Quigley, Marcus

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Engineering Conferences InternationalECI Digital ArchivesFifty Years Of Watershed Modeling - Past, PresentAnd Future Proceedings2012Beyond the Desktop: Embedded Modeling andCloud Based Real-Time Monitoring and ControlMarcus QuigleyGeosyntec, USAFollow this and additional works at: http://dc.engconfintl.org/watershedPart of the Civil and Environmental Engineering CommonsThis Article is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion in Fifty Years OfWatershed Modeling - Past, Present And Future by an authorized administrator of ECI Digital Archives. For more information, please contactfranco@bepress.com.Recommended CitationMarcus Quigley, "Beyond the Desktop: Embedded Modeling and Cloud Based Real-Time Monitoring and Control" in "Fifty Years OfWatershed Modeling - Past, Present And Future", A.S. Donigian, AQUA TERRA Consultants; Richard Field, US EPA (retired);Michael Baker Jr., Inc. Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/watershed/11Beyond the Desktop: Embedded Modeling and Cloud Based Real-Time Monitoring and ControlMarcus Quigley, P.E., D.WREPrincipal, Brookline, MAAlex Bedig, Project Engineer, Brookline, MAOverview• Background:– Initial Research Problem– The Internet-of-Things (IoT)– Types of Cloud Computing– Development of the IoT for Infrastructure Monitoring and Control (DRTC/OptiRTC)• Modeling-as-a-Service (MaaS)• What does all of this look like in the real world?• Closing thoughtsInitial Research Problem• Find the least expensive most flexible  means for monitoring and controlling  the physical environment and integrating internet based datastreams.Patent # 60/850,600 and 11/869,927Internet-of-Things (IoT)• Definitions:– Extending the virtual internet to physical objects– Physical computing – Enabled through IP based field deployed gatewayshttp://press.teleinteractive.net/media/blogs/tialife/InternetofThingsVector.svgSource: Constellation ResearchPerspectives on Internet-of-Things• National Intelligence Council - “Disruptive civil technologies: six technologies with potential impacts on US interests out to 2025”• Likely rapid adoption and ubiquity in a number of civil environments (e.g., water)• Cisco IBSG predicts there will be 25 billion devices connected to the Internet by 2015 and 50 billion by 2020.Types of CloudsInternet Based Weather Forecast or other internet data sources (Web service API)User Interface Web Services and User DashboardsAzure Tables/BlobsOptiRTC Service PlatformOptiRTC Data Aggregator and Decision SpaceData Logging and Telemetry SolutionsField Monitoring and Control(Sensors, Gauges, and Actuators)AlertsEmailTweetSMSVoice AutodialRapid Deployment Field “Kits”With Wireless SensorsArchitecture Highlights• Redundant across all cloud infrastructure.• Fully virtualized for fast recovery and upgrades without downtime.• Capable of allocating Compute resources differently for different tasks (web services, data harvesting, real-time and non real-time processing.• Reliable, flexible, accessible, secure, and proactively adaptive IT basis in Windows Azure.OptiRTCArchitectureIoT GatewayData Services/Sensors /ActuatorsClients, Field Technicians, and ConsultantsWindows AzureLoad balancerPublish/subscribe communicationsWeb RolesData Harvesting Relational, system Worker RolesData Processing Worker RolesNon Real-Time Processing RolesWork Queue3rd-partyServicesPost-Relational, and Distributed File System Data Storage Services with Geo-redundancyOptiRTCArchitectureIoT GatewayData Services/Sensors /ActuatorsClients, Field Technicians, and ConsultantsWindows AzureLoad balancerPublish/subscribe communicationsWeb RolesData Harvesting Relational, system Worker RolesData Processing Worker RolesNon Real-Time Processing RolesWork Queue3rd-partyServicesPost-Relational, and Distributed File System Data Storage Services with Geo-redundancyData Processing Roles“Post-Actions”• Algorithms within .NET implementations compliant with the OptiRTC post-action sequence interface.• Include simple logical operations, remote control operations, alerting and email notifications• …and complete advanced modeling routines.• This is just “Modeling-as-a-Service” (MaaS)• Shift from thinking that models are “products”MaaS SWMM Implementation• Downloaded SWMM 5.0.022 computational engine from http://www.epa.gov/nrmrl/wswrd/wq/models/swmm/swmm50022_engine.zip• Re-targeted compilation at x64 platform• Rebuilt SWMM engine for 64-bit Windows environment• Added startup installation of 64-bit C++ runtime library to OptiRTC DataProcessor VM image• Developed C# wrapper around SWMM engine interface• Configured DataProcessor VM emulation of NFTS file system to provide cache for SWMM engine I/OExample Use Case for SWMM MaaS• Use SWMM 5 to evaluate pre-development hydrologic conditions for a site in real-time– Use pre-development SWMM model– Use historical site precipitation record– Add last 48 hours conditions to calculate expected state. (or generate hot-start file from previous conditions)• Report output to Data Processor Roles– Real-time dashboard reporting– Use in distributed infrastructure control– “Match” Pre-development conditions (above Qc and below Qmax)– Distribution and publication via OptiRTC HTTP APIExample of MaaS Workflow - Setup• Edit model .INP input file to allow for run-time configuration of file system directory structure• Upload input file and necessary input data files to OptiRTC cloud storageDetermine real-time data source to use as current •precipitation record• Determine which model objects to report • Link results datastreams via control