Search Results (45)

With the rapidly increasing number of satellites and orbital debris, collision avoidance and reentry prediction are very important for space situational awareness. A precise orbital prediction through orbit determination is crucial to enhance the space safety. The two-line element (TLE) data sets are publicly available to users worldwide. However, the data sets have uneven qualities and biases, resulting in exponential growth of orbital prediction errors in the along-track direction. A hybrid algorithm of the simplex method and genetic algorithm is proposed to improve orbit determination accuracy using TLEs. The parameters of the algorithm are tuned to achieve the best performance of orbital prediction. Six satellites with consolidated prediction format (CPF) ephemeris and four satellites with precise orbit ephemerides (PODs) are chosen to test the performance of the algorithm. Compared with the results of the least-squares method and simplex method based on Monte Carlo simulation, the new algorithm demonstrated its superiorities in orbital prediction. The algorithm exhibits an accuracy improvement as high as 40.25% for 10 days of orbital prediction compared to that using the single last two-line element. In addition, six satellites are used to evaluate the time efficiency, and the experiments prove that the hybrid algorithm is robust and has computational efficiency. Full article

According to Prandtl’s boundary layer theory, the entrance length refers to the axial distance required for a flow to transition from its initial entry condition to a fully developed flow where the velocity profile stabilizes downstream. However, this theory remains applicable only under the assumption of Re ≫ 1, while its validity diminishes under low-Reynolds-number conditions. This study utilizes OpenFOAM based on the finite volume method to numerically examine Newtonian and viscoelastic fluids in a laminar circular pipe flow. The objective is to determine the range of Reynolds numbers for which the differential equations from within the Prandtl boundary layer theory are strictly valid. Additionally, the study explores the effects of Reynolds numbers (Re) ranging from 50 to 100, s solvent viscosity ratio (β) fixed at 0.3 and 0.7, and Weissenberg numbers (Wi) ranging from 0.2 to 5 on the entrance length and friction factor for the Oldroyd-B model. The results indicate the presence of a lower Reynolds number that impedes the attainment of the outcomes predicted by the Prandtl boundary layer theory for the entrance length. The inertia effect, the increase in solvent viscosity contribution, and the elastic effect exhibit a linear relationship with the entrance length and friction factor. Full article

Uncontrolled space debris reentries pose a significant challenge to air traffic management (ATM), often requiring widespread airspace closures to mitigate the perceived risks to aviation safety. In a previous study, we established the probability of collision during such events to be in the order of 10⁻⁷ and classified the event as “extremely remote” but requiring mitigation action. Analyzing the temporal dynamics, we concluded that any given location remains at risk for no more than one minute. Building on these findings, this paper will investigate advanced mitigation strategies to reduce the operational impact of such reentries. We propose utilizing dynamic airspace allocation techniques, using information derived by enhanced reentry prediction models and real-time tracking. Transforming the spatial problem of airspace closures into a temporal one, the study demonstrates the feasibility of confining closures to dynamically moving zones with minimal disruption. A simulation for the Long March 5B reentry case study illustrates the potential for such measures to improve efficiency while maintaining safety standards. Full article

This paper proposes a trajectory prediction method via the adaptive cost function to address the difficulties in inferring the attack intention and maneuver mode, as well as the accumulation of prediction error during the trajectory prediction of reentry glide vehicles. Firstly, the vehicle guidance task is divided into two distinct categories: conventional guidance and no-fly zone avoidance guidance. A task-matched time-varying parameter prediction model set is then constructed. Secondly, taking into account the maneuverability, guidance intent, and battlefield situation of the vehicle, an adaptive intent cost function adapted to the guidance task is proposed, which avoids the estimation failure problem caused by manually setting cost coefficients in traditional methods. Finally, long-term trajectory prediction of vehicles is achieved using Bayesian theory to infer the attack intent and parametric model with the maximum a posteriori probability. The results of the simulations demonstrate that the proposed prediction method is capable of accurately inferring the vehicle’s attack intention and parameter model, and of effectively reducing the accumulation of prediction errors and the time required for the algorithmic process compared to existing methods. Full article

