Tuesday, February 9, 2016

Boulder, CO, Selects SCADAWatch for Real-Time Operational Performance Monitoring and Reporting

Boulder, CO, Selects SCADAWatch for Real-Time Operational Performance Monitoring and Reporting
Colorado Utility Continues to Expand Implementation of Innovyze Advanced Smart Water Solutions
Broomfield, Colorado, USA, February 9, 2016

Innovyze, a leading global innovator of business analytics software and technologies for smart wet infrastructure, today announced that the City of Boulder, Colorado, has chosen the company’s revolutionary SCADAWatch software solution to support mission-critical network modeling applications, supplement system performance monitoring in real time, gain insight and drive operational improvements. The implementation also boosts the City’s real-time business intelligence, enabling it to run the SCADAWatch out-of-the-box zonal mass balance to determine non-revenue water and monitor everything from uptime to analytics. SCADAWatch allows users to access timely information by consolidating data from different platforms, providing a faster, cleaner window into real-time data. This expands the pool of employees who can easily access valuable observed data on any desktop.

Located at the base of the Rocky Mountain foothills 25 miles from Denver, Boulder is home to approximately 100,000 people, including the main campus students and faculty of the University of Colorado, the state’s largest university. Its water system includes four raw water supplies, two treatment facilities, three separate pressure zones, 500 miles (800 km) of pipeline, three pumping stations with 13 pumps (excluding emergency facilities), and six remote storage facilities.

SCADAWatch allows Boulder to leverage better and consolidate their hydraulic modeling, GIS and SCADA into one powerful business software application — allowing information flows that are faster, analytically optimized and actionable. Users can instantly create, view and interactively analyze presentation-quality reports on a real-time, information-rich business dashboard fully customized to their needs, getting an at-a-glance perspective on system performance. For example, operators can instantly determine non-revenue water and minimum night-time flows; summarize daily, weekly, monthly, seasonal and annual water consumption; determine the maximum, minimum, and average zonal pressures; and view other business performance indicators — all with the click of a mouse. The software also automatically generates detailed, high-fidelity performance reports and assists staff members with determining the best course of action based on specific performance objectives. Using accurate network performance and operational information from real-time data, Boulder can now key its management, engineering, and water quality employees in, helping them bolster real-time observation of its water distribution system’s vital signs and non-critical operational data such as water quality parameters.
“SCADAWatch consolidates all our key performance indicator measures and hydraulic and water quality data on a dashboard, giving us even faster real-time visualization of our water asset performance,” said Suzanne Givler, Boulder’s Water Quality Projects Coordinator. “We can now quickly access, analyze and drill down through a wealth of information for more effective data-driven decision-making and business process optimization in dynamic situations.”
“By choosing SCADAWatch, the state-of-the-art in our industry, the City of Boulder has demonstrated its commitment to using cutting edge technology to best serve its customers,” said Paul F. Boulos, Ph.D., BCEEM, Hon.D.WRE, Dist.D.NE, Dist.M.ASCE, NAE, President, COO and Chief Technical Officer of Innovyze. “It gives the City a deep, comprehensive view into the health and operations of their water infrastructure, specifically water quality variables, and enables them to improve operation of pumps and tanks to optimize operations with respect to tank fluctuations. It is the ultimate decision support tool for achieving optimal results. Innovyze is very proud to continue to partner in Boulder’s success.”
About InnovyzeInnovyze is a leading global provider of wet infrastructure business analytics software solutions designed to meet the technological needs of water/wastewater utilities, government agencies, and engineering organizations worldwide. Its clients include the majority of the largest UK, Australasian, East Asian and North American cities, foremost utilities on all five continents, and ENR top-rated design firms. With unparalleled expertise and offices in North America, Europe and Asia Pacific, the Innovyze connected portfolio of best-in-class product lines empowers thousands of engineers to competitively plan, manage, design, protect, operate and sustain highly efficient and reliable infrastructure systems, and provides an enduring platform for customer success. For more information, call Innovyze at +1 626-568-6868, or visit www.innovyze.com.

Innovyze Contact:Rajan Ray
Director of Marketing and Client Service Manager
Rajan.Ray@innovyze.com
+1 626-568-6868
- See more at: http://www.innovyze.com/news/1660

Saturday, February 6, 2016

Innovyze RDII Analyst for the Analysis and Calibration of RDII, DWF, DWF Patterns and GWI in Sewer Collection Systems

