Publications and Reports

1 2021 | 2 2020 | 3 2019 | 4 2018 | 5 2017 | 6 Pre-2016

The following are publications made possible by ARCC resources. Any UW faculty that would like to highlight the research that has benefited from ARCC resources are invited to contact us.

2021

Ecological outcomes of hybridization vary extensively in Catostomus fishes. Mandeville, E. G., Hall, R. O., & Buerkle, C. A. (2021). BioRxiv, 2021.01.20.427472. https://doi.org/10.1101/2021.01.20.427472  

Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy. Shastry, V., Adams, P. E., Lindtke, D., Mandeville, E. G., Parchman, T. L., Gompert, Z., & Buerkle, C. A. (2021). Molecular Ecology Resources, 21(5), 1434–1451. https://doi.org/10.1111/1755-0998.13330 

Cardiac response to adrenergic stress differs by sex and across the lifespan.Yusifov, A., Chhatre, V. E., Zumo, J. M., Cook, R. F., McNair, B. D., Schmitt, E. E., Woulfe, K. C., & Bruns, D. R. (2021). GeroScience. https://doi.org/10.1007/s11357-021-00345-x  

ARRU Phase Picker: Attention Recurrent‐Residual U‐Net for Picking Seismic P‐ and S‐Phase Arrivals. Liao, W., Lee, E., Mu, D., Chen, P., & Rau, R. (2021). Seismological Research Letters, 92(4), 2410–2428. https://doi.org/10.1785/0220200382  

Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel Execution, Haniye Kashgarani, and Lars Kotthoff (University of Wyoming): AAAI 2021 Workshop on Meta-Learning

Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs. Laughlin, D.C., Mommer, L., Sabatini, F.M. et al.  Nat Ecol Evol (2021). https://doi.org/10.1038/s41559-021-01471-7  

Global Vegetation Project: An Interactive Online Map of Open-Access Vegetation Photos. Fleri, J. R., Wessel, S. A., Atkins, D. H., Case, N. W., Albeke, S. E., & Laughlin, D. C. (2021).  Vegetation Classification and Survey2, 41–45. https://doi.org/10.3897/vcs/2021/60575  

The genetic population structure of Lake Tanganyika’s Lates species flock, an endemic radiation of pelagic top predators. Rick, J. A., Junker, J., Kimirei, I. A., Sweke, E. A., Mosille, J. B., Dinkel, C., Mwaiko, S., Seehausen, O., & Wagner, C. E. (2021). BioRxiv, 2021.04.23.441176. https://doi.org/10.1101/2021.04.23.441176  

Rapid synchronized fabrication of vascularized thermosets and composites. Garg, M., Aw, J.E., Zhang, X. et al. Nat Commun 12, 2836 (2021). https://doi.org/10.1038/s41467-021-23054-7   

A suite of rare microbes interacts with a dominant, heritable, fungal endophyte to influence plant trait expression. Harrison, J. G., Beltran, L. P., Buerkle, C. A., Cook, D., Gardner, D. R., Parchman, T. L., Poulson, S. R., & Forister, M. L. (2021). BioRxiv, 608729. https://doi.org/10.1101/608729  

Estimating complex ecological variables at high resolution in heterogeneous terrain using multivariate matching algorithms. Renne, R., Schlaepfer, D., Palmquist, K., Lauenroth, W., & Bradford, J. (2021). EcoEvoRxiv. https://doi.org/10.32942/osf.io/b2ux7 

Directly visualizing carrier transport and recombination at individual defects within 2D semiconductors. Hill, J. W., & Hill, C. M. (2021). Chemical Science, 12(14), 5102–5112. https://doi.org/10.1039/D0SC07033E  

Transported and presumed probability density function modeling of the Sandia flames with flamelet generated manifold chemistry. Jaganath, V., & Stoellinger, M. (2021).  Physics of Fluids, 33(4), 045123. https://doi.org/10.1063/5.0045726 

Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska. Klobucar, S. L., Rick, J. A., Mandeville, E. G., Wagner, C. E., & Budy, P. (2021). Ecology and Evolution, 11(7), 3040–3057. https://doi.org/10.1002/ece3.7211 

Breaking atomic-level ordering via biaxial strain in functional oxides: A DFT study. Rawat, K., Fong, D. D., & Aidhy, D. S. (2021). Journal of Applied Physics, 129(9), 095301. https://doi.org/10.1063/5.0039420 

A statistical approach for atomistic calculations of vacancy formation energy and chemical potentials in concentrated solid-solution alloys. Zhang, Y., Manzoor, A., Jiang, C., Aidhy, D., & Schwen, D. (2021). Computational Materials Science, 190, 110308. https://doi.org/10.1016/j.commatsci.2021.110308  

Effect of different point-defect energetics in Ni80X20 (X=Fe, Pd) on contrasting vacancy cluster formation from atomistic simulations. Arora, G., Bonny, G., Castin, N., & Aidhy, D. (2021). Materialia, 15, 100974. https://doi.org/10.1016/j.mtla.2020.100974  


2020

The Role of European Starlings (Sturnus vulgaris) in the Dissemination of Multidrug-Resistant Escherichia coli among Concentrated Animal Feeding Operations. Chandler, J.C., Anders, J.E., Blouin, N.A. et al. Sci Rep 10, 8093 (2020). https://doi.org/10.1038/s41598-020-64544-w  

Accuracy of de novo assembly of DNA sequences from double-digest libraries varies substantially among software. LaCava, M. E. F., Aikens, E. O., Megna, L. C., Randolph, G., Hubbard, C., & Buerkle, C. A. (2020). Molecular Ecology Resources, 20(2), 360–370. https://doi.org/10.1111/1755-0998.13108https://doi.org/10.1016/j.poly.2020.114461 

Estimating and accounting for genotyping errors in RAD-seq experiments. Bresadola, L., Link, V., Buerkle, C. A., Lexer, C., & Wegmann, D. (2020). Molecular Ecology Resources, 20(4), 856–870. https://doi.org/10.1111/1755-0998.13153  

Seismic evidence of glacial deposits inhibiting weathering of local bedrock at a snow-dominated subalpine watershed. Wang, W., Chen, P., Dueker, K., Lee, E.-J., Mu, D., & Keifer, I. (2020).  Earth and Planetary Science Letters, 549, 116517. https://doi.org/10.1016/j.epsl.2020.116517  

