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Spotlight

Bhagirath Addepalli

Bhagirath Addepalli

Hometown: Hyderabad, India

Program: PhD (Graduated May 2012)

Current Position: Microsoft Program Manager

Research Interests: Fundamental and applied research in fluid dynamics, inverse and optimization techniques, and statistical modeling and analysis of data. Specifically, interests include: laboratory experiments, computational fluid dynamics, Lagrangian random-walk modeling, development of novel case-specific objective functionals (metrics) for inverse problems, development of efficient and robust optimization and inversion techniques spanning deterministic, stochastic (frequentist), and Bayesian methods, multiple criteria decision making (MCDM - Pareto optimality), linear and nonlinear regression techniques for stochastic modeling, statistical modeling of time series data, model selection in inverse problems.

Publications:
A) Journal Publications / Pre-prints:
a) Addepalli, B., K. Sikorski, E.R. Pardyjak and M.S. Zhdanov. Source characterization of atmospheric releases using stochastic search and regularized gradient optimization. Inverse Problems in Science and Engineering, 2011. 19(8): p. 1097-1124.
b) Addepalli, B. and E.R. Pardyjak. A pseudo-metric to handle zero measurements and predictions in atmospheric inverse-source problems. Under review. Submitted to Inverse Problems in Science and Engineering.
c) Addepalli, B. and E.R. Pardyjak. Investigation of flow structure in step-up street canyons. Ready to be submitted to Boundary Layer Meteorology. Pre-print available upon request.
d) Addepalli, B. and E.R. Pardyjak. Study of flow fields in asymmetric step-down street canyons. Ready to be submitted to Boundary Layer Meteorology. Pre-print available upon request.
e) Addepalli, B., E.R. Pardyjak, P. Willemsen and D.E. Johnson. Urban form optimization for air quality applications using simulated annealing and genetic algorithms. Ready to be submitted to Atmospheric Environment. Pre-print available upon request.
f) Addepalli, B. Markov Chain Monte Carlo annealing for atmospheric inverse-source problems. To be submitted to Inverse Problems in Science and Engineering. Pre-print available upon request.

B) Peer-reviewed Conference Publications:
a) Addepalli, B., K. Sikorski, E.R. Pardyjak and M.S. Zhdanov. Quasi-Monte Carlo, Monte Carlo, and regularized gradient optimization methods for source characterization of atmospheric releases. in Dagstuhl Seminar Proceedings 09391, Algorithms and Complexity for Continuous Problems. 2009. Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany.
b) Addepalli, B. and E.R. Pardyjak. Study of flow fields in asymmetric step-down street canyons. in The International Workshop on Physical Modelling of Flow and Dispersion Phenomena (PHYSMOD). 2007. University of Orleans, France.

C) Conference Publications:
a) Pardyjak, E.R., Addepalli, B., et al., Impact of green infrastructure on urban microclimate and air quality, in the 8th International Conference on Urban Climate - ICUC 8. 2012: Dublin, Ireland.
b) Addepalli, B. and C. Sikorski, A note on objective functions for atmospheric inverse-source problems, in second National Conference in Advancing Tools and Solutions for Nuclear Material Detection. 2011: Salt Lake City, UT.
c) Addepalli, B. and C. Sikorski, Efficient adaption of simulated annealing and genetic algorithms to atmospheric inverse-source problems, in AIChE Annual Meeting. 2010: Salt Lake City, UT.
d) Addepalli, B. and C. Sikorski, Tools to characterize the source of hazardous releases, in 1st National Conference on Advancing Tools and Solutions for Nuclear Material Detection. 2010: Salt Lake City, UT.
e) Addepalli, B., M.J. Brown, E.R. Pardyjak and I. Senocak. Evaluation of the QUIC-URB wind model using wind-tunnel data for step-up street canyons, in Seventh Symposium on the Urban Environment. 2007: San Diego, CA.
f) Addepalli, B. and E.R. Pardyjak. 2D PIV Measurements of street canyon flow for buildings with varying angles and separation distances. in American Meteorological Society Sixth Symposium on the Urban Environment. 2006: Atlanta, GA.

