Early Achievements Track-Record

Through his research career so far (from 2007 to 2019) Dr Efthimiou has offered some important achievements in the Computational Fluid Dynamics (CFD) – Reynolds-averaged Navier Stokes (RANS) methodology. One of the achievements is the work of Efthimiou et al. (2016a) [1], where a combination was attempted between two research fields “RANS” and “statistics”, in order to be able to predict the upper tails of the concentration distribution functions (cdf). The knowledge of the upper tails of cdfs is very important for human exposure studies. The RANS methodology is able to predict the two concentration moments, the mean and the variance, by solving numerically their partial differential equations. These two concentration moments can be used for the calculation of the parameters of a statistical distribution e.g. the scale and shape parameters of a gamma distribution or the shape parameters of a beta distribution. Recently, Dr Efthimiou proved that the beta distribution has a better performance in describing concentration fluctuations in urban areas than the gamma distribution (Efthimiou et al., 2017a) [2]. Dr Efthimiou has examined also the performance of the hybrid model “RANS-beta” using wind tunnel experimental data [3].

The advantage of the CFD-RANS methodology to predict the concentration mean and variance has been used also for the prediction of the individual exposure i.e. the prediction of the expected peak dosages. Dr Efthimiou has published, as first author, many works on this subject, Efthimiou et al. (2017b) [4], Efthimiou et al. (2015) [5], Efthimiou and Bartzis (2011) [6], Efthimiou et al. (2011) [7]. The idea is based on the incorporation of a deterministic model, which can predict peak dosages at short time intervals, into the RANS methodology. It was found that the Bartzis et al. (2008) [8] model was the ideal deterministic model for this purpose. The input parameters of the Bartzis et al. (2008) [8] model were the concentration mean, variance and the autocorrelation time scale. Thus, the RANS methodology was optimized in order to predict reliably these parameters. The major achievement of his PhD, that was published in Efthimiou and Bartzis (2011) [6], was the calculation of the pollutant travel time in complex urban environments. It is known that the turbulent time scales of the wind and of the pollutant concentration are not the same near the source. Thus, the calculation of the pollutant travel time allowed the separation of the time scales near and far from the source. This resulted also in the optimization of the concentration variance prediction. Dr Efthimiou has more ideas for the optimization of the RANS methodology. One of them is to use the turbulent time scales of the concentration and the wind in order to calculate the eddy mass diffusivity (in the literature of CFD the eddy mass diffusivity is calculated using the Schmidt number the value of which is questionable).

Dr Efthimiou has calculated also, with CFD-RANS, dosage-based parameters from puff dispersion. In Efthimiou et al. (2017c) [9], the dispersion of only one puff was performed and its dosage-based parameters (dosage, peak concentration, arrival time, peak time, leaving time, ascent time, descent time and duration) were compared with the ensemble averaged dosage-based parameters of two wind tunnel experiments. It was found that the model presented a better performance for the temporal parameters (i.e. arrival time, peak time, leaving time, duration etc.) than for the dosage and the peak concentration. Also, the Lagrangian methodology has been examined in case of puff dispersion [10].

The previous works belong to the “forward problem” which means that boundary conditions for the velocity and the concentration are used and prediction of the flow field and the dispersion of an airborne material are performed in the urban environment. In this case someone knows exactly where the source is and what the source rate is. The output of the forward problem is the numerical results at selected locations (sensors) in order to compare them with the experiment. Dr Efthimiou works also, as postdoc researcher, on the “inverse problem” which means that the flow field and the real concentration measurements are known, and an endeavour is required so as to find where the source is and what the source rate is. This can be performed in CFD-RANS by solving numerically an adjoint equation (a differential equation for the source receptor function). Results of the inverse problem of one difficult scenario have been presented in the Harmo 17 conference (Efthimiou et al., 2016b) [11]. Also, two papers have been published in Atmospheric Environment [12-13], one in Nature Scientific Reports [14], one in Building and Environment [15] and he prepares one more publication which can been found in ResearchGate [16].

Dr Efthimiou is also very interested for wind engineering. He believes that for various practical applications e.g. for the optimal design and operation of a wind turbine is based on the reliable knowledge of the wind speed probabilities. The wind speed probabilities in the atmospheric surface layer is very difficult to predict due to atmospheric turbulence. It was found that an optimized beta distribution is the best solution for this purpose (Efthimiou et al., 2017e) [17]. The construction of this distribution has been performed with wind tunnel and Direct Numerical Simulation experiments and the validation has been performed with real field measurements. The advantages of this distribution are that it can predict all the wind speed percentiles (from the 1st to the 100th) and can be incorporated in the CFD-RANS methodology (Efthimiou et al., 2019) [18]. Also, he has majored contributed to two publications concerning the simulation of the atmospheric wind flow in urban environments using RANS [19] and Large Eddy Simulation (LES) [20].

