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1,206 results on '"Larsen, G."'

2. Whole genomes from bacteria collected at diagnostic units around the world 2020

Author

Nag, S, Larsen, G, Szarvas, J, Birkedahl, L, Gulyas, G, Ciok, W, Lagermann, T, Tafaj, S, Bradbury, S, Collignon, P, Daley, D, Dougnon, V, Fabiyi, K, Coulibaly, B, Dembele, R, Nikiema, G, Magloire, N, Ouindgueta, I, Hossain, Z, Begum, A, Donchev, D, Diggle, M, Turnbull, L, Levesque, S, Berlinger, L, Sogaard, K, Guevara, P, Valderrama, C, Maikanti, P, Amlerova, J, Drevinek, P, Tkadlec, J, Dilas, M, Kaasch, A, Westh, H, Bachtarzi, M, Amhis, W, Salazar, C, Villacis, J, Luzon, M, Palau, D, Duployez, C, Paluche, M, Asante-Sefa, S, Moller, M, Ip, M, Marekovic, I, Pal-Sonnevend, A, Cocuzza, C, Dambrauskiene, A, Macanze, A, Cossa, A, Mandomando, I, Nwajiobi-Princewill, P, Okeke, I, Kehinde, A, Adebiyi, I, Akintayo, I, Popoola, O, Onipede, A, Blomfeldt, A, Nyquist, N, Bocker, K, Ussher, J, Ali, A, Ullah, N, Khan, H, Gustafson, N, Jarrar, I, Al-Hamad, A, Luvira, V, Paveenkittiporn, W, Baran, I, Mwansa, J, Sikakwa, L, Yamba, K, Hendriksen, R, Aarestrup, F, Nag S., Larsen G., Szarvas J., Birkedahl L. E. K., Gulyas G. M., Ciok W. J., Lagermann T. M., Tafaj S., Bradbury S., Collignon P., Daley D., Dougnon V., Fabiyi K., Coulibaly B., Dembele R., Nikiema G., Magloire N., Ouindgueta I. J., Hossain Z. Z., Begum A., Donchev D., Diggle M., Turnbull L. A., Levesque S., Berlinger L., Sogaard K. K., Guevara P. D., Valderrama C. D., Maikanti P., Amlerova J., Drevinek P., Tkadlec J., Dilas M., Kaasch A., Westh H. T., Bachtarzi M. A., Amhis W., Salazar C. E. S., Villacis J. E., Luzon M. A. D., Palau D. B., Duployez C., Paluche M., Asante-Sefa S., Moller M., Ip M., Marekovic I., Pal-Sonnevend A., Cocuzza C. E., Dambrauskiene A., Macanze A., Cossa A., Mandomando I., Nwajiobi-Princewill P., Okeke I. N., Kehinde A. O., Adebiyi I., Akintayo I., Popoola O., Onipede A., Blomfeldt A., Nyquist N. E., Bocker K., Ussher J., Ali A., Ullah N., Khan H., Gustafson N. W., Jarrar I., Al-Hamad A., Luvira V., Paveenkittiporn W., Baran I., Mwansa J. C. L., Sikakwa L., Yamba K., Hendriksen R. S., Aarestrup F. M., Nag, S, Larsen, G, Szarvas, J, Birkedahl, L, Gulyas, G, Ciok, W, Lagermann, T, Tafaj, S, Bradbury, S, Collignon, P, Daley, D, Dougnon, V, Fabiyi, K, Coulibaly, B, Dembele, R, Nikiema, G, Magloire, N, Ouindgueta, I, Hossain, Z, Begum, A, Donchev, D, Diggle, M, Turnbull, L, Levesque, S, Berlinger, L, Sogaard, K, Guevara, P, Valderrama, C, Maikanti, P, Amlerova, J, Drevinek, P, Tkadlec, J, Dilas, M, Kaasch, A, Westh, H, Bachtarzi, M, Amhis, W, Salazar, C, Villacis, J, Luzon, M, Palau, D, Duployez, C, Paluche, M, Asante-Sefa, S, Moller, M, Ip, M, Marekovic, I, Pal-Sonnevend, A, Cocuzza, C, Dambrauskiene, A, Macanze, A, Cossa, A, Mandomando, I, Nwajiobi-Princewill, P, Okeke, I, Kehinde, A, Adebiyi, I, Akintayo, I, Popoola, O, Onipede, A, Blomfeldt, A, Nyquist, N, Bocker, K, Ussher, J, Ali, A, Ullah, N, Khan, H, Gustafson, N, Jarrar, I, Al-Hamad, A, Luvira, V, Paveenkittiporn, W, Baran, I, Mwansa, J, Sikakwa, L, Yamba, K, Hendriksen, R, Aarestrup, F, Nag S., Larsen G., Szarvas J., Birkedahl L. E. K., Gulyas G. M., Ciok W. J., Lagermann T. M., Tafaj S., Bradbury S., Collignon P., Daley D., Dougnon V., Fabiyi K., Coulibaly B., Dembele R., Nikiema G., Magloire N., Ouindgueta I. J., Hossain Z. Z., Begum A., Donchev D., Diggle M., Turnbull L. A., Levesque S., Berlinger L., Sogaard K. K., Guevara P. D., Valderrama C. D., Maikanti P., Amlerova J., Drevinek P., Tkadlec J., Dilas M., Kaasch A., Westh H. T., Bachtarzi M. A., Amhis W., Salazar C. E. S., Villacis J. E., Luzon M. A. D., Palau D. B., Duployez C., Paluche M., Asante-Sefa S., Moller M., Ip M., Marekovic I., Pal-Sonnevend A., Cocuzza C. E., Dambrauskiene A., Macanze A., Cossa A., Mandomando I., Nwajiobi-Princewill P., Okeke I. N., Kehinde A. O., Adebiyi I., Akintayo I., Popoola O., Onipede A., Blomfeldt A., Nyquist N. E., Bocker K., Ussher J., Ali A., Ullah N., Khan H., Gustafson N. W., Jarrar I., Al-Hamad A., Luvira V., Paveenkittiporn W., Baran I., Mwansa J. C. L., Sikakwa L., Yamba K., Hendriksen R. S., and Aarestrup F. M.

