Doctorat en Ciència i Tecnologia Aeroespacialshttp://hdl.handle.net/2117/1845562024-03-29T06:32:00Z2024-03-29T06:32:00ZFast urban flow predictions through Convolutional Neural NetworksCalafell Sandiumenge, JoanBustillo, JaimeGomez Gonzalez, SamuelRamírez Jávega, FranciscoRadhakrishnan, SarathLehmkuhl Barba, Oriolhttp://hdl.handle.net/2117/4051552024-03-24T04:53:37Z2024-03-22T11:49:42ZFast urban flow predictions through Convolutional Neural Networks
Calafell Sandiumenge, Joan; Bustillo, Jaime; Gomez Gonzalez, Samuel; Ramírez Jávega, Francisco; Radhakrishnan, Sarath; Lehmkuhl Barba, Oriol
Having real-time and accurate numerical predictions of urban wind flow can be extremely useful for developing tools intended to improve citizens’ life quality and health. However, traditional methods such as Computational Fluid Dynamics (CFD) are unsuitable for fast prediction. This work proposes using a Convolutional Neural Network (CNN) trained with a newly created vast dataset to enable fast and accurate flow predictions for any urban geometry. The dataset has been generated through high-fidelity CFD simulations of 30 different European Urban areas and 90 meteorological conditions. The geometries were selected to have a wide variety of urban flow patterns and geometrical features allowing the Neural Network (NN) to learn a representative range of urban flow conditions. Then, a CNN was trained to reproduce the urban wind flow for any urban geometry and meteorological condition. The strategy allows for predicting accurate mean wind flow in urban areas that have not been seen in training time, showing good generalization properties.
2024-03-22T11:49:42ZCalafell Sandiumenge, JoanBustillo, JaimeGomez Gonzalez, SamuelRamírez Jávega, FranciscoRadhakrishnan, SarathLehmkuhl Barba, OriolHaving real-time and accurate numerical predictions of urban wind flow can be extremely useful for developing tools intended to improve citizens’ life quality and health. However, traditional methods such as Computational Fluid Dynamics (CFD) are unsuitable for fast prediction. This work proposes using a Convolutional Neural Network (CNN) trained with a newly created vast dataset to enable fast and accurate flow predictions for any urban geometry. The dataset has been generated through high-fidelity CFD simulations of 30 different European Urban areas and 90 meteorological conditions. The geometries were selected to have a wide variety of urban flow patterns and geometrical features allowing the Neural Network (NN) to learn a representative range of urban flow conditions. Then, a CNN was trained to reproduce the urban wind flow for any urban geometry and meteorological condition. The strategy allows for predicting accurate mean wind flow in urban areas that have not been seen in training time, showing good generalization properties.Design, implementation and testing of a 24 GHz 5G band RFI detection payload for a PocketQubeGràcia i Solà, GuillemPodaru, StefanAlcántara Villaverde, AdriánGarcia Morilla, AlejandroContreras Benito, Luis JuanPerea Barbé, AlexandreCamps Carmona, Adriano Joséhttp://hdl.handle.net/2117/4019502024-02-25T06:04:11Z2024-02-15T08:29:44ZDesign, implementation and testing of a 24 GHz 5G band RFI detection payload for a PocketQube
Gràcia i Solà, Guillem; Podaru, Stefan; Alcántara Villaverde, Adrián; Garcia Morilla, Alejandro; Contreras Benito, Luis Juan; Perea Barbé, Alexandre; Camps Carmona, Adriano José
Due to the recent licensing of the 26 GHz 5G communications bands, the passive microeaves Remote Sensing community is concerned that spurious out-of-band emissions could interfere with the measurements acquired in the 23.8 GHz water vapor resonance band. For this reason, the Frequency Allocations in Remote Sensing (FARS) Technical Committee from the IEEE Geoscience and Remote Sensing Society (GRSS) has procured the development of a RFI monitoring payload to detect possible interferences in the 24 to 25 GHz band. This payload is compatible with the PocketQubes being developed in the framework of the "IEEE GRSS Open PocketQube Kit" educational initiative, and it will be used to measure and determine the occurrence of possible interferences close to the 23.8 GHz water vapor band. This work presents the design, implementation and testing of the 24 to 25 GHz RFI monitoring payload designed for a 1P PocketQube.