D) Conference Presentations:
a) Addepalli, B., E.R. Pardyjak, P. Willemsen and D.E. Johnson. GPU-MCDM: A new module of the Quick Urban and Industrial Complex (QUIC) dispersion modeling system for urban form optimization. in the 8th International Conference on Urban Climate - ICUC 8. 2012: Dublin, Ireland.
b) Addepalli, B., E.R. Pardyjak, P. Willemsen and D.E. Johnson. Development of a multiple criteria decision making (MCDM) tool for urban form optimization. in 92nd AMS Annual Meeting. 2012: New Orleans, LA.
c) Addepalli, B., E.R. Pardyjak, P. Willemsen and D.E. Johnson. Urban form optimization for air quality applications using simulated annealing and genetic algorithms. in Ninth Symposium on the Urban Environment. 2010: Keystone, CO.
d) Addepalli, B., M.J. Brown, E.R. Pardyjak and I. Senocak. Investigation of the flow structure around step-up, step-down, deep canyon, and isolated tall building configurations using wind-tunnel PIV measurements, in Seventh Symposium on the Urban Environment. 2007: San Diego, CA.
e) Addepalli, B., E.R. Pardyjak and M.J. Brown. The effect of geometry on the wake structure of a surface mounted obstacle. in 60th Annual Meeting of the APS Divison of Fluid Dynamics. 2007: Salt Lake City, UT.
f) Addepalli, B. and E.R. Pardyjak. Experimental investigation of the effect of Reynolds number and HΔ value on flow fields in street canyons with cubical Buildings. in American Physical Society, 59th Annual Meeting of the APS Division of Fluid Dynamics. 2006: Tampa Bay, FL.
g) Addepalli, B. and E.R. Pardyjak. 2D PIV measurements of flow between a pair of model buildings with varying geometries. in American Physical Society, 58th Annual Meeting of the Division of Fluid Dynamics. 2005: Chicago, IL.

E) Technical Reports:
a) Addepalli, B., C. Sikorski and E.R. Pardyjak. Source Characterization of atmospheric releases using quasi-random sampling and gradient optimization. Report submitted to the School of Computing, University of Utah. Report number: UUCS 09-001.
b) Nelson, M., B. Addepalli, D. Boswell and M.J. Brown. QUIC Start Guide (v 4.5). Los Alamos National Labratory. LA-UR-07-2799.

Contact: addbugs@gmail.com

Localized Distributed Power Generation: Economically Robust, Demand-Optimized Placement of Urban Energy Production Systems

Surface Temperature simulations from QUIC ENERGY

Supported by the National Science Foundation - NSF CBET/ENG 1512740

PIs: Eric Pardyjak, Rob Stoll, Amanda Smith

Students:
Arash Nemati Hayati (PhD student)
Carlo Bianchi (PhD student)
Hanieh Esagh (MS student)
Rich Didier (MS student)

Overview: We propose to develop an integrated building simulation and optimization framework for use in making decisions regarding placement of distributed power generation installation and its interaction with the surrounding built environment. The framework will be used to select and place distributed generation within cities, given the unique energy demands of particular buildings, the changes in energy demand and microclimate due to the presence of a power generation source, and the effect of climate and microclimate on power generation potential. Specifically, we are interested in understanding how urban infrastructure and microclimate affects energy demands and how effectively and economically a simple local generation system can meet those demands. We will focus on two commonly and technologically mature prime movers, the solar photovoltaic array and the natural gas turbine-generator set, simulated in conjunction with groups of residential or commercial buildings. Four U.S. cities are selected as locations for case studies to test the system, representing a range of climate types and average electricity prices: Phoenix, Salt Lake City, Minneapolis, and Atlanta. We hypothesize that the placement of a solar array or natural gas generator will be improved for different climates when microclimate and individual building characteristics are taken into account, compared with predictions made for a single building using climate data alone. We further hypothesize that including the benefits of ecosystem services into the optimization process will produce different urban landscape forms and energy installations.