Abstract

The Two Weeks in the World research project has resulted in a dataset of 3087 clinically relevant bacterial genomes with pertaining metadata, collected from 59 diagnostic units in 35 countries around the world during 2020. A relational database is available with metadata and summary data from selected bioinformatic analysis, such as species prediction and identification of acquired resistance genes.

Published
2023

4. Comparison of three DWM-based wake models at above-rated wind speeds

Author

Hanssen-Bauer, O. W., Doubrawa, P., Madsen, H. Aa, Asmuth, Henrik, Jonkman, J., Larsen, G. C., Ivanell, Stefan, Stenbro, R., Hanssen-Bauer, O. W., Doubrawa, P., Madsen, H. Aa, Asmuth, Henrik, Jonkman, J., Larsen, G. C., Ivanell, Stefan, and Stenbro, R.

Abstract

In this study we investigate three mid-fidelity wind turbine wake models based on the dynamic wake meandering (DWM) model principle, and compare their performance with a reference dataset, produced with large-eddy simulations using the actuator line model. The models are compared with respect to flow field, power, and loads on a row of four 5MW reference turbines experiencing above-rated wind conditions. In general, the DWM models show fairly good agreement with large-eddy simulation for the time-averaged flow fields, blade forces and power, with increasing differences along the turbine row. Also when comparing fatigue loads of blade root moments, the differences between the models increase further into the row, with deviations up to 25 % of the reference case. However, while the development in blade root moment fatigue along the turbine row is predominantly driven by the energy content at the frequency corresponding to the turbine's rotational period (1P) for the DWM models, the large-eddy simulation results suggest that the key drivers for the blade root and tower loads are the increase in meandering and energy at higher frequencies (> 1P) deeper into the turbine row. For the tower loads, the DWM models highly underestimate the fatigue for the waked turbines. From these results, we suggest priorities for future model developments so that robust model implementations can be used in wind farm design and operation.

Published
2023
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14. Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering

Author

Onnen, D., Larsen, G. C., Lio, W. H., Liew, J. Y., Kühn, M., Petrović, V., Onnen, D., Larsen, G. C., Lio, W. H., Liew, J. Y., Kühn, M., and Petrović, V.

Abstract

In a wind farm setting, the location of the wake to which a downwind turbine is exposed is of high interest. It can be used for closed-loop active wake control, ultimately leading to fatigue load reduction and higher power generation. This work proposes a method for dynamic tracking of the meandering wake centre. The rotor takes the role of a sensor, with its blades sampling through the incoming wind field. The measurement of the flapwise blade root bending moment and its decomposition into the non-rotating yaw and tilt moments is used. The latter are linked to the lateral and vertical wake location via a parametric model, tuned with training data from aeroelastic simulations. The implementation of an Extended Kalman Filter (EKF) adds robustness to the tracking and allows to include physical knowledge of the wake meandering to the estimation. For this, the governing equations of the dynamic wake meandering model (DWM) are used to describe the meandering motion as a random walk process. The performance, possibilities and limitations of the tracking method in various inflow conditions are shown and discussed. Generally the wake tracking works satisfying, with estimation errors below 10% of the rotor diameter under moderate turbulence intensity. The Extended Kalman Filter formulation provides the confidence in the tracked wake position. The work shows how this can be effectively used for wake impingement detection.