The intermittent nature of renewable energy sources (RES) restricts their widespread applications and reliability. Nevertheless, with advancements in the field of artificial intelligence, we can predict the variations in parameters such as wind speed and solar irradiance for the short, medium and long terms. As such, this research attempts to develop a machine learning (ML)-based framework for predicting solar irradiance at Muscat, Oman. The developed framework offers a methodological way to choose an appropriate machine learning model for long-term solar irradiance forecasting using Python’s built-in libraries. The five different methods, named linear regression (LR), seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), support vector regression (SVR), Prophet, k-nearest neighbors (k-NN), and long short-term memory (LSTM) network are tested for a fair comparative analysis based on some of the most widely used performance evaluation metrics, such as the mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²) score. The dataset utilized for training and testing in this research work includes 24 years of data samples (from 2000 to 2023) for solar irradiance, wind speed, humidity, and ambient temperature. Before splitting the data into training and testing, it was pre-processed to impute the missing data entries. Afterward, data scaling was conducted to standardize the data to a common scale, which ensures uniformity across the dataset. The pre-processed dataset was then split into two parts, i.e., training (from 2000 to 2019) and testing (from 2020 to 2023). The outcomes of this study revealed that the SARIMAX model, with an MSE of 0.0746, MAE of 0.2096, and an R² score of 0.9197, performs better than other competitive models under identical datasets, training/testing ratios, and selected features. Full article

Lunar DRO pinpoint return is the final stage of manned deep space exploration via a lunar DRO station. A re-entry capsule suffers from complicated dynamic and thermal effects during an entire flight. The optimization of the lunar DRO return trajectory exhibits strong non-linearity. To obtain a global optimal return trajectory, an entire-flight lunar DRO pinpoint return model including a Moon–Earth transfer stage and an Earth atmosphere re-entry stage is constructed. A re-entry point on the atmosphere boundary is introduced to connect these two stages. Then, an entire-flight global optimization framework for lunar DRO pinpoint return is developed. The design of the entire-flight return trajectory is simplified as the optimization of the re-entry point. Moreover, to further improve the design efficiency, a rapid landing point prediction method for the Earth re-entry is developed based on a deep neural network. This predicting network maps the re-entry point in the atmosphere and the landing point on Earth with respect to optimal control re-entry trajectories. Numerical simulations validate the optimization accuracy and efficiency of the proposed methods. The entire-flight return trajectory achieves a high accuracy of the landing point and low fuel consumption. Full article

In the last few years, many space surveillance initiatives have started to consider the problem represented by resident space object overpopulation. In particular, the European Space Surveillance and Tracking (EUSST) consortium is in charge of providing services like collision avoidance, fragmentation analysis, and re-entry, which rely on measurements obtained through ground-based sensors. BIRALES is an Italian survey radar belonging to the EUSST framework and is capable of providing measurements including Doppler shift, slant range, and angular profile. In recent years, the Music Approach for Track Estimate and Refinement (MATER) algorithm has been developed to retrieve angular tracks through an adaptive beamforming technique, guaranteeing the generation of more accurate and robust measurements with respect to the previous static beamforming approach. This work presents the design of a new data processing chain to be used by BIRALES to compute the angular track. The signal acquired by the BIRALES receiver array is down-converted and the receiver bandwidth is split into multiple channels, in order to maximize the signal-to-noise ratio of the measurements. Then, the signal passes through a detection block, where an isolation procedure creates, for each epoch, signal correlation matrices (CMs) related to the channels involved in the detection and then processes them to isolate the data stream related to a single detected source. Consequently, for each epoch and for each detected source, just the CM featuring the largest signal contribution is kept, allowing deriving the Doppler shift measurement from the channel illumination sequence. The MATER algorithm is applied to each CM stream, first estimating the signal directions of arrival, then grouping them in the observation time window, and eventually returning the target angular track. Ambiguous estimates may be present due to the configuration of the receiver array, which cause spatial aliasing phenomena. This problem can be addressed by either exploiting transit prediction (in the case of cataloged objects), or by applying tailored criteria (for uncatalogued objects). The performance of the new architecture was assessed in real operational scenarios, demonstrating the enhancement represented by the implementation of the channelization strategy, as well as the angular measurement accuracy returned by MATER, in both nominal and off-nominal scenarios. Full article

The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and reliability assessment. Given that uncertain aerodynamic heating loads manifest as a stochastic field over time, conventional surrogate models, typically accepting scalar random variables as inputs, face limitations in modeling them. Consequently, this paper introduces an effective characterization approach utilizing proper orthogonal decomposition (POD) to represent the uncertainties of aerodynamic heating. The augmented snapshots matrix is used to reduce the dimension of the random field by the decoupling method of independently spatial and temporal bases. The random variables describing material properties and geometric thickness are also employed as inputs for probabilistic analyses. An uncoupled POD Gaussian process regression (UPOD-GPR) model is then established to achieve highly accurate solutions for transient heat conduction. The model takes random heat flux fields as inputs and thermal response fields as outputs. Using a typical multi-layer TPS and thermal structure as two examples, probabilistic analyses are conducted. The mean square relative error of a typical multi-layer TPS is less than 4%. For the thermal structure, the averaged absolute error of the radiation and insulation layer is less than 25 °C and 6 °C when the maximum reaches 1200 °C and 150 °C, respectively. This approach can provide accurate and rapid predictions of thermal responses for TPS and thermal structures throughout their entire operating time when furnished with input heat flux fields and structural parameters. Full article