2024-02-15T08:29:44ZGràcia i Solà, GuillemPodaru, StefanAlcántara Villaverde, AdriánGarcia Morilla, AlejandroContreras Benito, Luis JuanPerea Barbé, AlexandreCamps Carmona, Adriano JoséDue to the recent licensing of the 26 GHz 5G communications bands, the passive microeaves Remote Sensing community is concerned that spurious out-of-band emissions could interfere with the measurements acquired in the 23.8 GHz water vapor resonance band. For this reason, the Frequency Allocations in Remote Sensing (FARS) Technical Committee from the IEEE Geoscience and Remote Sensing Society (GRSS) has procured the development of a RFI monitoring payload to detect possible interferences in the 24 to 25 GHz band. This payload is compatible with the PocketQubes being developed in the framework of the "IEEE GRSS Open PocketQube Kit" educational initiative, and it will be used to measure and determine the occurrence of possible interferences close to the 23.8 GHz water vapor band. This work presents the design, implementation and testing of the 24 to 25 GHz RFI monitoring payload designed for a 1P PocketQube.Description of the Rita payload aboard AlainSat-1, A 3U educational CubesatGonga Siles, AmadeuPérez Portero, AdriánFernandez Capon, Lara PilarGarcia Morilla, AlejandroGràcia i Solà, GuillemContreras Benito, Luis JuanRamos Castro, Juan JoséCamps Carmona, Adriano JoséJallad, Abdul-Halimhttp://hdl.handle.net/2117/4019452024-02-25T08:39:36Z2024-02-15T08:10:33ZDescription of the Rita payload aboard AlainSat-1, A 3U educational Cubesat
Gonga Siles, Amadeu; Pérez Portero, Adrián; Fernandez Capon, Lara Pilar; Garcia Morilla, Alejandro; Gràcia i Solà, Guillem; Contreras Benito, Luis Juan; Ramos Castro, Juan José; Camps Carmona, Adriano José; Jallad, Abdul-Halim
The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals.
2024-02-15T08:10:33ZGonga Siles, AmadeuPérez Portero, AdriánFernandez Capon, Lara PilarGarcia Morilla, AlejandroGràcia i Solà, GuillemContreras Benito, Luis JuanRamos Castro, Juan JoséCamps Carmona, Adriano JoséJallad, Abdul-HalimThe Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals.Characterization and verification of the Rita payload hyperspectral imager in Alainsat-1, as part of the 2nd IEEE GRSS student grand challengeContreras Benito, Luis JuanGonga i Siles, AmadeuCrisan, IeremiaPérez Portero, AdriánGarcia Morilla, AlejandroGràcia i Solà, GuillemRamos Castro, Juan JoséJallad, Abdul-HalimCamps Carmona, Adriano Joséhttp://hdl.handle.net/2117/4017642024-02-25T06:00:02Z2024-02-13T10:19:29ZCharacterization and verification of the Rita payload hyperspectral imager in Alainsat-1, as part of the 2nd IEEE GRSS student grand challenge
Contreras Benito, Luis Juan; Gonga i Siles, Amadeu; Crisan, Ieremia; Pérez Portero, Adrián; Garcia Morilla, Alejandro; Gràcia i Solà, Guillem; Ramos Castro, Juan José; Jallad, Abdul-Halim; Camps Carmona, Adriano José
The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI).
2024-02-13T10:19:29ZContreras Benito, Luis JuanGonga i Siles, AmadeuCrisan, IeremiaPérez Portero, AdriánGarcia Morilla, AlejandroGràcia i Solà, GuillemRamos Castro, Juan JoséJallad, Abdul-HalimCamps Carmona, Adriano JoséThe Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI).Numerical assessment of the effect of hydrogen enrichment of a technically premixed swirl-stabilized natural gas flamePachano Prieto, LeonardoSurapaneni, AnuragBoth, AmbrusAx, HolgerPetry, NiklasBoxx, IsaacMira Martínez, Danielhttp://hdl.handle.net/2117/3958602024-02-04T09:33:57Z2023-11-06T12:30:10ZNumerical assessment of the effect of hydrogen enrichment of a technically premixed swirl-stabilized natural gas flame
Pachano Prieto, Leonardo; Surapaneni, Anurag; Both, Ambrus; Ax, Holger; Petry, Niklas; Boxx, Isaac; Mira Martínez, Daniel
High-fidelity large eddy simulations (LES) are conducted for lean natural gas flames with different levels of hydrogen enrichment in a technically premixed swirl-stabilized combustor (PRECCINSTA) operated at atmospheric pressure. The modelling approach relies on tabulation of premixed flamelets and presumed-shape probability density functions (PDF) to account for subgrid turbulence-chemistry interactions. Results are presented for non-reacting and reacting conditions with 0, 40 and 50% hydrogen content in the natural gas. The influence of hydrogen-enrichment is investigated here by combining LES with Raman measurements. The assessment of LES shows good predictions of the flame stabilization mechanism, flow field and flame dynamics as compared to experiments. The natural gas flame develops a self-excited flow oscillation characterized as a precessing vortex core, which is well reproduced by the LES. The lean operation of the burner with natural gas shows a stable M-shape flame that transitions to a V-shape fully attached flame as the main fuel is blended with hydrogen. Raman measurements are compared with LES data to examine the flame structure and burning characteristics. It is concluded that hydrogen addition makes the flame more compact, induces higher reactivity of the fuel-air mixture and leads to a stable V-shape flame fully attached to the burner’s nozzle-cone.
