Merge branch 'generator_mod' into 'main'

DT-HYDRO Development Group:

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# DT-HYDRO-CFD

# DT-HYDRO

Data, models, and post processing scripts for CFD simulation of hydropower facilities

This package contains models, data, and post processing scripts for the simulation of of hydropower facilities.

The problem of accurately simulating the dynamic behavior of all primary components of a hydroelectric power plant is the primary concern of this package. The goal is to provide a full, accurate digital representation of all the physical processes and systems governing the behavior of a hydropower plant. This enables the engineer to make informed decisions on plant design, operation and maintenance schedules when faced with uncertain future electric load patterns.

The problem is defined by a system of coupled, nonlinear differential equations that govern the elastic water flow through the penstock, draft tube, and turbine coupled to the electro-mechanical generator model. Methods to solve these coupled differential equations in detail are provided by the software. Furthermore, an additional problem of computing and visualizing detailed flow field information within the highly complicated geometry of the turbine chamber in real-time is addressed by this software package by using state of the art reduced order modeling methods and CFD analysis.

The software solves the time dependent, one dimensional (1D) coupled mass and momentum balance equations governing the elastic flow of water through the penstock, turbine and draft tube in a hydroelectric facility using a high order finite volume based method. The numerical method is based on the Kurganov-Tadmor central method paired with the Monotonic Upstream-centered Scheme for Conservation Laws (MUSCL). This solution method accurately resolves the fast transient behavior of the flow, including water hammer. Additionally, the software estimates the full 3D flow field within the turbine chamber in real time, a feat that is made possible by leveraging pre-computed CFD results by utilizing a reduced order modeling method based on an efficient randomized singular value decomposition (SVD) driven proper orthogonal decomposition (POD) with POD-mode weight regression. The reduced order model of the 3D flow is directly coupled to the 1D elastic flow model so the entire flow field through the penstock and turbine system is resolved quickly and with high fidelity.

# Install

Or

    python setup.py develop

# Related Publications

For further reading please see:

Gurecky, William, Wang, Hong, and Ou, Shawn. Elastic Flow Modeling for Hydropower Digital Twins. United States: N. p., 2023. Web. doi:10.2172/1974317.  URL: https://www.osti.gov/biblio/1974317

Gurecky, William, Wang, Hong, and Ou, Shawn. One Dimensional Penstock Flow Models for Hydropower Digital Twin. United States: N. p., 2022. Web. doi:10.2172/1894197. URL: https://www.osti.gov/biblio/1894197

If you find this package useful, please consider citing the above works.

"""

Visualization for the propor Orthogonal decomposition (POD)

reduced order model (ROM) of the 3D cfd data through the

turbine chamber.

This Visualization script depends on the plotly and dash

python packages for fast live-updating 3D plots.

NOTE: You must FIRST run the pod_cfd_model.py script

before executing this script.

The data produced by

the pod_cfd_model.py script is visualized by this script

seperately.  This is to decouple the model from the plotting

procedure so that they may run on seperate processes.

Author: William Gurecky.  wll@ornl.gov.  June 2023

"""

import numpy as np

import h5py

import time

from dthydro_cfd.rom_cfd.pod_rom import PODInterpModel

# plotly imports

import plotly.graph_objects as go

import plotly.express as px

# dash imports

import dash

from dash import dcc, html

from dash.dependencies import Input, Output

# Connect to h5 file

db_pod_file = "db_dynamic_pod_model.h5"

f_db = h5py.File(db_pod_file, 'r', libver='latest', swmr=True)

app = dash.Dash(__name__)

# html.Div(id='live-vane-angle-text'),

app.layout = html.Div(

    html.Div([

        html.H4('Reduced Order Model (ROM) Live Feed'),

        dcc.Graph(id='live-update-graph-p'),

        dcc.Graph(id='live-update-graph-v'),

        dcc.Graph(id='live-update-graph-vane'),

        dcc.Interval(

            id='interval-component',

            interval=500, # in milliseconds

            n_intervals=0

        )

    ])

)

X_POINTS = f_db["X"][:].flatten()

