Yang, J., Stroh, A., Lee, S., Bagheri, S., Frohnapfel, B.
& Forooghi, P. (2024).
Assessment of Roughness Characterization Methods for Data-Driven Predictions.
Flow, Turbulence and Combustion,
113(2), 275-292.
https://doi.org/10.1007/s10494-024-00549-z
Colombo, M., Meng, Y., Poirier-Quinot, M., Rollet, A. L., Geeverding, A., Delcea, M., deMello, A. J. & Abou-Hassan, A. (2024).
Assessing the Effect of Magnetite Nanoflowers on Platelets in a Multiscale Approach in the Context of Thromboembolic Diseases.
ACS Applied Nano Materials,
7(17), 20085-20093.
https://doi.org/10.1021/acsanm.4c02715
Formentini, G., Martiny, T. A.
, Møller, C., Vernica, T. & Ramanujan, D. (2024).
Assessing the disassembly performance of washing machines through the design for circular disassembly methodology.
Proceedings of the Design Society ,
4, 1249-1258.
https://doi.org/10.1017/pds.2024.127
Pombo, D. V.
, Rincón, M. J., Bacher, P., Bindner, H. W., Spataru, S. V. & Sørensen, P. E. (2022).
Assessing stacked physics-informed machine learning models for co-located wind–solar power forecasting.
Sustainable Energy, Grids and Networks,
32, Article 100943.
https://doi.org/10.1016/j.segan.2022.100943
Zehtabiyan-Rezaie, N., Alvandifar, N., Saffaraval, F., Makkiabadi, M., Rahmati, N. & Saffar-Avval, M. (2019).
A solar-powered solution for water shortage problem in arid and semi-arid regions in coastal countries.
Sustainable Energy Technologies and Assessments,
35, 1-11.
https://doi.org/10.1016/j.seta.2019.05.015
Sawant , M., Thakare, S., Rao, A. P., Feijóo Lorenzo, A. E.
& Bokde, N. (2021).
A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics.
Energies,
14(8), Article 2041.
https://doi.org/10.3390/en14082041
Böttjer, T., Tola, D., Kakavandi, F., Wewer, C. R., Ramanujan, D., Gomes, C., Larsen, P. G. & Iosifidis, A. (2023).
A review of unit level digital twin applications in the manufacturing industry.
CIRP Journal of Manufacturing Science and Technology,
45, 162-189.
https://doi.org/10.1016/j.cirpj.2023.06.011
E. S. Chen, P., Bin, Y., I. A. Yang, X., Shi, Y.
, Abkar, M. & I. Park, G. (2023).
A priori screening of data-enabled turbulence models. Physical Review Fluids,
8(12), Article 124606.
https://doi.org/10.1103/PhysRevFluids.8.124606
Alzweighi, M., Tryding, J.
, Mansour, R., Borgqvist, E. & Kulachenko, A. (2023).
Anisotropic damage behavior in fiber-based materials: Modeling and experimental validation.
Journal of the Mechanics and Physics of Solids,
181, Article 105430.
https://doi.org/10.1016/j.jmps.2023.105430
Zhang, B., Endelt, B., Lang, L., Zhao, Y., Yan, S.
& Nielsen, K. B. (2022).
An inverse strategy to determine constitutive parameters of tubular materials for hydroforming processes.
Chinese Journal of Aeronautics,
35(6), 379-390.
https://doi.org/10.1016/j.cja.2021.11.007
Z. Bernstein, W., Tensa, M., Praniewicz, M., Kwon, S.
& Ramanujan, D. (2020).
An automated workflow for integrating environmental sustainability assessment into parametric part design through standard reference models.
Procedia CIRP,
90, 102-108.
https://doi.org/10.1016/j.procir.2020.02.058
Ma, P., Xia, R., Wang, X.
, Zhang, X., Królczyk, G., Gardoni, P. & Li, Z. (2022).
An active control method for vibration reduction of a single-link flexible manipulator.
Journal of Low Frequency Noise Vibration and Active Control,
41(4), 1497-1506.
https://doi.org/10.1177/14613484221094982
Rysgaard, S., Bjerge, K., Boone, W., Frandsen, E., Graversen, M.