Published
2022

19. Outstanding Survival and Regeneration Process by the Use of Intelligent Acellular Dermal Matrices and Mesenchymal Stem Cells in a Burn Pig Model

Author

Mansilla, E., Spretz, R., Larsen, G., Nuñez, L., Drago, H., Sturla, F., Marin, G.H., Roque, G., Martire, K., Díaz Aquino, V., Bossi, S., Gardiner, C., Lamonega, R., Lauzada, N., Cordone, J., Raimondi, J.C., Tau, J.M., Biasi, N.R., Marini, J.E., Patel, A.N., Ichim, T.E., Riordan, N., and Maceira, A.

Published
2010
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24. Production and Cost Assessment of Offshore Wind Power in the North Sea

Author

Sørensen, J N, Larsen, G C, and Cazin-Bourguignon, A

Subjects
SDG 13 - Climate Action
Abstract

A minimalistic model complex for determining the available wind power and associated costs related to the development of large–scale offshore wind power was recently developed by Sørensen and Larsen [1]. The model complex demands only a few global input parameters, such as turbine rotor diameter, nameplate capacity, area of wind farm, number of turbines, water depth, and mean wind speed Weibull parameters for the site. Using actual wind climate and bathymetric data for the North Sea, the model is in the present work used to map the annual energy production and levelized cost of energy (LCoE) for wind farms located in the North Sea. As a main conclusion, exploiting all locations with water depths down to about 45 m comply with the electrical power demand of Europe, which is about 3500 TWh/year. In this case, the LCoE is found to be about 7 ∈cents/kWh for wind farms consisting of 15 MW wind turbines and an interspacing between the wind turbines of 8 diameter, corresponding to 2 km.

Published
2021
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35. Tephrochronology

Author

ALLOWAY, B, primary, LARSEN, G, additional, LOWE, D, additional, SHANE, P, additional, and WESTGATE, J, additional

Published
2007
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38. ‘Driving’ innovation in construction organizations: a comparative case study of the design and construction of motor racing venues

Author

Larsen, G. D. and Leicht, R

Abstract

The culture of high-performance racing, whether Formula One, Nascar, or sports cars represents the continuous push for better performance. The research focuses upon understanding how stakeholders designing and building motor racing venues experience the innovation process through both new and refurbishment projects. This paper will provide a review of the literature relative to the nature of innovations within the construction setting, considering a range of theoretical and methodological approaches. The context of innovative designs and high-performance facilities serves as a novel exploration given that the nature of the facilities seems to attract these innovative solutions. Given this seeming repeatability of pursuit and success in innovation on this project type suggests that the context allows construction firms to successfully mobilize their innovative ideas and construction expertise. Using the captured data from two case study projects; Yas Marina Circuit in Abu Dhabi and the Daytona International Speedway in the USA, we explore the phenomenon around the mobilization of innovation in these contexts. Data is collected through extensive, unstructured interviews with key leadership in both projects to explore the emergent nature of innovation and the evolving facility design, construction, and operations. Innovation is born, resides and lives within a loosely and tightly knit network of stakeholders. We will connect the discursive nature of innovation in such settings and projects back to the innovation literature.

Published
2019

44. Low speed motion control experiments on a single point diamond turning machine using CMAC learning control algorithm

Author

Larsen, G. and Cetinkunt, S.

Subjects
Motion control devices -- Analysis, Turning, Diamond -- Analysis, Controllers (Computers) -- Usage, Brittleness -- Analysis, Machining -- Methods, Control systems -- Usage, Neural networks -- Usage, Servomechanisms -- Usage, PID controllers -- Usage, Engineering and manufacturing industries, Science and technology
Abstract

Diamond turning of brittle materials such as glass, ceramic, germanium, and zinc sulfide has been of considerable research interest in recent years due to applications in optics and precision engineering systems. When diamond turning brittle materials, material removal should be kept within the ductile regime to avoid subsurface damage (Evans, 1991; Nakasuji et al., 1990). It is generally accepted that ductile regime machining of brittle materials can be accomplished using extremely low depth of cut and feed rates. Furthermore, the tool positioning accuracy of the machine must be in the nanometer range to obtain optical quality machined parts with surface finish and profile accuracy on the order of 10 nm and 100 nm respectively (Nakasuji et al., 1990, Ueda et al., 1991). Nanometric level positioning accuracy of the machine tool axes is difficult particularly at low feed rates due to friction and backlash. Friction at extremely low feed rates is highly nonlinear due to the transition from stiction to Coulomb friction, and as such is very difficult to model. Standard proportional-integral-derivative (PID) type controllers are unable to deal with this large and erratic friction within the requirements of ultra precision machining. In order to compensate the effects of friction in the machine tool axes, a learning controller based on the Cerebellar Model Articulation Controller (CMAC) neural network is studied for servo-control. The learning controller was implemented using 'C' language on a DSP based controller for a single point diamond turning machine. The CMAC servo control algorithm improved the positioning accuracy of the single point diamond turning machine by a factor of 10 compared to the standard PID algorithm run on the same machine and control system hardware.

Published
1997

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