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Convergent Science

From EverybodyWiki Bios & Wiki




Convergent Science is an engineering software company headquartered in Madison, Wisconsin. The company develops, sells, and supports CONVERGE CFD software, a general purpose computational fluid dynamics (CFD) solver. The most common application of CONVERGE CFD software is simulating flow and combustion in internal combustion engines, but other applications include modeling gas turbines, fuel injectors and sprays, exhaust aftertreatment, pumps, compressors, fans, and blowers.

Company History

Company Origins

Convergent Science was established in 1997 as Convergent Thinking LLC in Madison, Wisconsin.[1][2] The company was founded as a CFD consulting firm[3] by a group of graduate students from the University of Wisconsin–Madison Engine Research Center (ERC),[1][4] including Peter Kelly Senecal, Keith Richards, Eric Pomraning, Daniel Lee, and David Schmidt.[5][2] At Convergent Thinking, the founders used the skills they gained during graduate school to provide CFD software support primarily to users of KIVA, an open source CFD software that the founders worked with at the ERC.[3][5] After spending much of their time generating CFD meshes for their customers,[3] they started writing a solver in 2001[6] that could automate mesh generation and thus take it out of the users’ hands. This solver, called MOSES (Modular Open Source Engine Simulation) during development, would later become Convergent Science’s CFD solver, CONVERGE.[7]

2008–Present

In 2008, Convergent Thinking LLC became Convergent Science, Inc.,[7] and CONVERGE CFD software became commercially available.[1][3] At the time, CONVERGE was marketed exclusively for modeling combustion and flow in internal combustion (IC) engines for use primarily in the automotive industry.[1] The company began to grow, and in 2012, a second Convergent Science office was opened in Texas.[6] In 2013, Convergent Science and IDAJ, a Japan-based company that distributes, supports, and provides consulting services for computer-aided engineering (CAE) products, entered into an agreement in which IDAJ would distribute and support CONVERGE in Japan, Korea, and China.[8]

In 2014, Convergent Science acquired majority shares in Ignite3D Engineering GmbH and renamed the company Convergent Science GmbH, thus opening the third Convergent Science office.[9] Convergent Science GmbH is headquartered in Linz, Austria. Also in 2014, Convergent Science held the first CONVERGE User Conference–North America,[6] originally called the CONVERGE User Group Meeting.[10][11]

The fourth Convergent Science office was opened in 2015 in Northville, Michigan,[12] and the fifth office was opened in 2017 in Pune, India [30, 44]. Also in 2017, the first CONVERGE User Conference–Europe was held in Vienna, Austria.[6][13] In 2018, the company had five office locations, distributors in North America, Europe, and Asia, and over 100 employees.[14]

Company Name

Convergent Science, Inc. refers to the American portion of the company; Convergent Science GmbH refers to the European branch of the company; and Convergent Science India, LLP refers to the Indian portion of the company. Convergent Science is an umbrella term that encompasses all branches of the company.[6]

Leadership

Convergent Science is structured such that there is no president. Instead, the company is lead by four vice presidents who treat their leadership role as a partnership.[3][5][15]

Current Leadership

Peter Kelly Senecal, Keith Richards, Eric Pomraning, and Daniel Lee are all co-founders, co-owners, and vice presidents of Convergent Science. Senecal, Richards, and Pomraning were also the original developers of CONVERGE. Rainer Rothbauer is a co-founder, co-owner, and general manager of Convergent Science GmbH, the European portion of Convergent Science.[15]

Software

Convergent Science’s flagship product is CONVERGE CFD software, which is comprised of the CONVERGE solver, the CONVERGE Studio graphical user interface (GUI), and CONVERGE chemistry tools.[6]

CONVERGE Solver

The CONVERGE solver is designed for solving reacting flows in systems with complex geometries and moving parts. One of the key features of CONVERGE is ‘autonomous meshing’, in which CONVERGE automatically creates and refines the computational mesh, thereby taking the meshing process out of the users’ hands.[1][16] CONVERGE’s autonomous meshing features include automatic mesh generation and Adaptive Mesh Refinement.

