Density Model Research Articles

Calculated brightness temperatures of solar structures compared with ALMA and Metsähovi measurements

AbstractThe Atacama Large Millimeter/submillimeter Array (ALMA) allows for solar observations in the wavelength range of 0.3–10 mm, giving us a new view of the chromosphere. The measured brightness temperature at various frequencies can be fitted with theoretical models of density and temperature versus height. We use the available ALMA and Metsähovi measurements of selected solar structures (quiet sun (QS), active regions (AR) devoid of sunspots, and coronal holes (CH)). The measured QS brightness temperature in the ALMA wavelength range agrees well with the predictions of the semiempirical Avrett–Tian–Landi–Curdt–Wülser (ATLCW) model, better than previous models such as the Avrett–Loeser (AL) or Fontenla–Avrett–Loeser model (FAL). We scaled the ATLCW model in density and temperature to fit the observations of the other structures. For ARs, the fitted models require 9%–13% higher electron densities and 9%–10% higher electron temperatures, consistent with expectations. The CH fitted models require electron densities 2%–40% lower than the QS level, while the predicted electron temperatures, although somewhat lower, do not deviate significantly from the QS model. Despite the limitations of the one‐dimensional ATLCW model, we confirm that this model and its appropriate adaptations are sufficient for describing the basic physical properties of the solar structures.

Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

In laminated composite materials design, optimization mainly targets the stacking sequence configuration, which is defined by the lamina thickness and fiber orientations within each layer. Recent studies emphasize the increasing role of Machine Learning in promoting innovative composite designs by facilitating the accurate modeling of essential properties such as strength and stiffness. This study introduces two metamodels that utilize feed-forward artificial neural networks, taking laminate thickness and fiber steering angles as input parameters. The output variables, including strain energy density and the Tsai-Wu failure index, enable the prediction of stacking sequence configurations for laminated materials, a capability confirmed in a case study. The results showcase neural network models with the ability to predict these variables, achieving coefficients of determination above 0.90 for testing data. Consequently, this modeling approach has the potential to be a tool for designers, aiding in decision-making processes for the subsequent optimization of stiffness and strength in structural components made of laminated composite materials.

Imaging Pleistocene volcanic edifices along the Egyptian Red Sea margin: Insights from reflection seismics and 3D constrained inversion of gravity and magnetic data

Several volcanic buildups have been documented using gravity and magnetic data at specific locations in the Northern Red Sea (NRS). Most of these volcanoes were never sampled, and only a few were imaged by seismic or bathymetry data. Furthermore, the confidentiality of commercial datasets does not allow adequate knowledge of these structures and their morphology and physical properties. In this study, 2D and 3D seismic reflection data and 3D constrained inversion of magnetic and gravity data have been used to infer densities and magnetic susceptibilities of volcanic features in the central part of the NRS and to evaluate the distribution and geometry. This study indicates that the results of the 3D inverted models show a good fit with the volcanic structures inferred from seismic data and, thus, that the inversion technique adopted can be a reliable tool for detecting other edifices where seismic data are missing. The 3D inverted density and magnetic susceptibility models suggest that at least eight isolated structures with similar gravity and magnetic properties exist in the study area. They are associated with high densities and magnetic susceptibility anomalies with average values of 2700 kg/m3 and 0.025 SI, respectively. These magmatic structures are deeply rooted in the sedimentary cover and emerge at elevations of 100 to >650 m relative to the seabed. They developed in a sector interpreted as containing thinned continental crust, accompanied by the ascension of magma through prominent rift-related normal faults. Magmatic activity likely started in the Pleistocene and continues to the present day.

Examination of the Effects of Level Density Models in Cross-Section Calculations of Some (p,x) Reactions on Natural Palladium

Examination of the Effects of Level Density Models in Cross-Section Calculations of Some (p,x) Reactions on Natural Palladium

X-ray diffraction analysis of orthorhombic SnSe nanoparticles by Williamson–Hall, Halder–Wagner and Size–Strain plot methods

SnSe nanoparticles were synthesized using the sonochemical exfoliation technique, employing SnSe single crystals. Various analysis, including Energy Dispersive Analysis of X-rays-Elemental mapping, X-ray Diffraction (XRD), High-Resolution Transmission Electron Microscopy, Ultra-Violet Diffused Reflectance Spectroscopy-Photoluminescence measurements, Raman spectroscopy, and X-ray Photoelectron Spectroscopy, were conducted to determine the chemical composition, crystalline phase, morphological characteristics, optical properties, vibrational modes, and oxidation states of the synthesized nanoparticles. The XRD peak broadening analysis, specifically employing Williamson–Hall (W-H), Size–Strain Plot (SSP), and Halder–Wagner Method (H-W), was employed to examine particle size and intrinsic strain. Additionally, three models within the W-H method, namely the uniform deformation model (UDM), uniform stress deformation model (USDM), and uniform deformation energy density model (UDEDM), were used to investigate physical and microstructural parameters such as strain, stress, and energy density. A comprehensive comparison of the average particle size results obtained from Williamson–Hall, Size–Strain, and Halder–Wagner Methods, along with results from HR-TEM and Particle Size Analysis, was carried out. The comprehensive comparison of results from different techniques adds credibility to the reported findings.

