One of the biggest unsolved problems of modern physics is the turbulence phenomena in fluid flow. The appearance of turbulence in a flow system is regularly determined by velocity and length scales of the system. If those scales are small the motion of the fluid is laminar, but at larger scales, disturbances appear and grow, leading the flow field to transition to a fully turbulent state. The prediction of transitional flow is critical for many complex fluid flow applications, such as aeronautical, aerospace, biomedical, automotive, chemical processing, heating and cooling systems, and meteorology. For example, in some cases the flow may remain laminar throughout a significant portion of a given domain, and fully turbulent simulations may produce results that can lead to inaccurate conclusions or inefficient design, due to an inability to resolve the details of the transition process. This work aims to develop, implement, and test a new model concept for the prediction of transitional flows using a linear eddy-viscosity RANS approach. The effects of transition are included through one additional transport equation for v2 as an alternative to the Laminar Kinetic Energy (LKE) framework. Here v2 is interpreted as the energy of fully turbulent, three-dimensional velocity fluctuations. This dissertation presents two new single-point, physics-based turbulence models based on the transitional methodology mentioned above. The first one uses an existing transitional model as a baseline which is modified to accurately capture the physics of fully turbulent free shear flows. The model formulation was tested over several boundary layer and free shear flow test cases. The simulations show accurate results, qualitatively equal to the baseline model on transitional boundary layer test cases, and substantially improved over the baseline model for free shear flows. The second model uses the SST k-w fully turbulent model and again the effects of transition are included through one additional transport equation for v2. An initial version of the model is presented here. Simplicity of the formulation and ease of extension to other baseline models are two potential advantages of the new method.