Automatic Mesh Generation

Using a modified cut-cell Cartesian grid generation method, CONVERGE automatically generates an orthogonal, structured mesh at runtime based on a few user-defined parameters.[1][17] The grid is regenerated at every time-step to accommodate moving geometries, so the mesh is never deformed, stretched, or skewed.[18]

Adaptive Mesh Refinement (AMR)

As the mesh is regenerated at each time-step, CONVERGE automatically refines the mesh in areas with complex phenomena, such as moving geometries or fluctuating temperatures or flow velocities.[18][19][20] Concurrently, cells are eliminated from the mesh in areas with low activity, where a highly refined mesh is not necessary for accurate results.[18] AMR is intended to ensure the simulation is resolved enough to achieve the desired level of accuracy while minimizing the overall cell count and thus minimizing runtime.[21]

Other CONVERGE Features

The CONVERGE flow solver is fully integrated with a detailed chemical kinetics solver.[17] CONVERGE contains a number of advanced physical models, including turbulence,[22] multi-phase fluid flow,[23] spray,[24] and radiation.[25] CONVERGE additionally allows for conjugate heat transfer[26] and fluid-structure interaction modeling.[27]

CONVERGE Studio

The CONVERGE Studio GUI includes both pre- and post-processing tools for the CONVERGE solver. Before running a CFD simulation in CONVERGE, the CONVERGE Studio pre-processing tools can be used to prepare the surface geometry, configure input files, and set up the reaction mechanism. After running a CONVERGE simulation, the line plotting module and the 3D visualization module in CONVERGE Studio can be used to visualize and interpret the simulation results.[28][29]

CONVERGE Chemistry Tools

CONVERGE includes a number of chemistry tools for the SAGE detailed chemistry solver. These tools include the 0D solver, 1D solver, mechanism reduction, mechanism merge, mechanism tuning, and surrogate blender. The 0D, or autoignition, solver calculates ignition delay for a given set of fuel parameters, e.g., temperature, pressure, equivalence ratio.[30] The 1D solver calculates laminar flame speeds.[31] To reduce computational time, the mechanism reduction tool eliminates species and reactions that have the least effect on the simulation results.[32] The mechanism merge tool combines two reaction mechanisms into one,[33] and the mechanism tuning tool optimizes reaction mechanisms to meet specified performance targets.[34] In CONVERGE Studio, the surrogate blender tool approximates real fuels through multi-component surrogates whose properties match those of the target fuel.[35]

CONVERGE Genetic Optimization (CONGO)

The CONVERGE Genetic Optimization (CONGO) utility automates the process for running a genetic algorithm or design of experiments (DoE) for optimization and model interrogation. CONGO is a separate executable from CONVERGE and can thus be used for either CONVERGE or non-CONVERGE studies.[36][37]

Third Party Integration

Convergent Science works with other engineering software companies to extend the capabilities of CONVERGE and CONVERGE Studio.

CONVERGE Studio has a Polygonica add-on that allows CONVERGE Studio users access to advanced surface preparation and repair tools.[38] CONVERGE Studio also has a Sculptor software interface for performing surface deformation and morphing.[38] For post-processing, CONVERGE comes with a Tecplot for CONVERGE license, which allows CONVERGE users to visualize 3D simulation results.[39]

CONVERGE is also integrated into Gamma Technologies’ GT-SUITE in the form of CONVERGE Lite, a reduced version of CONVERGE that is packaged with every GT-SUITE license.[40]

Applications

CONVERGE is a multi-purpose CFD solver and has thus been applied to a variety of application areas. Initially designed for IC engine applications, CONVERGE is widely used by automotive companies[18][41][42][43][44][45] and in the racing industry.[20][46] CONVERGE has been used to model the combustion,[47] spray,[48] turbulence,[49] and emissions[50] in IC engines for a range of fuels, such as gasoline,[51][52][53] diesel,[54][55] natural gas,[56] dual fuel or multi-fuels,[57][58] and alternative fuels.[59]

Fuel injection and sprays are other application areas in which CONVERGE has frequently been employed. Published peer-reviewed articles demonstrate the use of CONVERGE for studying the effects of injection characteristics, such as injector location[60] and design,[61] injection timing,[62][63] fuel temperature,[64] fuel mass,[63] and nozzle orientation[60] on combustion,[62] emissions,[63] spray breakup,[65] and spray-wall interaction.[66] Injection and spray studies have been performed for a variety of fuels, including gasoline,[67] diesel,[66] natural gas,[68] and hydrogen.[69]