Y_POINTS = f_db["Y"][:].flatten()

Z_POINTS = f_db["Z"][:].flatten()

# data thinning

PTX = np.random.randint(0, len(X_POINTS), size=6000)

P_ = None

from collections import deque

vane_history = deque(maxlen=50)

time_history = deque(maxlen=50)

# Multiple components can update everytime interval gets fired.

@app.callback(Output('live-update-graph-p', 'figure'),

              Input('interval-component', 'n_intervals'))

def update_graph_live(n):

    global P_

    # Read data from database

    P_ = f_db["P"][:]

    # scale pressure to be consisent with model turbine

    # inlet pressure

    p_inlet = 500.e3  # Pa

    p_scale = p_inlet - np.max(P_)

    fig = px.scatter_3d(

            x=X_POINTS[PTX],

            y=Y_POINTS[PTX],

            z=Z_POINTS[PTX],

            color=P_[PTX] + p_scale,

            range_color=[101.e3, 400.e3],

            opacity=0.95,

            size_max=4,

            title="Pressure (Pa)",

            )

    fig.update_traces(marker=dict(size=3))

    fig.update_layout(

        coloraxis_colorbar=dict(title="Pressure (Pa)",),

    )

    # Draw new figure

#     fig = go.Figure(data=go.Scatter3d(

#         x=X_POINTS[PTX],

#         y=Y_POINTS[PTX],

#         z=Z_POINTS[PTX],

#         mode='markers',

#         marker=dict(size=3, color=P_[PTX], opacity=0.95,

#                     colorbar=dict(thickness=5, title="Pressure (Pa)", ),

#                     ),

#         customdata=P_[PTX],

#         hovertemplate=('%{customdata}%'),

#         ))

    # preserve zoom, pan settings

    # from: https://stackoverflow.com/questions/68798315/how-to-update-plotly-plot-and-keep-ui-settings

    fig['layout']['height'] = 700  # px

    fig['layout']['uirevision'] = 'const'

    return fig

# Multiple components can update everytime interval gets fired.

@app.callback(Output('live-update-graph-v', 'figure'),

              Input('interval-component', 'n_intervals'))

def update_graph_live_v(n):

    # Read data from database

    pred_vx = f_db["VX"][:][PTX]

    pred_vy = f_db["VY"][:][PTX]

    pred_vz = f_db["VZ"][:][PTX]

    v_mag = np.sqrt(pred_vx ** 2. + pred_vy ** 2. + pred_vz ** 2.)

    fig_v = go.Figure(data=[go.Cone(

        x=X_POINTS[PTX],

        y=Y_POINTS[PTX],

        z=Z_POINTS[PTX],

        u=pred_vx,

        v=pred_vy,

        w=pred_vz,

        # sizemode="absolute",

        sizeref=3,

        # colorbar=dict(thickness=8, title="Velocity (m/s)"),

        ),

        go.Scatter3d(

         x=X_POINTS[PTX],

         y=Y_POINTS[PTX],

         z=Z_POINTS[PTX],

         mode='markers',

         marker=dict(size=2, color=v_mag, opacity=0.65,

                     colorbar=dict(thickness=5, title="Velocity (m/s)", ),

                     ),

            )

        ]

        )

    fig_v['layout']['height'] = 700  # px

    fig_v['layout']['uirevision'] = 'const'

    return fig_v

# Multiple components can update everytime interval gets fired.

@app.callback(Output('live-update-graph-vane', 'figure'),

              Input('interval-component', 'n_intervals'))

def update_graph_live_vane(n):

    global vane_history

    global time_history

    t = f_db["t"][:]

    va = f_db["vane_angle"][:]

    vane_history.append(va[0])

    time_history.append(t[0])

    fig_vane = go.Figure(data=go.Scatter(

        x=np.asarray(list(time_history)),

        y=np.asarray(list(vane_history)),

        ))

    fig_vane['layout']['uirevision'] = 'const'

    return fig_vane

if __name__ == "__main__":

    # init the dash app

    app.run_server(debug=True)