Publicat en accés obert amb el permís explícit de l'editorial.
2023-11-06T12:30:10ZPachano Prieto, LeonardoSurapaneni, AnuragBoth, AmbrusAx, HolgerPetry, NiklasBoxx, IsaacMira Martínez, DanielHigh-fidelity large eddy simulations (LES) are conducted for lean natural gas flames with different levels of hydrogen enrichment in a technically premixed swirl-stabilized combustor (PRECCINSTA) operated at atmospheric pressure. The modelling approach relies on tabulation of premixed flamelets and presumed-shape probability density functions (PDF) to account for subgrid turbulence-chemistry interactions. Results are presented for non-reacting and reacting conditions with 0, 40 and 50% hydrogen content in the natural gas. The influence of hydrogen-enrichment is investigated here by combining LES with Raman measurements. The assessment of LES shows good predictions of the flame stabilization mechanism, flow field and flame dynamics as compared to experiments. The natural gas flame develops a self-excited flow oscillation characterized as a precessing vortex core, which is well reproduced by the LES. The lean operation of the burner with natural gas shows a stable M-shape flame that transitions to a V-shape fully attached flame as the main fuel is blended with hydrogen. Raman measurements are compared with LES data to examine the flame structure and burning characteristics. It is concluded that hydrogen addition makes the flame more compact, induces higher reactivity of the fuel-air mixture and leads to a stable V-shape flame fully attached to the burner’s nozzle-cone.Large-scale coherent structures in an asymmetrically heated channel flowGarcía Berenguer, MarinaSilva, Lucas Gasparino Ferreira daLehmkuhl Barba, OriolRodríguez Pérez, Ivette Maríahttp://hdl.handle.net/2117/3947452023-12-24T12:06:36Z2023-10-09T11:01:55ZLarge-scale coherent structures in an asymmetrically heated channel flow
García Berenguer, Marina; Silva, Lucas Gasparino Ferreira da; Lehmkuhl Barba, Oriol; Rodríguez Pérez, Ivette María
A compressible direct numerical simulation (DNS) has been conducted to analyze the behavior of low-speed flows submitted to a strong temperature gradient in a fully developed and asymmetrically heated turbulent channel flow. The focus is to understand the relevance of the variable thermophysical properties and their impact on the heat transfer mechanisms. The simulations have been performed at mean friction Reynolds number Ret m = 400 submitted to a high-temperature ratio between the two walls (Thot/Tcold = 2). The results show an increased flow mixing and recirculation due to the temperature difference between the walls.
2023-10-09T11:01:55ZGarcía Berenguer, MarinaSilva, Lucas Gasparino Ferreira daLehmkuhl Barba, OriolRodríguez Pérez, Ivette MaríaA compressible direct numerical simulation (DNS) has been conducted to analyze the behavior of low-speed flows submitted to a strong temperature gradient in a fully developed and asymmetrically heated turbulent channel flow. The focus is to understand the relevance of the variable thermophysical properties and their impact on the heat transfer mechanisms. The simulations have been performed at mean friction Reynolds number Ret m = 400 submitted to a high-temperature ratio between the two walls (Thot/Tcold = 2). The results show an increased flow mixing and recirculation due to the temperature difference between the walls.InSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studiesMirmazloumi, SeyedmohammadWassie, YismawNava, LorenzoCuevas González, MaríaCrosetto, MicheleMontserrat, Oriolhttp://hdl.handle.net/2117/3935542024-02-25T12:10:23Z2023-09-15T11:32:27ZInSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studies
Mirmazloumi, Seyedmohammad; Wassie, Yismaw; Nava, Lorenzo; Cuevas González, María; Crosetto, Michele; Montserrat, Oriol
Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.