CONVERGE is also used for non-IC engine applications. The first peer-reviewed journal article using CONVERGE to model gas turbine combustion was published in 2014.[70] Since then, CONVERGE has been used to predict ignition and relight,[71] lean blow-out,[72][73] and emissions[74][75] in gas turbines. In the field of exhaust aftertreatment, CONVERGE has been used to model urea spray and to predict urea deposit formation.[76][77] CONVERGE has also been used to model a variety of compressors, expanders, pumps, and fans, including reciprocating compressors,[78][79] scroll compressors,[80] twin-screw compressors and expanders,[81][82] gerotor pumps,[83] blood pumps,[84] and centrifugal fans.[85]

Collaborators

[86]

Convergent Science maintains a number of collaborations with various institutions, including Argonne National Laboratory, IFP Energies nouvelles, CMT Motores Térmicos – Universitat Politècnica de València, Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, Sandia National Laboratories, Engine Research Center – University of Wisconsin–Madison, and the University of Massachusetts Amherst.


References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 1.5 Haight, Brent. “Convergent Science Eyes Gas Compression.” Gas Compression Magazine, November 2017. https://gascompressionmagazine.com/2017/11/14/convergent-science-eyes-gas-compression/.
  2. ↑ 2.0 2.1 “Diesel Breeding.” Mechanical Engineering 124, no. 9 (2002): 53. doi: 10.1115/1.2002-SEP-4.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 Knowles, Robin. “EP13 - Kelly Senecal - Convergent Science.” Produced by CFD Engine. Talking CFD. November 15, 2016. Podcast, MP3 audio, 31:30. https://www.cfdengine.com/podcast/kelly-senecal-convergent-science/.
  4. ↑ Wood, Matthew. “Wisconsin entrepreneurs CONVERGE on engine optimization: BTN LiveBIG.” Big Ten Network, October 14, 2016. http://btn.com/2016/10/14/wisconsin-entrepreneurs-converge-on-engine-optimization-btn-livebig/.
  5. ↑ 5.0 5.1 5.2 Tenenbaum, David. “Engine software from UW spinoff being used around the world.” University of Wisconsin-Madison News, September 20, 2016. https://news.wisc.edu/engine-software-from-uw-spinoff-being-used-around-the-world/.
  6. ↑ 6.0 6.1 6.2 6.3 6.4 6.5 “Media Kit and Brand Guidelines: Convergent Science and CONVERGE CFD Software.” Convergent Science online. Last modified August 2018. https://api.convergecfd.com/wp-content/uploads/cs-media-kit.pdf.
  7. ↑ 7.0 7.1 Senecal, Kelly. “CONVERGE: 10 YEARS OF PERSEVERANCE AND SUCCESS… AND AUTONOMOUS MESHING!” Convergent Science Blog, March 20, 2018. https://convergecfd.com/blog/10-years-autonomous-meshing.
  8. ↑ “Convergent Science Inc. and IDAJ Enter Strategic Agreement.” CFD Review, March 8, 2013. http://www.cfdreview.com/biz/13/03/08/1441247.shtml.
  9. ↑ “CSI FORMS CONVERGENT SCIENCE GMBH AND OPENS NEW OFFICE IN AUSTRIA.” Convergent Science Press (press release), December 3, 2014. https://convergecfd.com/press/csi-forms-convergent-science-gmbh-and-opens-new-office-in-austria.
  10. ↑ “CONVERGENT SCIENCE SUCCESSFULLY CONCLUDES FIRST ANNUAL USER GROUP MEETING.” Convergent Science Press (press release), October 10, 2014. https://convergecfd.com/press/convergent-science-successfully-concludes-first-annual-user-group-meeting.
  11. ↑ “2014: LOOKING BACK AND MOVING FORWARD.” Convergent Science Press (press release), February 9, 2015. https://convergecfd.com/press/2014-looking-back-and-moving-forward.
  12. ↑ “CONVERGENT SCIENCE ADDS NEW OFFICE IN THE MOTOR CITY.” Convergent Science Press (press release), July 21, 2015. https://convergecfd.com/press/convergent-science-detroit.