One of the most powerful InfoSWMM and H2OMAP SWMM Application Tools from Innovyze is RDII Analyst.  RDII Analyst will separate out the Groundwater base flow, Dry Weather Flow (DWF), DWF Patterns, estimate the Wet Weather flow component or RDII  Rainfall -Derived Infiltration Inflow (I&I) and use a Genetic Algorithm to find  the best fit 12 RTK parameters for RDII modeling.  This powerful tool can  be used for RTK flow in InfoSWMM, H2OMAP SWMM, InfoSewer, SWMM 5 and InfoWorks ICM.  Figure 1 shows one of the  end results of RDII Analyst – a correlation plot of Observed versus Calibrated RDII Volume for the simulated events.
RDII Analyst is a significant improvement over the EPA SSOAP program, performs QA/QC of rainfall and flow monitoring data and decomposes the flow data into Dry-Weather Flow (DWF) and Wet-Weather Flow (RDII) components using criteria such as rainfall threshold. The DWF component is further analyzed to construct a DWF pattern that can be used to simulate the collection system using InfoSWMM. The DWF pattern is then assigned to the source nodes that contribute DWF to the meter location in proportion to sewershed areas or based on other criteria. The RDII component is then analyzed to determine RDII events and to calibrate parameters of the RTK synthetic unit hydrograph so that the RDII flow simulated by the RTK method closely matches the RDII flow obtained by the decomposition process. The RTK unit hydrograph parameters are calibrated with genetic algorithm optimization. The calibrated RTK parameters and the DWF patterns are then passed to InfoSWMM to carry out detailed dynamic flow routing through the sewer system and evaluate system response to support the development of an optimal capital improvement program. You can read the World Environmental and Water Resources Congress paper by Misgana and Boulos (2008) with a complete description and validation of the RDII Analyst workflow process for both RDII Analyst and for the InfoSWMM Calibrator Add-On in the InfoSWMM Suite (Boulos, 2005)
RDII00002
Figure 1. Correlation plot of Observed versus Calibrated RDII Volume for the simulated events.
The steps in using RDII Analyst are both simple and powerful, the main steps are:
  • Import flow monitoring data and rainfall data into RDII Analyst
  • Perform QA/QC for the flow data and the rainfall data
  • Determine dry day flows and create hourly DWF pattern for weekend and weekdays
  • Determine the groundwater flow component of the dry days flows
  • Determine RDII flow time series
  • Identify RDII events, and perform linear regression analysis on the RDII depth and rainfall depth calculated for each RDII event
  • Run Genetic Algorithms based calibration of the RTK hydrograph parameters and review calibration results
  • Export the DWF patterns, the GWF time series and the calibrated RTK parameters to InfoSWMM
In this overview of the steps in the remainder of the blog post, you’ll learn how the RDII Analyst tool works in general and what terminology is used to describe each step. This is recommended reading for anyone who is new to RDII Analyst.  If you are experienced in RDII decomposition, you can probably just skim it when needed.

Step 1.  Define the Flow and Rainfall Data

The flow and rainfall data are imported by each monitored Node location.  The flow and  rainfall format are defined along with the monitored data time intervals.  The rainfall and flow do not have to be at the same interval or cover exactly the same time period. The Flow Data Tab displays the flow data that has been read from the flow data file. The display consists of the data area showing Date Time and Value field that is read from the data file. The Rainfall Data Tab displays the flow data that has been read from the rainfall data file. The display consists of the data area showing Date Time and Value field that is read from the data file.
Figure 2. Imported Flow Data in RDII Analyst
Figure 2. Imported Flow Data in RDII Analyst
Figure 3. Imported Rainfall Data in RDII Analyst
Figure 3. Imported Rainfall Data in RDII Analyst

Step 2.  DWF Extraction

The DWF Mean and Patterns are extracted from the Flow Time Series and the residual is used as the basis of the RTK parameter estimation. The dry day flows identified for weekdays and the weekend are further analyzed to determine hourly DWF patterns that can be used to model DWF in InfoSWMM . The DWF pattern presents average hourly DWF values across all dry days for both weekdays and weekend. The DWF pattern is given both graphically and in report form as shown below. The DWF patterns can be exported to InfoSWMM and are assigned to nodes that contribute flow to the meter location proportional to sewershed area or equally among all nodes.
Once determined, the dry days flows are presented both in report form and in graph form for weekend and for weekdays as shown below. The graph shows average daily flow for each dry day, and upper bound and the lower bounds. The upper bound refers to mean flow of all dry day flows plus standard deviation multiplier *standard deviation of dry day flows. The lower bound refers to mean flow of all dry day flows minus standard deviation multiplier *standard deviation of dry day flows. The bounds help the user visually identify outliers and, if necessary, discard those days from further consideration.
Figure 4. Define the Methods used in the DWF Extraction.
Figure 4. Define the Methods used in the DWF Extraction.
Figure 5. Mean and QA/QC Graph for the DWF Extraction.
Figure 5. Mean and QA/QC Graph for the DWF Extraction.
Figure 6. DWF Pattern found by RDII Analyst
Figure 6. DWF Pattern found by RDII Analyst
Figure 7. Estimated Weekly GroundWater Flow.
Figure 7. Estimated Weekly GroundWater Flow.

Step 3.  Ground Water Base Flow Extraction

The DWF Mean and Patterns are extracted from the Flow Time Series and the residual is used as the basis of the RTK parameter estimation.  As part of the DWF Extraction, the GW flow can be estimated and later exported to InfoSWMM and a Time Series (Figure 7). The groundwater flow time series can be exported to InfoSWMM as external inflow and can be assigned to nodes that contribute flow to the meter location proportional to sewershed area or equally among all nodes.