There are also participations of Dr Efthimiou in projects concerning indoor air quality. He has participated in the OFFICAIR project (http://www.officair-project.eu/) and he has performed CFD-RANS simulations of hygrothermal and concentration parameters in real offices [21]. Also, during the EPHECT project (https://esites.vito.be/sites/ephect/Pages/home.aspx), he developed a software that computes the emission rate of various hazardous consumer products (Dimitroulopoulou et al., 2015 [22]; Bartzis et al., 2015 [23]).

There are also participations of Dr Efthimiou in projects concerning environmental radioactivity (the PREPARE project, https://prepare-eu.org/) with participation in two publications [24-25].

Recently, Dr Efthimiou has proved that a Langevin-type equation can be incorporated in the RANS methodology in order to calculate inlet and boundary conditions for atmospheric LES (Efthimiou, 2018) [26]. He used this method in the paper Argyropoulos et al. (2017) [27] for the Harmo 18 conference and we predicted very good LES results for the concentration.

Dr Efthimiou this period examines if the maximum values and the statistics of various physical quantities of science such as the wind speed (in Atmospheric Sciences), the cosmic ray (in Astrophysics), the material intensity (in Economy), the blood speed (in Medicine) etc. correlate with the fluctuation intensity and the autocorrelation time scale. The most recent paper that has been published in a journal of the University of Yale is Efthimiou et al. (2018) [28].


1) Efthimiou G.C., Andronopoulos S., Tolias I., Venetsanos A., 2016a. Prediction of the upper tail of concentration probability distributions of a continuous point source release in urban environments. Environ Fluid Mech, 16, 5, 899–921, DOI 10.1007/s10652-016-9455-2 (http://rdcu.be/j93f).

2) Efthimiou G.C. Andronopoulos S., Bartzis J.G., 2017a. Evaluation of probability distributions for concentration fluctuations in a building array, Physica A, 484, 104–116.

3) Efthimiou G.C., 2019. Prediction of four concentration moments of an airborne material released from a point source in an urban environment. Journal of Wind Engineering and Industrial Aerodynamics, 184, 247-255.

4) Efthimiou G.C., Andronopoulos S., Bartzis J.G., Berbekar E., Harms F., Leitl B. 2017b. CFD-RANS prediction of individual exposure from continuous release of hazardous airborne materials in complex urban environments, Journal of Turbulence, 18, 2, 115-137.

5) Efthimiou G.C., Berbekar E., Harms F., Bartzis J.G., Leitl B., 2015. Prediction of the peak concentration and concentration distribution of a continuous point source release in a semi-idealized urban canopy using CFD-RANS modelling, Atmospheric Environment, 100, 48-56.

6) Efthimiou G.C., Bartzis J.G., 2011. Atmospheric dispersion and individual exposure of hazardous materials. Journal of Hazardous Materials, 188, 375-383.

7) Efthimiou G.C., Bartzis J.G., Koutsourakis N., 2011. Modelling concentration fluctuations and individual exposure in complex urban environments. Journal of Wind Engineering and Industrial Aerodynamics, 99, 349-356.

8) Bartzis J.G., Sfetsos A., Andronopoulos S., 2008. On the individual exposure from airborne hazardous releases: the effect of atmospheric turbulence, J Hazard Mater, 150, 76–82.

9) Efthimiou G.C., Andronopoulos S., Bartzis J.G., 2017c. Prediction of dosage-based parameters from the short-duration release of airborne materials in urban environments, Meteorology and Atmospheric Physics. DOI 10.1007/s00703-017-0506-0.

10) S. Andronopoulos, J. G. Bartzis, G. C. Efthimiou, A. Venetsanos. Puff dispersion variability assessment through Lagrangian and Eulerian modelling based on the JU2003 campaign, Boundary Layer Meteorology, 171, 3, 395-422.

11) Efthimiou G.C., Andronopoulos S., Venetsanos A.G., Kovalets I.V., Kakosimos K., Argyropoulos Christos D., 2016b. Validation of a data assimilation method for estimation of the location and rate of an unknown point source of passive atmospheric pollutant in a complex urban environment, 17th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, 9-12 May, Budapest, Hungary

12) Efthimiou G.C., Kovalets I.V., Venetsanos A., Andronopoulos S., Argyropoulos C.D., Kakosimos K. 2017d. An optimized inverse modelling method for determining the location and strength of a point source releasing airborne material in urban environment, Atmospheric Environment, 170, 118-129.