  13. ↑ Senecal, Kelly. “2017: A YEAR OF GLOBAL GROWTH.” Convergent Science Blog, December 18, 2017. https://convergecfd.com/blog/2017-a-year-of-global-growth.
  14. ↑ “CONVERGENT SCIENCE CELEBRATES TEN YEARS OF CONVERGE CFD SOFTWARE.” Convergent Science Press (press release), September 14, 2018. https://convergecfd.com/press/convergent-science-celebrates-ten-years-of-converge-cfd-software.
  15. ↑ 15.0 15.1 “Leadership.” Convergent Science online. Accessed November 23, 2018. https://convergecfd.com/about/leadership.
  16. ↑ Alba, Michael. “University Built CFD Software Uses Natural Selection Strategies to Optimize Engines.” Engineering.com, September 29, 2016. https://www.engineering.com/DesignSoftware/DesignSoftwareArticles/ArticleID/13239/University-Built-CFD-Software-Uses-Natural-Selection-Strategies-to-Optimize-Engines.aspx.
  17. ↑ 17.0 17.1 Ford, Jason. “Automatic meshing function brings new dimension to CFD emissions modelling.” The Engineer, September 24, 2015. https://www.theengineer.co.uk/issues/sept-2015-online/automatic-meshing-function-brings-new-dimension-to-cfd-emissions-modelling/.
  18. ↑ 18.0 18.1 18.2 18.3 Thornton, John. “Convergent Science improves CFD software.” Automotive Testing Technology International, February 17, 2016. https://www.automotivetestingtechnologyinternational.com/features/convergent-science-improves-cfd-software.html.
  19. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 22.8.2: Adaptive Mesh Refinement - amr.in.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  20. ↑ 20.0 20.1 “Roush Yates Engines Announces Technical Partner.” News. Performance Racing Industry, April 8, 2016. https://performanceracing.com/news/roush-yates-engines-announces-technical-partner.
  21. ↑ Austin-Morgan, Tom. “CONVERGE CFD software enables in-cylinder simulations to run faster than ever.” Eureka Magazine, October 21, 2016. http://www.eurekamagazine.co.uk/design-engineering-news/converge-cfd-software-enables-in-cylinder-simulations-to-run-faster-than-ever/147111/.
  22. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 15: Turbulence Modeling.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  23. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 17: Volume of Fluid (VOF) Modeling.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  24. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 12: Discrete Phase Modeling.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  25. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 19: Radiation Modeling.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  26. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 16: Conjugate Heat Transfer.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  27. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 18: Fluid-Structure Interaction Modeling.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  28. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Foreword.” CONVERGE Studio 2.4 Manual. Madison, WI: Convergent Science, 2018.
  29. ↑ “Ease of Use.” Convergent Science online. Accessed December 28, 2018. https://convergecfd.com/benefits/ease-of-use.
  30. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 13.16: Zero-Dimensional Combustion Utilities.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  31. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 13.17: One-Dimensional Combustion Utilities.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  32. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 13.18: Mechanism Reduction” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  33. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 9.1.6: Mechanism Merge.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  34. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 9.1.7 Mechanism Tune.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  35. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 4.1.2: Surrogate Blender.” CONVERGE Studio 2.4 Manual. Madison, WI: Convergent Science, 2018.
