Exact answer known1D · free surfaceCandidate

Sewer pipes running part full

Does a drainage model get the depth right in a round pipe?

Test A1D-001
Chow (1959)
Version 1.0.0

Right answer: Exact solution · Chow (1959)
Results: 5 models · closest under 0.01 % off · see them ↓
Must: keep every pipe and manhole depth within 1 cm

Sketch, not to scale.

The model file

The suite writes this EPA SWMM 5 input file from the test's inputs. Each engine's own copy, as it ran, is in Run it yourself under its result below.

Download A1D-001.inp 4 KB Open it on the map →

On the map: click any element to see its line in the file and every engine's result there over time.

What happens

Three lines of 1 m sewer pipes, on a steady slope, carry three different flows that fill them to about a quarter, a half and three quarters. The depth in each pipe is fixed exactly by Manning's formula for a circle.

Why it matters

Pipe capacity checks, surcharge warnings and flood risk all start from the depth in a partly full pipe. This is the simplest thing a sewer model has to get right.

A good model must

keep every pipe and manhole depth within 1 cm
pass the full inflow down every pipe

Exact answer and models

Depth along each line of pipes

Three separate lines of pipes, each carrying a different flow; the exact depth is the same in every pipe of a line.

Point at the chart to read every model's value; click a name to hide or show it.

Difference from the exact answer at every manhole and pipe

Shaded: the pass limit, ±10 mm.

Point at the chart to read every model's value; click a name to hide or show it.

How models did

In rank order: by each model's percent difference from the right answer, the largest over the test's accuracy checks (lower is better; results within 10 % of each other share a rank). Mass error, timing and NSE are shown beside it, not folded in. Each model runs the same inputs. Compare them in the results viewer →

Rank	Model	Difference	Largest difference in	Run time
1	Hydra urban drainage	<0.01 %	Worst depth error	0.30 s
2=	OpenSWMM 6 dynamic wave	0.027 %	Worst depth error	0.73 s
2=	SWMM 5.1.15 previous release	0.027 %	Worst depth error	0.40 s
2=	EPA SWMM 5.2.4 dynamic wave	0.027 %	Worst depth error	0.41 s
5	OpenSWMM 6 finite volume	1.4 %	Worst depth error	1.4 s

Hydra

Hydra 17 (NEER AI, Rust): its urban-drainage engine on the SWMM data model, run through its CLI with default settings

#1 under 0.01 % off

Run time, finest grid: 0.30 s · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, network.json as a SWMM input file)

Worst depth error

• 0.00051 mm off

<0.01 % of the 1 m pipe diameter

Typical flow error

• < 0.0001 % off

Exact to rounding (0.0005 mm): Hydra computes the circular section exactly, where the SWMM engines interpolate a 51-entry table (0.27 mm off).

Scored with method 3.0 · Score card: Hydra urban drainage (17.0.0/4.1.1)

Run it yourself

Hydra urban drainage (17.0.0/4.1.1) · runs on Linux · produced by scripts/run_drainage_cases.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
HYDRA=$(python scripts/fetch_hydra.py .hydra)  # the pinned Hydra 17 CLI
python scripts/run_drainage_cases.py A1D-001 --engines hydra-uds --hydra-exe "$HYDRA"

The model files the suite wrote for this run (.zip, 1.3 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

The test itself (inputs, exact answer, checks): A1D-001.zip. Check the score card with benchmark1d2d verify-card: its signature by the suite's public development key shows the card has not changed since it was written, not who wrote it.

