Space Domain Research Articles

Sharp Hausdorff content estimates for accessible boundaries of domains in metric measure spaces of controlled geometry

Sharp Hausdorff content estimates for accessible boundaries of domains in metric measure spaces of controlled geometry

Highly Strong and Tough Transparent Aramid Nanofiber Aerogel Films for Passive Heating Energy‐Efficient Windows

AbstractAerogel energy‐efficient glass can effectively reduce building energy consumption, but the low light transmission and weak mechanical properties greatly limit the application of aerogel in energy‐efficient windows. Herein, a modified aramid nanofiber aerogel with outstanding mechanical strength (1.80 MPa, pull over 30000 times its weight), transparency (94.38% in the infrared region), and resistance to extreme environments (thermal insulation, frost resistance, flame retardancy, and self‐extinguishing) is designed and synthesized using nanowire domain space structure strategy. The aerogel used for energy‐efficient windows to reduce energy consumption in buildings is demonstrated. When the aerogel energy‐efficient windows are applied in winter, the average temperature of the energy‐efficient windows during the period from 10:00 a.m. to 4:00 p.m. is 4.01 and 7.01 °C higher than the ordinary glass house and the outdoor temperature, respectively. Carbon reduction calculations show that even in the coldest provinces of China, poly(terephthaloyl chloride‐co‐phenylenediamine‐co‐2‐(4‐Aminophenyl)‐1H‐benzimidazol‐5‐amine) (PTPA) aerogel for passive heating can reduce CO2 emissions by 94.14 Kg m−2 per year. This study provides a new strategy for synthesizing transparent aramid aerogels and a new way to develop passive heating and warming energy‐efficient windows for the next generation of energy‐efficient and environmentally friendly buildings.

Analytical formulations for nitrogen oxides emissions estimation of a hydrogen fueled Synergetic Air-Breathing Rocket Engine (SABRE) in air-breathing mode

The space sector is undergoing rapid expansion due to the transformations it is experiencing, evolving from government-funded traditional space initiatives to commercially driven NewSpace activities. The surge of government and private investments in the space domain has positioned it as one of the world's fastest-growing sectors, making it crucial to assess the impact of already operational launch assets and to adopt design-to-sustainability strategies for under-development and future launchers. To actively contribute to this transition, this paper proposes a methodology for deriving novel analytical formulations to estimate nitrogen oxide (NOx) emissions of a hydrogen-fueled reusable access-to-space vehicle. Throughout the paper, the Skylon spaceplane and its Synergetic Air Breathing Rocket Engine are used as a case study. In particular, the SABRE engine presents a highly integrated dual propulsion architecture supporting both air-breathing and rocket modes. This study specifically focuses on the former, enabling the Single Stage To Orbit (SSTO) Skylon reaching the hypersonic speed regime before transitioning to rocket mode. This paper proposes novel analytical formulations to be integrated into the Fuel Flow (FF) and P3-T3 methods for estimating NOx Emission Index (EINOx), since the early design phase. These estimation techniques currently enable the calculation of the emission index of pollutants and GreenHouse Gases (GHGs) produced by any subsonic aero-engines powered by traditional fuels (i.e. kerosene) only. The methodology presented in this paper allows for upgrading the original mathematical formulations of the methods to ensure the applicability to high-speed flight and hydrogen fuel conditions. This involves the propulsive modelling of the engine and the chemical-kinetic modelling of the combustion process, representative of various on-ground and in-flight operating conditions, to generate updated and reliable propulsive and emissive databases for the engine. To this end, 0D chemical-kinetic air/hydrogen combustion numerical simulations are employed. The results of the performance analysis and emission modeling of the SABRE engine are reported and discussed. Regarding the calculated EINOx trend for the air-breathing phase of the engine operation, it reaches a peak of 45 gNOx/kgH2burnt under hypersonic conditions. Through the analysis of correlations between the propulsive characteristics of the SABRE and its NOx production, a series of parameters (such as Mach Number, Fuel to Air Ratio, Water to Fuel Ratio, and others) are selected to be integrated into the original formulations of the P3-T3 and FF methods via mathematical interpolation to adapt them to the case study. Finally, the EINOx trends resulting from the application of the new H2-P3T3 and H2-FF methods to the SABRE are graphically reported, along with the corresponding estimation errors relative to the reference trend from the engine emission database. Additionally, a discussion regarding the chemical-physical justifications for the mathematical contributions to the formulations for EINOx of the newly introduced parameters is provided.

