In this work, we propose a simple and efficient generator of random fiber distribution with diverse fiber volume fractions for unidirectional fiber reinforced composites by a random fiber removal method. From the representative volume element consisting of 100 fibers that have a maximum fiber volume fraction of about 65% in this work, also termed the master model, we randomly eliminate fibers to match the predefined fiber volume fraction ranging from 60%, 55%, 45%, 35%, 25%, 15%, and 5%, which ar lower than that of the master model. Accordingly, 100 representative volume element samples for each fiber volume can be constructed in a straightforward manner.
To demonstrate the performance of the proposed algorithm, its fiber locations are verified in terms of statistical spatial metrics, such as the nearest neighbor orientation, Ripley’s K function, and pair distribution function. Further more, the elastic properties and the anisotropic ratios of the generated representative volume elements are investigated and compared to those of other random fiber generation algorithms.
Furthermore, the transverse mechanical properties of unidirectional composite modeled with fiber, matrix, and interphase is predicted with the representative volume elements and is calibrated by adjusting the properties and thickness of the interphase by referring to the test results. While the conventional representative volume elements modeled with fiber and matrix shows high predictive accuracy for the longitudinal mechanical properties, but it shows some deviations in the transverse mechanical properties. In order to compensate such gaps, the interphase region is employed, and its mechanical properties are adjusted to improve the prediction accuracy according to various elastic modulus, thickness, and strength parameters. As a result, the deviation of the transverse elastic modulus and strength is reduced significantly similar to the test results of the unidirectional composites with the accuracy of the longitudinal mechanical properties preserved.