OpenSWMM 6

OpenSWMM Engine v6 (HydroCouple's SWMM 6, pre-release) with dynamic-wave routing

#2= 0.027 % off

Run time, finest grid: 0.73 s · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, network.json as a SWMM input file)

Worst depth error

• 0.27 mm off

0.027 % of the 1 m pipe diameter

Typical flow error

• < 0.0001 % off

Scored with method 3.0 · Score card: OpenSWMM Engine v6 dynamic wave (6.0.0a4.dev1)

Run it yourself

OpenSWMM Engine v6 dynamic wave (6.0.0a4.dev1) · runs on Linux · produced by scripts/run_drainage_cases.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python -m venv .venv-openswmm
.venv-openswmm/bin/python -m pip install "openswmm==6.0.0a4.dev1" numpy
python scripts/run_drainage_cases.py A1D-001 --engines openswmm-dw --openswmm-python .venv-openswmm/bin/python

The model files the suite wrote for this run (.zip, 1.3 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

SWMM 5.1.15

EPA SWMM 5.1.15, the release before 5.2: on the parity tests compared with SWMM 5.2.4, on the exact tests scored like any other engine

#2= 0.027 % off

Run time, finest grid: 0.40 s · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, network.json as a SWMM input file)

Worst depth error

• 0.27 mm off

0.027 % of the 1 m pipe diameter

Typical flow error

• < 0.0001 % off

Scored with method 3.0 · Score card: EPA SWMM 5.1.15

Run it yourself

EPA SWMM 5.1.15 · version data5.1.15 · runs on Linux · produced by scripts/run_drainage_cases.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python3.10 -m venv .venv-swmm51 && .venv-swmm51/bin/python -m pip install "swmm-toolkit==0.10.0"  # EPA SWMM 5.1.15
python scripts/run_drainage_cases.py A1D-001 --engines swmm515 --swmm51-python .venv-swmm51/bin/python

The model files the suite wrote for this run (.zip, 1.3 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

EPA SWMM 5.2.4

EPA SWMM 5.2.4 with dynamic-wave routing; on the channel tests, the channel is written as one conduit per grid cell

#2= 0.027 % off

Run time, finest grid: 0.41 s · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (DYNWAVE routing, network.json as a SWMM input file)

Worst depth error

• 0.27 mm off

0.027 % of the 1 m pipe diameter

Typical flow error

• < 0.0001 % off

Scored with method 3.0 · Score card: EPA SWMM 5.2.4

Run it yourself

EPA SWMM 5.2.4 · runs on Linux · produced by scripts/run_drainage_cases.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python scripts/run_drainage_cases.py A1D-001 --engines swmm524

The model files the suite wrote for this run (.zip, 1.3 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

OpenSWMM 6

OpenSWMM Engine v6 with its new explicit finite-volume routing (FLOW_ROUTING FV): one cell per conduit on the channel tests, its default cells per pipe on the drainage networks

#5 1.4 % off

Run time, finest grid: 1.4 s · INTEL(R) XEON(R) PLATINUM 8573C, 2 cores, 15.6 GB, Linux 6.17.0-1022-azure (GitHub Actions (github-hosted, ubuntu24))

Settings: swmm (FV routing, network.json as a SWMM input file)

Worst depth error

• 1.4 cm off

1.4 % of the 1 m pipe diameter

Typical flow error

• < 0.0001 % off

Accurate mid-line (under 2 mm), but up to 1.4 cm too deep in the pipes next to the normal-depth outfalls and 7 mm too deep in the first pipes below the inflows. EPA SWMM 5.2.4 holds normal depth to 0.3 mm everywhere.

Scored with method 3.0 · Score card: OpenSWMM Engine v6 finite volume (6.0.0a4.dev1)

Run it yourself

OpenSWMM Engine v6 finite volume (6.0.0a4.dev1) · runs on Linux · produced by scripts/run_drainage_cases.py in track-a.yml.

git clone https://github.com/scott-jeffers/benchmark1d2d && cd benchmark1d2d
git checkout 71dc10fc07a9
python -m pip install -e ".[dev,engines]"
python -m venv .venv-openswmm
.venv-openswmm/bin/python -m pip install "openswmm==6.0.0a4.dev1" numpy
python scripts/run_drainage_cases.py A1D-001 --engines openswmm-fv --openswmm-python .venv-openswmm/bin/python

The model files the suite wrote for this run (.zip, 1.3 KB): the exact inputs the solver read (one folder per grid, where the test has several), with their SHA-256.

