772 research outputs found
Videos with results from the paper "Optimal 3D time-energy trajectory planning for AUVs using ocean general circulation models" by Albarakati S., Lima R.M., Theußl T., Hoteit I., Knio O
No full textVideos with results from the paper "Optimal 3D time-energy trajectory planning for AUVs using ocean general circulation models" by Albarakati S., Lima R.M., Theußl T., Hoteit I., Knio
Videos with results from the paper "Multi-objective risk-aware path planning in uncertain transient currents: an ensemble-based stochastic optimization approach." by Albarakati S., Lima R.M., Theußl T., Hoteit I., Knio O
No full textVideos with results from the paper "Multi-objective risk-aware path planning in uncertain transient currents: an ensemble-based stochastic optimization approach." by Albarakati S., Lima R.M., Theußl T., Hoteit I., Knio
Geological CO2 Storage Capacity in Aquifers in Saudi Arabia
No full textCarbon capture and storage (CCS) is crucial for Saudi Arabia's net-zero goal by 2060. The provided data provide an estimate of CO2 storage capacities in Saudi Arabia's sedimentary basins, incorporating data from public sources. We assessed 17 basins for CO2 storage in deep saline aquifers. Eastern basins, including Eastern Arabian Basin and Interior Homocline-Central Arch, are most suitable, while Western Saudi Arabia has fewer favorable basins, except Umm Luj, Yanbu, and Jeddah basins. The details are published in “Evaluation of Geological CO2 Storage Potential in Saudi Arabian Sedimentary Basins”, Earth-Science Reviews, by Jing Ye, Abdulkader Afifi, Feras Rowaihy, Guillaume Baby, Arlette De Santiago, Alexandros Tasianas, Ali Hamieh, Aytaj Khodayeva, Mohammed Juaied, Timothy Meckel, Hussein Hoteit**Corresponding author: [email protected]
OPTIMIZING THE INVESTMENT STRATEGY OF FULLY RENEWABLE ENERGY-WATER SYSTEMS
No full textI would like to express my sincerest gratitude to Professor Omar M. Knio for his
guidance and support over the past two years. Thank you for providing me important
feedback when developing our models and writing the thesis.
Many thanks to Dr. Ricardo M. Lima for his advice and constant encouragement.
His expertise in the technical and mathematical aspects of this work proved vital.
Working together was truly an enriching experience.
I appreciate the support of Professor Ibrahim Hoteit, and the members of his
research group, Dr. Hari Dasari and Dr. Sivareddy Sanikommu. Their climate data
was essential to develop our models.
Finally I would like to thank King Abdullah University of Science and Technol
ogy (KAUST) as this work was supported by its Center for Excellence for NEOM
Research, and utilized its Core Labs resource
Samples of wind power and electricity prices used in the manuscript "Risk-averse stochastic programming vs. adaptive robust optimization: a virtual power plant application"
No full textDescription of the files with the wind power and electricity prices scenarios used in the manuscript:
"Risk-averse stochastic programming vs. adaptive robust optimization: a virtual power plant application" by Ricardo M. Lima, Antonio J. Conejo, Loic Giraldi, Olivier Le Maitre, Ibrahim Hoteit, and Omar Knio, submitted to the INFORMS Journal on Computing.
This file describes the wind power and electricity prices for two weeks used in the manuscript above.
Please refer to the manuscripts above for a detailed description of the data sources, sampling process, and its characteristics.
The sample average approximation methodology involves two stages:
1) optimization stage;
2) bound estimation stage.
Data for the the stochastic programming approach
The optimization stage uses sample sizes of N=10, 50, 100, 500, and 5000; and for each sample size,
M = 30 optimization replications are performed.
This data is available in the following files:
wp_week{X}_N{Y}_M{Z}.csv
pp_week{X}_N{Y}_M{Z}.csv
where
X:={1,2} is the week number
Y:={10,50,100,500,5000} is the sample size
Z:={1,...,30} is the replication number
The bound estimation stage, the lower bound on the true optimal objective function value is estimated in two steps:
1) for each distinct first-stage solution obtained from the optimization replications, a lower bound is estimated using T=30 samples of size N=25,000, and
2) the first-stage solution with the best lower bound from the previous step is selected and a new lower bound is estimated using S=30 samples of size N=25,000.
The data is available in the following files
wp_week{X}_N{Y}_T{Z}.csv
pp_week{X}_N{Y}_T{Z}.csv
wp_week{X}_N{Y}_S{Z}.csv
pp_week{X}_N{Y}_S{Z}.csv
where
X:={1,2} is the week number
Y:={25000} is the sample size
Z:={1,...,30} is the replication number
Data for the adaptive robust optimization approach
The optimization stage uses uncertainty sets defined in these files:
pp_week{X}_pf.csv
pp_week{X}_95ci.csv
wp_week{X}_ensemble.csv
where
X:={1,2} is the week number
The lower bound estimation stage requires only the second step above, using S = 30 samples of size N=25,000.