Step 4.  Export DWF Pattern and DWF Means to the Domain in InfoSWMM

Assign DWF Pattern: This tool assigns the hourly DWF patterns developed for weekdays and weekends to InfoSWMM nodes that contribute flow to the monitoring site. The DWF pattern is allocated to the contributing nodes either proportional to sewershed area of each contributing node, or simply equally among all contributing nodes. The user must assign an ID to be used as the weekday and weekend pattern name. The assignment could be limited to nodes in a domain by checking the Assign to Domain Nodes option.
Figure 8. Export Dialog to export the DWF means and DWF patterns to the Node DWF DB Table or the Patterns DB Table in InfoSWMM.
Figure 8. Export Dialog to export the DWF means and DWF patterns to the Node DWF DB Table or the Patterns DB Table in InfoSWMM.

Step 5.  Create the RDIII or Wet Weather Time Series

RDII flow is the difference between the corrected monitoring flow data, and the sum of average hourly DWF pattern and the groundwater flow time series. Once the hourly DWF pattern and the groundwater flow components are identified, the sum of the two components would be subtracted from the corrected flow data to determine the RDII flow component.
Figure 9. The estimated WWF or RDII time series after extaction fo the mean DWF and DWF Pattern.
Figure 9. The estimated WWF or RDII time series after extaction fo the mean DWF and DWF Pattern.

Step 6.  Calibrate the 12 RDII Parameters

One of the objectives of decomposing flow monitoring data into dry weather flow and wet weather flow components is to improve the accuracy of modeling the wet-weather flow component. In H2OMAP SWMM, RDII flow is modeled using the RTK method as previously described. The RTK method requires definition of up to 12 parameters. Proper choice of these parameters is crucial for accurate modeling of the RDII flow. Traditionally, RDII UH parameters are assigned using a tedious and inexact trial-and-error process in which the parameters are manually adjusted in an iterative fashion to closely match wet-weather flow data with the RDII flow generated by the simulation model using the assumed RTK parameters. Since there are a vast number of possible combinations of RTK values, evaluating all options this way may not be manageable, and even knowledgeable modelers often fail to obtain good results. RDII Analyst uses Genetic Algorithms (GA) optimization to automatically determine the UH parameters that best match the RDII time series generated by the RDII Analyst with the RDII flow estimated using H2OMAP SWMM.
The RDII calibration tool is launched using Analysis -> Calibrate RDII Parameters or using from tools. Minimum and maximum value for each parameter should be defined using the RTK Parameters Range dialog editor.
The calibration tool systematically searches for the best set of parameters that matches the RDII flow simulated by H2OMAP SWMM with RDII time series determined by the decomposition process. The parameter values would be searched within the minimum and the maximum ranges assigned by the user on dialog editor shown above. The model would adjust the nominal parameter values assigned by the user on H2OMAP SWMM RDII hydrograph dialog editor (see below) by a randomly selected multiplier within the range assigned for the parameter and chooses the optimal set of adjustments. The Tributary Area may be taken from the sewershed area defined in H2OMAP SWMM’s Hydrograph page, or the user can directly specify area of the tributary sewersheds. In addition, the user has the option to use sewershed area of the nodes defined in a domain. The Ensure that R1>R2>R3 option ensures that the RDII flow contributed by the first triangle (fast flow contribution) would be higher than contribution of the second triangle (intermediate flow), and contribution of the second triangle would be higher than that of the third triangle (slow flow).
Domain Node option is checked, sewershed area of the nodes included in the domain will be considered. Nodes in the domain will not contribute sewershed area. Once the parameters are assigned, hitting the OK button would initiate the calibration dialog box (see below).
Options: Some Genetic Algorithms (GAs) parameters may be defined using the options page initiated by clicking the Options button on the Calibrate RDII Parameters dialog box.
Initial Population: Represents the number of initial solution candidates considered by the GAs calibrator. Each solution candidate contains a value H2OMAP SWMM the assigned range for each RTK parameters. The higher the Initial Population, the better the calibration results would be. However, the calibration process takes more run time as the number of population increases.
Max. Generation: Represents the maximum number of iterations required to complete the calibration process. One generation represents running the model for initial population number of times, and each simulation represents different solution candidates. The higher the maximum generation, the better the chance of improving the calibration. Again, the improvement comes at the cost of more calibration run time. The calibration process can stop before reaching the maximum generation if there is no improvement in results from generation to generation.
Mutation Rate: represents the percentage of solution candidates whose one or more parameter values needs to be randomly altered to inject new and potentially better solution candidates into the search process during each generation. The value must be within zero and one, and typical value is 0.1.
Calibration Results: Upon completing the calibration run, the best RTK parameter values would be reported as shown below. The percentage adjustment and then actual parameter values suggested are reported in the last two columns. In addition, graphical comparison of the RDII flow generated by the calibrated parameters and the RDII time series generated by the decomposition process would be provided to visually analyze the calibration results
Figure 10. The 12 RTK parameters can be estimated with min and max constraints to find the best fit parameters.
Figure 10. The 12 RTK parameters can be estimated with min and max constraints to find the best fit parameters.