  36. ↑ Richards, K. J., P. K. Senecal, and E. Pomraning. “Chapter 21: CONGO - Optimization and Model Interrogation Utility.” CONVERGE 2.4 Manual. Madison, WI: Convergent Science, 2018.
  37. ↑ Probst, Dan, Mandhapati Raju, Peter Kelly Senecal, Ahmed Abdul Moiz, Pinaki Pal, Janardhan Kodavasal, Sibendu Som, and Yuanjiang Pei. "Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators.” Paper presented at ASME 2018 Internal Combustion Engine Division Fall Technical Conference, San Diego, CA, November 2018.
  38. ↑ 38.0 38.1 “Third-Party Integration.” Convergent Science online. Accessed December 27, 2018. https://convergecfd.com/benefits/third-party-integration
  39. ↑ “CONVERGE licenses to include complimentary Tecplot 360 license, creating powerful and seamless CFD and visualization.” Tecplot online (press release), May 31, 2018. https://www.tecplot.com/2018/05/31/tecplot-convergent-science-announce-partnership/.
  40. ↑ “GT-SUITE Product Options.” Gamma Technologies LLC. Accessed December 28, 2018. https://www.gtisoft.com/gt-suite/product-options/.
  41. ↑ “Ford Uses CONVERGE CFD.” Digital Engineering, October 24, 2016. https://www.digitalengineering247.com/article/ford-uses-converge-cfd/.
  42. ↑ Wood, Matthew. “Wisconsin entrepreneurs CONVERGE on engine optimization: BTN LiveBIG.” Big Ten Network, October 14, 2016. http://btn.com/2016/10/14/wisconsin-entrepreneurs-converge-on-engine-optimization-btn-livebig/.
  43. ↑ Rathinam, B., F. Ravet, C. Servant, L. Delahaye, and U. Naithani. “Experimental and Numerical Investigations of Tumble Motion on an Optical Single Cylinder Engine.” SAE Technical Paper, no. 2015-01-1698 (2015). doi: 10.4271/2015-01-1698.
  44. ↑ Sawada, R. "Study for Cycle Variation of Flow in Engine Cylinder by Measurement and CFD." Paper presented at the 2015 JSAE Annual Congress, Yokohama, Japan, May 2015.
  45. ↑ Park, S. and T. Furukawa. “Validation of Turbulent Combustion and Knocking Simulation in Spark-Ignition Engines Using Reduced Chemical Kinetics.” SAE Technical Paper, no. 2015-01-0705 (2015). doi: 10.4271/2015-01-0750.
  46. ↑ Thornton, John. “Case study: CFD at ECR Engines.” Automotive Testing Technology International, April 1, 2015. https://www.automotivetestingtechnologyinternational.com/features/case-study-cfd-at-ecr-engines.html.
  47. ↑ Colin, O., S. Chevillard, J. Bohbot, P. K. Senecal, E. Pomraning, and M. Wang. “Development of a Species-Based Extended Coherent Flamelet Model (SB-ECFM) for Gasoline Direct Injection Engine (GDI) Simulations.” Paper presented at ASME 2018 Internal Combustion Engine Division Fall Technical Conference, no. ICEF2018-9684, San Diego, CA, November 2018.
  48. ↑ Senecal, P. K., E. Pomraning, K. J. Richards, S. Som. “Grid-Convergent Spray Models for Internal Combustion Engine CFD Simulations.” Journal of Energy Resources Technology 136, no. 1 (2013). doi: 10.1115/1.4024861.
  49. ↑ Pei, Y., S. Som, E. Pomraning, P. K. Senecal, S. A. Skeen, J. Manin, and L. M. Pickett. “Large Eddy Simulation of a Reacting Spray Flame with Multiple Realizations Under Compression Ignition Engine Conditions.” Combustion and Flame 162, no. 12 (2015): 4442-4455. doi: 10.1016/j.combustflame.2015.08.010.
