<p>Space-filling designs are widely used in computer experiments to build effective metamodels with limited prior information, as they enable thorough exploration of the design space by uniformly distributing points. However, many existing designs perform poorly in low-dimensional projections, particularly when only a few factors are active. Uniform projection designs address this limitation by optimizing point distribution across low-dimensional subspaces, ensuring uniformity in all dimensions while maintaining desirable distance and column-orthogonality properties. Existing methods for constructing such designs often rely on complex algorithms or can only generate designs with large factor-to-run ratios. In this work, the authors propose a simple approach for constructing uniform projection Latin hypercube designs by employing orthogonal arrays. The proposed method is particularly effective when the number of factors is much smaller than the number of runs. Both theoretical and numerical results demonstrate that the designs produced by the proposed method perform well with respect to the uniform projection, low-dimensional stratification, maximin distance, and column-orthogonality criteria.</p>