13) Ivan V. Kovalets, George C. Efthimiou, Spyros Andronopoulos, Alexander G. Venetsanos, Konstantinos Kakosimos, Christos D. Argyropoulos. Inverse identification of unknown finite-duration air pollutant releases in urban environment, Atmospheric Environment, 181, 82–96.

14) Argyropoulos, C. D.; Elkhalifa, S.; Fthenou, E.; Efthimiou, G. C.; Andronopoulos, S.; Venetsanos, A.; Kovalets, I. V.; Kakosimos, K. E., 2018. Source reconstruction of airborne toxics based on acute health effects information. Scientific Reports, 8, (1), 5596.

15) George C. Efthimiou, Ivan V. Kovalets, Christos D. Argyropoulos, Alexandros Venetsanos, Spyros Andronopoulos, Konstantinos Kakosimos, 2018. Evaluation of an inverse modelling methodology for the prediction of a stationary point pollutant source in complex urban environments, Building and Environment, 143, 107–119.

16) George C. Efthimiou, Ivan V. Kovalets, Alexandros Venetsanos, Spyros Andronopoulos, Christos D. Argyropoulos, Konstantinos Kakosimos. Presentation of new techniques for the prediction of the source location and rate of airborne materials in urban environments, the paper can be found as a working paper in ResearchGate.

17) Efthimiou G.C., Hertwig D., Andronopoulos S., Bartzis J.G., Coceal O., 2017e. A statistical model for the prediction of wind-speed probabilities in the atmospheric surface layer, Boundary Layer Meteorology, 163, 2, 179-201.

18) G.C. Efthimiou, P. Kumar, S.G. Giannissi, A.A. Feiz, S. Andronopoulos, 2019. Prediction of the wind speed probabilities in the atmospheric surface layer, Renewable Energy, 132, 921-930.

19) Denise Hertwig, George C. Efthimiou, John G. Bartzis, Bernd Leitl, 2012. CFD-RANS model validation of turbulent flow in a semi-idealized urban canopy, Journal of Wind Engineering and Industrial Aerodynamics, 111, 61–72.

20) I. C. Tolias, N. Koutsourakis, D. Hertwig, G.C. Efthimiou, A.G. Venetsanos, J.G. Bartzis, 2018. Large Eddy Simulation study on structure of turbulent flow in a complex city, Journal of Wind Engineering & Industrial Aerodynamics, 177, 101–116.

21) Nektarios Koutsourakis, John G. Bartzis, George C. Efthimiou, Ioannis Sakellaris, 2019. CFD studies of pollutant spatial distribution in a large office, International Journal of Environment and Pollution.

22) Dimitroulopoulou S., Trantallidi M., Carrer P., Efthimiou G.C., Bartzis J.G., 2015. EPHECT II: Exposure assessment to household consumer products, Science of the Total Environment, 536, 890–902.

23) Bartzis, J., Wolkoff, P., Stranger, M., Efthimiou, G., Tolis, E., Maes, F., Nørgaard, A. W., Ventura, G., Kalimeri, K., Goelen, E., Fernandes, O., 2015. On organic emissions testing from indoor consumer products’ use, Journal of Hazardous Materials, 285, 37–45.

24) S. Andronopoulos, T. Schichtel, G. Efthimiou, J.G. Bartzis, 2016. Updates of the atmospheric dispersion models inside the Local Scale Model Chain of RODOS regarding particles. Radioprotection 51(HS2), S101-S103.

25) Xiaole Zhang, George Efthimiou, Yan Wang, Meng Huang, 2018. Comparisons between a new point kernel-based scheme and the infinite plane source assumption method for radiation calculation of deposited airborne radionuclides from nuclear power plants, Journal of Environmental Radioactivity, 184-185, 32-45.

26) G. C. Efthimiou, 2018. Prediction of wind speed time series in the near-surface atmospheric surface layer using the CFD-RANS methodology, the paper can be found as a working paper in ResearchGate.

27) C.D. Argyropoulos, G. C. Efthimiou, S. Andronopoulos, K.E. Kakosimos, N.C. Markatos. Μodelling of toxic contaminants dispersion during a real industrial accident using large eddy simulation and RANS models. 18th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, 9-12 October 2017 Bologna, Italy.

28) G. C. Efthimiou, P. Kalimeris, S. Andronopoulos, J. G. Bartzis, 2018. Statistical projection of Material Intensity: Evidence from the global economy and 107 countries, Journal of Industrial Ecology, 22, 6, 1465-1472.