Dr. Patricia Gharagozloo is a Fluid and Thermal Sciences Engineer at Sandia National Laboratories[I] in Livermore, CA. Gharagozloo holds a PhD in Mechanical Engineering from Stanford University, and she is an expert in heat transfer and fluid dynamics modeling simulation and code development. Her current research spans multiple fields, including algae-based biofuels, advanced cooling techniques and reactive flows. At Sandia, Gharagozloo leads the development of the validated algae growth model and collaborates with industry and academic partners to validate and expand this model. She conducts computational fluid dynamics (CFD), heat transfer and growth kinetics modeling of algae photobioreactors, particularly outdoor raceway ponds.
In addition to her research, Gharagozloo is dedicated to supporting diversity initiatives in the STEM fields. Through Sandia, she has volunteered for the LHS Engineer Academy to mentor high school students on science fair projects in green energy and the Sandia Women’s Connection Math & Science Awards to advise and encourage outstanding young women in math and science. During her time at Sandia, she also has served as a team lead for a Diversity and Inclusion Action Team to reform hiring policies and started a parenting group for working scientists. Outside of the lab, she enjoys spending time and traveling with her husband and two children.
1. Why is computational modeling essential within the algal biomass value chain?
Computational modeling has the ability to impact algal biomass production by explaining the reasons behind productivity reductions, optimizing designs and methods to determine the best ways to improve productivity, and predicting productivities given various conditions for input into techno-economic analysis (TEA) and life-cycle assessment (LCA) models. Algal biomass growth depends on many factors, including nutrient availability, light, temperature, pH, and salinity. The computational model decouples the effects of these factors and determines the primary causes of reduced growth. By adjusting the parameters that we can control, we can optimize pond designs and cultivation methodologies to improve algal productivities. Additionally, the variations in algal productivities predicted from our model by varying the inputs to the pond and methods for cultivation can be input into TEA and LCA models to determine the energetic and economic impacts of these changes.
2. What sparked your interest in applying dynamic modeling approaches to evaluating algal biomass systems?
After joining Sandia, I was interested in getting involved in energy-related research. A co-worker was applying a model for algae growth in large estuaries to smaller raceway ponds. An opportunity arose for me to convert the model to a more flexible CFD code (e.g., Fluent). After coding the initial algae growth model and adding in salinity effects, it became apparent that a fully integrated CFD and growth model could facilitate improved design of cultivation systems. Since then, I have been working to validate the fully integrated model to effectively predict fluid-flow regimes and temperature variations in ponds of various sizes.
3. What are the major implications for algal biomass production that you have discovered through your ATP3 work?
One of the main contributions of our model has been looking at the effect of harvesting frequency, dilution rate and concentration on algae productivity. We have identified potential productivity improvements by changing harvesting frequency and dilution rate. We also have shown that the optimal harvesting strategy changes due to weather and must be varied between the seasons.
You can read more about our work in modeling algal growth through the following publications:
Gharagozloo et al. (2014) Analysis and modeling of Nannochloropsis growth in lab, greenhouse, and raceway experiments. Journal of Applied Phycology. 26(6): 2303-2314. http://doi.org/10.1007/s10811-014-0257-y
Drewry et al. (2015) A Computational Fluid Dynamics Model of Algal Growth: Development and Validation. Transactions of the ASABE. 58(2): 203-213. http://doi.org/10.13031/trans.58.10372
4. How can ATP3 provide support for other groups interested in applying modeling to their algal biomass systems?
ATP3 can support modeling of algal biomass systems to help design and optimize systems, select appropriate strains and predict productivities based on weather data for the geographic locations of interest. Contact us to find out more information on how to partner with ATP3.
[I] Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. Sandia has major R&D responsibilities in national security, energy and environmental technologies and economic competitiveness.