A study on the optimization of jet engines for combat aircraft

In the paper the optimization of jet engines for combat aircrafts is discussed. This optimization is referred to the selection of the valúes of the fan pressure ratio or by—pass ratio; máximum turbine inlet temperature; overall pressure ratio and máximum reheat temperature which optimize the specific fuel consumption and the ratio of the thrust to the nozzle throat área, assuming this last parameter to be an indicative of the thrust/weight ratio. The selection is carried out taking into account the aircraft missions for which the engine design is optimized

The ASSET project as a training tool for energy transition

[EN] The ASSET project aims to provide a holistic and scalable solution for research, innovation and education by creating functional networks. These networks are intended to be created between energy companies, universities, training actors, energy and environmental authorities, policy makers and, more generally, citizens who are sensitive to environmental issues and the quality of energy transition processes. The ASSET project delivers the framework and the tools to create and share knowledge and competences needed to tackle the energy transition by supporting training. As a highlight of this approach to education, a strong interdisciplinary component oriented to social sciences is added in an area with an exclusive technological vocation. This transition seeks to push towards a low-carbon society in order to make the energy sector sustainable. To reach this goal, ASSET intends to strengthen the skills of sector operators, to cultivate new talents with multidisciplinary skills, and to intensify research and network industry. Therefore, the final target is to promote innovation and strengthen understanding of the importance of reducing carbon emissions. Over the course of the project, 23 learning graph models and more than 40 educational programs are being developed, in addition to a portfolio of challenges and case studies on the subject. The actors involved will be able to search for the programs available - online and on-campus - on the ASSET website and if a search is unsuccessful, a request can be sent for the creation of content necessary for their target market. The main tools that have been developed through the ASSET project are; the Learning Graph tool, the Marketplace tool and the EMMA platform. The Learning graph tool allows for the creation and sharing of learning structures, as well as the use of existing study materials. The Marketplace tool allows the searching through the available training offer, to request courses on demand, or to offer own training programmes. Finally, the EMMA platform offers a wide range of MOOC (Massive Online Open Courses), mainly in English and with the possibility of being translated into several languages. Universitat Politècnica València (UPV) is participating in the project as one of the academic actors that is developing courses and MOOCs in the area of Energy Storage. In this way, the UPV contributes to the identification of learning needs, the application of the ASSET method and tools to its teaching material, and the delivery of this teaching material. Specifically, the course being developed is called "Hydrogen as an Energy Vector". The course provides the fundamentals of hydrogen technology, using it to store energy and further develop the concept of its use as an energy vector. The course follows the blended format, combining online elements, through a MOOC (EMMA platform) and face-to-face teaching carried out at the university facilities. In the paper, we will present the main ASSET tools, the lessons learned in the development of course materials during the lifetime project and the analysis of the results of this experience.This work was supported by the European Commission though the project A Holistic And Scalable Solution For Research, Innovation And Education In Energy Transition (European Union's Horizon 2020 research and innovation programme under grant agreement number 837854).Zúñiga Saiz, P.; Sánchez-Diaz, C. (2021). The ASSET project as a training tool for energy transition. IATED Academy. 4354-4363. https://doi.org/10.21125/inted.2021.08884354436

Undifferentiated round cell sarcoma of the broad ligament

Sarcomas of the broad ligament are very uncommon. To our knowledge, there are no cases published of undifferentiated round cell sarcoma of the broad ligament. Round cell sarcomas are a rare and very aggressive variant, which due to their sensitivity to chemotherapy, have an acceptable prognosis. We report the case of a 27-year-old woman who presented with a pelvic mass with a 7-cm diameter placed on the right broad ligament. After surgery, she was diagnosed with undifferentiated round cell sarcoma of the broad ligament. The patient received adjuvant chemotherapy and radiotherapy, and after 12 years of follow-up, she still remains asymptomatic. Proper differential diagnoses as well as an appropriate adjuvant therapy after surgical treatment seem to be essential to obtain good oncological outcomes in this rare entit