Boundary of recent activation and seismicity on the platform part of Ukraine

The purpose of the work is to clarify the boundaries of the recent activation (RA) zone in the platform part of Ukraine. Such limitations are evident for the Alpine Carpathian geosyncline and the Hercynian-Cimmerian Scythian plate. For the East European Platform, the edge of the zone is determined by the magnitude of the mantle gravity anomaly. This option gives a more definite result than the previously used complex of geological and geophysical data. The applied technique, in addition to the usual two-dimensional density modeling along DSS profiles, includes the processing of the obtained data by kri­ging. This allows you to fill in the area between the profiles and trace the detailed shape of the zone boundary. The magnitude of the anomaly at the edge of the active region was calculated. In the center of the zone, the studied perturbation (the difference between the gravitational effect of the crust and the observed field) is 30—40 mGl, at the edge — 20 mGl. An estimate of the error in determining the anomaly was made, it turned out to be at the level of 10 mGl. The value at the boundary is less than the tripled error. In this situation, a special procedure is needed to detect spatial variations in the position of the boundary. For this, for the first time, two of its calculation options were used: using kri­ging and using artificial profiles located between the real ones. The crustal sections under the artificial ones were average between the real ones, the observed field on the artificial profile was used. An average difference in the boundary position of 13 km was obtained. The practically important question of the ecological consequences of manifestations of activity in the RA zone on the platform is considered. Seismicity does not pose a significant threat, as does the release of helium with anomalous isotopy and hydrogen. Catastrophes happen precisely during the exploitation of gas and coal deposits, and not as a result of the natural evolution of these objects.

Subcooled forced convection boiling flow measured using high-resolution techniques at the COSMOS-L facility and accompanying CFD simulation employing an interface-tracking scheme

This paper describes state-of-the-art experimental measurement techniques and computational fluid dynamics methods applied to subcooled forced convection boiling flow at 2-3 bar, both featuring high spatial- and time-resolution measurements. The objectives are: (i) to provide the measured boiling flow data, e.g. average and RMS profiles of axial and radial velocities and bubble size distributions, and (ii) to evaluate the capabilities of state-of-the-art numerical simulation techniques to model the observed phenomena, by comparison between measured data and numerical prediction. In the experiment, the bubble velocities in the axial and radial directions were measured using laser Doppler anemometry, and the bubble size was estimated by a shadowgraphy method. The simulation results, incorporating an interface capturing technique, showed good agreement with measurement in terms of the wall temperature and axial velocity, but underestimation of the radial velocity, and overestimation of bubble sizes. The paper shows that the discrepancies between measurement and simulation may be traced to: (i) the uncertainties in the nucleation site density model; (ii) too coarse a grid being adopted, which is not able to resolve the thermal boundary layer around the bubbles; and (iii) spurious numerical bubble coalescence, a feature not seen in the experiment. The mechanism of bubble lift-off from the hot surface, without any sliding motion, as observed in the experiment, is discussed in detail, based on the simulation results, and the condensation on the bubble cap as the bubble is ‘sucked’ into the bulk flow is also examined.

Analysis of Linkage between Long-Term Morphological Spatial Pattern Analysis and Vegetation Carbon Storage of Forests in Hunan, China

The carbon sequestration of forest ecosystems plays a pivotal role in constraining global warming and mitigating climate change. The landscape pattern of forests is being altered due to the combined effects of climate change and human interference. Furthermore, the relationship between forest pattern changes and carbon storage distribution in a long time series remains unclear. Therefore, it is necessary to examine the relationship between forest patterns and carbon density, investigating the variations and similarities in the changes in carbon density across different modes of pattern change over time, and suggestions for forest planning were provided from a perspective focused on pattern change to enhance carbon storage. The Google Earth Engine (GEE) platform’s random forest model was used to map the spatial distribution of forests in Hunan Province for 1996 and 2020, followed by analyzing the correlation between the changes in forest patterns using the morphological spatial pattern analysis (MSPA) and carbon density simulated by the model. Results show that the net growth rate ((area in 2020-area in 1996)/area in 2020) of the forest in Hunan increased 26.76% between 1996 and 2020. The importance scores for the decade average temperature, short-wave length infrared band 1 (SWIR-1), and slope were the highest metrics in the model of carbon density, and were 0.127, 0.107 and 0.089, respectively. The vegetation carbon storage in Hunan Province increased by 31.02 Tg, from 545.91 Tg to 576.93 Tg in 25 years. This study demonstrates that vegetation carbon storage is influenced by the pattern type in both newly established and pre-existing forests (p < 0.05). The findings of this study offer empirical evidence to support forest management strategies targeted at enhancing carbon sequestration.

