Hromadka II TV1 and Prasada Rao2*
1Department of Engineering-Mathematics, United States Military Academy, USA
2Department of Civil and Environmental Engineering, USA
*Corresponding author: Prasada Rao, Department of Civil and Environmental Engineering, California State University, Fullerton, USA
Submission: October 13, 2021;Published: October 26, 2021
ISSN: 2639-0574 Volume4 Issue5
The advances made in computational algorithms, computing power and visualization
tools are motivating researchers to develop new numerical models or enhance the predictive
abilities of existing numerical models. These advances include a combination of enhanced
flow equations, improved discretization techniques that reduce the flow equations to
algebraic equations, grid methodology, a better understanding of the flow phenomena, highperformance
computers, optimization tools, interfaces to enter and process the data input,
post-processor visualization and analysis tools. These next-generation computational models
are facilitating in capturing the flow physics at various spatial and temporal scales that were
not possible until two decades back, primarily due to limitations in computing power. As this
limitation eases, numerical modeling will continue to be at the forefront for advancing the
frontiers of knowledge across all modeling disciplines.
The reliability of these enhanced models is measured by validating their results with
standard benchmark tests. Experiments play an important role, and their data vastly improves
the understanding of physical flow. Using the measured physical data as a benchmark for
testing various numerical models has been a standard practice across all disciplines. To this
end, the physical experiments and the associated measurements need to be done in sufficient
detail for a range of flow scenarios to arrive at an acceptable dataset for calibrating, verifying
and validating the models. Model validation with experimental benchmark data, although
highly recommended, is not feasible for all flow cases due to the costs, time, and limitations in
the equipment to measure chosen flow variables in the flow domain. Because the numerical
model can simulate complex flow phenomena across different scales, it might not be feasible
to obtain the corresponding experimental data. In its absence, using the output from other
numerical models as the benchmark data is the second approach to validate the models of
interest.
Legacy numerical models are those that were primarily developed before the 1990s and
were primarily written in Fortran 77. Characteristic features of these legacy models include
(a) they are based on strong mathematical foundational, (b) they have been rigorously
tested by users who have spent millions of hours in using them for their applications, and
(c) their applications at that time were constrained by available computational resources.
While some of the legacy models have evolved with time, others are still available for free in
public domains, and their popularity among the modeling community varies from discipline
to discipline. Their solution is reliable and can be used with confidence as benchmark data to
measure the performance details of current-day models.
In the field of computational hydraulics, one of the legacy models is the Diffusion
Hydrodynamic Model (DHM), which was developed for the United States Geological Survey
(USGS) in the 1980s (https://pubs.er.usgs.gov/publication/wri874137). DHM solves the two-dimensional overland flow coupled with one-dimensional
open channel flow equations and includes interfaces between these
two flow regimes using source and sink term approximations. For
simulating flows where inertial forces dominate over frictional
forces, as in many types of floods, the solution of the twodimensional
diffusion wave equation will suffice. The model source
code is written in Fortran 77 and can approximate various hydraulic
effects as backwater, drawdown, channel overflow, storage and
ponding. The model has been extensively tested for different flow
scenarios, as detailed in the above document. Its solution has been
compared with experimental, theoretical and numerical data. The
model’s companion website www.diffusionhydrodynamicmodel.
com has the source code and documentation along with various
applications for which the model was applied.
The next-generation models that are now more popular for
modeling free surface flows include HEC-RAS 2D, TUFLOW, MIKE
21, FLOW-3D, Open-FOAM, WSPG, and XP SWMM. Other models are
also being developed and are gaining momentum. The theoretical
pinning’s of these models and their applications have been well
documented in the literature.
When one utilizes a computational model to develop opinions
related to a problem in engineering and science, it is appropriate
to also provide a validation of the computational results. Such
validation includes an examination of the developed computational
model with a variety of plausible modeling parameters leading
to a general sensitivity evaluation as to modeling performance
versus choice of model parameters (and also boundary conditions).
Another type of evaluation is to evaluate the choice of computational
model used. That is, there is a space of computational model
outcomes that depend upon selection choices of problem boundary
conditions and modeling parameter values. But there is also a
space of computational outcomes that depend upon the selection
of modeling genre and the choice of computational model features.
For example, the comparison of meshless models versus meshed
or gridded, or celled models can deliver a significant difference in
computational outcomes. An approach presented in this note is to
model the target problem using a computational modeling approach
developed during the initial model evolution stage (typically, in the
1970’s and 1980’s-time frame) but generally prior to about 1990.
Such a computational model suitable for use in computational
validation is the USGS Diffusion Hydrodynamic Model, which is
useful in the computational validation assessment of the more
modern computational models. The DHM is recommended to be
called a “Legacy” type model where its computer code, FORTRAN
77, remains available from the web and in hard-copy report format.
The use of DHM to test the computational outcome from a more
modern computational model provides a second opinion as to the
veracity of the modeling effort and related outcomes.
© 2021 Prasada Rao. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and build upon your work non-commercially.