  50. ↑ Luo, Z., M. Raju, and P. K. Senecal. “Application of Dynamic Mechanism Reduction for Detailed Soot Modeling in Internal Combustion Engine Simulations.” Paper presented at the 9th US National Combustion Meeting, Cincinnati, OH, May 2015.
  51. ↑ Pal, Pinaki, Yunchao Wu, Tianfeng Lu, Sibendu Som, Yee Chee See, and Alexandra Le Moine. “Multidimensional Numerical Simulations of Knocking Combustion in a Cooperative Fuel Research Engine.” Journal of Energy Resources Technology 140, no. 10 (2018). doi: 10.1115/1.4040063.
  52. ↑ Scarcelli, R., K. Richards, E. Pomraning, P. K. Senecal, T. Wallner, and J. Sevik. “Cycle-To-Cycle Variations in Multi-Cycle Engine RANS Simulations.” SAE Technical Paper, no. 2016-01-0593 (2016). doi: 10.4271/2016-01-0593.
  53. ↑ Yang, S., E. Pomraning, and M. Jia. “Simulations of Gasoline Engine Combustion and Emissions Using a Chemical-Kinetics-Based Turbulent Premixed Combustion Approach.” Journal of Automobile Engineering (2016). doi: 10.1177/0954407016661448.
  54. ↑ Senecal, P. K., K. J. Richards, E. Pomraning, T. Yang, M. Z. Dai, R. M. McDavid, M. A. Patterson, S. Hou, and T. Shethaji. “A New Parallel Cut-Cell Cartesian CFD Code for Rapid Grid Generation Applied to In-Cylinder Diesel Engine Simulations.” SAE Technical Paper, no. 2007-01-0159 (2007). doi: 10.4271/2007-01-0159.
  55. ↑ Senecal, P. K., E. Pomraning, J. W. Anders, M. R. Weber, C. R. Gehrke, C. J. Polonowski, and C. J. Mueller. “Predictions of Transient Flame Lift-off Length With Comparison to Single-Cylinder Optical Engine Experiments.” Journal of Engineering for Gas Turbines and Power 136, no. 11 (2014). doi: 10.1115/1.4027653.
  56. ↑ Mashayekh, A., T. J. Jacobs, M. Patterson, and J. Etcheverry. “Prediction of Air-Fuel Ratio Control of a Large Bore Natural Gas Engine Using Computational Fluid Dynamic Modeling of Reed Valve Dynamics.” International Journal of Engine Research (2017). doi: 10.1177/1468087416686224.
  57. ↑ Pasunurthi, S., R. Jupudi, S. Wijeyakulasuriya, S. R. Gubba, H. Im, M. J. Ali, R. Primus, A. Klingbeil, and C. Finney. “Cycle to Cycle Variation Study in a Dual Fuel Operated Engine.” SAE Technical Paper, no. 2017-01-0772 (2017). doi: 10.4271/2017-01-0772.
  58. ↑ Yousefi, A., H. Guo, and M. Birouk. “Effect of Diesel Injection Timing on the Combustion of Natural Gas/Diesel Dual-Fuel Engine at Low-High Load and Low-High Speed Conditions.” Fuel 235 (2018): 838-846. doi: 10.1016/j.fuel.2018.08.064.
  59. ↑ Ganji, P. R., V. R. K. Raju, and S. S. Rao. “Computational Optimization of Biodiesel Combustion Using Response Surface Methodology.” Thermal Science 21, no. 1B (2017): 465-473. doi: 10.2298/TSCI161229031G.
  60. ↑ 60.0 60.1 Karaya, Y., S. Addepalli, and J. Mallikarjuna. “Effect of Fuel Injector Location and Nozzle-Hole Orientation on Mixture Formation in a GDI Engine: A CFD Analysis.” SAE Technical Paper, no. 2018-01-0201 (2018). doi: 10.4271/2018-01-0201.
  61. ↑ Broatch, A., X. Margot, R. Novella, and J. Gomez-Soriano. “Impact of the injector design on the combustion noise of gasoline partially premixed combustion in a 2-stroke engine.” Applied Thermal Engineering 119, no. 5 (2017): 530-540. doi: 10.1016/j.applthermaleng.2017.03.081.
  62. ↑ 62.0 62.1 Yousefi, Amin, Hongsheng Guo, and Madjid Birouk. “Effect of diesel injection timing on the combustion of natural gas/diesel dual-fuel engine at low-high load and low-high speed conditions.” Fuel 235 (2019): 838-846. doi: 10.1016/j.fuel.2018.08.064.
