Education

Research Description

Dr. Mahinthakumar is interested in large scale modeling of subsurface flow and transport, parallel and distributed computing, optimization and inverse problems, water distribution system analysis

Honors and Awards

• National Science Foundation CAREER Award, 2003
• NCSU Faculty Research and Professional Development Award, 2001
• US Fulbright Scholar to South Africa, 2017

Publications

Supervised Machine Learning Approaches for Leak Localization in Water Distribution Systems: Impact of Complexities of Leak Characteristics

Basnet, L., Brill, D., Ranjithan, R., & Mahinthakumar, K. (2023), JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 149(8). https://doi.org/10.1061/JWRMD5.WRENG-6047

A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks

Daniel, I., Pesantez, J., Letzgus, S., Fasaee, M. A. K., Alghamdi, F., Berglund, E., … Cominola, A. (2022), JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 148(6). https://doi.org/10.1061/(ASCE)WR.1943-5452.0001535

Correcting Power Leakage Equation for Improved Leakage Modeling and Detection

Kabaasha, A. M., Zyl, J. E., & Mahinthakumar, G. ''K. (2020), JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 146(3). https://doi.org/10.1061/(ASCE)WR.1943-5452.0001172

GRAPS: Generalized Multi-Reservoir Analyses using probabilistic streamflow forecasts

Xuan, Y., Ford, L., Mahinthakumar, K., De Souza Filho, A., Lall, U., & Sankarasubramanian, A. (2020), Environmental Modelling & Software, 133, 104802. https://doi.org/10.1016/j.envsoft.2020.104802

Reducing error in water distribution network simulations with field measurements

Ricca, H., Patskoski, J., & Mahinthakumar, G. (2020), JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH, 8(1), 15–27. https://doi.org/10.1080/23249676.2020.1719218

Modeling chloramine decay in full-scale drinking water supply systems

Ricca, H., Aravinthan, V., & Mahinthakumar, G. (2019), WATER ENVIRONMENT RESEARCH, 91(5), 441–454. https://doi.org/10.1002/wer.1046

Multiphase Procedure to Design District Metered Areas for Water Distribution Networks

Pesantez, J. E., Berglund, E. Z., & Mahinthakumar, G. (2019), JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 145(8). https://doi.org/10.1061/(ASCE)WR.1943-5452.0001095

Repurposing an energy system optimization model for seasonal power generation planning

de Queiroz, A. R., Mulcahy, D., Sankarasubramanian, A., Deane, J. P., Mahinthakumar, G., Lu, N., & DeCarolis, J. F. (2019), Energy, 181, 1321–1330. https://doi.org/10.1016/j.energy.2019.05.126

The role of cross-correlation between precipitation and temperature in basin-scale simulations of hydrologic variables

Seo, S. B., Das Bhowmik, R., Sankarasubramanian, A., Mahinthakumar, G., & Kumar, M. (2019), Journal of Hydrology, 570, 304–314. https://doi.org/10.1016/j.jhydrol.2018.12.076

Assessing the effects of water restrictions on socio-hydrologic resilience for shared groundwater systems

Al-Amin, S., Berglund, E. Z., Mahinthakumar, G., & Larson, K. L. (2018), Journal of Hydrology, 566, 872–885. https://doi.org/10.1016/j.jhydrol.2018.08.045

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Grants

Coal Ash Groundwater Modeling Review and Technical Support

NC Department of Environmental Quality (DEQ)(1/01/22 - 6/30/24)

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Coal Ash Groundwater Modeling Review and Technical Support

Sponsor: NC Department of Environmental Quality (DEQ)

Start Date: 1/01/22

End Date: 6/30/24

Abstract

The Department, in conjunction with Dr. Kumar Mahinthakumar acting in the capacity of a modeling advisor, will conduct a review of six groundwater flow and transport (F-T) models that are being developed by Dr. Falta at Clemson University to help select basin-closure options and evaluate groundwater remediation alternatives. The Division of Water Resources (DWR) will lead a small review team (probably two representatives from DWR and a representative from the Division of Waste Management [DWM]) that will: 1) work with Dr. Mahinthakumar to review the models, 2) request information from Dr. Falta as/if needed, and 3) make recommendations to the Department regarding the quality of the models for their intended use. Both Dr. Mahinthakumar and the Department’s review team will have the capability to run the models and conduct sensitivity analyses. After initial discussions associated with each model, the groundwater model advisor will provide the Department/review team with a technical memorandum for that model. Assistance may be requested from Dr. Falta to initiate model runs so that the Department’s time is spent on the review process itself rather than dealing with software.