METASAT's Model Based Design Solutions

METASAT is a recently started project (January 2023) in the Horizon Europe programme, in the SPACE call, coordinated by the Barcelona Supercomputing Center (BSC). METASAT will develop model-based design (MBD) solutions for high performance on-board processors such as multicores, Graphics Processing Units (GPUs) and Artificial Intelligence (AI) Accelerators. While the developed tools and methodologies are particularly focusing on the space domain, reusability to other safety critical domains is also a project goal. This talk will provide an overview of the solutions which will be developed during the project, which will be centered around the open source TASTE framework used at the European Space Agency (ESA), which leverages AADL.

Control of Instabilities in Resonant Converters Using Half‐Period Time Delay Output Feedback

ABSTRACTBifurcation phenomena in power electronic converters lead to undesired instabilities. In this paper, different types of bifurcations are identified, which can occur in a series load resonant converter with the variation of the parameters for example, input voltage, load resistance, and so forth. These instabilities are associated mainly to three different bifurcations: (a) Neimark–Sacker, (b) saddle–node, and (c) symmetry breaking bifurcations. Due to the symmetry property of load resonant converters, a half‐period time delay output feedback control is used to avoid all of these instabilities. The ideal time delay is realized by a first‐order all‐pass‐filter. The stability analysis is performed and the values of the feedback gains are determined to identify the stable domain in the parameter space. By identifying the unstable orbits, the analytical stability analysis helps to understand as well as to control the mechanisms of these instabilities, which extends the stability domain of the system. This study can also be extended to other load resonant converters where the symmetry property naturally exists.

A High-Impedance Fault Detection Method for Active Distribution Networks Based on Time–Frequency–Space Domain Fusion Features and Hybrid Convolutional Neural Network

Traditional methods for detecting high-impedance faults (HIFs) in distribution networks primarily rely on constructing fault diagnosis models using one-dimensional zero-sequence current sequences. A single diagnostic model often limits the deep exploration of fault characteristics. To improve the accuracy of HIF detection, a new method for detecting HIFs in active distribution networks is proposed. First, by applying continuous wavelet transform (CWT) to the collected zero-sequence currents under various operating conditions, the time–frequency spectrum (TFS) is obtained. An optimized algorithm, modified empirical wavelet transform (MEWT), is then used to denoise the zero-sequence current signals, resulting in a series of intrinsic mode functions (IMFs). Secondly, the intrinsic mode functions (IMFs) are transformed into a two-dimensional spatial domain fused image using the symmetric dot pattern (SDP). Finally, the TFS and SDP images are synchronized as inputs to a hybrid convolutional neural network (Hybrid-CNN) to fully explore the system’s fault features. The Sigmoid function is utilized to achieve HIF detection, followed by simulation and experimental validation. The results indicate that the proposed method can effectively overcome the issues of traditional methods, achieving a detection accuracy of up to 98.85% across different scenarios, representing a 2–7% improvement over single models.

Snap-through of functionally graded graphene origami-enabled auxetic metamaterial doubly curved nonlinear shells