  63. ↑ 63.0 63.1 63.2 Doohyun, Kim, Angela Violi, and André Boehman. “The Effects of Injection Timing and Injected Fuel Mass on Local Charge Conditions and Emissions for Gasoline Direct Injection Engines.” Paper presented at ASME 2017 Internal Combustion Engine Division Fall Technical Conference, no. ICEF2017-3623. doi: 10.1115/ICEF2017-3623.
  64. ↑ Jing, Daliang, Hongxue Zhao, Yanfei Li, Hengjie Guo, Jianhua Xiao, and Shi-Jin Shuai. “Numerical Investigation on the Effect of Fuel Temperature on Spray Collapse and Mixture Formation Characteristics in GDI Engines.” SAE Technical Paper, no. 2018-01-0311 (2018). doi: 10.4271/2018-01-0311.
  65. ↑ Ochiai, Naoya, Jun Ishimoto, Akira Arioka, Nobuhiko Yamaguchi, Yuzuru Sasaki, and Nobuyuki Furukawa. “Integrated Computational Study for Total Atomization Process of Primary Breakup to Spray Droplet Formation in Injector Nozzle.” SAE Technical Paper, no. 2016-01-2202 (2016). doi: 10.4271/2016-01-2202.
  66. ↑ 66.0 66.1 Zhao, Le, Roberto Torelli, Xiucheng Zhu, Jeffrey Naber, Seong-Young Lee, Sibendu Som, Riccardo Scarcelli, and Mehdi Raessi. “Evaluation of Diesel Spray-Wall Interaction and Morphology around Impingement Location.” SAE Technical Paper, no. 2018-04-03 (2018). doi: 10.4271/2018-01-0276.
  67. ↑ Saha, Kaushik, Priyesh Srivastava, Shaoping Quan, P. K. Senecal, Eric Pomraning, Sibendu Som. “Modeling the Dynamic Coupling of Internal Nozzle Flow and Spray Formation for Gasoline Direct Injection Applications.” SAE Technical Paper, no. 2018-01-0314. doi: 10.4271/2018-01-0314.
  68. ↑ Li, Menghan, Qiang Zhang, Xiaori Liu, Yuxian Ma, and Qingping Zheng. “Soot emission prediction in pilot ignited direct injection natural gas engine based on n-heptane/toluene/methane/PAH mechanism.” Energy 163 (2018): 660-681. doi: 10.1016/j.energy.2018.08.102.
  69. ↑ Shahsavan, Martia, Mohammadrasool Morovatiyan, and J. Hunter Mack. “The Influence of Mixedness on Ignition for Hydrogen Direct Injection in a Constant Volume Combustion Chamber.” Paper presented at the Spring Technical Meeting of the Eastern States Section of the Combustion Institute, State College, PA, March 2018. https://www.researchgate.net/publication/325425453_The_Influence_of_Mixedness_on_Ignition_for_Hydrogen_Direct_Injection_in_a_Constant_Volume_Combustion_Chamber.Shahsavan, Martia, Mohammadrasool Morovatiyan, and J. Hunter Mack. “The Influence of Mixedness on Ignition for Hydrogen Direct Injection in a Constant Volume Combustion Chamber.” Paper presented at the Spring Technical Meeting of the Eastern States Section of the Combustion Institute, State College, PA, March 2018. https://www.researchgate.net/publication/325425453_The_Influence_of_Mixedness_on_Ignition_for_Hydrogen_Direct_Injection_in_a_Constant_Volume_Combustion_Chamber.
  70. ↑ Drennan, Scott A. and Gaurav Kumar. “Demonstration of an Automatic Meshing Approach for Simulation of a Liquid Fueled Gas Turbine with Detailed Chemistry.” Paper presented at the 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Cleveland, OH, July 2014. doi: 10.2514/6.2014-3628.
  71. ↑ Drennan, Scott A. and Gaurav Kumar. “Demonstrating Accurate Gas Turbine Ignition and Relight with Detailed Chemistry and Autonomous Meshing.” Paper presented at the 2018 Joint Propulsion Conference, Cincinnati, OH, July 2018. doi: 10.2514/6.2018-4681.
  72. ↑ Hasti, Veeraraghava Raju, Prithwish Kundu, Gaurav Kumar, Scott A. Drennan, Sibendu Som, Sang Hee Won, Frederick L. Dryer, and Jay P. Gore. “Lean blow-out (LBO) computations in a gas turbine combustor.” Paper presented at the 2018 Joint Propulsion Conference, Cincinnati, OH, July 2018. doi: 10.2514/6.2018-4958.