post-actions• Eventually completely through web API as wellMaaS SWMM Process in OptiRTCData HarvesterRolesVirtual file Data ProcessorRolesTime-series Object Store3) Contingent on SWMM output, real-world infrastructure state automatically changed in real-timesystemWeb ServicesRoles1) On-site rainfall  data collected in real-time via IoT web services 2) Pre-development SWMM model run with last 3 days of on-site rainfall data input4) Time-series results extracted from SWMM output and persisted in cloud-based data store.5) Public-facing web dashboards display recent system history and provide manual override options.Some Other Use Cases for MaaS• Self-calibration of models from streaming empirical data• Hydrologic/Hydraulic forecasting and control– DSS – Reservoir Operation– Light weight flood warning systems• Batch Model Processing (scale up/scale down)• Other boundary conditions (stream flow)• Adaptive management • “time shifted” hydrology• Integrated QA/QC – everything is preserved• There are many more….What does this look like in the real world?CollaboratorsTechnology Application:Advanced Rainwater Harvestingand Harvesting System RetrofitsTechnology Application: Advanced Rainwater HarvestingSimplest Definition: Drain storage in advance of predicted rainfall or other triggerPilot System: NC StateAdvanced Rainwater HarvestingInternet Gateway (Powered by ioBridge and OptiRTC)CisternsPilot System: NC State Advanced Rainwater HarvestingAutomatic Drain ValveIrrigation PumpOverflows from TanksPilot System: NC StateAdvanced Rainwater HarvestingNC State PilotSystem Behavior Week of 9/20/2011Forecast Datastream70% ThresholdNC State PilotSystem Behavior Week of 9/20/2011QPF and POP Forecast Datastream (Threshold of 70%)NC State Pilot – Dashboard (1-min refresh)System Behavior Week of 4/5/2012 11:52 AMNC State Pilot – Dashboard (1-min refresh)System Behavior Week of 4/5/2012 2:06 PMNC State Pilot – Dashboard (1-min refresh)System Behavior Week of 4/6/2012 12:14 AMNC State Pilot – Dashboard (1-min refresh)System Behavior Week of 4/6/2012 12:14 AMNC State Pilot – Dashboard (1-min refresh)System Behavior Week of 4/6/2012 8:38 AMNC State Pilot – Dashboard (1-min refresh)System Behavior Week of 4/6/2012 3:34 PMNC State PilotAnalysis of Monitoring Results • 3.5 month period• Captured 90.6% of the total runoff volume.• Conventional rainwater harvesting system with same demand profile would have captured  48.7% of the total runoff volume NC State Pilot – Dashboard (1-min refresh)System Behavior 9/172012 11:12 PMNC State Pilot – Dashboard (1-min refresh)System Behavior 9/17/2012 11:12 PMNC State Pilot – Dashboard (1-min refresh)System Behavior 9/18/2012 7:10 AMNC State Pilot – Dashboard (1-min refresh)System Behavior 9/18/2012 9:16 AMPilot Site: Washington, DCEngine House #3Pilot Site: Washington, DC - Engine House #3: DesignEngine House #25: DesignDDOE Pilot as Potential Prototype Conceptual Rendering of Site in Kansas City, MOProject Site: Rainwater Harvesting Site LocationsSt. Louis, MO Harvesting Garden RenderingRenaissance PlacePre-constructionRenaissance PlaceGeo-fabric placement Pad compactionRenaissance PlaceInitial excavation Cistern placement (10,000 gal)Harvesting system cisterns Controlled discharge valveChattanooga Main Terrain Park Harvesting RetrofitChattanooga Main Terrain Park Harvesting RetrofitTechnology Application: Detention/Retention/Flood Control RetrofitsDepth Time Series and Average Hydraulic Residence Time for Passive Outlet Technology Application: Modeled Wetland Pond/water Feature RetrofitsNorth Carolina Design ( collaboration with Bill Hunt)Average Hydraulic Residence Time (hrs)13 daysDepth Time Series and Average Hydraulic Residence Time for Actively Controlled Outlet Average Hydraulic Residence Time (hrs)24 daysBrooklyn Botanical Garden – Pond Control for CSO Mitigation Pond RTC Outlet Control Retrofit – Austin, TXRTC Outlet Control StructureExisting Pond Outlet Control Structure – Austin, TXOutlet Control StructureTechnology Application: Active Blue and Green RoofsTechnology Application: Active Green Roof Make real-time forecast based decisions on when and how much to drain or irrigate the roof Make storage volume available for stormwatervolume and peak control Reduce irrigation wasteActive Irrigation Valve Actively Controlled Green Roof - SAP Headquarters (Collaboration with Roofmeadow)SAP HQ – Dashboard (1-min refresh)System Behavior 9/18/2012 9:12 AMTechnology Application: Controlled Underdrain BioretentionTechnology Application: Controlled Under Drain BioretentionGwinnett County, GAControlled Underdrain Bioretention and Cistern RetrofitTechnology Application: Portable and Wireless Sensor RTC MonitoringWireless OptiRTCPermanent Install WirelessPortable Field KitsCost/Benefit Analysis and ResearchWERF/GLPFCost/Benefit Analysis Cost/Benefit Analysis Comparison of maximum benefit vs. tank storage capacity of controlled and passive system. Cost/Benefit Analysis Comparison of maximum benefit vs. tank storage capacity of controlled and passive system with tank costs incorporated ($5.00/ft3) Closing• Much more fundamental research to be done• Solve the general case (if possible)• Low-cost, reliable, and highly functional sensors and sensor platforms will change everything• Do not fear “dis”-integration and web API strategies.

Beyond the Desktop: Embedded Modeling and Cloud Based Real-Time Monitoring and Control

https://dc.engconfintl.org/cgi/viewcontent.cgi?article=1019&amp;context=watershed

Beyond the Desktop: Embedded Modeling and Cloud Based Real-Time Monitoring and Control

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