GPU-accelerated automatic microseismic monitoring algorithm (GAMMA) and its application to the 2019 ridgecrest earthquake sequence. Lee, E. J., Liao, W. Y., Mu, D., Wang, W., & Chen, P. (2020). Seismological Research Letters, 91(4), 2062–2074. https://doi.org/10.1785/0220190323  

Multiwindow weighted stacking of surface-wave dispersion. Pasquet, S., Wang, W., Chen, P., & Flinchum, B. A. (2021).  GEOPHYSICS, 86(2), EN39–EN50. https://doi.org/10.1190/geo2020-0096.1  

Learning to Continually Learn. Beaulieu, S., Frati, L., Miconi, T., Lehman, J., Stanley, K. O., Clune, J., & Cheney, N. (2020).  ArXiv:2002.09571 [Cs, Stat]. http://arxiv.org/abs/2002.09571  

A deep active learning system for species identification and counting in camera trap images. Norouzzadeh M, Morris D, Beery S, Joshi N, Jojic N, Clune J (2020).  Methods in Ecology & Evolution (to appear). Currently available at http://arxiv.org/abs/1910.09716  

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2. Tabak MA, Norouzzadeh M, Wolfson D, Newton E, Boughton R, Ivan J, Odell E, Newkirk E, Conrey R, Stenglein J, Iannarilli F, Erb J, Brook R, Davis A, Lewis J, Walsh D, Beasley J, VerCauteren K, Clune J, Miller R (2020).  Ecology and Evolution.

Evidence for the amnion-fetal gut-microbial axis in late gestation beef calves. Hummel, G. L., Woodruff, K. L., Austin, K. J., Smith, T. L., & Cunningham-Hollinger, H. C. (2020). Translational Animal Science, 4(Supplement_1), S174–S177. https://doi.org/10.1093/tas/txaa138  

Influence of the maternal rumen microbiome on development of the calf meconium and rumen microbiome. Woodruff, K. L., Hummel, G. L., Austin, K. J., Smith, T. L., & Cunningham-Hollinger, H. C. (2020). Translational Animal Science, 4(Supplement_1), S169–S173. https://doi.org/10.1093/tas/txaa136  

The distance microbial ecology of the bovine placenta at parturition. Hummel, G. L., K. L. Woodruff, K. J. Austin, T. L. Smith, and H. C. Cunningham-Hollinger. (2020)  Abstract. Accepted. Annual Meeting of the Society for the Study of Reproduction. Vancouver, BC. July 2020.

Changes in early milk composition has subsequent effects on microbial composition of the rumen. Nin-Velez, A. , J. Duncan, H. Cunningham-Hollinger, K. Austin, K. Cammack, W. Lamberson, and R. Cockrum. (2020)  Abstract. Journal of Dairy Science. 103:270. American Dairy Science Association Annual Meeting.

Applications of Data Assimilation Methods on a Coupled Dual Porosity Stokes Model. Hu, X., & Douglas, C. C. (2020). In V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, & J. Teixeira (Eds.), Computational Science – ICCS 2020 (pp. 72–85). Springer International Publishing. https://doi.org/10.1007/978-3-030-50433-5_6  

A Terminal Rh Methylidene from Activation of CH2Cl2. Morrow, T. J., Gipper, J. R., Christman, W. E., Arulsamy, N., & Hulley, E. B. (2020).Organometallics, 39(13), 2356–2364. https://doi.org/10.1021/acs.organomet.0c00031 

Platinum ethylene dimerization catalysts: Diphosphine vs. diimine ancillary ligand effects. Debnath, S., Basu, S., Schmidt, B. M., Adams, J. J., Arulsamy, N., & Roddick, D. M. (2020). Polyhedron, 181, 114461. https://doi.org/10.1016/j.poly.2020.114461

Plant Invasion Has Limited Impact on Soil Microbial α-Diversity: A Meta-Analysis. Custer, G. F., & van Diepen, L. T. A. (2020).Diversity, 12(3), 112. https://doi.org/10.3390/d12030112  

Structural and Functional Dynamics of Soil Microbes following Spruce Beetle Infestation. Custer, G. F., van Diepen, L. T. A., & Stump, W. L. (n.d.). Applied and Environmental Microbiology, 86(3), e01984-19. https://doi.org/10.1128/AEM.01984-19 

Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data. Harrison, J. G., Calder, W. J., Shastry, V., & Buerkle, C. A. (2020). Molecular Ecology Resources, 20(2), 481–497. https://doi.org/10.1111/1755-0998.13128 

Whole-genome duplication and host genotype affect rhizosphere microbial communities. Ponsford, J. C. B., Hubbard, C. J., Harrison, J. G., Maignien, L., Buerkle, C. A., & Weinig, C. (2020). BioRxiv, 822726. https://doi.org/10.1101/822726  

Pronghorn Migrations and Barriers: Predicting Corridors Across Wyoming’s Interstate 80 to Restore Movement. Robb, B. S. (n.d.). [M.S., University of Wyoming]. Retrieved July 22, 2021, from https://www.proquest.com/docview/2487894966/abstract/1066E709C66843B9PQ/1  

An order N log N parallel solver for time-spectral problems. Ramezanian, D., Mavriplis, D., & Ahrabi, B. R. (2020). Journal of Computational Physics, 411, 109319. https://doi.org/10.1016/j.jcp.2020.109319  

Hover Predictions Using a High-Order Discontinuous Galerkin Off-Body Discretization. Kara, K., Brazell, M. J., Kirby, A. C., Mavriplis, D. J., & Duque, E. P. (2020). In AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-0771  

Sensitivity Analysis for Aero-Thermo-Elastic Problems Using the Discrete Adjoint Approach. Kamali, S., Mavriplis, D. J., & Anderson, E. M. (n.d.). In AIAA AVIATION 2020 FORUM. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-3138  

Advances in the Pseudo-Time Accurate Formulation of the Adjoint and Tangent Systems for Sensitivity Computation and Design. Padway, E., & Mavriplis, D. J. (2020). In AIAA AVIATION 2020 FORUM. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-3136https://doi.org/10.2514/6.2020-3138 

