodology applied to build a testing machine to determine the sub-critical fracture index in a rock, based on double torsion testing in order to characterize naturally fractured formations such as those located in the Colombian Llanos Foothill Basin . These formations have been subjected to cyclic loads over time, causing fractures that trend to spread at sub-critical stress intensity values. Similarly, it presents the results of testing conducted on nine specimens of the Tambor Formation from 2 different outcrops to establish the testing traceability in the equipment.

Valve-plate wear in axial piston pumps reduces volumetric efficiency, and data-driven classifiers often report near-perfect accuracy in grading it. This paper asks how much of such accuracy reflects the fault rather than the recording. Outlet and precharge pressure, leakage flow and housing vibration at four points were recorded on a test bench for five induced wear states (estimated volumetric efficiency 94.9–69.5%) at an unloaded and a loaded (≈700 psi) operating point, in a single campaign. Statistical features were classified with support vector machines, random forests and shallow neural networks under protocols of increasing difficulty. Record-level cross-validation separates the five states with 94–100% accuracy from pressure or from any single accelerometer. Leave-one-condition-out validation shows that only a coarse decision generalises to a wear state absent from training: significant degradation (efficiency below about 80%, where the pump loses its precharge pressure) is detected by the pressure channels at both operating points and by three accelerometers at the loaded point, whereas the fine grading and a continuous efficiency estimate (mean absolute error 3.6–7.0 points) do not transfer, nor do models between operating points. The results are delimited to one campaign with one mounting per wear state; multi-session validation on naturally worn components is required.