Not run yet: InfoWorks ICM, MIKE+ (MOUSE), PCSWMM, XPSWMM, Flood Modeller, HEC-RAS

Run it and add yours →

Status of this test

Candidate. To become frozen it needs: maintainer review recorded.

Download inputs (.zip)Check your results in the browser Cite this test View on GitHub

Technical details for reviewers Checks, reference, inputs, sources

Checks and why each limit was chosen

Check	Difference measured as a percentage of	Latest	Why this check
normal_depth Linf of h · in the score	of the 1 m pipe diameter	–	Worst node or pipe depth within 1 cm (1 % of the diameter). SWMM-family engines store circular-pipe area and hydraulic radius in 51-entry tables and interpolate between them; that alone moves a normal depth by a few millimetres.
pipe_flow L2 of Q · in the score	already a percentage	–	Pipe flows within 0.5 %: at steady state every pipe carries its chain's inflow.
convergence not applicable	–	–	A link–node network with a closed-form (or 1e-11 integrated) reference at fixed elements: there is no grid to refine.

Reference

Exact solution from references.drainage.uniform_flow.

Promotion checklist

Only frozen tests have fixed inputs and answers. This list is computed from the manifest and the published score cards; the rules are in the method.

✓Data licence verifiedfor candidateanalytic-public
✓All inputs availablefor candidateall committed or fetched by SHA-256
✓Reference (truth) availablefor candidateanalytic generator
✓Every tolerance justifiedfor candidateeach gating criterion has a rationale
✓A published run passesfor frozen4 passing score card(s)
✓Order of accuracy shownfor frozennot applicable: A link–node network with a closed-form (or 1e-11 integrated) reference at fixed elements: there is no grid to refine.
✓Two independent codes passfor frozenpassing codes: EPA SWMM 5.1.15, EPA SWMM 5.2.4, Hydra urban drainage, OpenSWMM Engine v6 dynamic wave
✗Maintainer review recordedfor frozenno maintainer review recorded

Inputs

File	Role	Format	SHA-256	Get it
`inputs/network.json`	network	json	`69bd619bc8fa…`	Download

About this test

Physics

Three independent chains of ten 1.0 m circular pipes (100 m each, Manning \(n = 0.013\), slope 0.002) carry 0.15, 0.5 and 1.0 m³/s from a constant inflow to an outfall at normal depth. At steady state the flow in every pipe is uniform, so its depth is the root of

\[ Q = \frac{1}{n} A R^{2/3} S^{1/2}, \quad A = \tfrac{D^2}{8}(\theta - \sin\theta),\ P = \tfrac{D\theta}{2},\ \theta = 2\arccos(1 - 2y/D) \]

solved by bisection to \(10^{-13} D\): 0.253, 0.480 and 0.765 m.

What it checks

The circular section and the friction law, with nothing else in play. Every pipe and every interior manhole is scored; the inflow and outfall manholes are not (their depth depends on how an engine treats a chain's ends), but the last pipe is, so an engine's normal-depth outfall must hold normal depth.

SWMM-family engines store a circular pipe's area and hydraulic radius in 51-entry tables; EPA SWMM 5.2.4 is 0.27 mm off for that reason. An engine with exact geometry agrees to rounding.

Citation

Chow, V. T. (1959). Open-Channel Hydraulics. McGraw-Hill. Rossman, L. A. (2017). SWMM Reference Manual Volume II – Hydraulics. EPA/600/R-17/111.

Sources

Chow, V. T. (1959). Open-Channel Hydraulics.
Rossman, L. A. (2017). Storm Water Management Model Reference Manual Volume II – Hydraulics (EPA/600/R-17/111). www.epa.gov/water-research/storm-water-management-model-swmm

Test A1D-001_circular_pipe_normal_depth · version 1.0.0 · data licence analytic-public