  73. ↑ Hasti, Veeraraghava Raju, Prithwish Kundu, Gaurav Kumar, Scott A. Drennan, Sibendu Som, and Jay P. Gore. “A Numerical Study of Flame Characteristics during Lean Blow-Out in a Gas Turbine Combustor.” Paper presented at the 2018 Joint Propulsion Conference, Cincinnati, OH, July 2018. doi: 10.2514/6.2018-4955.
  74. ↑ Drennan, Scott A., Gaurav Kumar, Erlendur Steinthorsson, and Adel Mansour. “Unsteady Simulations of a Low NOx LDI Combustor for Environmentally Responsible Aviation Engines.” Paper presented at ASME Turbo Expo 2015: Turbine Technical Conference and Exposition, Montreal, Canada, June 2015. doi: 10.1115/GT2015-43802.
  75. ↑ Drennan, Scott A. and Gaurav Kumar. “Using LES Simulations to Predict Pilot Fuel Split Emissions Effects in an Industrial Gas Turbine Combustor with Automatic Meshing.” Paper presented at the 55th AIAA Aerospace Sciences Meeting, Grapevine, TX, January 2017. doi: 10.2514/6.2017-1059.
  76. ↑ Sun, Yong, Saurabh Sharma, Bruce Vernham, Keiko Shibata, and Scott Drennan. “Urea Deposit Predictions on a Practical Mid/Heavy Duty Vehicle After-Treatment System.” SAE Technical Paper, no. 2018-01-0960 (2018). doi: 10.4271/2018-01-0960.
  77. ↑ Zheng, Guanyu. “CFD Modeling of Urea Spray and Deposits for SCR Systems.” SAE Technical Paper, no. 2016-01-8077 (2016). doi: 10.4271/2016-01-8077.
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  79. ↑ Rowinski, David and Kenneth Davis. “Modeling Reciprocating Compressors Using A Cartesian Cut-Cell Method With Automatic Mesh Generation.” Paper presented at the 23rd International Compressor Engineering Conference at Purdue, West Lafayette, IN, July 2016. https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=3510&context=icec.
  80. ↑ Rowinski, D., H.-D. Pham, and T. Brandt. “Modeling a Scroll Compressor Using a Cartesian Cut-Cell Based CFD Methodology with Automatic Adaptive Meshing.” Paper presented at the 24th International Compressor Engineering Conference at Purdue, no. 1252, West Lafayette, IN, July 2018.
  81. ↑ Rowinski, David, Alexander Nikolov, and Andreas Brümmer. “Modeling a dry running twin-screw expander using a coupled thermal-fluid solver with automatic mesh generation.” IOP Conference Series: Materials Science and Engineering 425, no. 1 (2018). doi: 10.1088/1757-899X/425/1/012019.
  82. ↑ Rowinski, David. “Modeling flows in twin screw machines with autonomous meshing.” Presented at the 2018 CONVERGE User Conference-North America, Madison, WI, September 2018. https://api.convergecfd.com/wp-content/uploads/CSI_Rowinski.pdf.
  83. ↑ Rowinski, David. “New Applications in Multi-phase Flow Modeling with CONVERGE: Gerotor Pumps, Fuel Tank Sloshing, and Gear Churning.” Presented at the 2018 CONVERGE User Conference-Europe, Bologna, Italy, March 2018. https://api.convergecfd.com/wp-content/uploads/David-Rowinski_Multiphase-Modeling-Gearbox-Power-Losses-Oil-Pump-Cavitation-and-Fuel-Tank-Sloshing.pdf.
  84. ↑ Jhun, Choon‐Sik, Christopher Siedlecki, Lichong Xu, Branka Lukic, Raymond Newswanger, Eric Yeager, John Reibson, Joshua Cysyk, William Weiss, and Gerson Rosenberg. “Stress and Exposure Time on von Willebrand Factor Degradation.” Artificial Organs (2018). doi: 10.1111/aor.13323.
  85. ↑ Li, Y., D. H. Rowinski, K. Bansal, and K. R. Reddy. “CFD Modeling and Performance Evaluation of a Centrifugal Fan Using a Cut-Cell Method with Automatic Mesh Generation and Adaptive Mesh Refinement.” Paper presented at the 24th International Compressor Engineering Conference at Purdue, no. 1533, West Lafayette, IN, July 2018.
  86. ↑ “Collaborators.” Convergent Science online. Accessed December 27, 2018. https://convergecfd.com/about/collaborators.


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