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A. Pastor-Rodriguez
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1
The uncertainty on Thermospheric Mass Density (TMD), as derived from atmospheric models, can reach extremely high values. This effect is noteworthy in Low Earth Orbit (LEO), where atmospheric drag is the main perturbing force, as well as the most uncertain. LEO harbours almost 18,000 space objects at the end of 2021, around 60% of the total space debris population, and the rate of growth is increasing every year. Increasing the accuracy of TMD models, and thus the uncertainty characterisation, is important to ensure space environment sustainability in this congested and contested region. Accurate TMD modelling is a decisive factor in all space applications below the exopause, from LEO mission design to Space Situational Awareness (SSA) service provision: from conjunction assessment to re-entry and fragmentation analysis To enhance empirical TMD models, atmospheric density observations derived from satellite measurements are assimilated.
This paper presents a novel approach for assimilating thermospheric density observations into atmospheric models to improve the accuracy of orbit predictions in short- to medium- term propagations. First, Global Navigation Satellite System (GNSS) derived density data from Swarm satellites are ingested from the publicly available Level 2 data products of the European Space Agency (ESA). In a second step, density data is assimilated into the empirical model NRMLSISE-00, using Principal Component Analysis (PCA) to decompose into the main temporal and spatial modes, providing useful physical insight into the main variables driving the model. Thirdly, the model is tested on several cases, whose data was not assimilated, such as LEO satellites that are well-tracked with GNSS-derived positions: Sentinel, and GRACE. The model is also tested with objects with less accurate reference trajectories, such as catalogued space debris in LEO. Finally, the orbits are propagated, using the improved drag model that includes the neutral density from the assimilation of the GNSS-derived observations into NLRMSISE-00. The accuracy of the method is assessed and compared to non-assimilated models. During the discussion of the results, other sources of uncertainty are analysed. To name a few, geomagnetic activity, solar radiation pressure coefficient, attitude knowledge, and spacecraft parameters such as mass, area, drag coefficient, and so on. The improvement on the state accuracy and uncertainty realism after a medium-term propagation is analysed and the application to catalogue maintenance discussed.
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This paper presents a novel approach for assimilating thermospheric density observations into atmospheric models to improve the accuracy of orbit predictions in short- to medium- term propagations. First, Global Navigation Satellite System (GNSS) derived density data from Swarm satellites are ingested from the publicly available Level 2 data products of the European Space Agency (ESA). In a second step, density data is assimilated into the empirical model NRMLSISE-00, using Principal Component Analysis (PCA) to decompose into the main temporal and spatial modes, providing useful physical insight into the main variables driving the model. Thirdly, the model is tested on several cases, whose data was not assimilated, such as LEO satellites that are well-tracked with GNSS-derived positions: Sentinel, and GRACE. The model is also tested with objects with less accurate reference trajectories, such as catalogued space debris in LEO. Finally, the orbits are propagated, using the improved drag model that includes the neutral density from the assimilation of the GNSS-derived observations into NLRMSISE-00. The accuracy of the method is assessed and compared to non-assimilated models. During the discussion of the results, other sources of uncertainty are analysed. To name a few, geomagnetic activity, solar radiation pressure coefficient, attitude knowledge, and spacecraft parameters such as mass, area, drag coefficient, and so on. The improvement on the state accuracy and uncertainty realism after a medium-term propagation is analysed and the application to catalogue maintenance discussed.
...
The uncertainty on Thermospheric Mass Density (TMD), as derived from atmospheric models, can reach extremely high values. This effect is noteworthy in Low Earth Orbit (LEO), where atmospheric drag is the main perturbing force, as well as the most uncertain. LEO harbours almost 18,000 space objects at the end of 2021, around 60% of the total space debris population, and the rate of growth is increasing every year. Increasing the accuracy of TMD models, and thus the uncertainty characterisation, is important to ensure space environment sustainability in this congested and contested region. Accurate TMD modelling is a decisive factor in all space applications below the exopause, from LEO mission design to Space Situational Awareness (SSA) service provision: from conjunction assessment to re-entry and fragmentation analysis To enhance empirical TMD models, atmospheric density observations derived from satellite measurements are assimilated.
This paper presents a novel approach for assimilating thermospheric density observations into atmospheric models to improve the accuracy of orbit predictions in short- to medium- term propagations. First, Global Navigation Satellite System (GNSS) derived density data from Swarm satellites are ingested from the publicly available Level 2 data products of the European Space Agency (ESA). In a second step, density data is assimilated into the empirical model NRMLSISE-00, using Principal Component Analysis (PCA) to decompose into the main temporal and spatial modes, providing useful physical insight into the main variables driving the model. Thirdly, the model is tested on several cases, whose data was not assimilated, such as LEO satellites that are well-tracked with GNSS-derived positions: Sentinel, and GRACE. The model is also tested with objects with less accurate reference trajectories, such as catalogued space debris in LEO. Finally, the orbits are propagated, using the improved drag model that includes the neutral density from the assimilation of the GNSS-derived observations into NLRMSISE-00. The accuracy of the method is assessed and compared to non-assimilated models. During the discussion of the results, other sources of uncertainty are analysed. To name a few, geomagnetic activity, solar radiation pressure coefficient, attitude knowledge, and spacecraft parameters such as mass, area, drag coefficient, and so on. The improvement on the state accuracy and uncertainty realism after a medium-term propagation is analysed and the application to catalogue maintenance discussed.
