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TOUGH Team Participates in Lab-Corps Entrepreneurial Training Program2 min read

by Maryann Villavert on July 19, 2016

Climate and Ecosystem Sciences Division Energy Geosciences Division Events Geochemistry Department Hydrogeology Department

Animation of TOUGH simulation

TOUGH simulation of enhanced oil recovery using steam injection and associated reservoir deformations (animation).


Three members of the TOUGH development team (Yingqi Zhang, Nic Spycher, and Stefan Finsterle)—scientists from the Energy Geosciences Division—participated in Lab-Corps, a six-week entrepreneurial boot camp, or “cohort”, facilitated by the U.S. Department of Energy’s (DOE’s) National Renewable Energy Laboratory, which aims at accelerating the transfer of clean energy technologies from national laboratories to the commercial marketplace.

The team was tasked to evaluate the market potential of LBNL’s TOUGH (Transport Of Unsaturated Groundwater and Heat) suite of multiphase flow simulators for geothermal reservoir engineering and other application areas. Hypotheses about the needs and problems of potential customers were formulated and then tested by conducting 54 interviews with companies and individuals that are directly or indirectly involved with simulation technology. The information gathered in this customer discovery process was continuously analyzed to generate a business model that describes:

  • what the key value propositions are for each customer segment,
  • how a relationship with the customer can be established and maintained,
  • how the technology can be delivered to the customer,
  • what resources and partnerships are required to perform key activities, and
  • how large the costs and revenue streams are likely to be.

The instructions and critical feedback from the Lab-Corps faculty and the active participation in the customer discovery process provided the team with valuable insights into the strengths and shortcomings of the TOUGH simulator, including which current and new application areas are particularly suitable for TOUGH. Moreover, the team was able to more clearly identify the challenges faced by users of the TOUGH software, be they in academia, government research institutes, or private industry.

The TOUGH team speaks with prospective industry partners at Lab Corps "Industry Night" event.  (Paul Zoby, CEO of American Energy Assets and members of the TOUGH team. Photo by J. De La Rosa, National Renewable Energy Laboratory.)

The TOUGH team speaks with prospective industry partners at Lab Corps “Industry Night” event. (Paul Zoby, CEO of American Energy Assets with members of the TOUGH team. Photo by J. De La Rosa, NREL)

Finsterle recalls his experience as “a unique opportunity and learning experience. Not only did we obtain a better understanding of the commercialization process, but also a recognition and appreciation of the way scientific projects in an applied research environment can be improved by approaching technology development from an entrepreneurial perspective.”

As an immediate outcome of Lab-Corps, LBNL’s Innovation and Partnership Office and the TOUGH developers discuss the revision of the licensing and pricing options for the TOUGH software. Go to the TOUGH website to learn more about current code developments, documentation of capabilities and applications, as well as training courses and symposia.

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January 18, 2023

Commercializing Berkeley Lab inventions is an important part of the Lab’s mission, and one that requires strong communication skills. For example, Lab inventors need to be able to pitch their ideas to external partners and potential funders.  The annual Berkeley Lab Pitch Competition occurred on October 27, 2022 and is a part of an entrepreneurship…

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New Report Explores Revolutionary Environmental Artificial Intelligence Infrastructure5 min read

January 10, 2023

In a collaborative effort between the U.S. Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) and DOE’s Advanced Scientific Computing Research (ASCR) program, as well as with community experts, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held from October through December 2021. BER developed the process as the Model-Experiment paradigm, or ModEx, and a report released this fall outlines the key takeaways of last year’s event.

A Q&A With Postdoc Kunxiaoja Yuan3 min read

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  Kunxiaojia Yuan received her Bachelor’s of Engineering in remote sensing and Ph.D. in geographic information engineering from Wuhan University. She is a postdoctoral researcher in EESA, with a research focus on global carbon, energy, and water cycle analysis and model evaluation using machine learning and causal inference. What motivated you to pursue a postdoc…

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