The lightweight design of thin-walled curved structures as spherical shells are frequently implemented in architecture, aerospace, mechanical, automotive, nuclear, and defense structures. Under the transverse loads, shells may largely deform and hence snap from one equilibrium position to the other. Thus, this work aims to develop a mathematical model and computational solution to investigate the nonlinear bending and snap-through behavior of doubly curved auxetic metamaterial shell subjected to transversal loading, for the first time. The shell is composed of several layers through the shell thickness, each layer is manufactured from a copper (Cu) matrix reinforced with a specified weight fraction of graphene origami auxetic metamaterial (GOAM). The mechanical properties of the GOAM shell panel are presented and described by functions of the GOAM volume fraction and folding degree. Three types of GOAM-distributions are considered, which are U-type, X-type, and O-type. The theoretical framework of Kirchhoff–Love hypotheses for thin shells and von Karman type nonlinearity are used to derive the governing equations. The differential quadrature method (DQM) is implemented to discretize the space domain and convert the nonlinear partial differential equation to nonlinear algebraic equations in terms of displacement field. An efficient incremental iterative procedure is developed to solve the nonlinear equations and predict the snap-through behavior. A model conversion and validation with isotropic spherical shell is considered. Several numerical results are conducted considering the effects of changing GOAM content, distribution pattern, folding degree, and shell curvature and thickness. For panels exhibiting snap-through behavior, increasing the shell curvature leads to higher snap-through limiting load; however, the instability gap is enlarged.

A sizing model for a tube in tube sensible thermal energy storage heat exchanger with solid storage material

Sensible thermal energy storage (STES) systems are generally the most affordable and least complex type of thermal energy storage systems available. The main question when modeling STES systems is the outlet state of the heat transfer fluid for a given inlet condition. Traditional heat exchanger design methods such as the logarithmic mean temperature difference method do not apply to TES systems since these systems do not operate in steady state. Other design methods are only applicable to specific cases such as packed bed systems with a thermocline. The present paper proposes a novel framework for STES system design with solid, stationary storage material. The framework is validated based on the case of a tube in tube STES system. The novel design model characterizes the local conductive heat transfer in the Laplace domain. This local behavior is integrated over the surface of the heat exchanger using three methods: a height split, a height lumped and a fully lumped approach. Each method corresponds to a different assumption regarding the temperature gradient in the storage material and will therefore be applicable to different cases. By numerically inverting the Laplace transform, a prediction is obtained of the outlet temperature of the HTF in the time domain. The solution is compared to the result of a computational fluid dynamics (CFD) reference model for five selected cases, with the CFD requiring calculation times in the order of days on a dedicated server while the novel model requires seconds on a laptop. This difference in calculation time is the result of the need of the CFD model to discretize the domain in space and apply a time stepping algorithm. The transfer function approach requires neither discretization nor time stepping but it does require a numerical inverse Laplace transform. The transfer function approach has a good fit with the CFD predictions. Furthermore, the quality of the fit and the behavior of the system is predicted based on the Biot number, the thermocline width ratio and the cross conduction number where the latter two numbers are derived in the present paper. The novel approach can provide a method to obtain sizing models for STES systems with a solid storage material.

Data Generation for Machine Learning Interatomic Potentials and Beyond.

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

Correlating the 3D Morphology of Polymer-Based Battery Electrodes with Effective Transport Properties.

Polymer-based batteries represent a promising candidate for next-generation batteries due to their high power densities, decent cyclability, and environmentally friendly synthesis. However, their performance essentially depends on the complex multiscale morphology of their electrodes, which can significantly affect the transport of ions and electrons within the electrode structure. In this paper, we present a comprehensive investigation of the complex relationship between the three-dimensional (3D) morphology of polymer-based battery electrodes and their effective transport properties. In particular, focused ion beam scanning electron microscopy (FIB-SEM) is used to characterize the 3D morphology of three polymer-based electrodes which differ in material composition. The subsequent segmentation of FIB-SEM image data into active material, carbon-binder domain and pore space enables a comprehensive statistical analysis of the electrode structure and a quantitative morphological comparison of the electrode samples. Moreover, spatially resolved numerical simulations allow for computing effective properties of ionic and electronic transport. The obtained results are used for establishing analytical regression formulas which describe quantitative relationships between the 3D morphology of the electrodes and their effective transport properties. To the best of our knowledge, this is the first time that the 3D structure of polymer-based battery electrodes is quantitatively investigated at the nanometer scale.

