The objective of this study is to present a finite-element advection-diffus
ion scheme for the steady scalar transport equation. The novelty is the use
of two advection-diffusion schemes in combination in a way which ensures t
he satisfaction of the monotonicity property in their matrix equation. Comm
on to these two fundamental finite-element models is that matrix equations
are all classified to Be irreducibly diagonal dominant. The resulting M-mat
rix finite-element method is the method of choice to resolve sharp profiles
in the flow. The first finite-element method unconditionally provides mono
tonic solutions. The gain in the stability is due to the introduction of th
e upwind information along the local streamline. The second basic scheme is
classified as conditionally monotonic and is well salted to predicting low
er Peclet number flows. This Petrov-Galerkin finite-element model manifests
itself by the use of Legendre polynomials to span finite-element spaces. A
n inherent feature of this formulation is the orthogonal property, which en
ables a considerable saving in the numerical evaluation of integral terms.
Computational evidence reveals that the Legendre-polynomial finite-element
model can provide more accurate solutions in low Peclet number conditions.
As the Peclet number is increased to higher values that forbid a monotonic
solution, the unconditionally monotonic finite-element model is used to com
plement the Legendre-polynomial finite-element model. This helps enhance th
e stability. A combined formulation renders a composite scheme that offers
promise to optimize the scheme performance. In order to show that the prese
nt composite scheme is computationally efficient, the method needs to be ri
gorously tested against available analytic results. This composite scheme w
as found to provide monotonic solutions under high and low Peclet number co
nditions and provided accurate solutions at less computational cost. Use of
this composite scheme promises a wider range of practical problems that ca
n be modeled numerically.