2023-09-15T11:32:27ZMirmazloumi, SeyedmohammadWassie, YismawNava, LorenzoCuevas González, MaríaCrosetto, MicheleMontserrat, OriolEarly alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.Fresh volcanic aerosols injected in the atmosphere during the volcano eruptive activity at the Cumbre Vieja area (La Palma, Canary Islands): Temporal evolution and vertical impactCordoba Jabonero, CarmenSicard, MichaëlBarreto Velasco, ÁfricaToledano, CarlosLópez Cayuela, María ÁngelesComerón Tejero, AdolfoGarcía Rodríguez, OmairaCarvajal Pérez, Clara VioletaGil Díaz, CristinaRamos López, RamónMuñoz Porcar, ConstantinoRodríguez Gómez, Alejandro Antoniohttp://hdl.handle.net/2117/3924432023-11-12T04:06:41Z2023-07-27T17:12:28ZFresh volcanic aerosols injected in the atmosphere during the volcano eruptive activity at the Cumbre Vieja area (La Palma, Canary Islands): Temporal evolution and vertical impact
Cordoba Jabonero, Carmen; Sicard, Michaël; Barreto Velasco, África; Toledano, Carlos; López Cayuela, María Ángeles; Comerón Tejero, Adolfo; García Rodríguez, Omaira; Carvajal Pérez, Clara Violeta; Gil Díaz, Cristina; Ramos López, Ramón; Muñoz Porcar, Constantino; Rodríguez Gómez, Alejandro Antonio
For the first time in fifty years, the Cumbre Vieja volcanic area (La Palma, Canary Islands, Spain) erupted on September 19, 2021, giving birth to a new volcano. Fresh volcanic aerosols were continuously injected into the troposphere at different height levels, decreasing with time until the end of December 2021 (15 weeks duration). A wide set of different instrumentation was deployed all over the Island in order to evaluate the effects of the volcanic plumes on the atmosphere and the air quality. For the first time, a long-term study of the relative mass contribution and vertical impact of the volcanic components, ash and non-ash particles separately, during the eruptive activity was carried out in this work. In particular, a polarized Micro-Pulse Lidar (P-MPL) was deployed at Tazacorte (at around 8 km west from the volcano) in 24/7 operation from October 17, 2021 until the end of the volcano activity (11 weeks) for vertical monitoring of the volcanic particles. First, a statistical study of the mass conversion factors for mass concentration estimation of the volcanic (ash and non-ash) particles was performed by using the AERONET sun/sky-photometer dataset at Fuencaliente (at around 18 km south from the volcano). A representative mass conversion factor was obtained for ash and non-ash particles: 1.89 ± 0.53 and 0.31 ± 0.06 g m-2, respectively, with no dependence on time and optical depth. Second, these factors were used to calculate the ash and non-ash mass concentrations from P-MPL observations. Ash particles dominated 11% of the time and mostly until week 3 (i.e. week 7 from the volcanic eruption). Their mass concentration decreased by one order of magnitude: the relative ash mass contribution was 73 ± 18% with a total mass loading of 566 ± 281 mg m-2 at week 1, reducing gradually down to 38 ± 32% and 120 ± 49 mg m-2, respectively, at week 11. Layer-to-layer, it decreased with increasing layer-height; no ash was detected above 4 km at the end of the volcanic period. Third, in order to analyse the potential AERONET underestimation of the coarse mass conversion factor due to the 15 µm cutoff effect in the AERONET retrieval, two worst-case-scenarios (WCS) were examined, representing aged-like ash particles (WCS1, 4-µm radius) and fresh-like (WCS2, 10-µm radius). For both scenarios, the mass concentration of the volcanic plumes exceeded the first contamination level (>200 µg m-3, as defined by the UK Meteorological Office) up to 5–6 km height mostly during week 1 and up to 1–2 km until week 9. The extreme contamination level (>2000 µg m-3, aircraft flight limitations) was only exceeded from week 1 to week 6 under WCS2 conditions. This work infers a new long-term insight on the volcanic matter injected in the atmosphere with relevance for Air Quality issues and air traffic safety policies.