, Thomas Høye, T., Jensen, B., Johnen, G.
, Antoni Jackowicz-Korczynski, M., Taylor Kerby, J., Kortegaard, S.
, Mastepanov, M., Melvad, C., Schmidt Mikkelsen, P., Mortensen, K., Nørgaard, C.
, Poulsen, E., Riis, T., Sørensen, L. & Røjle Christensen, T. (2022).
A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas.
HardwareX,
12, Article e00331.
https://doi.org/10.1016/j.ohx.2022.e00331
Frandsen, L. N., Kristensen, C. K., Haahr-Lillevang, L., Klingaa, C. G., Mohanty, S.
& Pedersen, M. M. (2023).
A method for developing predictive models of quality metrics and gas flow variables for 316L PBF-LB/M printed components based on image analysis. In C. Nisbet, D. Phillips & O. Riemer (Eds.),
European Society for Precision Engineering and Nanotechnology, Conference Proceedings: Copenhagen 2023 (pp. 115-118). Euspen.
Shehab, E., Meiirbekov, A., Amantayeva, A., Suleimen, A., Tokbolat, S.
, Sarfraz, S. & Ali, M. H. (2021).
A fuzzy logic-based cost modelling system for recycling carbon fibre reinforced composites.
Polymers,
13(24), Article 4370.
https://doi.org/10.3390/polym13244370
Migliorini, M.
, Doll, U., Lawson, N. J., Melnikov, S. M., Steinbock, J., Dues, M., Zachos, P. K., Röhle, I. & MacManus, D. G. (2025).
Advancements on the use of Filtered Rayleigh Scattering (FRS) with Machine learning methods for flow distortion in Aero-Engine intakes.
Experimental Thermal and Fluid Science,
160, 111325. Article 111325.
https://doi.org/10.1016/j.expthermflusci.2024.111325
Mahato, M., Hwang, W. J.
, Tabassian, R., Oh, S., Nguyen, V. H., Nam, S., Kim, J. S., Yoo, H., Taseer, A. K., Lee, M. J., Zhang, H., Song, T. E. & Oh, I. K. (2022).
A Dual-Responsive Magnetoactive and Electro–Ionic Soft Actuator Derived from a Nickel-Based Metal–Organic Framework.
Advanced Materials,
34(35), Article 2203613.
https://doi.org/10.1002/adma.202203613
Kunrath, K., Leka, S., Vestergaard, L. S., Presser, M. & Ramanujan, D. (2024).
A Digital Tool for Scaffolding Innovation Learning in Engineering Education with Local Industry Needs.
International Journal of Engineering Education,
40(4), 801-814.
Shehab, E., Meiirbekov, A., Amantayeva, A., Suleimen, A., Tokbolat, S.
& Sarfraz, S. (2021).
A cost modelling system for recycling carbon fiber-reinforced composites.
Polymers,
13(23), Article 4208.
https://doi.org/10.3390/polym13234208
Yang, J., Velandia, J., Bansmer, S., Stroh, A.
& Forooghi, P. (2023).
A comparison of hydrodynamic and thermal properties of artificially generated against realistic rough surfaces.
International Journal of Heat and Fluid Flow,
99, Article 109093.
https://doi.org/10.1016/j.ijheatfluidflow.2022.109093
Høye, T. T., Dyrmann, M., Kjær, C., Nielsen, J., Bruus, M., Mielec, C. L., Vesterdal, M. S.
, Bjerge, K., Madsen, S. A., Jeppesen, M. R. & Melvad, C. (2022).
Accurate image-based identification of macroinvertebrate specimens using deep learning — How much training data is needed? PeerJ,
10, Article e13837.
https://doi.org/10.7717/peerj.13837
Gao, X., Zhang, Y., Haahr-Lillevang, L., Vedel-Smith, N. K.
& Andriollo, T. (2023).
3D strain pattern in additively manufactured AlSi10Mg from digital volume correlation. Heliyon,
9(12), Article e23186.
https://doi.org/10.1016/j.heliyon.2023.e23186