Intellectual Merit: There is a critical need for decision makers to have a place-based framework that allows them to understand the complex interactions and tradeoffs between demand moderating urban form options, and distributed power generation opportunities. We hypothesize that a site-specific optimal mix of distributed power generation and microscale building demand reduction strategies exists that can minimize both internal and external costs resulting in more sustainable cities. Previous work using the QUIC-EnviSim package has demonstrated the importance of considering the built environment together with its local, natural environment for calculating energy and mass fluxes in the urban environment; and has produced a computational software package capable of performing these advanced simulations. Similarly, meeting the energy demands of a building should depend on consideration of the building’s environment and neighboring buildings; and a computational package, EnergyPlus, capable of performing detailed individual building simulations, is available from the DOE. We propose to combine the two simulation packages in such a way that building simulations conducted in EnergyPlus will use QUIC-EnviSim environmental data, and building performance calculations from EnergyPlus will inform the QUIC simulations. We will also add custom code simulating the two proposed types of distributed generation systems, and continue to license the new software as open-source, so that additional prime movers can be incorporated.

Broader Impacts: These simulation tools will be developed in a Multi-criteria Decision Making environment designed to aid urban planners, engineers and architects in placing and designing buildings and surrounding landscaping in ways that integrate best with distributed generation capacity. Through workshops, stakeholder’s input will be directly integrated into the project, enabling a more fluid integration with practioners. The understanding gained from this project will guide utility companies and public utility planners in developing plans for expanding power generation in urban areas while reducing the investment risk associated with additional central generation capacity. Further, the knowledge gained from this project will help communities and developers in placing distributed power generation within existing groups of buildings in a way that provides an economic benefit to the power consumers. Simulation data results for the cities and scenarios will be made available in an archival form to researchers, and the modified EnergyPlus-QUIC-EnviSim model will be released upon request, along with the custom power generation packages and instructions for developing and incorporating other types of DG. This project will provide interdisciplinary training in engineering, computer science, economics, urban planning, and energy policy for graduate students directly via involvement in the project, as well as more broadly through a new Energy Systems course that will be developed. A summer outreach program will also be developed to target underrepresented groups.

Publications:

  • Nemati Hayati, A., Rob Stoll, J.J. Kim, T. Harman, M.A. Nelson, M.J. Brown, and E. R. Pardyjak, Comprehensive evaluation of fast-response, Reynolds-Averaged Navier-Stokes, and Large-Eddy Simulation methods against high spatial resolution wind-tunnel data in step-down street canyons, Boundary-Layer Meteor., 164(2), 217-247, 2017.
  • Girard, P., D. Nadeau, E.R. Pardyjak, M. Overby, P. Willemsen, R. Stoll, B.N. Bailey, and M.B. Parlange, Validation of the QUIC-URB wind solver and QESRadiant radiation-transfer model using a dense array of urban meteorological observations, Urban Climate, DOI: 10.1016/j.uclim.2017.08.006, 2017.
  • Rahman, A., A.D. Smith. “Predicting Fuel Consumption for Commercial Buildings with Machine Learning Algorithms,” Submitted to Energy and Buildings, revised version under review.
  • Bianchi, C., S.M. Lucich, A.D. Smith. “Demand matching algorithm for photovoltaic array sizing and sensitivity analysis to temporal availability of building energy data,” Submitted to Sustainable Energy Technologies and Assessments, under review.
  • Pardyjak, E.R. and R. Stoll, Improving measurement technology for the design of sustainable cities, Meas. Sci. and Technol., 28(9), 092001, 2017.