2023-07-27T17:12:28ZCordoba Jabonero, CarmenSicard, MichaëlBarreto Velasco, ÁfricaToledano, CarlosLópez Cayuela, María ÁngelesComerón Tejero, AdolfoGarcía Rodríguez, OmairaCarvajal Pérez, Clara VioletaGil Díaz, CristinaRamos López, RamónMuñoz Porcar, ConstantinoRodríguez Gómez, Alejandro AntonioFor the first time in fifty years, the Cumbre Vieja volcanic area (La Palma, Canary Islands, Spain) erupted on September 19, 2021, giving birth to a new volcano. Fresh volcanic aerosols were continuously injected into the troposphere at different height levels, decreasing with time until the end of December 2021 (15 weeks duration). A wide set of different instrumentation was deployed all over the Island in order to evaluate the effects of the volcanic plumes on the atmosphere and the air quality. For the first time, a long-term study of the relative mass contribution and vertical impact of the volcanic components, ash and non-ash particles separately, during the eruptive activity was carried out in this work. In particular, a polarized Micro-Pulse Lidar (P-MPL) was deployed at Tazacorte (at around 8 km west from the volcano) in 24/7 operation from October 17, 2021 until the end of the volcano activity (11 weeks) for vertical monitoring of the volcanic particles. First, a statistical study of the mass conversion factors for mass concentration estimation of the volcanic (ash and non-ash) particles was performed by using the AERONET sun/sky-photometer dataset at Fuencaliente (at around 18 km south from the volcano). A representative mass conversion factor was obtained for ash and non-ash particles: 1.89 ± 0.53 and 0.31 ± 0.06 g m-2, respectively, with no dependence on time and optical depth. Second, these factors were used to calculate the ash and non-ash mass concentrations from P-MPL observations. Ash particles dominated 11% of the time and mostly until week 3 (i.e. week 7 from the volcanic eruption). Their mass concentration decreased by one order of magnitude: the relative ash mass contribution was 73 ± 18% with a total mass loading of 566 ± 281 mg m-2 at week 1, reducing gradually down to 38 ± 32% and 120 ± 49 mg m-2, respectively, at week 11. Layer-to-layer, it decreased with increasing layer-height; no ash was detected above 4 km at the end of the volcanic period. Third, in order to analyse the potential AERONET underestimation of the coarse mass conversion factor due to the 15 µm cutoff effect in the AERONET retrieval, two worst-case-scenarios (WCS) were examined, representing aged-like ash particles (WCS1, 4-µm radius) and fresh-like (WCS2, 10-µm radius). For both scenarios, the mass concentration of the volcanic plumes exceeded the first contamination level (>200 µg m-3, as defined by the UK Meteorological Office) up to 5–6 km height mostly during week 1 and up to 1–2 km until week 9. The extreme contamination level (>2000 µg m-3, aircraft flight limitations) was only exceeded from week 1 to week 6 under WCS2 conditions. This work infers a new long-term insight on the volcanic matter injected in the atmosphere with relevance for Air Quality issues and air traffic safety policies.State-of-art products for real time scintillation monitoring and consolidated requirements for the activityGonzález Casado, GuillermoJuan Zornoza, José MiguelYin, YuTimote Bejarano, Cristhian CamiloSanz Subirana, JaumeRovira Garcia, Adriàhttp://hdl.handle.net/2117/3919012023-10-22T00:04:09Z2023-07-21T07:49:32ZState-of-art products for real time scintillation monitoring and consolidated requirements for the activity
González Casado, Guillermo; Juan Zornoza, José Miguel; Yin, Yu; Timote Bejarano, Cristhian Camilo; Sanz Subirana, Jaume; Rovira Garcia, Adrià
The first phase of the RT-WMIS activity comprises two Work Packages (WPs). WP 10 is aimed at performing a revision of the state-of-the-art RT products and requirements for the RT-WMIS activity. On the other hand, WP 20 is devoted to carry out the software design for the subsequent implementation of the RT-WMIS tool. The present technical note (TN-1) describes the activities developed in the framework of WP 10.