Adjoint Based Optimization of a Slotted Natural Laminar Flow Wing for Ultra Efficient Flight. Mavriplis, D. J., Yang, Z., & Anderson, E. M. (2020). In AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-1292https://doi.org/10.2514/6.2020-3138 

Development and Validation of a High-Fidelity Aero-Thermo-Elastic Analysis Capability. Kamali, S., Mavriplis, D. J., & Anderson, E. M. (2020). In AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-1449  

An implicit block ILU smoother for preconditioning of Newton–Krylov solvers with application in high-order stabilized finite-element methods. Ahrabi, B. R., & Mavriplis, D. J. (2020). Computer Methods in Applied Mechanics and Engineering, 358, 112637. https://doi.org/10.1016/j.cma.2019.112637  

Locally Engineering and Interrogating the Photoelectrochemical Behavior of Defects in Transition Metal Dichalcogenides.  Hill, J. W.; Fu, Z.; Tian, J.; Hill, C. M. J. Phys. Chem. C, 2020, 124 (31), 17141–17149. https://doi.org/10.1021/acs.jpcc.0c05235

Mycoplasma bovis Infections in Free-Ranging Pronghorn, Wyoming, USA. Malmberg, J. L., O’Toole, D., Creekmore, T., Peckham, E., Killion, H., Vance, M., Ashley, R., Johnson, M., Anderson, C., Vasquez, M., Sandidge, D., Mildenberger, J., Hull, N., Bradway, D., Cornish, T., Register, K. B., & Sondgeroth, K. S. (2020). Emerging Infectious Diseases, 26(12), 2807–2814. https://doi.org/10.3201/eid2612.191375  

Distribution and Habitat Associations of Spotted Skunks in Wyoming. Riotto, R. (n.d.). [M.S., University of Wyoming]. Retrieved July 22, 2021, from https://www.proquest.com/docview/2489183380/abstract/1D7BB5101F8241FEPQ/1  

Theory-based Reynolds-averaged Navier–Stokes equations with large eddy simulation capability for separated turbulent flow simulations. Heinz, S., Mokhtarpoor, R., & Stoellinger, M. (2020). Physics of Fluids, 32(6), 065102. https://doi.org/10.1063/5.0006660 

Pronghorn population genomics show connectivity in the core of their range. LaCava, M. E. F., Gagne, R. B., Stowell, S. M. L., Gustafson, K. D., Buerkle, C. A., Knox, L., & Ernest, H. B. (2020). Journal of Mammalogy, 101(4), 1061–1071. https://doi.org/10.1093/jmammal/gyaa054  

Novel hybrid finds a peri-urban niche: Allen’s Hummingbirds in southern California. Godwin, B. L., LaCava, M. E. F., Mendelsohn, B., Gagne, R. B., Gustafson, K. D., Love Stowell, S. M., Engilis, A., Tell, L. A., & Ernest, H. B. (2020). Conservation Genetics, 21(6), 989–998. https://doi.org/10.1007/s10592-020-01303-4  

Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys. Arora, G., & Aidhy, D. S. (2020). Metals, 10(8), 1072. https://doi.org/10.3390/met10081072  

∑3 Twin Boundaries in Gd2Ti2O7 Pyrochlore: Pathways for Oxygen Migration. Gupta, A. K., Arora, G., Aidhy, D. S., & Sachan, R. (2020). ACS Applied Materials & Interfaces, 12(40), 45558–45563. https://doi.org/10.1021/acsami.0c12250 

Predicting vibrational entropy of fcc solids uniquely from bond chemistry using machine learning. Manzoor, A., & Aidhy, D. S. (2020). Materialia, 12, 100804. https://doi.org/10.1016/j.mtla.2020.100804  

Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model. Ott, C. W., Adhikari, B., Alexander, S. P., Hodza, P., Xu, C., & Minckley, T. A. (2020). Fire, 3(4), 71. https://doi.org/10.3390/fire3040071  


2019

Climatic niche predicts the landscape structure of locally adaptive standing genetic variation. (2019). Chhatre, V. E., Fetter, K. C., Gougherty, A. V., Fitzpatrick, M. C., Soolanayakanahally, R. Y., Zalesny, R. S., & Keller, S. R.   BioRxiv, 817411. https://doi.org/10.1101/817411  

Maternal influences on the calf rumen microbiome and subsequent impacts on performance and efficiency. (2019). Cunningham-Hollinger, H. C.  Proceedings paper. Range Beef Cow Symposium.

Metagenomic analysis of rumen populations in week old calves as altered by maternal late gestational nutrition and mode of delivery. (2019). Christensen II, T. A., K. J. Austin, K. M. Cammack, and H. C. Cunningham-Hollinger. Abstract. Undergraduate Poster Competition. Western Section of the American Society of Animal Science. Boise, ID. June, 2019.

Influence of the late gestation maternal rumen microbiome on the calf meconium and early rumen microbiome. (2019). Woodruff, K. L., G. L. Hummel, K. J. Austin, T. L. Smith, and H. C. Cunningham-Hollinger.  Abstract. Poster Presentation. Midwest Section of the American Society of Animal Science. Omaha, NE. March 2020

The fitness benefits of genetic variation in circadian clock regulation. Salmela, M. J., & Weinig, C. (2019). Current Opinion in Plant Biology, 49, 86–93. https://doi.org/10.1016/j.pbi.2019.06.003  

Plant host identity and soil macronutrients explain little variation in sapling endophyte community composition: Is disturbance an alternative explanation? Griffin, E. A., Harrison, J. G., Kembel, S. W., Carrell, A. A., Wright, S. J., & Carson, W. P. (2019). Journal of Ecology, 107(4), 1876–1889. https://doi.org/10.1111/1365-2745.13145 

Rarity does not limit genetic variation or preclude subpopulation structure in the geographically restricted desert forb Astragalus lentiginosus var. Piscinensis. Harrison, J. G., Forister, M. L., Mcknight, S. R., Nordin, E., & Parchman, T. L. (2019). American Journal of Botany, 106(2), 260–269. https://doi.org/10.1002/ajb2.1235 