Step 7.  Examine the Calibration Report

The number of trial runs made so far, the maximum number of trials to be made and the best fitness obtained from the trail runs made so far would be shown while the model is running. If there is no significant improvement in the fitness for some time (i.e., from generation to generation), then the calibration process would be stopped. Once the calibration run is completed, the RDII flow simulated by the optimal RTK parameters identified by the calibration process would be compared graphically with the RDII time series obtained from the decomposition process. In addition, the optimal RTK parameters identified by the model would also be presented in table form.
RDII Analyst can further analyze the RDII time series to identify RDII events. Breaking RDII time series into separate events can enable a better understanding of the RDII process and aid in process of calibrating the model. Event definition depends on the values assigned for the inputs given in the RDII EVENT IDENTIFICATION dialog box shown below.
Minimum Rainfall Volume: represents the minimum rainfall depth that needs to be collected from “continuous” rain to initiate an event. By continuous rain, it means that for two rainfall occurrences to be considered as one event the time interval between the successive rains should not exceed the interevent time threshold defined by the user.
Minimum RDII Flow: represents the minimum RDII flow that should be generated as the result of the rainfall collected over the duration to accept the occurrence as an event.
Minimum Length of the Event (hr): refers to the minimum length of time that the RDII flow should exceed the minimum RDII flow for the occurrence to be accepted as an event.
Interevent Time Threshold (hr): refers to the length of time needed to separate two successive events. If two rainfall occurrences are separated by duration shorter than the Interevent Time Threshold, then the two rainfall occurrences are considered as one event.
Length of Time for Rain to become RDII (hr): This input refers to average time span for a rainfall event to start contributing RDII to the collection system. Depending on this input, the RDII event identification algorithm tests if an RDII flow has occurred within the Length of Time for Rain to become RDII (hr) after a rainfall event.
Tributary Area: This input is used to compute RDII flow depth based on RDII flow volume determined for each event. RDII Analyst can use the sewershed area defined in InfoSWMM’s RTK Hydrograph page, or the user can directly specify area of the tributary sewersheds. In addition, the user has the option to use sewershed area of the nodes defined in a domain. If the Use Domain Node option is checked, sewershed area of the nodes included in the domain will be considered. Nodes in the domain will not contribute sewershed area.
Figure 11. A Calibration Graph of the Monitored and Predicted RDII Time Series.
Figure 11. A Calibration Graph of the Monitored and Predicted RDII Time Series.

Step 8. RDII Event Analysis Results

Linear Regression Results: A linear regression equation is developed between the RDII depth and rainfall depth identified for each event. Slope of the regression equation represents the fraction of rainfall depth that enters the sewer system in the form of RDII (i.e., a representative R for all events).
Figure 12. Correlation plot of Observed versus Calibrated RDII Volume for the simulated events.
Figure 12. Correlation plot of Observed versus Calibrated RDII Volume for the simulated events.

Step 9.  Export the RDII RTK Parameters to InfoSWMM

This function assigns either the RTK parameters determined by the calibration tool or the RDII time series determined by decomposing the flow monitoring data to InfoSWMM nodes that contribute flow to the monitoring site. The RTK parameters could be exported to InfoSWMM and assigned to RDII hydrographs for each contributing node. The time series is assigned to the nodes as external inflow. The RDII time series is allocated to the contributing nodes either proportional to sewershed area of each contributing node, or simply equally among all contributing nodes. The user must provide a name for the Hydrograph and/or the Time Series. The assignment could be limited to nodes in a domain by checking the Assign to Domain Nodes option. Please note that if both the GWF time series and the RDII time series are exported into InfoSWMM , only the time series exported last would be available for use. InfoSWMM takes only one exported external inflow time series at a time.
Figure 13. The 12 RTK parameters can now be exported back to InfoSWMM and H2OMAP SWMM.
Figure 13. The 12 RTK parameters can now be exported back to InfoSWMM and H2OMAP SWMM.
Figure 14. The 12 RTK parameters in the Operations tab of the InfoSWMM Attribute Browser.
Figure 14. The 12 RTK parameters in the Operations tab of the InfoSWMM Attribute Browser.
Figure 15. GA options in RDII Analyst
Figure 15. GA options in RDII Analyst
Figure 16. How the Exported DWF looks in InfoSWMM graphs of lateral flow at nodes
Figure 16. How the Exported DWF looks in InfoSWMM graphs of lateral flow at nodes

Tuesday, February 2, 2016

How Depression Storage works for #SWMM5 #HYDROLOGY in #INFOSWMM #iNFOWORKS_ICM #H2OMAP_SWMM

Here is how depression storage works on the pervious area of a Subcatchment:

1. Rainfall starts on a Subcatchment without any existing storage,

2. The Rainfall is added to the Storage Depth of the Subcatchment,

3. Infiltration and Evaporation occur if the Storage Depth is positive with the limit that more than the Storage Depth cannot be infiltrated or evaporated per time step

4. The new Storage depth after Infiltration and Evaporation is computed

5. If the new Storage depth is greater than the Depression Storage, then you have Runoff

6. The new Storage depth after Runoff is computed

7. Repeat with a new time step

a. Infiltration then occurs at all times there is a positive Storage Depth

b. Runoff only occurs when the Storage Depth is greater than the Depression Storage

How Depression Storage works for in
Embedded image permalink


Innovyze Releases InfoWorks WS Generation V16.0, Delivering Powerful and Even More Comprehensive Smart Water Network Modeling

Innovyze Press Release
 Insider BlogLinkedInTwitterYouTubeYouTube
Innovyze Releases InfoWorks WS Generation V16.0, Delivering Powerful and Even More Comprehensive Smart Water Network Modeling
New Version Features Expanded RTC and Flushing Capabilities, Multiple Usability Enhancements and Comprehensive Real-Time Data Feeds
Broomfield, Colorado, USA, February 2, 2016 — Innovyze, a leading global innovator of business analytics software and technologies for smart wet infrastructure, today announced the release of InfoWorks WS V16.0, enhanced with new benefits and valuable modeling capabilities that make it easier for users to arrive at fast, accurate solutions and enhance their productivity.

Whether configured as a component of a corporate modeling solution or a stand-alone desktop application,InfoWorks WS combines a fast relational database, powerful hydraulic computational engine and comprehensive spatial analysis tools to create a single, flexible smart water network modeling application that excels in both steady-state and extended period dynamic simulations.

InfoWorks WS uses an enhanced version of the WesNet engine, renowned for its computational speed and ability to cope with large and complex networks. A full range of simulation capabilities is standard, including dynamic water quality and sediment modeling, fire flow assessment, pipe criticality analysis, demand area and leakage analysis, energy use and cost calculations, and auto-calibration of networks. User Programmable Control (UPC) allows modelers to optimize water network operating regimes by changing state of control elements based on the status of sensors.

The latest version dramatically enhances speed, efficiency and usability — providing users with revolutionary workflows and unique capabilities. The flushing scheduler now allows users to specify shear stress as a flushing criterion (in addition to velocity) as well as the elapsed time between flushing operations. Options have also been added for direct SCADAWatch connectivity, allowing easier calibration and setup of demand patterns; application of SQL expression themes to networks without results; and special color-coding of symbols based on control or result status. These important capabilities deliver superior ease of use and productivity, providing the user with extreme flexibility within project modeling workflows.

For a complete listing of InfoWorks WS V16.0 features and capabilities, visit the “What’s New” section in online help.

“As a trusted partner, Innovyze plays an important role in helping our customers create ever more accurate and reliable water distribution network models to help them optimize their CAPEX and OPEX, better sustain more resilient systems, and enhance quality of service,” said Paul F. Boulos, Ph.D., BCEEM, Hon.D.WRE, Dist.D.NE, Dist.M.ASCE, NAE, President, COO and Chief Technical Officer of Innovyze. “The latest InfoWorks WS release introduces a significant range of user-requested capabilities designed to extend their engineering simulation possibilities and simplify their modelling experience — advances that enable them to more effectively meet their business goals and gain competitive advantage.”

Pricing and Availability
InfoWorks WS V16.0 is now available worldwide by subscription. Subscription members can immediately download the new version free of charge directly from www.innovyze.com. The Innovyze Subscription Program is a friendly customer support and software maintenance program that ensures the longevity and usefulness of Innovyze products. It gives subscribers instant access to new functionality as it is developed, along with automatic software updates and upgrades. For the latest information on the Innovyze Subscription Program, visit www.innovyze.comor contact your local Innovyze Channel Partner.
About Innovyze
Innovyze is a leading global provider of wet infrastructure business analytics software solutions designed to meet the technological needs of water and wastewater utilities, government agencies, and engineering organizations worldwide. Its clients include the majority of the largest UK, Australasia and North American cities, foremost utilities on all five continents, and ENR top-rated design firms. With unparalleled expertise and offices in North America, Europe, and Asia Pacific, the Innovyze connected portfolio of best-in-class product lines empowers thousands of engineers to competitively plan, manage, design, protect, operate and sustain highly efficient and reliable infrastructure systems, and provides an enduring platform for customer success. For more information, call Innovyze at +1 626-568-6868, or visit www.innovyze.com.