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PFI-TT: Real Time Leakage Detection and Pressure Management in Water Distribution Systems Using Routine Pressure Measurement

National Science Foundation (NSF)(8/01/19 - 10/31/23)

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PFI-TT: Real Time Leakage Detection and Pressure Management in Water Distribution Systems Using Routine Pressure Measurement

Sponsor: National Science Foundation (NSF)

Start Date: 8/01/19

End Date: 10/31/23

Abstract

Leakage in bulk water pipelines is a major problem for many water utilities as it leads to significant economic losses and cause service disruptions threatening public safety. Most utilities currently employ intrusive methods based on acoustic or infrared signals that can be expensive, time consuming, and require trained personnel. Non-intrusive methods that currently exist require significant computational resources and are not suitable for real-time application. We have developed extremely fast leakage detection algorithms that are suitable for real-time application. These methods rely on a hydraulic model and routine pressure measurements and were developed as part of an NSF project funded from 2011-2016. In partnership with DC Water, Lakewood City (CA), TAGO (an Internet of Things (IoT) company based in Raleigh, NC), and Citilogics (a smart water analytics company based in Cincinnati, OH) we will conduct a proof-of-concept study for isolated sections of each utilities’ water networks. Data from existing pressure sensors as well as new sensors installed at strategic locations in the network will be used to validate our algorithms using experimentally simulated leakage scenarios. Citilogics will provide necessary expertise and software for processing the real time data suitable for inputs into the hydraulic model from AMI, SCADA, and CMMS systems. TAGO will build an IoT framework for acquiring the pressure data (from sensors) in real-time and for integrating our algorithms and associated filters on cloud resources. Partner utilities will install the pressure sensors as needed and execute the experiment. NCSU will design the experiment, build the hydraulic model and the leak detection analytics, test the IoT architecture, and evaluate the potential for commercialization.

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Groundwater Model Review and Technical Support for NC Coal Ash Waste Facilities

NC Department of Environmental Quality (DEQ)(11/01/18 - 11/30/20)

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Groundwater Model Review and Technical Support for NC Coal Ash Waste Facilities

Sponsor: NC Department of Environmental Quality (DEQ)

Start Date: 11/01/18

End Date: 11/30/20

Abstract

The Department, in conjunction with Dr. Kumar Mahinthakumar acting in the capacity of a modeling advisor, will conduct a review of six groundwater flow and transport (F-T) models that are being developed by Dr. Falta at Clemson University to help select basin-closure options and evaluate groundwater remediation alternatives. The Division of Water Resources (DWR) will lead a small review team (probably two representatives from DWR and a representative from the Division of Waste Management [DWM]) that will: 1) work with Dr. Mahinthakumar to review the models, 2) request information from Dr. Falta as/if needed, and 3) make recommendations to the Department regarding the quality of the models for their intended use. Both Dr. Mahinthakumar and the Department’s review team will have the capability to run the models and conduct sensitivity analyses. After initial discussions associated with each model, the groundwater model advisor will provide the Department/review team with a technical memorandum for that model. Assistance may be requested from Dr. Falta to initiate model runs so that the Department’s time is spent on the review process itself rather than dealing with software.

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Resilience-based Modeling and Analysis to Support Master Planning in Drinking Water Infrastructure Systems

National Science Foundation (NSF)(8/15/18 - 7/31/23)

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Resilience-based Modeling and Analysis to Support Master Planning in Drinking Water Infrastructure Systems

Sponsor: National Science Foundation (NSF)

Start Date: 8/15/18

End Date: 7/31/23

Abstract

Recent surveys of the national water industry warn of looming costs for capital improvements for drinking water systems in the coming decades [1,2]. One AWWA report estimates that over $1 trillion are needed over the next 25 years, and $1.7 trillion over the next 40 years; about half of this investment would cover the renewal and replacement of aging pipes, and half would pay for system expansions to accommodate population changes [1]. At the same time, SCADA and sensor systems, monitoring and modeling software are facilitating real-time operational decision making in water utilities as never before. These short-term operational decisions, as well as capital improvements such as system expansions, equipment or technology upgrades, and price structures, affect system performance with respect to long-term master planning goals such as system resilience, cost and resource sustainability. Currently, no systematic decision support methodologies exist to optimize the timing of the needed investments over the 25 or 40 year horizon, assessing the criticality of these decisions for overall system vulnerability and resource optimization. The proposed project aims to build a resilience modeling framework using a water utility’s in-house data and models to bridge the gap between short-term operations and the medium- and long-term decisions that influence master planning objectives such as system resilience. The objectives of this research are: develop a general purpose resilience modeling framework that integrates computational tools and data to expand the optimization capacity of available data and infrastructure component models from short-term operational to long-term planning horizons; build, demonstrate and test the modeling framework using a case study water utility’s data and models to provide in-house decision support for optimal timing and balance between short-term performance and long-term objectives; and develop an open-source water system resilience library that is compatible with a standard resilience modeling language and water infrastructure system modeling tools.