The Role of Heating in the Electrochemical Response of Plasmonic Nanostructures under Illumination. Maley, M.; Hill, J. W.; Saha, P.; Walmsley, J. D.; Hill, C. M. J. Phys. Chem. C, 2019, 123 (19), 12390–12399. https://doi.org/10.1021/acs.jpcc.9b01479

Sex differentiation and a chromosomal inversion lead to cryptic diversity in Lake Tanganyika sardines. Junker, J., Rick, J. A., McIntyre, P. B., Kimirei, I., Sweke, E. A., Mosille, J. B., Werli, B., Dinkel, C., Mwaiko, S., Seehausen, O., & Wagner, C. E. (2019). BioRxiv, 800904. https://doi.org/10.1101/800904  

Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context. Mandeville, E. G., Walters, A. W., Nordberg, B. J., Higgins, K. H., Burckhardt, J. C., & Wagner, C. E. (2019). Molecular Ecology, 28(16), 3738–3755. https://doi.org/10.1111/mec.15175 


2018

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm. Huizinga J, Clune J (2018) https://arxiv.org/abs/1807.03392  

Deep curiosity search: Intra-life exploration improves performance on challenging deep reinforcement problems. Stanton C, Clune J (2018) NeurIPS Deep Reinforcement Learning Workshop.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018). Proceedings of the National Academy of Sciences, 115(25), E5716–E5725. https://doi.org/10.1073/pnas.1719367115  

Machine learning to classify animal species in camera trap images: Applications in ecology. Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., Vercauteren, K. C., Snow, N. P., Halseth, J. M., Salvo, P. A. D., Lewis, J. S., White, M. D., Teton, B., Beasley, J. C., Schlichting, P. E., Boughton, R. K., Wight, B., Newkirk, E. S., Ivan, J. S., Odell, E. A., Brook, R. K., … Miller, R. S. (2019). Methods in Ecology and Evolution, 10(4), 585–590. https://doi.org/10.1111/2041-210X.13120 

Investigation of maternal breed and rearing type on the calf rumen microbiome from day 28 through weaning. Austin, K. J., Cunningham, H. C., Powell, S. R., Carpenter, K. T., & Cammack, K. M. (2018). Translational Animal Science, 2(suppl_1), S125–S129. https://doi.org/10.1093/tas/txy034  

Maternal influences on beef calf rumen microbiome in the first 4 weeks of life. Powell, S. R., H. C. Cunningham, K. J. Austin, and K. M. Cammack. (2018) Accepted Abstract. Undergraduate Student Poster Competition. (Proc. Western Section of the American Society of Animal Science). Bend, Oregon. June 2018.

Potential response of the rumen microbiome to mode of delivery from birth through weaning. Cunningham, H. C., Austin, K. J., Powell, S. R., Carpenter, K. T., & Cammack, K. M. (2018). Translational Animal Science, 2(suppl_1), S35–S38. https://doi.org/10.1093/tas/txy029  

Influence of maternal factors on the rumen microbiome and subsequent host performance. Cunningham, H. C., Austin, K. J., & Cammack, K. M. (2018). I Translational Animal Science, 2(suppl_1), S101–S105. https://doi.org/10.1093/tas/txy058  

Mode of delivery influence on the early calf rumen microbiome. Cunningham, H. C., K. J. Austin, K. T. Carpenter, S. R. Powell, and K. M. Cammack. (2018)  Accepted. (Abstr.) Poster presented at Rowett-INRA Gut Microbiology: No longer the forgotten organ. June, 2018. Aberdeen, Scotland.

Effects of maternal breed on the early calf rumen microbiome. Cammack, K. M., H. C. Cunningham, K. J. Austin, H. C. Barton, and K. T. Carpenter. (2018) Accepted. (Abstr.) Poster presented at Rowett-INRA Gut Microbiology: No longer the forgotten organ. June, 2018. Aberdeen, Scotland.

Maternal influences on early calf rumen volatile fatty acid profile. Powell, S. R., H. C. Cunningham, K. J. Austin, K. M. Cammack, and D. C. Rule. (2018) Accepted. (Abstr.) Undergraduate poster competition at Midwest Section of the American Society of Animal Science Annual Meeting. Omaha, NE. March, 2018.

The influence of maternal breed on early calf rumen microbiome. Cunningham, H. C., K. J. Austin, K. M. Cammack, G. Conant, and W. R. Lamberson. (2018) The influence of maternal breed on early calf rumen microbiome. Accepted. (Proceedings) 11th World Congress on Genetics Applied to Livestock Production. W.R. Lamberson Presenting. Auckland, New Zealand. February, 2018.

Effects of mode of delivery on the young calf rumen microbiome. Cunningham, H. C., K. J. Austin, K. M. Cammack, J. C. McEwan, C. D. Moon, and A. McCulloch. (2018) Accepted. (Abstr.) Poster presented at Plant and Animal Genome XXVI. San Diego, CA. January 2018.

Potential role of maternal nutrition during late gestation on early calf rumen microbiome. Austin, K. J., H. C. Cunningham, K. M Cammack, J. C. McEwan, C. D. Moon, and A. McCulloch. (2018) Accepted. (Abstr.) Poster presented at Plant and Animal Genome XXVI. San Diego, CA. January 2018.

The plant circadian clock influences rhizosphere community structure and function. Hubbard, C. J., Brock, M. T., van Diepen, L. T., Maignien, L., Ewers, B. E., & Weinig, C. (2018). The ISME Journal, 12(2), 400–410. https://doi.org/10.1038/ismej.2017.172  

Rhizosphere microbes and host plant genotype influence the plant metabolome and reduce insect herbivory. Hubbard, C. J., Li, B., McMinn, R., Brock, M. T., Maignien, L., Ewers, B. E., Kliebenstein, D., & Weinig, C. (2018). BioRxiv, 297556. https://doi.org/10.1101/297556  

Dark-Field Scattering Spectroelectrochemistry Analysis of Hydrazine Oxidation at Au Nanoparticle-Modified Transparent Electrodes. Ma, Y.; Highsmith, A. L.; Hill, C. M.; Pan, S. J. Phys. Chem. C, 2018, 122 (32), 18603–18614. https://doi.org/10.1021/acs.jpcc.8b05112