Innovyze Contact
Rajan Ray
Director of Marketing and Client Service Manager
Rajan.Ray@innovyze.com
+1 626 568-6868

Tuesday, January 26, 2016

IWLive 6.5 Technology Enhancements Increase Fidelity and Power of Comprehensive Real-Time Water Network Modeling, Operation and Management

IWLive 6.5 Technology Enhancements Increase Fidelity and Power of Comprehensive Real-Time Water Network Modeling, Operation and Management

Latest Version Strengthens Day-to-Day Business Analytics and Expands Real-Time Water Network Monitoring and Modeling Capabilities, Regulatory Reporting

Broomfield, Colorado, USA, January 26, 2016

Innovyze, a leading global innovator of business analytics software and technologies for smart wet infrastructure, today announced the worldwide release of Generation V6.5 of IWLive, adding many unique new capabilities and enhancements for comprehensive real-time water network modeling. The release delivers major advancements for operating and sustaining safe, reliable, more efficient and resilient water supply and distribution systems, including major upgrades to facilitate improved collaboration, efficiency and user functionality. The developments have been specifically designed based on customer feedback to further meet the needs of water utilities and their engineering consultants.

A complete solution for real-time network hydraulic and water quality modeling, monitoring, forecasting and SCADA integration, IWLive equips water utilities with powerful, mission-critical tools that are both predictive and reactive. It continuously assesses system performance and alerts operators to problems that may arise in the coming minutes, hours, or days, allowing them to quickly determine time of onset and severity and develop reliable response strategies to minimize downstream effects.

Designed for use in the water distribution control room, IWLive gives operators unprecedented decision making ability in developing real-time response strategies. They can run accurate hydraulic simulations that factor in energy costs, weather, real time or delayed SCADA telemetry, demand history, and valve and pump control scenarios. Beyond increasing efficiency and reducing energy consumption, IWLive can help operators understand the impact of their systems on CO2 emissions as well as the effects of main breaks, pump and reservoir shutdowns, and other scheduled maintenance, and minimize the impact on the customer’s side.

The fully optimized IWLive interface allows operators to see a map of all water infrastructures, including background maps. Highlighted color coding shows predicted problem areas; a single click produces a detailed map showing pipes, valves, pumps, reservoirs and other water assets. Animation of the map illustrates the development of the problem, while graphs show simulated pressures and reservoir levels. IWLive can be accessed remotely over a VPN, and configured to send critical warning messages via SMS or email. It can also interface directly with SCADAWatch, allowing utilities to monitor everything from uptime to analytics; instantly compute non-revenue water; increase productivity for day-to-day tasks; react faster to important events; identify opportunities to drive operational efficiencies as they happen; improve pressure and leakage management; optimize business performance; and operate in a more modern, data-driven environment.

The newest version of IWLive touts a number of new features and enhancements that enhance efficiency and improve the modeling experience. They include more flexibility in the display of key simulation results (e.g., more minima and maxima, summary and time-varying results on object properties); comprehensive results replay control in InfoWater Configuration Manager; improved control of the user interface (e.g., graph and grid menus, graph defaults, scroll bars); expanded modeling control (e.g., log files, profile and script editor); faster solution algorithms; and more powerful post-processing. These features contribute to reducing the time and effort required to investigate and solve complex modeling questions associated with water network operations and management.

For a complete listing of new features and capabilities in IWLive V6.5, visit the “What’s New” section in the online help.

IWLive V6.5 builds on the foundation of previous releases, directly addressing our customers’ feedback by incorporating important capabilities that improve users’ experience and their ability to monitor, strengthen and optimize their water infrastructures,” said Paul F. Boulos, Ph.D., BCEEM, Hon.D.WRE, Dist.D.NE, Dist.M.ASCE, NAE, President, COO and Chief Technical Officer of Innovyze. “This release will create a compelling advantage for water utilities worldwide, making it easier for them to operate, manage and sustain high-performing, highly efficient water systems — imperative in today’s economy — and meet the needs of their customers.”

Pricing and Availability
IWLive V6.5 is now available worldwide by subscription. Subscription members can immediately download the new version free of charge directly from www.innovyze.com. The Innovyze Subscription Program is a friendly customer support and software maintenance program that ensures the longevity and usefulness of Innovyze products. It gives subscribers instant access to new functionality as it is developed, along with automatic software updates and upgrades. For the latest information on the Innovyze Subscription Program, visit www.innovyze.com or contact your local Innovyze Channel Partner.
About InnovyzeInnovyze is a leading global provider of wet infrastructure business analytics software solutions designed to meet the technological needs of water/wastewater utilities, government agencies, and engineering organizations worldwide. Its clients include the majority of the largest UK, Australasian, East Asian and North American cities, foremost utilities on all five continents, and ENR top-rated design firms. With unparalleled expertise and offices in North America, Europe and Asia Pacific, the Innovyze connected portfolio of best-in-class product lines empowers thousands of engineers to competitively plan, manage, design, protect, operate and sustain highly efficient and reliable infrastructure systems, and provides an enduring platform for customer success. For more information, call Innovyze at +1 626-568-6868, or visit www.innovyze.com.
Innovyze Contact:Rajan RayDirector of Marketing and Client Service Manager
Rajan.Ray@innovyze.com
+1 626-568-6868
- See more at: http://www.innovyze.com/news/1658/IWLive_6.5_Technology_Enhancements_Increase_Fidelity_and_Power_of_Comprehensive_Real-Time_Water_Network_Modeling,_Operation_and_Management#sthash.p0IycMhD.dpuf

Monday, January 25, 2016

Updated - Innovyze St Venant Solutions for InfoSWMM, InfoSewer, H2OMap SWMM and InfoWorks ICM and InfoWorks ICM SE

Updated - Innovyze St Venant Solutions for InfoSWMM, InfoSewer, H2OMap SWMM and InfoWorks ICM and InfoWorks ICM  SE

This help file topic contrast the St Venant Solution for InfoSWMMH2OMap SWMM image1130 , InfoSewer/H2OMap Sewerimage1138 and ICM/ICM SEimage1136.