wp_week{X}_N{Y}_S{Z}.csv
pp_week{X}_N{Y}_S{Z}.csv
where
X:={1,2} is the week number
Y:={25000} is the sample size
Z:={1,...,30} is the replication numberResearch reported in this publication was supported by research funding from KAUST
Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework
No full textAn ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As an advanced technology in reservoir surveillance, crosswell EM tomography can be used to estimate a cross-sectional conductivity map and associated saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this information into reservoir simulation in combination
with other available observations is expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.Support for authors Yanhui Zhang and Ibrahim Hoteit is provided by the research project “Efficient Integration of Electromagnetic
Tomography into Reservoir History Matching,” which is funded by Saudi Aramco. The authors also thank Wim Mulder and Marwan
Wirianto for providing the multigrid EM forward solver that formed the basis for our inversion
Eddy Detection Using Reanalysis Datasets
No full textOceanic eddies are ubiquitous in oceans and play a major role in several parameters that include ocean energy transfer, nutrients distribution and air-sea interaction. Typically, eddy detection algorithms are based on single physical parameter, geometrics or other handcrafted features. To achieve better performances, we aim to develop a new approach to fuse multi-variable features for eddy detection. We will investigate lumping satellite datasets of Sea surface height, Sea surface temperature, Salinity in addition to full model solution velocity field through the inclusion of information (correlation) between the datasets
The climatology of the Red Sea – part 2: the waves
No full textThe wave climatology of the Red Sea is described based on a 30-year hindcast generated using WAVEWATCH III configured on a 5-km resolution grid and forced by Red Sea reanalysis surface winds from the advanced Weather Research and Forecasting model. The wave simulations have been validated using buoy and altimeter data. The four main wind systems in the Red Sea characterize the corresponding wave climatology. The dominant ones are the two opposite wave systems with different genesis, propagating along the axis of the basin. The highest waves are generated at the centre of the Red Sea as a consequence of the strong seasonal winds blowing from the Tokar Gap on the African side. There is a general long-term trend toward lowering the values of the significant wave height over the whole basin, with a decreasing rate depending on the genesis of the individual systems
The climatology of the Red Sea – part 1: the wind
No full textThe wind climatology of the Red Sea is described based on a 30-year high-resolution regional reanalysis generated using the Advanced Weather Research Forecasting model. The model was reinitialized on a daily basis with ERA-Interim global data and regional observations were assimilated using a cyclic three-dimensional variational approach. The reanalysis products were validated against buoy and scatterometers data. We describe the wind climatology and identify four major systems that determine the wind patterns in the Red Sea. Each system has a well-defined origin, and consequently different characteristics along the year. After analysing the relevant features of the basin in terms of their climatology, we investigate possible long-term trends in each system. It is found that there is a definite tendency towards lowering the strength of the wind speed, but at a different rate for different systems and periods of the year
Comparison of the performance of the Cepheid Xpert HemosIL Factor II and Factor V and the ViennaLab FV-PTH-MTHFR StripAssay kits for molecular thrombophilia profiling
No full textAims: To compare the performance of two assays used for the detection of mutations-polymorphisms in the Factor V, Factor II, and methylenetetrahydrofolate reductase genes among patients referred for the management of a thrombotic event. Materials and Methods: We tested 40 different patient samples using two assays, the ViennaLab FV-PTH-MTHFR StripAssay and the Cepheid Xpert HemosIL. Results: The two assays were 100percent concordant in their produced results with no samples failing the testing procedures in both. Conclusion: This is the first report to evaluate the performance of the ViennaLab FV-PTH-MTHFR StripAssay and the Cepheid Xpert HemosIL. Both assays can be introduced to the operation of molecular diagnostic laboratories to cover the referrals from different disciplines, especially in tertiary care centers with emergency departments. © Mary Ann Liebert, Inc.Arslan S, 2011, MOL BIOL REP, V38, P2395, DOI 10.1007-s11033-010-0373-y; Collins FS, 2001, JAMA-J AM MED ASSOC, V285, P540, DOI 10.1001-jama.285.5.540; Fekih-Mrissa N, 2013, J STROKE CEREBROVASC, V22, P465, DOI 10.1016-j.jstrokecerebrovasdis.2013.03.011; Gessoni G, 2012, CLIN CHIM ACTA, V413, P814, DOI 10.1016-j.cca.2012.01.016; Gil-Prieto R, 2009, NUTR METAB, V6, DOI 10.1186-1743-7075-6-39; Hoteit R, 2012, GENET TEST MOL BIOMA, V16, P223, DOI 10.1089-gtmb.2011.0114; Hoteit R, 2012, GENET TEST MOL BIOMA, V16, P459, DOI 10.1089-gtmb.2011.0220; Jadaon MM, 2011, MEDITERR J HEMATOL I, V3; Margaglione M, 1999, THROMB HAEMOSTASIS, V82, P1583; Poort SR, 1996, BLOOD, V88, P3698; Puri M, 2013, J PERINAT MED, V16, P1; Shaheen K, 2012, CLEV CLIN J MED, V79, P265, DOI 10.3949-ccjm.79a.110720
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