2023-07-21T07:49:32ZGonzález Casado, GuillermoJuan Zornoza, José MiguelYin, YuTimote Bejarano, Cristhian CamiloSanz Subirana, JaumeRovira Garcia, AdriàAislamiento y confinamiento: análisis del rendimiento humano de una tripulación análoga en simulación analógica espacialSequeda, JosephBejarano, IngridCampos, CristianMalpica, DiegoCortés, DiegoJiménez Sánchez, GiovanniBuitrago Leiva, Jeimmy Natalyhttp://hdl.handle.net/2117/3913832023-10-15T05:13:55Z2023-07-18T10:28:32ZAislamiento y confinamiento: análisis del rendimiento humano de una tripulación análoga en simulación analógica espacial
Sequeda, Joseph; Bejarano, Ingrid; Campos, Cristian; Malpica, Diego; Cortés, Diego; Jiménez Sánchez, Giovanni; Buitrago Leiva, Jeimmy Nataly
Los tripulantes en misiones análogas participan en misiones espaciales simuladas que experimentan aislamiento y confinamiento con el fin de educar y realizar experimentos de ciencia, tecnología, ingeniería entre otros. Estos tripulantes análogos suelen ser estudiantes o personas dispuestos a desarrollar una carrera en la ciencia espacial y la industria. En este artículo se describe la primera simulación análoga lunar realizada por profesionales militares de la Fuerza Aérea Colombiana, misión que fue diseñada para proporcionar capacitación eficiente para futuras operaciones que van encaminadas en la línea del desarrollo del programa nacional de capacitación de astronautas en Colombia. La misión THOR (Team of Human Operation Research) se realizó en agosto del 2022, con el apoyo de un Centro de Control de Misión (MCC), misión análoga de aislamiento y confinamiento de siete días la cual tenía como objetivo promover el desarrollo cognitivo, físico, fisiológico, psicológico y tecnológico durante esta misión espacial simulada. La misión THOR fue la misión número 50 del Centro de Entrenamiento para Astronautas Análogos (AATC), se dividió en una tripulación dentro del hábitat compuesta por 5 tripulantes análogos con roles específicos basados en su experiencia, antecedentes y adecuados a los roles proporcionados por el AATC, y dos miembros de tripulación externos adicionales que brindaron apoyo remoto e investigación externa. Durante el periodo de misión se realizaron pruebas tales como aprendizaje espacial, memoria de trabajo, abstracción, velocidad sensorio-motora, orientación espacial, identificación de emociones, razonamiento abstracto, toma de decisiones de riesgo, dinámica e equipo, calidad y cantidad de sueño, puntuaciones de fatiga, intervalo R-R, mediante actigrafia de muñeca y antropometría, la vigilancia psicomotora, la percepción del tiempo y tareas críticas en el hábitat se midió simulando una misión corta en la superficie lunar mediante encuesta NASA-TLX, así como las aplicaciones móviles Brainess y Subjective TimePerception. Los sujetos fueron expuestos a crioterapia y aceleraciones.
2023-07-18T10:28:32ZSequeda, JosephBejarano, IngridCampos, CristianMalpica, DiegoCortés, DiegoJiménez Sánchez, GiovanniBuitrago Leiva, Jeimmy NatalyLos tripulantes en misiones análogas participan en misiones espaciales simuladas que experimentan aislamiento y confinamiento con el fin de educar y realizar experimentos de ciencia, tecnología, ingeniería entre otros. Estos tripulantes análogos suelen ser estudiantes o personas dispuestos a desarrollar una carrera en la ciencia espacial y la industria. En este artículo se describe la primera simulación análoga lunar realizada por profesionales militares de la Fuerza Aérea Colombiana, misión que fue diseñada para proporcionar capacitación eficiente para futuras operaciones que van encaminadas en la línea del desarrollo del programa nacional de capacitación de astronautas en Colombia. La misión THOR (Team of Human Operation Research) se realizó en agosto del 2022, con el apoyo de un Centro de Control de Misión (MCC), misión análoga de aislamiento y confinamiento de siete días la cual tenía como objetivo promover el desarrollo cognitivo, físico, fisiológico, psicológico y tecnológico durante esta misión espacial simulada. La misión THOR fue la misión número 50 del Centro de Entrenamiento para Astronautas Análogos (AATC), se dividió en una tripulación dentro del hábitat compuesta por 5 tripulantes análogos con roles específicos basados en su experiencia, antecedentes y adecuados a los roles proporcionados por el AATC, y dos miembros de tripulación externos adicionales que brindaron apoyo remoto e investigación externa. Durante el periodo de misión se realizaron pruebas tales como aprendizaje espacial, memoria de trabajo, abstracción, velocidad sensorio-motora, orientación espacial, identificación de emociones, razonamiento abstracto, toma de decisiones de riesgo, dinámica e equipo, calidad y cantidad de sueño, puntuaciones de fatiga, intervalo R-R, mediante actigrafia de muñeca y antropometría, la vigilancia psicomotora, la percepción del tiempo y tareas críticas en el hábitat se midió simulando una misión corta en la superficie lunar mediante encuesta NASA-TLX, así como las aplicaciones móviles Brainess y Subjective TimePerception. Los sujetos fueron expuestos a crioterapia y aceleraciones.