This paper presents a novel approach for assimilating thermospheric density observations into atmospheric models to improve the accuracy of orbit predictions in short- to medium- term propagations. First, Global Navigation Satellite System (GNSS) derived density data from Swarm satellites are ingested from the publicly available Level 2 data products of the European Space Agency (ESA). In a second step, density data is assimilated into the empirical model NRMLSISE-00, using Principal Component Analysis (PCA) to decompose into the main temporal and spatial modes, providing useful physical insight into the main variables driving the model. Thirdly, the model is tested on several cases, whose data was not assimilated, such as LEO satellites that are well-tracked with GNSS-derived positions: Sentinel, and GRACE. The model is also tested with objects with less accurate reference trajectories, such as catalogued space debris in LEO. Finally, the orbits are propagated, using the improved drag model that includes the neutral density from the assimilation of the GNSS-derived observations into NLRMSISE-00. The accuracy of the method is assessed and compared to non-assimilated models. During the discussion of the results, other sources of uncertainty are analysed. To name a few, geomagnetic activity, solar radiation pressure coefficient, attitude knowledge, and spacecraft parameters such as mass, area, drag coefficient, and so on. The improvement on the state accuracy and uncertainty realism after a medium-term propagation is analysed and the application to catalogue maintenance discussed.
The LAgrangian Kite SimulAtor (LAKSA) is a freely available software for the dynamic analysis of tethered flying vehicles, such as kites and fixed-wing drones, applied to airborne wind energy generation. This software comprises four simulators. The one, two and four-line simulators, which consider flexible but inelastic tethers, are based on minimal coordinate Lagragian formulations and can be used for the analysis of fly and ground generation systems, kite-based traction systems, and kitesurfing applications, respectively. The configuration of the mechanical system in the fourth simulator can be defined by the user, who can select the number of flying vehicles and the properties of the elastic and flexible tethers linking them. In all the software tools, the kites or tethered fixed-wing drones are represented as rigid bodies and the dynamic equations of the tether-bridle-vehicle systems, together with the user-defined and time-dependent control variables, are solved self-consistently. Academic and research analysis can take advantage of the modularity of the simulators and their inputs and outputs interfaces, which follow a common and user-friendly architecture.
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The LAgrangian Kite SimulAtor (LAKSA) is a freely available software for the dynamic analysis of tethered flying vehicles, such as kites and fixed-wing drones, applied to airborne wind energy generation. This software comprises four simulators. The one, two and four-line simulators, which consider flexible but inelastic tethers, are based on minimal coordinate Lagragian formulations and can be used for the analysis of fly and ground generation systems, kite-based traction systems, and kitesurfing applications, respectively. The configuration of the mechanical system in the fourth simulator can be defined by the user, who can select the number of flying vehicles and the properties of the elastic and flexible tethers linking them. In all the software tools, the kites or tethered fixed-wing drones are represented as rigid bodies and the dynamic equations of the tether-bridle-vehicle systems, together with the user-defined and time-dependent control variables, are solved self-consistently. Academic and research analysis can take advantage of the modularity of the simulators and their inputs and outputs interfaces, which follow a common and user-friendly architecture.
Abstract
(2017)
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Gonzalo Sánchez-Arriaga, A. Pastor-Rodríguez, Manuel García-Villalba, M. Sanjurjo-Rivo, R.B. Moreno, Roland Schmehl
Analytical mechanics techniques are applied to the construction of three kite flight simulators with applications to airborne wind energy generation and sport uses. All of themwere developed under a minimal coordinate formulation approach. This choice has the main advantage of yielding a set of ordinary differential equations free of algebraic constraints, a feature that distinguishes the simulators from codes based on classical mechanics formulation and improves their robustness and efficiency. The first simulator involves a kite with a flexible tether, a bridle of variable geometry, and several on-board wind turbines. Such a simulator, which models the tether by a set of rigid bars linked with ideal joints, can be used to study the on-ground generation of electrical energy through yo-yo pumping maneuvers and also the onboard generation by the wind turbines. The second simulator models a kite linked to the ground by two rigid control lines. This numerical tool is aimed at the study of the dynamics of acrobatic kites and the traction analysis of giant kites to propel cargo ships. The third and last simulator of this work considers a kite with four control lines similar to the ones used in kitesurf applications. Two of them are rigid tethers of constant length that connect the leading edge of the kite with a fixed point at the ground. The other two tethers are elastic and they link a control bar with the trailing edge of the kite. The performances of the simulators in terms of computational cost and parallelization efficiency are discussed. Their architectures and user interfaces are similar, and appropriate to carry out trade-off and optimization analyses for airborne wind energy generation.
...
Analytical mechanics techniques are applied to the construction of three kite flight simulators with applications to airborne wind energy generation and sport uses. All of themwere developed under a minimal coordinate formulation approach. This choice has the main advantage of yielding a set of ordinary differential equations free of algebraic constraints, a feature that distinguishes the simulators from codes based on classical mechanics formulation and improves their robustness and efficiency. The first simulator involves a kite with a flexible tether, a bridle of variable geometry, and several on-board wind turbines. Such a simulator, which models the tether by a set of rigid bars linked with ideal joints, can be used to study the on-ground generation of electrical energy through yo-yo pumping maneuvers and also the onboard generation by the wind turbines. The second simulator models a kite linked to the ground by two rigid control lines. This numerical tool is aimed at the study of the dynamics of acrobatic kites and the traction analysis of giant kites to propel cargo ships. The third and last simulator of this work considers a kite with four control lines similar to the ones used in kitesurf applications. Two of them are rigid tethers of constant length that connect the leading edge of the kite with a fixed point at the ground. The other two tethers are elastic and they link a control bar with the trailing edge of the kite. The performances of the simulators in terms of computational cost and parallelization efficiency are discussed. Their architectures and user interfaces are similar, and appropriate to carry out trade-off and optimization analyses for airborne wind energy generation.