Analytical Solution for Electromagnetic Performance Analysis of Permanent Magnet Synchronous Motor with a Parallel Magnetized Cylindrical Permanent Magnet

High-speed direct-drive permanent magnet synchronous motors (PMSMs), supported by elastic foil gas bearings, have broad applications, such as in microcompressors. However, some problems remain to be solved for the electrical performance analysis of PMSMs. For example, there is presently no related analytical model that can be used in rotor dynamics expression for this type of PMSM. This study aimed to establish theoretical models for electromagnetic force density and torque. The process involved both theoretical and experimental research. The analytic models of air gap magnetic density, electromagnetic force density, and electromagnetic performance were established for a PMSM with a parallel magnetized cylindrical permanent magnet. The analytic calculation was conducted, and the results of the analytic model were obtained. The analytical model of the electromagnetic torque and force can be applied in theoretical research on rotor dynamics. The model provides a theoretical basis and method for studying the influence of the electromagnetic load on rotor dynamics. A finite element simulation analysis of the electrical performance of the PMSM was carried out. An electrical performance experiment was conducted. The deviation between the experimental result and the theoretical value was less than 4%. This result indicated that the analytic models could be used in a dynamics analysis of compressors that are directly driven by a PMSM for application in engineering and industrial contexts.

Drag-based analytical optimal de-orbiting guidance from low earth orbit via Deep Neural Networks

Controlled de-orbiting is crucial for a low earth orbiting (LEO) satellite or capsule intending to land in a desired location and to prevent damage to people and property on the ground caused by debris. Drag modulation is one possible mechanism to control de-orbiting, exploiting atmospheric drag variation to reduce the necessary orbital energy from the initial conditions to the re-entry interface. In this context, this paper proposes a novel targeted de-orbiting Artificial Neural Network (ANN) and drag-based guidance algorithm for a LEO artificial satellite. It relies on the combination of empirical and analytical relations with Deep Neural Networks (DNNs) to estimate the main parameters of the optimal control law in real time. The training set is composed of several optimal control solutions generated with a previously developed optimal control algorithm, exploiting the formulation in equinoctial modified orbital parameters to manage the large time scale of the problem and the computational cost. An innovative procedure for the training set generation led to the achievement of general results with a limited number of samples. The successful outcome of the guidance algorithm on over 1000 cases demonstrates its robustness and generalization of results. To conclude, a Linear Quadratic Regulator (LQR) feedback control is applied to one case to deal with a more realistic and uncertain density model.

Gravity-induced ice compaction and subsurface porosity on icy moons

Our understanding of the surface porosity of icy moons and its evolution with depth remains limited, including the precise scale at which ice compaction occurs under self-weight pressure. This parameter is of crucial interest for the correct interpretation of current remote sensing data (spectroscopy in the visible, infrared to passive microwave) but also for planetary exploration when designing a lander, a rover or a cryobot. In situ exploration of the ice crust would require knowledge about subsurface porosity. This study employs a compaction model solely driven by overburden pressure based on prior research. The formulation for density as a function of depth, incorporates an essential parameter: the ice compaction coefficient. To determine this coefficient, we fit our depth-dependent density model to existing data obtained from Earth-based measurements of ice cores in Antarctica and North Greenland. Our results yield a typical lengthscale for ice compaction on Earth of approximately 20.1±0.6m , consistent with the existing literature. We apply the model to Europa, which due to its lower gravity, has a typical ice compaction scale of 150±4m, when assuming an Earth-like compaction coefficient. We compare it with the depths scanned by current spaceborne data and find that porosity can be considered constant when accounting only for gravity-induced compaction.