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Water Distribution Modeling Experiments with the Cary Network

Sensus USA, Inc.(10/01/14 - 7/31/16)

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Water Distribution Modeling Experiments with the Cary Network

Sponsor: Sensus USA, Inc.

Start Date: 10/01/14

End Date: 7/31/16

Abstract

NCSU’s CCEE department will work with Sensus and Town of Cary (ToC) in designing and executing a series of experiments to evaluate NCSU optimization algorithms (Phases 1-3). Phases 1-3 are expected to take 1 year and will require 1 graduate student from the NCSU CCEE department. Depending on the success of these experiments, additional phases may be carried out with the goal of developing software modules for efficient management and real time operation and control of urban water distribution systems. Activities beyond the experimental phase will be contingent on developing a successful intellectual property agreement between NCSU and Sensus.

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Cybersees: Type II: Cyber-Enabled Water and Energy Systems Sustainability Utilizing Climate Information

National Science Foundation (NSF)(9/01/14 - 2/28/21)

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Cybersees: Type II: Cyber-Enabled Water and Energy Systems Sustainability Utilizing Climate Information

Sponsor: National Science Foundation (NSF)

Start Date: 9/01/14

End Date: 2/28/21

Abstract

Continually increasing water demand (due to population growth) and fuel costs threaten the reliability of water and energy systems and also increase operational costs. In addition, both natural climatic variability and the impacts of global climate change increase the vulnerability of these two systems. For instance, reservoir systems depend on precipitation; whereas power systems demand depend on mean daily temperature. Currently, these systems use seasonal averages for their short-term (0-3 months) management, which ignores uncertainty in the climate, thereby resulting in increased spillage and reduced hydropower. While seasonal climate forecasts contain appreciable levels of skill over parts of the US in both winter and summer, the uptake of these forecasts for water and energy systems management has been limited due to lack of a coherent approach to assimilate probabilistic forecasts into management models. We systematically analyze various scenarios that aim at improving the performance of these systems utilizing the multimodel climate forecasts and a high performance computing (HPC) framework.

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WSC - Category 3: Collaborative Research: Water Sustainability under Near-term Climate Change : A Cross-Regional Analysis Incorporating Socio-Ecological Feedbacks and Adaptations

National Science Foundation (NSF)(9/01/12 - 8/31/18)

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WSC - Category 3: Collaborative Research: Water Sustainability under Near-term Climate Change : A Cross-Regional Analysis Incorporating Socio-Ecological Feedbacks and Adaptations

Sponsor: National Science Foundation (NSF)

Start Date: 9/01/12

End Date: 8/31/18

Abstract

The main objective of the proposed research is to understand, identify and quantify uncertainties related to the freshwater sustainability under near-term climate change and population growth by incorporating adaptive responses, feedbacks as well as the needs of hydro-ecological systems and human-environmental systems through a cross-regional synthesis. The specific tasks associated with this objective are: 1) Reduce the uncertainty in targeted hydro-ecological variables under near-term climate change by combining projections from multiple GCMs (from AR5), regional climate models (from NAARCAP) and hydrological models (VIC, SWAT and MODFLOW) 2) Ingest the hindcasts and future projections (i.e., time series) of the targeted variables in reservoir models, groundwater models and ecological models to quantify the resilience and vulnerability of water infrastructure systems and ecological systems over the target basins 3) Identify key policy interventions, supply augmentation and demand reduction measures that will ensure freshwater sustainability under projected population growth and climate change 4) Incorporate the identified consumer feedbacks and societal adaptations with the water infrastructure models through an agent-based model 5) Perform an integrative cross-regional analyses to identify critical tipping points and feedback mechanisms that will reduce the uncertainty in freshwater sustainability under near-term climate change and population growth

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AN ADAPTIVE LEAK DETECTION AND RISK ANALYSIS FRAMEWORK FOR URBAN WATER DISTRIBUTION SYSTEMS

National Science Foundation (NSF)(8/15/11 - 7/31/16)

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AN ADAPTIVE LEAK DETECTION AND RISK ANALYSIS FRAMEWORK FOR URBAN WATER DISTRIBUTION SYSTEMS

Sponsor: National Science Foundation (NSF)