1.  Assumptions for the St. Venant Equations

The assumptions behind Lumped and Distributed Models along with the assumptions of the St Venant Equations.   InfoSWMMH2OMap SWMM, InfoSWMM, H2OMap SWMM, SWMM5, ICM and ICM SE are all Distributed models for Unsteady flow.  InfoSWMM and InfoSWMMH2OMap SWMM have options for direct steady flow.  ICM and InfoSWMM can also use a quasi steady flow solution.   All of these Innovyze models use the Continuity Equation and Momentum equation for routing flows in links.  The numerical solution differs between the three Innovyze main  platforms:
image1138[1] InfoSewer and H2OMap Sewer
image1130[1] InfoSWMM,  H2OMap SWMM and SWMM 5
image1136[1] ICM and ICM SE
image242
image243
image241
Continuity Equation
image489
Various Forms of the Momentum Equation
image488

2.  Muskingum-Cunge for InfoSewer and H2OMap Sewer image1138[2]

rimage143image540
The continuity (mass conservation) equation is:
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where
x          =          distance along the pipe (longitudinal direction of sewer)
A          =          flow cross-sectional area normal to x
y          =          coordinate direction normal to x on a vertical plane
d          =          depth of flow of the cross section, measured along y direction
Q         =          discharge through A
V          =          cross-sectional average velocity along x direction
S0         =          pipe slope, equal to sin θ
θ          =          angle between sewer bottom and horizontal plane
Sf            =          friction slope
g             =          gravitational acceleration
t           =          time
β          =          Boussinesq momentum flux correction coefficient for velocity distribution
These complete unsteady flow equations (momentum together with continuity) along with appropriate initial and boundary conditions are rather tedious and computationally expensive to solve, especially for large sewer collections systems. As a result, acceptable simplifications and improved solution methods have been proposed including non-inertial, kinematic wave and dynamic wave simplifications. Hydraulically, the dynamic wave approach is the most accurate model among the approximations. The Muskingum-Cunge explicit diffusion wave dynamic flow routing model, obtained by neglecting local acceleration term in the momentum equation, is the most commonly used dynamic wave model.
In  InfoSewer /H2OMap Sewer Proimage1138[3] unsteady open channel (free surface) flow is simulated using Muskingum-Cunge technique whereas pressurized flow in any pipe is modeled assuming the pipe is flowing full and the energy equation is applied to the entire pipe section.
Muskingum-Cunge image1138[4]:
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where
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Here c is the dynamic wave celerity and B is the top width at normal depth for discharge Q. This highly efficient and accurate flow routing algorithm is used by InfoSewer /H2OMap Sewer Pro to track the spatial and temporal variation of flows throughout the collection system.
In this method (a.k.a., one sweep explicit solution method), the network flow dynamic equations are formulated by using an explicit finite difference scheme such that the flow depth, discharge, or velocity at a given location and the current time can be solved explicitly from the known information at the previous locations at the same time level, as well as known information at the previous time level. Thus, the solution is obtained segment by segment, pipe by pipe, over a given time interval for the entire sewer network before progressing to the next interval for another sweep of individual solutions of the network flow equations for the entire network. A variable time step approach (based on the Courant number image544 is used to minimize numerical dispersion and ensure robustness and stability of the numerical scheme. Complex flow attenuation calculations can be explicitly carried out to more accurately simulate the movement and transformation of sanitary sewer flows in the collection system.
An excellent review and comparison between simulated and observed hydrographs of the various numerical methods for solving unsteady flow in simple and compound channels was presented by Chatila (Chatila 2003). In terms of overall performance, the Muskingum unsteady solution scheme compared favorably and proved to be a simple and reliable method avoiding complicated mathematical and numerical computations for the cases considered.
Flooding at manholes and wet wells in InfoSewer /H2OMap Sewer Pro is not modeled during an extended period dynamic simulation. Instead, the flows at the flooded structures are conserved and are not lost by the occurrence of flooding at the manholes. In actual flooding situations, flows may be diverted away from the flooded structures and out of the sewer collection system. However, a surcharged pipe or manhole is generally an indication of poor hydraulic performance of the sewer system. InfoSewer/Pro assumes that the downstream pipes of flooded manholes are flowing full.
Sanitary sewer systems are typically designed to flow less than full and have an upper-pressure limit of 4 to 6 psi. Sewer systems operating under pressurized flow condition may run the risk of violating local, state, and federal health codes. The USEPA regulations would also be in violation if raw sewage were discharged into the ground, potentially affecting groundwater. For these reasons, pressurized flows in sanitary sewers not designed to sustain pressures can be dangerous and in some cases can present an unlawful activity.