Evaluation of Alternatives for Energy Supply from fuel Cells in Compact Cities in the Mediterranean Climate. Case Study: City of Valencia

[EN] A study of energy supply alternatives was carried out based on a cogeneration fuel cell system fed from the natural gas network of compact Mediterranean cities. As a case study it was applied to the residential energy demands of the L'Illa Perduda neighbourhood, located in the east of the city of Valencia and consisting of 4194 residential cells. In total, eight different alternatives were studied according to the load curve, the power of the system, the mode of operation and the distribution of the fuel cells. In this way, the advantages and disadvantages of each configuration were found. This information, together with the previous study of the energy characteristics of the neighbourhood, enabled selection of the most promising configuration and to decide whether or not to recommend investment. The chosen configuration was a centralised system of phosphoric acid fuel cells in cogeneration, with approximately 4 MW of thermal power and an operating mode that varied according to the outside temperature. In this way, when heating is required, the plant adjusts its production to the thermal demand, and when cooling is required, the plant follows the electrical demand. This configuration presented the best energy results, as it achieved good coverage of thermal (62.5%) and electrical (88.1%) demands with good primary energy savings (28.36 GWh/year). However, due to the high power of the system and low maturity (i.e., high costs) of this technology, would be necessary to make a large initial economic investment of 15.2 Meuro.This research was funded by Catedra de Transicion Energetica Urbana (UPV-Las NavesFVCiE). Grant number 20210096. This work was supported by a grant of the Cátedra de Transición Energética Urbana UPV-Las Naves-FVCiE, which is chair at Universitat Politència de València (UPV) in collaboration with the city hall of Valencia. (Grant number: 20220027).Martínez Reverte, I.; Gómez-Navarro, T.; Sánchez-Diaz, C.; Montagud- Montalvá, C. (2022). Evaluation of Alternatives for Energy Supply from fuel Cells in Compact Cities in the Mediterranean Climate. Case Study: City of Valencia. Energies. 15(12):1-30. https://doi.org/10.3390/en15124502130151

Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

[EN] Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is essential to detect in real time if the inflatable cavities are malfunctioning (presence of air leakage). Two classification predictive models were obtained, one for each cavity typology, which must discern between the ¿Right¿ or ¿Leak¿ states. The cavity pressure signals were digitally processed, from which a set of features were extracted and selected. The predictive models were obtained from the features, and a prior classification of the signals between the two possible states was used as input to different su-pervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the balloon-type was 99.62%, while that of the bellows-type was 100%, repre-senting an encouraging result. Once the models are validated with data generated in animal model tests and subsequently in exploratory clinical tests, their incorporation in the software device will ensure patient safety during small bowel exploration.The study was funded by the Spanish Ministry of Economy and Competitiveness through Project (PI18/01365) and by the UPV/IIS LA Fe through the (Endoworm 3.0) Project. CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with the assistance of the European Regional Development Fund.Zazo-Manzaneque, R.; Pons-Beltrán, V.; Vidaurre, A.; Santonja, A.; Sánchez-Diaz, C. (2022). Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System. Sensors. 22(14):1-18. https://doi.org/10.3390/s22145211118221

Techno‐environmental analysis of the use of green hydrogen for cogeneration from the gasification of wood and fuel cell

This paper aims to evaluate the use of wood biomass in a gasifier integrated with a fuel cell system as a low carbon technology. Experimental information of the wood is provided by the literature. The syngas is purified by using pressure swing adsorption (PSA) in order to obtain H2 with 99.99% purity. Using 132 kg/h of wood, it is possible to generate 10.57 kg/h of H2 that is used in a tubular solid oxide fuel cell (TSOFC). Then, the TSOFC generates 197.92 kW. The heat generated in the fuel cell produces 60 kg/h of steam that is needed in the gasifier. The net efficiency of the integrated system considering only the electric power generated in the TSOFC is 27.2%, which is lower than a gas turbine with the same capacity where the efficiency is around 33.1%. It is concluded that there is great potential for cogeneration with low carbon emission by using wood biomass in rural areas of developing countries e.g., with a carbon intensity of 98.35 kgCO2/MWh when compared with those of natural gas combined cycle (NGCC) without and with CO2 capture i.e., 331 kgCO2/MWh and 40 kgCO2/MWh, respectively. This is an alternative technology for places where biomass is abundant and where it is difficult to get electricity from the grid due to limits in geographical location

Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

[EN] Proton Exchange Membrane Fuel Cell (PEMFC) fuel cells is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that allow deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a Nexa Ballard 1.2 kW fuel cell; therefore, it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them taken from literature and two proposed in this work. Finally, the model with the identified parameters was validated with real data.This research was funded by COLCIENCIAS (Administrative department of science, technology and innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-ColombiaAriza-Chacón, HE.; Correcher Salvador, A.; Sánchez-Diaz, C.; Pérez-Navarro, Á.; García Moreno, E. (2018). Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm. Energies. 11(8):1-15. https://doi.org/10.3390/en11082099S115118Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6Wang, Y., Chen, K. S., Mishler, J., Cho, S. C., & Adroher, X. C. (2011). A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research. Applied Energy, 88(4), 981-1007. doi:10.1016/j.apenergy.2010.09.030Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., & Harris, T. J. (1995). Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell: I . Mechanistic Model Development. Journal of The Electrochemical Society, 142(1), 1-8. doi:10.1149/1.2043866Tao, S., Si-jia, Y., Guang-yi, C., & Xin-jian, Z. (2005). Modelling and control PEMFC using fuzzy neural networks. Journal of Zhejiang University-SCIENCE A, 6(10), 1084-1089. doi:10.1631/jzus.2005.a1084Amphlett, J. C., Mann, R. F., Peppley, B. A., Roberge, P. R., & Rodrigues, A. (1996). A model predicting transient responses of proton exchange membrane fuel cells. Journal of Power Sources, 61(1-2), 183-188. doi:10.1016/s0378-7753(96)02360-9Mo, Z.-J., Zhu, X.-J., Wei, L.-Y., & Cao, G.-Y. (2006). Parameter optimization for a PEMFC model with a hybrid genetic algorithm. International Journal of Energy Research, 30(8), 585-597. doi:10.1002/er.1170YE, M., WANG, X., & XU, Y. (2009). Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization. International Journal of Hydrogen Energy, 34(2), 981-989. doi:10.1016/j.ijhydene.2008.11.026Askarzadeh, A., & Rezazadeh, A. (2011). A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. International Journal of Hydrogen Energy, 36(8), 5047-5053. doi:10.1016/j.ijhydene.2011.01.070El-Fergany, A. A. (2018). Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser. IET Renewable Power Generation, 12(1), 9-17. doi:10.1049/iet-rpg.2017.0232Li, Q., Chen, W., Wang, Y., Liu, S., & Jia, J. (2011). Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization. IEEE Transactions on Industrial Electronics, 58(6), 2410-2419. doi:10.1109/tie.2010.2060456Ali, M., El-Hameed, M. A., & Farahat, M. A. (2017). Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer. Renewable Energy, 111, 455-462. doi:10.1016/j.renene.2017.04.036Sun, Z., Wang, N., Bi, Y., & Srinivasan, D. (2015). Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm. Energy, 90, 1334-1341. doi:10.1016/j.energy.2015.06.081Gong, W., Yan, X., Liu, X., & Cai, Z. (2015). Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy, 86, 139-151. doi:10.1016/j.energy.2015.03.117Turgut, O. E., & Coban, M. T. (2016). Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm. Ain Shams Engineering Journal, 7(1), 347-360. doi:10.1016/j.asej.2015.05.003Al-Othman, A. K., Ahmed, N. A., Al-Fares, F. S., & AlSharidah, M. E. (2015). Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method. Arabian Journal for Science and Engineering, 40(9), 2619-2628. doi:10.1007/s13369-015-1711-0Methekar, R. N., Prasad, V., & Gudi, R. D. (2007). Dynamic analysis and linear control strategies for proton exchange membrane fuel cell using a distributed parameter model. Journal of Power Sources, 165(1), 152-170. doi:10.1016/j.jpowsour.2006.11.047KUNUSCH, C., HUSAR, A., PULESTON, P., MAYOSKY, M., & MORE, J. (2008). Linear identification and model adjustment of a PEM fuel cell stack. International Journal of Hydrogen Energy, 33(13), 3581-3587. doi:10.1016/j.ijhydene.2008.04.052Li, C.-H., Zhu, X.-J., Cao, G.-Y., Sui, S., & Hu, M.-R. (2008). Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. Journal of Power Sources, 175(1), 303-316. doi:10.1016/j.jpowsour.2007.09.049Fontes, G., Turpin, C., & Astier, S. (2010). A Large-Signal and Dynamic Circuit Model of a $\hbox{H}_{2}/\hbox{O}_{2}$ PEM Fuel Cell: Description, Parameter Identification, and Exploitation. IEEE Transactions on Industrial Electronics, 57(6), 1874-1881. doi:10.1109/tie.2010.2044731Cheng, S.-J., & Liu, J.-J. (2015). Nonlinear modeling and identification of proton exchange membrane fuel cell (PEMFC). International Journal of Hydrogen Energy, 40(30), 9452-9461. doi:10.1016/j.ijhydene.2015.05.109Buchholz, M., & Krebs, V. (2007). Dynamic Modelling of a Polymer Electrolyte Membrane Fuel Cell Stack by Nonlinear System Identification. Fuel Cells, 7(5), 392-401. doi:10.1002/fuce.200700013Meiler, M., Schmid, O., Schudy, M., & Hofer, E. P. (2008). Dynamic fuel cell stack model for real-time simulation based on system identification. Journal of Power Sources, 176(2), 523-528. doi:10.1016/j.jpowsour.2007.08.051Wang, C., Nehrir, M. H., & Shaw, S. R. (2005). Dynamic Models and Model Validation for PEM Fuel Cells Using Electrical Circuits. IEEE Transactions on Energy Conversion, 20(2), 442-451. doi:10.1109/tec.2004.842357Restrepo, C., Konjedic, T., Garces, A., Calvente, J., & Giral, R. (2015). Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy. IEEE Transactions on Industrial Informatics, 11(2), 548-559. doi:10.1109/tii.2014.2317982Salim, R., Nabag, M., Noura, H., & Fardoun, A. (2015). The parameter identification of the Nexa 1.2 kW PEMFC’s model using particle swarm optimization. Renewable Energy, 82, 26-34. doi:10.1016/j.renene.2014.10.012Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.03

Studying the topological organization of the cerebral blood flow fluctuations in resting state

In this paper the cerebral blood flow (CBF) in resting state obtained from SPECT imaging is employed as a hemodynamics descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in CBF between pairs of regions was measured by computing the Pearson correlation coefficient across 31 normal subjects. We demonstrated the CBF connectivity matrices follow 'small-world' attributes similar to previous studies using different modalities of neuroimaging data (MRI, fMRI, EEG, MEG). The highest concurrent fluctuations in CBF were detected between homologous cortical regions (homologous callosal connections). It was found that the existence of structural core regions or hubs positioned on a high proportion of shortest paths within the CBF network. These were anatomically distributed in frontal, limbic, occipital and parietal regions that suggest its important role in functional integration. Our findings point to a new possibility of using CBF variable to investigate the brain networks based on graph theory in normal and pathological states. Likewise, it opens a window to future studies to link covariation between morphometric descriptors, axonal connectivity and CBF processes with a potential diagnosis applications