Probing Electrocatalysis at Individual Au Nanorods via Correlated Optical and Electrochemical Measurements. Saha, P.; Hill, J. W.; Walmsley, J. D.; Hill, C. M. Anal. Chem., 2018, 90 (21), 12832–12839. https://doi.org/10.1021/acs.analchem.8b03360

Effect of atomic order/disorder on Cr segregation in Ni-Fe alloys. Arora, G., Rawat, K. D., & Aidhy, D. S. (2018). Journal of Applied Physics, 124(11), 115303. https://doi.org/10.1063/1.5027521 

Entropy contributions to phase stability in binary random solid solutions. Manzoor, A., Pandey, S., Chakraborty, D., Phillpot, S. R., & Aidhy, D. S. (2018). Npj Computational Materials, 4(1), 1–10. https://doi.org/10.1038/s41524-018-0102-y  

Classical interatomic potential for quaternary Ni–Fe–Cr–Pd solid solution alloys. Bonny, G., Chakraborty, D., Pandey, S., Manzoor, A., Castin, N., Phillpot, S. R., & Aidhy, D. S. (2018). Modelling and Simulation in Materials Science and Engineering, 26(6), 065014. https://doi.org/10.1088/1361-651X/aad2e7  

Effect of atomic order/disorder on vacancy clustering in concentrated NiFe alloys. Chakraborty, D., Harms, A., Ullah, M. W., Weber, W. J., & Aidhy, D. S. (2018). Computational Materials Science, 147, 194–203. https://doi.org/10.1016/j.commatsci.2018.02.011  


2017

Cr-induced fast vacancy cluster formation and high Ni diffusion in concentrated Ni-Fe-Cr alloys. Chakraborty, D., & Aidhy, D. S. (2017). Journal of Alloys and Compounds, 725, 449–460. https://doi.org/10.1016/j.jallcom.2017.07.140  

Segregation and binding energetics at grain boundaries in fluorite oxides. Arora, G., & Aidhy, D. S. (2017). Journal of Materials Chemistry A, 5(8), 4026–4035. https://doi.org/10.1039/C6TA09895A  


Pre-2016

EvolvingAI

  • Clune J, Mouret J-B, Lipson H (2013) The evolutionary origins of modularity. Proceedings of the Royal Society B. 280: 20122863.

  • Huizinga J, Mouret J-B, Clune J (2014) Evolving neural networks that are both modular and regular: HyperNEAT plus the Connection Cost Technique. Proceedings of the Genetic and Evolutionary Computation Conference.

  • Nguyen A, Yosinski J, Bengio Y, Dosovitskiy A, Clune J (2016) Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. arXiv 1612.00005.

  • Stanton C, Clune J (2016) Curiosity Search: Producing generalists by encouraging individuals to continually explore and acquire skills throughout their lifetime. PLoS ONE 11(9): e0162235.

  • Nguyen A, Yosinski J, Clune J (2016) Understanding Innovation Engines: Automated creativity and improved stochastic optimization via Deep Learning. Evolutionary Computation.

  • Nguyen A, Dosovitskiy A, Yosinski J, Brox T, Clune J (2016) Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Advances in Neural Information Processing Systems (NIPS)

  • Norouzzadeh M, Clune J (2016) Neuromodulation improves the evolution of forward models. Proceedings of the Genetic and Evolutionary Computation Conference. Roby Velez, Jeff Clune (2016) Identifying core functional networks and functional modules within artificial neural networks via subsets regression. Proceedings of the Genetic and Evolutionary Computation Conference.

  • Mengistu H, Lehman J, Clune J (2016) Evolvability Search: Directly selecting for evolvability in order to study and produce it. Proceedings of the Genetic and Evolutionary Computation Conference.

  • Huizinga J, Mouret JB, Clune J (2016) Does aligning phenotypic and genotypic modularity improve the evolution of neural networks? Proceedings of the Genetic and Evolutionary Computation Conference.

  • Nguyen A, Yosinski J, Clune J (2016) Multifaceted Feature Visualization: Uncovering the different types of features learned by each neuron in deep neural networks. Visualization for Deep Learning workshop. International Conference on Machine Learning.

  • Mengistu H, Huizinga J, Mouret JB, Clune J (2016) The evolutionary origins of hierarchy.

Stochasticaquiferinv

  • Dongdong Wang, Ye Zhang, Subsurface Data Integration and Stochastic Inversion Assessing Aquifer Parameter and Boundary Condition Uncertainty, Journal of Hydrology, in revision

  • Jianying Jiao, Ye Zhang, Julian Zhu, Direct Hydraulic Characterization for diverse soil types under Infiltration and Evaporation, Transport in Porous Media, accepted for publication.

  • Jianying Jiao, Ye Zhang (2016) Direct Method of Hydraulic Conductivity Structure Identification for Subsurface Transport Modeling, Journal of Hydrologic Engineering, 10.1061/(ASCE)HE.1943-5584.0001410, 04016033.

Geologiccarbonseq

  • Minh Nguyen, Ye Zhang, Jun Li, Xiaochun Li, Bing Bai, Haiqing Wu, Ning Wei, Philip Stauffer (2017) A Geostatistical Study in Support of CO2 Storage in Deep Saline Aquifers of the Shenhua CCS Project, Energy Procedia.

In Review:

  • Mingkan Zhang, Ye Zhang, Peter Lichtner, Effect of Multiscale Permeability Heterogeneity on Developing Convective Mixing in Geological Carbon Sequestration, International Journal of Greenhouse Gas Control.

  • Minh C. Nguyen1*, Ye Zhang1, Philip H. Stauffer2 (2017) Object-based Modeling and Sensitivity Analysis in Support of CO2 Storage in Deep Saline Aquifers at the Shenhua site, Ordos basin, China, IJGHGC.

Elecdisp

  • Robert Godby, Tara Righetti, Kipp Coddington, Dalia Patino-Echeverri, and Temple Stoellinger, “The role of energy models: characterizing the uncertainty of the future electricity system to design more efficient and effective laws and regulations,”, George Washington Journal of Energy & Environmental Law, in press.