Nonlinear thermal flutter of variable angle tow curved panels with elastically restrained edges in supersonic airflow by generalized Galerkin method

Variable Angle Tows (VAT) composite materials can effectively transfer major stresses from the interior region to the edges through in-plane load redistribution, thereby significantly enhancing their structural performance. Therefore, correct handling of boundary conditions is crucial in analyzing VAT structures. Considering that most practical engineering cases involve non-ideal or non-classical boundary support, this paper proposes an efficient method to address the nonlinear thermal flutter problem of elastic supported VAT curved panels in supersonic airflow. Assuming the fiber orientation angle varies in four different ways along the x-direction, the basic equations are established based on the von Kármán large deformation plate theory and the first-order piston theory. The generalized Galerkin method, capable of handling arbitrary elastic boundary constraints, is used to solve the problem in the space domain, while the Runge-Kutta method is applied in the time domain. Numerical examples illustrate how elastically restrained conditions, fiber orientation angles, height-rise ratio, and thermal loads, along with aerodynamic pressure, influence flutter responses of VAT panels. The presented method can be used for the aeroelastic stability design of VAT curved panels under arbitrary boundary conditions.

Application of Machine Learning and Hydrological Models for Drought Evaluation in Ungauged Basins Using Satellite-Derived Precipitation Data

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. SPI values were calculated from the precipitation data obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a hydrological model for calculating discharge and drought in the overall basin. It uses random forest (RF) and support vector regression (SVR) as machine learning models for SSI prediction for time scales of 1- and 3-month periods, which are widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the machine learning model’s results. The results from the prediction model, hydrological model, and the station data show better correlation. The coefficients of determination obtained for 1-month SSI are 0.842 and 0.696, and those for the 3-month SSI are 0.919 and 0.862 in the RF and SVR models, respectively. These results also revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one, with the better prediction result being from the SVR model. The hydrological model-evaluated SSI has 0.885 and 0.826 coefficients of determination for the 1- and 3-month time durations, respectively. The results and discussion in this research will aid planners and decision-makers in managing hydrological droughts in basins.

The Future of Radar Space Observation in Europe—Major Upgrade of the Tracking and Imaging Radar (TIRA)

The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they can be operated at any time and under any weather conditions. The Tracking and Imaging Radar (TIRA) is one of the largest space observation radars in the world. It consists of a 34m Cassegrain antenna, a precise tracking radar, and a high-resolution imaging radar. Since the 1990s, TIRA contributes to the field of space domain awareness by tracking and imaging space objects and by monitoring the debris population. Due to new technologies, modern satellites become smaller, and satellite extensions become more compact. Thus, sensitive high-resolution space observation systems are needed to detect, track, and image these space objects. To fulfill these requirements, TIRA is undergoing a major upgrade. The current imaging radar in the Ku band will be replaced by a new radar with improved geometrical and radiometric resolution operating in the Ka band. Due to its wideband fully polarimetric capability, the new imaging radar will increase the analysis and characterization of space objects. In addition, the tracking radar in the L band is also being currently refurbished. Through its novel modular structure and open design, highly flexible radar modes and precise tracking concepts can be efficiently implemented for enhanced space domain awareness. The new TIRA system will mark the start of a new era for space observation with radar in Europe.

An Integrated Taylor Expansion and Least Squares Approach to Enhanced Acoustic Wave Staggered Grid Finite Difference Modeling

The staggered grid finite difference method has emerged as one of the most commonly used approaches in finite difference methodologies due to its high computational accuracy and stability. Inevitably, discretizing over time and space domains in finite difference methods leads to numerical artifacts. This paper introduces a novel approach that combines the widely used Taylor series expansion with the least squares method to effectively suppress numerical dispersion. We have derived the coefficients for the staggered grid finite difference method by integrating Taylor series expansions with the least squares method. To validate the effectiveness of our approach, we conducted analyses on accuracy, dispersion, and stability, alongside simple and complex numerical examples. The results indicate that our method not only inherits the capabilities of the original Taylor series and least squares methods in suppressing numerical dispersion across small and medium wavenumber ranges but also surpasses the original methods. Moreover, it demonstrates robust dispersion suppression capabilities at high wavenumber ranges.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains.

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.

Shrinking shrimp-shaped domains and multistability in the dissipative asymmetric kicked rotor map.