During reentry, the high temperatures experienced by near-space hypersonic vehicles result in surface ablation, generating ablative particles. These particles become part of a plasma, commonly referred to as a “dusty plasma sheath” in radar remote sensing. The dusty plasma model, integral in radar studies, involves extensive charge and dynamic interactions among dust particles. Previous derivations assumed that the dust particle radius significantly surpassed the Debye radius, leading to the neglect of dust radius effects. This study, however, explores scenarios where the dust particle radius is not markedly smaller than the Debye radius, thereby deducing the charging process of dusty plasma. The derived equations encompass the Debye radius, charging process, surface potential, and charging frequency, particularly considering larger dust particle radii. Comparative analysis of the dusty plasma model, both before and after modification, reveals improvements when dust particles approach or exceed the Debye length. In essence, our study provides essential equations for understanding dusty plasma under realistic conditions, offering potential advancements in predicting electromagnetic properties and behaviors, especially in scenarios where dust particles closely align with or surpass the Debye radius. Full article

Atmospheric drag stands out as the predominant non-gravitational force acting on satellites in Low Earth Orbit (LEO), with altitudes below 2000 km. This drag exhibits a strong dependence on the thermospheric mass density (TMD), a parameter of vital significance in the realms of orbit determination, prediction, collision avoidance, and re-entry forecasting. A multitude of empirical TMD models have been developed, incorporating contemporary data sources, including TMD measurements obtained through onboard accelerometers on LEO satellites. This paper delves into three different TMD modelling techniques, specifically, Fourier series, spherical harmonics, and artificial neural networks (ANNs), during periods of geomagnetic quiescence. The TMD data utilised for modelling and evaluation are derived from three distinct LEO satellites: GOCE (at an altitude of approximately 250 km), CHAMP (around 400 km), and GRACE (around 500 km), spanning the years 2002 to 2013. The consistent utilisation of these TMD data sets allows for a clear performance assessment of the different modelling approaches. Subsequent research will shift its focus to TMD modelling during geomagnetic disturbances, while the present work can serve as a foundation for disentangling TMD variations stemming from geomagnetic activity. Furthermore, this study undertakes precise TMD modelling during geomagnetic quiescence using data obtained from the GRACE (at an altitude of approximately 500 km), CHAMP (around 400 km), and GOCE (roughly 250 km) satellites, covering the period from 2002 to 2013. It employs three distinct methods, namely Fourier analysis, spherical harmonics (SH) analysis, and the artificial neural network (ANN) technique, which are subsequently compared to identify the most suitable methodology for TMD modelling. Additionally, various combinations of time and coordinate representations are scrutinised within the context of TMD modelling. Our results show that the precision of low-order Fourier-based models can be enhanced by up to 10 % through the utilisation of geocentric solar magnetic coordinates. Both the Fourier- and SH-based models exhibit limitations in approximating the vertical gradient of TMD. Conversely, the ANN-based model possesses the capacity to capture vertical TMD variability without manifesting sensitivity to variations in time and coordinate inputs. Full article

Background and Objectives: As the latest research encourages the ultrasound-guided infraclavicular central venous approach, due to the lateral puncture site displacement, in comparison to the anatomical landmark technique based on subclavian vein catheterization, the need to re-calculate the optimal catheter insertion length and possibly to rename the punctuated vessel emerges. Although naming a particular anatomical structure is a nomenclature issue, a suboptimal catheter position can be associated with multiple life-threatening complications and must be avoided. The main study objective is to determine the optimal catheter insertion length by the most proximal ultrasound-guided, in-plane infraclavicular central vein approach, to compare results with the anatomical landmark technique based on subclavian vein catheterization and to clarify the punctuated anatomical structure. Materials and Methods: 109 patients were enrolled in this study. All procedures were performed according to the same catheterization protocol. In order to determine optimal insertion length, chest X-ray scans with an existing catheter were performed. The definition of punctuated vessel was based on computer tomography and evaluated by radiologists. Independent predictors for optimal insertion length were identified, prediction equations were generated. Results: The optimal catheter insertion length is approximately 1.5 cm longer than estimated by Pere’s formula and can be accurately calculated based on anthropometric data. Computed tomography revealed: five cases with subclavian vein puncture and three cases with axillary vein puncture. Conclusions: Even the most proximal ultrasound-guided infraclavicular central vein access does not guarantee subclavian vein catheterization. A more accurate term could be infraclavicular central venous access, with the implication that the entry point could be through either subclavian or axillary veins. The optimal insertion length is approximately 1.5 cm deeper than the length determined for the anatomical landmark technique based on subclavian vein catheterization. Full article

