{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Purpose:** \n", "The existing transfer station is inadequate and improperly installed (subjects pump to undue wear and tear). A new transfer station will be designed to increase reliability and performance." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from Water import Pipe, Pump, tools" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target MDD flow = 4.1 gpm Target ADD flow = 1.9 gpm\n", "Using 28 gpm to fill tank in 18 hours.\n" ] } ], "source": [ "# maximum daily demand\n", "MDD = 425 # gpd/ERU\n", "ADD = 200 # gpd/ERU\n", "N = 14\n", "\n", "Q_MDD = (MDD/1440) * N\n", "Q_ADD = (ADD/1440) * N\n", "\n", "flows = 'Target MDD flow = {:.1f} gpm Target ADD flow = {:.1f} gpm'.format(Q_MDD, Q_ADD)\n", "Q = 28 # GPM\n", "print(flows)\n", "print('Using', Q, 'gpm to fill tank in 18 hours.')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Suction Side Static Low Pressure = 1.00 ft\n", "Suction Side Static High Pressure = 13.00 ft\n", "Discharge Side Static Pressure = 119.17 psig\n", "Elevation change from BPS to top of Operational Storage at the Upper Vusario Tank = 275 ft\n" ] } ], "source": [ "# elevations\n", "station_elevation = 1079 # ft\n", "storage_elevation = 1341 # ft\n", "OS_water_level = 13 # ft\n", "\n", "suction_static_low = 1 # ft\n", "suction_static_high = 13 # ft\n", "\n", "print('Suction Side Static Low Pressure = {:.2f} ft'.format(suction_static_low))\n", "print('Suction Side Static High Pressure = {:.2f} ft'.format(suction_static_high))\n", "\n", "print('Discharge Side Static Pressure = {:.2f} psig'.format(tools.ft2psi(storage_elevation +\\\n", " OS_water_level -\\\n", " station_elevation)))\n", "\n", "print('Elevation change from BPS to top of Operational Storage at the Upper Vusario Tank = {} ft'.format(storage_elevation +\\\n", " OS_water_level -\\\n", " station_elevation))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "**Losses**\n", "\n", "Major losses are calculated using the Hazen Williams equation \n", "$$h_{l}=\\bigg{(}\\frac{Q}{C}\\bigg{)}^{1.85}\\bigg{(}\\frac{10.45 L}{d^{4.87}}\\bigg{)}$$ \n", "Minor losses are cacluated using the Darcy-Weisbach Equation \n", "$$h_{l}=K_{l}\\frac{V^{2}}{2g}$$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#### Pipe and Fitting Definitions ####\n", "# suction side \n", "pipe_tnk2bps = Pipe(length=20, size = 4, kind='PVC')\n", "pipe_tnk2bps.fitting('elbow_90', 'standard_flanged', 2)\n", "pipe_tnk2bps.fitting('valve', 'gate', 1)\n", "\n", "pipe_bps2pmp = Pipe(length=6, size=2, kind='STEEL', sch=40)\n", "pipe_bps2pmp.fitting('elbow_90', 'standard_threaded', 2)\n", "pipe_bps2pmp.fitting('tee_through', 'standard_threaded', 2)\n", "pipe_bps2pmp.fitting('tee_branch', 'standard_threaded', 1)\n", "\n", "# discharge side\n", "pipe_pmp2dh = Pipe(length=1, size=1.5, kind='STEEL', sch=40)\n", "pipe_pmp2dh.fitting('elbow_90', 'standard_threaded', 1)\n", "pipe_pmp2dh.fitting('tee_through', 'standard_threaded', 1)\n", "pipe_pmp2dh.fitting('tee_branch', 'standard_threaded', 1)\n", "pipe_pmp2dh.fitting('valve', 'butterfly', 1)\n", "pipe_pmp2dh.fitting('valve', 'tilt_disc_check', 1)\n", "\n", "pipe_dischargeHeader = Pipe(length=4, size=2, kind='STEEL', sch=40)\n", "pipe_dischargeHeader.fitting('elbow_90', 'standard_flanged', 1)\n", "pipe_dischargeHeader.fitting('tee_through', 'standard_flanged', 2)\n", "pipe_dischargeHeader.fitting('valve', 'butterfly', 1)\n", "\n", "pipe_bps2strg = Pipe(length=2000, size=3, kind='PVC', sch=40)\n", "pipe_bps2strg.fitting('valve', 'gate', 1)\n", "pipe_bps2strg.fitting('tee_branch', 'standard_flanged', 2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Suction Losses: 0.52 ft, Discharge Losses: 5.95 ft\n" ] } ], "source": [ "#### Calculating Major and Minor Losses\n", "# suction side losses (H1) \n", "suction_losses = pipe_tnk2bps.get_losses(flow=Q) + pipe_bps2pmp.get_losses(flow=Q) \n", "\n", "# discharge side losses (H2)\n", "discharge_losses = pipe_pmp2dh.get_losses(flow=Q) +\\\n", " pipe_dischargeHeader.get_losses(flow=Q) +\\\n", " pipe_bps2strg.get_losses(flow=Q)\n", " \n", "\n", "# print result\n", "result = 'Suction Losses: {:.2f} ft, Discharge Losses: {:.2f} ft'.format(suction_losses, discharge_losses)\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "At supply storage low level Total Dynamic Head from pump discharge to Operational Storage Water Level = TDH = 280.47 ft or 121.5 psi\n", "At supply storage high level Total Dynamic Head from pump discharge to Operational Storage Water Level = TDH = 268.47 ft or 116.3 psi\n", "\n" ] } ], "source": [ "discharge_head = storage_elevation + OS_water_level + discharge_losses - station_elevation\n", "suction_head_low = suction_static_low - suction_losses\n", "suction_head_high = suction_static_high - suction_losses\n", "TDH_low = discharge_head - suction_head_low\n", "TDH_high = discharge_head - suction_head_high\n", "result = '''\n", "At supply storage low level Total Dynamic Head from pump discharge to Operational Storage Water Level = TDH = {:.2f} ft or {:.1f} psi\n", "At supply storage high level Total Dynamic Head from pump discharge to Operational Storage Water Level = TDH = {:.2f} ft or {:.1f} psi\n", "'''.format(TDH_low, tools.ft2psi(TDH_low), TDH_high, tools.ft2psi(TDH_high) )\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Pumping Requirements**\n", "\n", "Horse Power Calculation: \n", "$$hp_{water}=(Q)(TDH)\\bigg{(}\\frac{1\\ psi}{2.308\\ ft}\\bigg{)}\\bigg{(}\\frac{1\\ hp}{1714 (psi\\ gpm)}\\bigg{)}$$ \n", "$$hp_{break}=\\frac{hp_{water}}{\\eta_{pump}} \\quad hp_{input}=\\frac{hp_{break}}{\\eta_{motor}}$$\n", "\n", "$\n", "\\begin{align}\n", "\\text{where:}\\quad \\eta_{pump}=0.6 \\quad \\eta_{motor}=0.9\n", "\\end{align}\n", "$\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FLOW = 28.00 gpm HEAD = 280.47 ft or 121.54 psi Total HP = 3.67 hp\n" ] } ], "source": [ "hp = tools.calc_hp(flow_rate=Q, head=TDH_low)\n", "psi = tools.ft2psi(TDH_low)\n", "reqs = 'FLOW = {:.2f} gpm HEAD = {:.2f} ft or {:.2f} psi Total HP = {:.2f} hp'.format(Q, TDH_low, psi, hp[2])\n", "print(reqs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**System Curve**" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from numpy import arange\n", "sys_x = arange(0, Q+1)\n", "sys_y_low = []\n", "sys_y_high = []\n", "for x in sys_x:\n", " s_loss = pipe_tnk2bps.get_losses(flow=x) + pipe_bps2pmp.get_losses(flow=x)\n", "\n", "\n", " d_loss = pipe_pmp2dh.get_losses(flow=x) +\\\n", " pipe_dischargeHeader.get_losses(flow=x) +\\\n", " pipe_bps2strg.get_losses(flow=x)\n", " dis_head = storage_elevation + OS_water_level + d_loss - station_elevation\n", " suc_head = suction_static_low - s_loss\n", " suc_head_high = suction_static_high - s_loss\n", " head = dis_head - suc_head\n", " head2 = dis_head - suc_head_high\n", " sys_y_low.append(head)\n", " sys_y_high.append(head2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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