Surcharge image1138[5]

Sewer pipes can flow full with water under pressure, which is often known as surcharge flow. Surcharge flow occurs in under-designed pipes (or under extreme flows) when the flow rate Q exceeds the full pipe capacity Qf .
Flow conditions are unstable at the transition between open-channel (free surface) flow and full pipe flow. A wave or surge can induce full flow in the pipe in the unstable range. Surcharge in sewer pipes is modeled in InfoSewer /H2OMap Sewer Pro using energy and continuity principles. The energy equation between sections 1 and 2 in a pipe can be written as:
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Here z denotes the invert elevation; d represents the water depth; and HL designates the head loss between sections 1 and 2. The energy equation is used to determine the difference in hydraulic grade line elevation (which is added at the upstream manholes) needed to pass downstream flows under the surcharge condition.
The procedure for analyzing surcharge in sewer pipes is illustrated using the figure below as a reference.
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Assuming that pipe 4 (between manholes 4 and 5) is under-designed, Q4 will exceed its full flow capacity and the hydraulic grade line at manhole 4 will increase based on energy consideration to allow Q4 to pass through pipe 4 (note that water always flows from higher to lower energy) as continuity must be satisfied. This forces the hydraulic grade line at manhole 3 to increase in order for Q3 to pass through pipe 3. The procedure continues upstream until the slope of the energy grade line needed to transport the flow allows open-channel flow condition to occur in the pipe. The projected hydraulic grade line will then intersect the uniform water surface flow to complete the backwater curve.
The energy equation is also used to model the flow in siphons, which can occur in adverse pipes. InfoSewer assumes that the siphon flows full, with a continuous liquid column throughout it.

Flow Attenuation image1138[6]

When a flow hydrograph is injected and propagates downstream in sewer pipes the bulk of the water will normally travel slower than its induced disturbance or wave. That is, if the water is injected with a tracer then the tracer lags behind the disturbance. The speed of the disturbance depends on parameters such as depth, width and flow velocity. This disturbance will tend to flatten, or spread out, the peak flow in the downstream direction along the sewer pipes.
Flow attenuation in a sewer system is defined as the process of reducing the peak flow rate by redistributing the same volume of flow over a longer period of time as a result of friction (resistance), internal storage and diffusion along the sewer pipes. InfoSewer /H2OMap Sewer Pro uses the distributed Muskingum-Cunge flow routing method based on diffusion analogy, which is capable of accurately predicting hydrograph attenuation or peak flow damping effects (peak subsidence). The method is attractive since the routing parameters can be directly calculated as a function of pipe and flow properties, is applicable for a wide range of flow conditions, and does not require calibration or any iterative scheme. The Muskingum coefficients are derived from the pipe diameter, length, discharge, dynamic wave celerity, and slope of the flow. The magnitude of attenuation depends on parameters such as the peak discharge, the curvature of the hydrograph, and the width of flow. An example of flow attenuation process as a hydrograph is routed through a sewer system is illustrated in the figure below.
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Hydrograph Aggregation/Flow Accumulation image1138[7]

Proper aggregation of multiple hydrographs with distinct time steps is essential in a sewer collection system as the flows are routed in both time and space. Aggregation normally occurs when laterals are merging around manholes and wet wells. This can create offset of time-steps, which can affect accurate determination of flow peaks and volumes. InfoSewer /H2OMap Sewer Pro utilizes a highly accurate dynamic hydrograph aggregation method that allows preservation of both flow peaks and flow volumes when multiple hydrographs with different time steps are added. The method is Lagrangian in nature and tracks the hydrograph ordinates as they are transported along the sewer pipes and mix together at manholes and wet-wells. A variable time step is used to minimize numerical dispersion, enhance stability, and maximize computational efficiency. See the User Guide for more information on Extended Period Simulations.

3. H2OMap SWMM and InfoSWMM image1130[2]

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4. ICM and ICM SE image1136[2]

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5. A common look at the Equations for ICMimage1136[3], ICM  SEimage1136[4]. InfoSWMMimage1130[3] and H2OMap SWMMimage1130[4]

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6. ICM 2D and InfoSWMM 2D Equations

ICM 2D image1136[5] and InfoSWMM image1130[5] share the same computational engine as described on the Innovyze Blog
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As the scheme is an explicit solution it does not require iteration to achieve stability within defined tolerances like the ICM 1D scheme or the iterative solution in InfoSWMM.  Instead, for each element, the required timestep is calculated using the Courant-Friedrichs-Lewy condition in order to achieve stability, where the Courant-Friedrichs-Lewy condition is

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Time Step for 2D Models

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