An interesting feature in dissipative nonlinear systems is the emergence of characteristic domains in parameter space that exhibit periodic temporal evolution, known as shrimp-shaped domains. We investigate the parameter space of the dissipative asymmetric kicked rotor map and show that, in the regime of strong dissipation, the shrimp-shaped domains repeat themselves as the nonlinearity parameter increases while maintaining the same period. We analyze the dependence of the length of each periodic domain with the nonlinearity parameter, revealing that it follows a power law with the same exponent regardless of the dissipation parameter. Additionally, we find that the distance between adjacent shrimp-shaped domains is scaling invariant with respect to the dissipation parameter. Furthermore, we show that for weaker dissipation, a multistable scenario emerges within the periodic domains. We find that as the dissipation gets weaker, the ratio of multistable parameters for each periodic domain increases, and the area of the periodic basin decreases as the nonlinearity parameter increases.

Embedded State Estimation for Optimization of Cislunar Space Domain Awareness Constellation Design

The traffic in cislunar space is expected to increase over the coming years, leading to a higher likelihood of conjunction events among active satellites, orbital debris, and noncooperative satellites. This increase necessitates enhanced space domain awareness (SDA) capabilities that include state estimation for targets of interest. Both Earth surface-based and space-based observation platforms in geosynchronous orbit or below face challenges such as range, exclusion, and occlusion that hinder observation. Motivated by the need to place space-based observers in the cislunar space regime to overcome these challenges, this paper proposes a cislunar SDA constellation design and analysis framework that integrates state estimation into an optimization problem for determining the placement of observers for optimal state estimation performance on a set of targets. The proposed multiobserver placement optimization problem samples from a range of possible target orbits. Upon convergence, the optimized constellation is validated against a broader set of targets to assess its effectiveness. Two comparative analyses are presented to evaluate the effects of changes in the sensor tasking procedure and sensor fidelity on the optimized constellation, comparing these to a single observer baseline case. The results demonstrate that the optimized constellations can provide accurate state estimation for various orbit families.

High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.

Analyzing the European Union’s CSDP and the evolution of European security and defense training at the ESDC: prospects and pathways

ABSTRACT This article presents a thorough examination of the European Union’s Common Security and Defense Policy (CSDP) in comparison with the educational programs offered by the European Security and Defense College (ESDC), with a specific focus on the High-Level CSDP Course. We conducted a comprehensive analysis using a meta-synthesis research approach. Our research commenced with a Systematic Literature Review and was further validated through a qualitative case study, which included interviews with the 2023 high-Level CSDP course participants. Our research findings highlight the need to prioritize emerging topics such as climate security and identify new areas in cyber and space domains to potentially expand the CSDP. We emphasized the importance of developing strategic approaches to facilitate the dissemination of comprehensive CSDP knowledge generated by the ESDC to higher education institutions. This initiative aims to equip future EU leaders with the necessary knowledge and skills to effectively engage in CSDP-related matters.

Analytical modelling and analysis of the meander-line coil EMATs

The meander-line coil electromagnetic acoustic transducer (EMAT) is widely used in the field of ultrasonic nondestructive testing due to its convenience to generate specific mode of guided waves. Some design methods of the meander-coil EMATs are developed in the frequency-wavenumber domain while others in the time–space domain. In this paper, a unified theoretical framework is developed by proposing an analytical model from the system perspective. Signal transfers between different physical fields in EMAT excitation, wave propagation and EMAT reception are represented as linear time–space-invariant systems. Taking the Rayleigh wave EMAT detection as an example, the analytical model for the transfer functions of these systems is established. The analytical model is experimentally verified by different Rayleigh wave detection techniques: the conventional EMAT, the spatial pulse compression (SPC) EMAT, temporal-spatial pulse compression (TSPC) EMAT and detection cases employing the same receiving EMAT. From the system perspective, the received signal of EMAT is interpreted as the response of the filter system to the input signal. It is found that the meander-coil EMAT can be regarded as the frequency domain expression during the detection. And the frequency domain expression plays different roles in different techniques.