  • Robert Godby and Roger Coupal, “The Potential Impact of Rate-based or Mass-based Rules on Coal-Producing States under the Clean Power Plan,” with Roger Coupal, Electricity Journal, Vol. 29:6, July 2016.

  • Robert Godby and Roger Coupal, “A Comparison of Clean Power Plan Forecasts for Wyoming: the Importance of Implementation and Modeling Assumptions,” with Roger Coupal, Electricity Journal, Vol. 29:1, February 2016, pp. 53-62.

  • Greg Torell, “Essays on Electricity Modeling, Transmission Congestion and Carbon Policy,” Ph.D. Dissertation, Department of Economics and Finance, University of Wyoming, July, 2016.

  • Robert Godby, Roger Coupal, and David Taylor, “An Assessment of Wyoming's Competitiveness to attract new Wind Development, and the Potential impacts such development may bring the State", prepared for the Wyoming Business Council and Carbon County Economic Development Corp (Wyoming), September 2016.

Qdots

  • Choi, J. K., Tohgha, U., Pap, L., Elliott, K, W., Leonard, B. M., Kubelka, J., Dzyuba, S. V., Varga, K., and Balaz, M. (2016) “Ligand-induced inversion of chirality in CdSe and CdS quantum dots without changing the stereochemistry of the capping ligand.” ACS Nano 10, 3809-3815

Twpm

  • Wood, Aaron D., Charles F. Mason, and David Finnoff, “OPEC, the Seven Sisters, and Oil Market Dominance: An Evolutionary Game Theory and Agent-Based Modeling Approach,” Journal of Economic Behavior and Organization, 2016 v. 132 (Part B), pp. 66-78

HOD

  • S. Eftekharzadeh, A. D. Myers, J. F. Hennawi, S. G. Djorgovski, G. T. Richards, A. A. Mahabal, M. J. Graham (2017) "Clustering on very small scales from a large sample of confirmed quasar pairs: Does quasar clustering track from Mpc to kpc scales?"

Chemcal

  • Bradley M Schmidt, Jeramie J. Adams, Suman Debnath, Navamoney Arulsamy, and Dean M Roddick: "Catalytic Ethylene dimerization by (PP)PtMe(C2H4)+ Complexes: Phosphine Steric and Electronic Effects"

Simturtle

  • Poster presentation at PICES 2016 (North Pacific Marine Science Organization), November 2-13, San Diego, USA “Project Sim Turtle: novel approaches for understanding biophysical interactions in the mesoscale”, by Cheryl S. Harrison, Nathan Putman, Jessica Y. Luo, Qingfeng Li, Mathew Long

Preamble

In Review:

  • Juliano, T. W., T. R. Parish, D. A. Rahn, and D. Leon, 2017: An Atmospheric Hydraulic Jump in the Santa Barbara Channel, J. Applied Meteor. Clim.

Pecan

  • Parish, T. R., R. D. Clark, 2017: On the Initiation of the 20 June 2015 Great Plains Low-Level Jet. J. Applied Meteor. Clim., (conditionally accepted).

Wcl

  • Rebecca Pauly - M.S. Department of Atmospheric Science, December 2015. Thesis title: An Evaluation of PBL Parameterizations Utilizing Compact Airborne Raman Lidar Data

Coalgasif

  • J. Cai, S. Roy, M.F. Modest, A comparison of specularly reflective boundary conditions and rotationally invariant formulations for Discrete Ordinate Methods in axisymmetric geometries, J. Quant. Spectrosc. Radiat. Transfer., 182, 75-86, 2016

Qco

  • Moorhouse, Williford, Double Covers of Symplectic Dual Polar Space Graphs, Discrete Mathematics 339 (2016) p. 571-588. Moorhouse, Sun, Williford, The Eigenvalues of the graphs D(4,q). accepted to Journal of Combinatorial Theory, Series B.

Rd-hea

  • Gaurav Arora and Dilpuneet S. Aidhy, (2017) "Segregation and binding energetics at grain boundaries in fluorite oxides"

Turbulencemodeling

  • Mokhtarpoor R., Heinz S. and Stoellinger M. (2016), “Dynamic Unified RANS-LES Simulations of High Reynolds Number Separated Flows”, Phys. Fluids 28, 095101/1-36. Roy R., and Stoellinger M. (2017), “Large Eddy Simulation of Wind Flow Over Complex Terrain: The Bolund Hill Case”, AIAA Aviation 2017, Grapevine, TX, AIAA Paper 17-1160.

  • Han Y., and Stoellinger M. (2017), “Large eddy simulation of atmospheric boundary layer flows over complex terrain with varying stability conditions”, AIAA Aviation 2017, Grapevine, TX, AIAA Paper 17-1161.

  • Mokhtarpoor R., Heinz S., Stoellinger M., and Balakumar P. (2017), “Dynamic Large Eddy Simulation: Analysis of Stability and Realizability”, AIAA Aviation 2017, Grapevine, TX, AIAA Paper 17-0992.

  • Mokhtarpoor R., Heinz S., and Stoellinger M. (2017), “LES and Hybrid RANS-LES of High Reynolds Number Separated Flows”, AIAA Aviation 2017, Grapevine, TX, AIAA Paper 17-0994.

  • Han Y., Stoellinger M., and Naughton J. (2016), “Large eddy simulation for atmospheric boundary layer flow over flat and complex terrains”, J. Phys.: Conf. Ser. 753 03204.

  • Ashton N. and Stoellinger M. (2016), "Computation of Turbulent Flow in a Rotating Pipe using the Elliptic Blending Reynolds Stress Model", 46th AIAA Fluid Dynamics Conference, AIAA AVIATION Forum, (AIAA 2016-3943).

Inbrev

  • Chhatre, VE and Emerson KJE (2017) StrAuto: Automation and Parallelization of STRUCTURE Analysis.

F3dt

  • Subsurface fault geometries in Southern California illuminated through Full-3D Seismic Waveform Tomography (F3DT), En-Jui Lee and Po Chen, Tectonophysics, DOI: 10.1016/j.tecto.2017.03.005, 2017.

  • Improved Basin Structures in Southern California Obtained Through Full-3D Seismic Waveform Tomography (F3DT), En-Jui Lee and Po Chen, Seismological Research Letters, Volume 87, Number 4, July/August, 2016.

  • Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression, Peter Lindstrom, Po Chen, En-Jui Lee, Computers and Geosciences (2016), pp. 45-54.

  • pSIN: a scalable, Parallel algorithm for Seismic INterferometry of large-N ambientnoise data, Po Chen, Nicholas J. Taylor, Ken G. Dueker, Ian S. Keifer, Andra K. Wilson, Casey L. McGuffy, Christopher G. Novitsky, Alec J. Spears, W. Steven Holbrook, Computers and Geosciences (2016), pp. 88-95.

Rotarywingcfd

  • (InPreparation)MichaelJ.Brazell, AndrewC.Kirby, DimitriJMavriplis. ”Ahigh-orderdiscontinuousGalerkin octree-based AMR solver for overset simulations,” 2017 AIAA Aviation Forum, Denver, CO., June 2017.

  • (In Preparation) Andrew C. Kirby, Michael J. Brazell, Rajib Roy, Behzad Ahrabi, Dimitri J Mavriplis, Michael Stoellinger, Jay Sitaraman. ”Wind Farm Simulations Using an Overset hp-Adaptive Approach with Blade-Resolved Turbine Models,” 2017 AIAA Aviation Forum, Denver, CO., June 2017.

  • Donya Ramezanian and Dimitri J. Mavriplis. ”An Order N log 2(N) Parallel Solver for Time Spectral Problems”, 55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2017-1219).

  • Dimitri J. Mavriplis, Enrico Fabiano, and Evan Anderson. ”Recent Advances in High-Fidelity Multidisciplinary Adjoint-Based Optimization with the NSU3D Flow Solver Framework”, 55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2017-1669).

  • Behzad Reza Ahrabi and Dimitri J. Mavriplis. ”Scalable Solution Strategies for Stabilized FiniteElementFlowSolversonUnstructuredMeshes”, 55thAIAAAerospaceSciencesMeeting, AIAASciTech Forum, (AIAA 2017-0517).

  • Shervin Sammak, Arash G. Nouri, Michael J. Brazell, Dimitri J. Mavriplis, and Peyman Givi. ”Discontinuous Galerkin-Monte Carlo Solver for Large Eddy Simulation of Compressible Turbulent Flows”, 55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2017-0982).

  • Dimitri Mavriplis, Evan Anderson, Ray S. Fertig, and Mark Garnich. ”Development of a High-Fidelity Time-Dependent Aero-Structural Capability for Analysis and Design”, 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA SciTech Forum, (AIAA 2016-1175).

  • Enrico Fabiano and Dimitri J. Mavriplis. ”Adjoint-Based Aerodynamic Design On Unstructured Meshes”, 54th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2016-1295).

  • Michael J. Brazell, Jay Sitaraman, Dimitri J. Mavriplis. An overset mesh approach for 3D mixed element high order discretizations. Journal of Computational Physics. Vol. 322, 33-51, 2016, Elsevier.

  • Asitav Mishra, Dimitri Mavriplis, and Jay Sitaraman. Time-Dependent Aeroelastic AdjointBased Aerodynamic Shape Optimization of Helicopter Rotors in Forward Flight. AIAA Journal, December 2016, Vol. 54, No. 12 : pp. 3813-3827.

  • Michael J. Brazell, Andrew Kirby, Jayanarayanan Sitaraman, and Dimitri J. Mavriplis. ”A Multi-Solver Overset Mesh Approach for 3D Mixed Element Variable Order Discretizations”, 54th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2016-2053).

  • Andrew C. Kirby, Michael J. Brazell, Jay Sitaraman, Dimitri J. Mavriplis. ”An Overset Adaptive High-Order Approach for Blade-Resolved Wind Energy Applications,” Paper presented at AHS Forum 72, West Palm Beach FL, May 2016.

  • Enrico Fabiano and Dimitri J. Mavriplis. ”Adjoint-based Aeroacoustic Design-Optimization of Flexible Rotors in Forward Flight,” Paper presented at AHS Forum 72, West Palm Beach FL, May 2016. Alfred Gessow Forum Best Paper Award

Euo

  • Vitaly Proshchenko and Yuri Dahnovsky, Weak d 0 ferromagnetism: Zn vacancy condensation in ZnS nanocrystals, J. Phys.: Condens. Matter 29, 025803 (20017).

  • Andrew J. Yost, Artem Pimachev, Chun-Chih Ho, Seth B. Darling, Leeyih Wang, Wei-Fang Su, Yuri Dahnovsky, and TeYu Chien, Coexistence of Two Electronic Nano-Phases on a CH3NH3PbI3−xClx Surface Observed in STM Measurements, ACS Appl. Mater. Interfaces, 2016, 8 (42), pp 29110–29116.

  • A. Pimachev, U. Poudyal, V. Proshchenko, W. Wang and Yu. Dahnovsky, Large enhancement in photocurrent by Mn doping in CdSe/ZTO quantum dot sensitized solar cells, Phys. Chem. Chem. Phys., 18, 26771-26776, (2016) DOI: 10.1039/c6cp04263e.

  • G. Rimal, A. K. Pimachev, A. J. Yost, U. Poudyal, S. Maloney, W. Wang, TeYu Chien, Yu. Dahnovsky, and J. Tang, Giant photocurrent enhancement by transition metal doping in quantum dot sensitized solar cells, Appl. Phys. Lett. 119, 103901 (2016).

  • F. V. Kusmartsev, V. D. Krevchik, M. B. Semenov, D. O. Filatov, A. M. Shorokhov, A. A. Bukharev, Yu. I. Dahnonovsky, A. V. Nikolaev, N. A. Pyataev, R. B. Zaitsev, P. V. Krevcjk, I. A Egorov, K. Yamamoto, A. K. Aringazin, Phonon assisted resonant tunneling and its phonon control. JETP Lett. 104 (6), 392-397 (2016) (Russ. JETP Lett., 104, 406-412 (2016)).

  • Vitaly Proshchenko, Anri Karanovich, and Yuri Dahnovsky, Surface-Bulk Model for d0 Ferromagnetism in ZnS Quantum Dots, J. Phys. Chem. C, 120, 11253-11261 (2016).