How to improve the computational efficiency of flow field simulations around irregular objects in near-continuum and continuum flow regimes has always been a challenge in the aerospace re-entry process. The discrete velocity method (DVM) is a commonly used algorithm for the discretized solutions of the Boltzmann-BGK model equation. However, the discretization of both physical and molecular velocity spaces in DVM can result in significant computational costs. This paper focuses on unlocking the key to accelerate the convergence in DVM calculations, thereby reducing the computational burden. Three versions of DVM are investigated: the semi-implicit DVM (DVM-I), fully implicit DVM (DVM-II), and fully implicit DVM with an inner iteration of the macroscopic governing equation (DVM-III). In order to achieve full implicit discretization of the collision term in the Boltzmann-BGK equation, it is necessary to solve the corresponding macroscopic governing equation in DVM-II and DVM-III. In DVM-III, an inner iterative process of the macroscopic governing equation is employed between two adjacent DVM steps, enabling a more accurate prediction of the equilibrium state for the full implicit discretization of the collision term. Fortunately, the computational cost of solving the macroscopic governing equation is significantly lower than that of the Boltzmann-BGK equation. This is primarily due to the smaller number of conservative variables in the macroscopic governing equation compared to the discrete velocity distribution functions in the Boltzmann-BGK equation. Our findings demonstrate that the fully implicit discretization of the collision term in the Boltzmann-BGK equation can accelerate DVM calculations by one order of magnitude in continuum and near-continuum flow regimes. Furthermore, the introduction of the inner iteration of the macroscopic governing equation provides an additional 1–2 orders of magnitude acceleration. Such advancements hold promise in providing a computational approach for simulating flows around irregular objects in near-space environments. Full article

This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible. Full article

The SARS-CoV-2 virus, the causative agent of COVID-19, is known for its genetic diversity. Virus variants of concern (VOCs) as well as variants of interest (VOIs) are classified by the World Health Organization (WHO) according to their potential risk to global health. This study seeks to enhance the identification and classification of such variants by developing a novel bioinformatics criterion centered on the virus’s spike protein (SP1), a key player in host cell entry, immune response, and a mutational hotspot. To achieve this, we pioneered a unique phylogenetic algorithm which calculates EIIP-entropy as a distance measure based on the distribution of the electron–ion interaction potential (EIIP) of amino acids in SP1. This method offers a comprehensive, scalable, and rapid approach to analyze large genomic data sets and predict the impact of specific mutations. This innovative approach provides a robust tool for classifying emergent SARS-CoV-2 variants into potential VOCs or VOIs. It could significantly augment surveillance efforts and understanding of variant characteristics, while also offering potential applicability to the analysis and classification of other emerging viral pathogens and enhancing global readiness against emerging and re-emerging viral pathogens. Full article

A large amount of heat flux from aerodynamic heating acts on reusable spacecraft; thus, an accurate heat flux prediction around spacecraft reentry is essential for developing a high-performance reusable spacecraft. Although the approximate convective-heating equations can calculate the heat flux with high efficiency and sufficient fidelity, the heat flux should be evaluated over a thousand times for the entire trajectory in multidisciplinary analyses. For these reasons, it is necessary to develop an efficient method for calculating the heat flux for multidisciplinary analysis. In this paper, an efficient method for heat flux calculation that is adoptable by multidisciplinary analyses for hypersonic vehicles, such as spacecraft, is developed. Approximate convective-heating equations were adopted to relieve the computational cost of estimating the heat flux, and an adaptive time step method for heat flux calculations was developed to reduce the number of heat flux calculations required across the entire flight trajectory. A dynamic factor was introduced that adjusts the time step between each instance of the heat flux calculation. Since the time step using this factor could increase under low heat flux conditions, the number of heat flux calculations decreases by approximately one-tenth with over 90% accuracy. Therefore, the efficiency was improved with high accuracy using the adaptively-determined time step according to this dynamic factor. Full article