Start Date: 8/15/11

End Date: 7/31/16

Abstract

Urban water distribution systems (WDS) are prone to leaks, deterioration, and contaminant intrusion. The risks associated with these conditions can be spatially diverse due to flow conditions, network characteristics, and external factors. The key to long term sustainability of these systems is to identify high risk regions and develop sound operational procedures and preventive maintenance plans. This project will develop an adaptive leak detection and contaminant intrusion framework that utilizes real time pressure, flow, and water quality data driven by a high performance simulation optimization engine. The research will also develop a risk analysis capability that could be used to analyze economic and public health risks associated with gradual leaks and contaminant intrusion occurring in day to day operations. In a recently completed project funded by NSF, the research team has developed an adaptive high performance simulation-optimization engine and associated optimization methodologies for contaminant source identification and sampling design in water distribution systems. While the proposed work will build on this work to an extent, a number of new developments are planned including: (a) a new simulation-based leak detection and contaminant intrusion detection methodology that uses routinely measured pressure, flow and water quality measurements (e.g., from a SCADA system), (b) a methodology for incorporating spatially varying macro indicators (e.g., pipe attributes) to improve the search process, (c) a methodology for incorporating demand uncertainty in leak detection, and (d) a risk assessment methodology that targets economic losses from leaks and public health risks from contaminant intrusion. The developed framework could be used by decision makers to make operational decisions and to develop long term maintenance and expansion plans. The system can also be used by decision makers to evaluate the risk reduction potential of different response and maintenance actions. In collaboration with a local utility, the leak detection and risk assessment framework developed through this research will be applied and validated using data from a mid-sized urban area. The major objectives are to: (a) develop a parallel simulation-optimization leak detection and contaminant intrusion methodology that uses routinely measured pressure and water quality data; (b) develop a Markov Chain Monte Carlo (MCMC) methodology to incorporate prior information and demand uncertainty into the leak and contaminant intrusion detection framework; (c) develop a methodology for evaluating economic and public health risks associated with leaks and contaminant intrusion; (d) test and evaluate the computational framework and the associated components for a mid- sized urban area; and (e) integrate and disseminate the research results in classroom teaching for college students, as well as continuing education and short courses for practitioners. Intellectual Merit. While complementing other related NSF funded efforts, the proposed research hinges on a number of paradigm shifting and transformational developments, including the conjunctive use of routine operational measurements and external factors for generating leak and contaminant intrusion maps in real time, incorporating uncertainties in a probabilistic framework, and the use of high performance computing technologies to enable real time computation. Broader Impact. Besides addressing one of the nation?s critical infrastructure security priorities in a real setting, the project contributes in general to the development of adaptive simulation methods and optimization algorithms that can harness high end computing resources. The computational framework developed through this proposal will set the stage for using emerging automated data collection systems for real time characterization.

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Development of a Scalable Parallel I/O Module for Environmental Management Applications

US Dept. of Energy (DOE)(7/27/10 - 9/30/12)

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Development of a Scalable Parallel I/O Module for Environmental Management Applications

Sponsor: US Dept. of Energy (DOE)

Start Date: 7/27/10

End Date: 9/30/12

Abstract

This project entails the development and optimization of software algorithms that read/write data sets from/to parallel file systems in an efficient and scalable manner. In this context, scalable means that the simulators read/write performance does not degrade significantly as the number of cores grows. It is accepted (and well understood) that parallel I/O will not scale as efficiently as the parallel scientific algorithms in the simulator due to hardware limitations beyond the scope of the project. The products generated by the project will include software code composed of functions and/or subroutines that read and write data sets in parallel and perform the read/write operations associated with simulation checkpoint/restart.

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PERI: Application Engagement

US Dept. of Energy (DOE)(6/07/07 - 12/15/12)

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PERI: Application Engagement

Sponsor: US Dept. of Energy (DOE)

Start Date: 6/07/07

End Date: 12/15/12

Abstract

The overall goal of this SciDAC-2 project is to develop and maintain a world class Performance Engineering Research Institute (PERI) that will address the growing challenges of achieving good performance of grand challenge applications on highly complex emerging high-end computing (HEC) systems. This center will function on the basis of a tripartite research plan encompassing: (1) performance modeling and prediction; (2) automatic performance optimization; and (3) performance engineering of high profile applications. The work performed in this proposal will primarily address the third component with appropriate interactions with the first two components. The major thrust of this third major component, application engagement, is to maintain direct interactions with SciDAC applications through passive, active, or ?tiger team? liaisons. Approximately 30% of the total PERI resources are devoted to this activity, including tiger teams that will focus on particular codes.

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