  • Vitaly Proshchenko, Sabit Horoz, Jinke Tang, and Yuri Dahnovsky, Room temperature d0 ferromagnetism in ZnS nanocrystals, Journal of Applied Physics 119, 223901 (2016)

Solvatedaminoacids

  • Chi, H., Welch, W. R. W., Kubelka, J. and Keiderling, T. A. (2013) "Insight into the packing pattern of 2 fibrils. A model study of glutamic acid-rich oligomers with 13C isotopic edited vibrational spectroscopy." Biomacromolecules, 14, 3880-3891.

  • Welch, W. R. W., Keiderling, T. A. and Kubelka, J. (2013) "Structural analyses of experimental 13C edited amide I' IR and VCD for peptide -sheet aggregates and fibrils using DFT-based spectral simulations." J. Phys. Chem. B., 117, 10359-10369.

  • Welch, W. R. W., Kubelka, J. and Keiderling, T. A. (2013) "Infrared, VCD and Raman spectral simulations for -sheet structures with various isotopic labels, inter-strand and stacking arrangements using Density Functional Theory." J. Phys. Chem. B., 117, 10343-10358.

  • Welch, W. R. W. and Kubelka, J. (2012) “DFT-Based Simulations of Amide I ' IR Spectra of a small protein in a solution using an empirical electrostatic map with a continuum solvent model.” J. Phys. Chem. B 116, 10739-10747

Brqtl

  • Brock MT, Lucas LK, Anderson NA, Rubin MJ, Markelz RJC, Covington MF et al (2016). Genetic architecture, biochemical underpinnings and ecological impact of floral UV patterning. Molecular Ecology 25(5): 1122-1140.

  • Yarkhunova Y, Edwards CE, Ewers BE, Baker RL, Aston TL, McClung CR et al (2016). Selection during crop diversification involves correlated evolution of the circadian clock and ecophysiological traits in Brassica rapa. New Phytologist 210(1): 133-144.

In Review:

  • Baker, RL, WF Leong, MT Brock, M Rubin, N An, S Welch, and C Weinig. In revision. Leveraging Bayesian inference and high-throughput remote sensing indices for quantitative genetic analyses of Function-Valued Traits. Theoretical and Applied Genetics.

  • Brock MT, RL Winkelman, MJ Rubin, CE Edwards, BE Ewers, and C Weinig. Accepted pending revisions. Floral allocation currencies are affected by distinct environmental stresses, show whorl × environment interactions, and exhibit whorl-specific QTL. Heredity.

  • Rubin M, MT Brock, A Davis, Z German, M Knapp, S Welch, S Harmer, J Maloof, S Davis, and C Weinig. In revision. Circadian rhythms vary over the growing season and correlate with fitness components. Molecular Ecology.

In Prep:

  • Baker, RL, WF Leong, N An, S Welch, and C Weinig. In prep. Predicting developmental phenotypes based on genotypes: A Bayesian function-valued trait approach to phenotypic plasticity in Brassica rapa leaves. For Genetics.

  • Hubbard C, MT Brock, L van Diepen, L Maignien, and C weinig. In prep. The circadian clock influences rhizosphere community structure and function. For ISME Journal.

Inversion

  • Pafeng, J., S. Mallick, and H. Sharma, 2017, Prestack waveform inversion of three-dimensional seismic data—an example from the Rock Springs Uplift, Wyoming, USA, Geophysics, 82, B1-B12, doi: 10.1190/Geo2016-0079.1.

  • Mallick, S., D. Mukherjee, L. Shafer, and E. Campbell-Stone, 2017, Azimuthal anisotropy analysis of P-wave seismic data and estimation of the orientation of the in situ stress fields – an example from the Rock-Springs uplift, Wyoming, USA: Geophysics, 82, no. 2, B63-B77.

  • Sharma, H., S. Mallick, S. Verma, and E. Campbell, 2017, Azimuthal anisotropy analysis of multiazimuth P-wave seismic data—an example from the Rock-Springs uplift, Wyoming, USA, First draft prepared for submission to Geophysics.

In Review:

  • Sharma, H., S. Mallick, S. Verma, and E. Campbell, 2017, Azimuthal anisotropy analysis to estimate the in-situ stress orientation and the relative fracture density- a real data example, SEG Technical Program Expanded Abstracts.

  • Ayani, M., S. Mallick, J. Hunziker, and L. MacGragor, 2017, Inversion of frequency domain marine controlled-source electromagnetic data using genetic algorithm, SEG Technical Program Expanded Abstracts.

  • Jia, L., and S. Mallick, 2017, Finite Element Reverse Time Prestack Depth Migration – Concerns and Discussions, SEG Technical Program Expanded Abstracts.

  • Jia, L. and S. Mallick, 2017, A new imaging condition for the reverse time prestack depth migration, SEG Technical Program Expanded Abstracts.

  • Jia, L., S. Mallick, W. S. Holbrook, and W. Fortin, 2017, Joint prestack waveform inversion and acoustic reverse time migration, SEG Technical Program Expanded Abstracts.

Lessf

  • Reza Mokhtarpoor, Stefan Heinz and Michael Stoellinger,“Dynamic unified RANS-LES simulations of high Reynolds number separated flows”, Phys.Fluids 28, 095101, 2016.

  • R. Mokhtarpoor, S. Heinz and M. K. Stoellinger, Dynamic Unified RANS-LES Simulations of Periodic Hill Flow at High Reynolds Number. TSFP-10, Chicago, IL, USA, TSFP Paper 2017-xx-x, 1-6.

  • R. Mokhtarpoor, S. Heinz and M. K. Stoellinger, Realizability and Stability Analysis of Dynamic LES. TSFP-10, Chicago, IL, USA, TSFP Paper 2017-P-xx, 1-6.

  • R. Mokhtarpoor, S. Heinz, M. Stoellinger and P. Balakumar, “Dynamic Large Eddy Simulation: Analysis of Stability and Realizability”. AIAA SciTech 2017, Grapevine, TX, AIAA Paper 17-0992.

  • R. Mokhtarpoor, S. Heinz and M. Stoellinger, “LES and Hybrid RANS-LES of High Reynolds Number Separated Flows”. AIAA SciTech 2017, Grapevine, TX, AIAA Paper 17-0994.