A comparative study in estimating of crystallite sizes of synthesized and natural hydroxyapatites using Scherrer Method, Williamson-Hall model, Size-Strain Plot and Halder-Wagner Method

In this study, an approach was made to create hydroxyapatite (HAp) crystals on nanoscale ranges from CaCO3 and H3PO4 utilizing a wet chemical process. Both fish and cow bones were used effectively for making bone powder as the natural sources of HAp. X-ray diffraction approach proved that powdered bone and HAp were in the crystalline phase. The determination of crystallite size in synthesized HAp crystals and bone powders was investigated using a range of techniques, including Linear Straight-Line Method (LSLM), Williamson-Hall Method (WHM), Size-Strain Plot (SSP), Halder-Wagner Method (HWM), and Sahadat-Scherrer Method (SSM). We also employed different Williamson-Hall models to quantify the stress and strain of the powdered materials for all diffraction peaks, including the Uniform Deformation Model (UDM), Uniform Stress Deformation Model (USDM), and Uniform Deformation Energy Density Model (UDEDM). Williamson-Hall is regarded as the best approach out of all of them since it was used to find the energy density, lattice stress, lattice strain, and crystallite size of the formed crystal. All the results from each model were compared to check the variation of analyzed data. The obtained results confirmed that there was an interrelation between the particle sizes of the Size Strain Plot and the Williamson-Hall method.

The invisible black widow PSR J1720-0534: implications for the electron density towards the North Polar Spur

Abstract Radio emission from pulsars can be used to map out their distances through dispersion measure (DM), which quantifies the amount of radio pulse dispersion. However, this method relies on accurately modeling the free electron density in the line of sight. Here, we present a detailed study of the multi-wavelength emission from PSR J1720-0534, a black widow compact binary millisecond pulsar discovered in 2021, which the latest electron density model of the Galaxy (YMW16; Yao et al. 2017) places at only 191 pc. We obtained and analyzed deep multi-wavelength observations in the γ-ray (Fermi-LAT, 2008-2022), optical (LCO, 2.7-h), near-infrared (NOT, 3.5-h) and X-ray (Swift-XRT, 10 ksec) bands. We found no significant detection of γ-ray, optical, near-infrared, or X-ray counterparts around the radio-timing position of PSR J1720-0534, which we thus nickname ‘the invisible black widow’. Employing the most constraining near-infrared limit (J > 23.4 mag), we established a lower limit on the source distance, d > 1.1 kpc, assuming conservative properties for the black widow companion star. This distance lower limit differs drastically (by a factor of more than five) from the YMW16 DM distance estimate. We attribute this difference to the inclusion in the YMW16 model of a large and dense component towards the North Polar Spur. Considering our results and recent parallax distances to other pulsars in this direction, we argue that such a local and large component in the electron density model of the Galaxy is unnecessary.

Estimating the contribution of thermal processes to generation of seismicity in the junction zone of the Chuya Depression and the Kyrgyz Ridge of the Northern Tien Shan

This paper presents the attempt to estimate the contribution of thermal processes in the Earth’s crust to the generation of seismicity by the example of the seismically active region of the Chuya depression and the Kyrgyz ridge of the Northern Tien Shan. For this purpose, we use the models of temperature, petrophysical characteristics, and elastic moduli constructed in the previous works. The silica content model based on seismic tomography data is used to build a lithotype model. The constructed thermal conductivity model is utilized, along with the temperature model, to create a depth model of heat flow density. Based on the density, elastic moduli, and temperature models, the shear and thermoelastic stress models in the rocks are constructed. Their comparison with the distribution of earthquake hypocenters suggests that on the scale of the seismically active zone of the Chuya depression and the Kyrgyz ridge of the Northern Tien Shan, seismicity is mainly determined by the thermomechanical effect resulting from the hot ascending flow of acid magma from the upper mantle beneath the Muyunkum–Narat median mass.

Building an ab initio solvated DNA model using Euclidean neural networks.

Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecules in solution due to the need to include large numbers of solvent molecules. We present a machine-learned electron density model based on a Euclidean neural network framework that includes a built-in understanding of equivariance to model explicitly solvated double-stranded DNA. By training the machine learning model using molecular fragments that sample the key DNA and solvent interactions, we show that the model predicts electron densities of arbitrary systems of solvated DNA accurately, resolves polarization effects that are neglected by classical force fields, and captures the physics of the DNA-solvent interaction at the ab initio level.

Phase fluctuations-induced bit error ratio of deep-space optical communication systems during superior solar conjunction

Deep-space optical communication has garnered increasing attention for its high data transfer rate, wide bandwidth, and high transmission speed. However, coronal plasma turbulence severely degrades optical signals during superior solar conjunction. In this study, we introduce the models for plasma density and generalized non-Kolmogorov turbulence power spectrum. Based on these models, we derive the variance of the phase fluctuations with the assistance of the Rytov theory in the weak turbulence regime involving various variables, such as turbulence outer scale, spectral index, relative fluctuation factor, and wavelength. Subsequently, we evaluate the bit error ratio (BER) performance of the deep-space optical communication system, considering phase fluctuations and intensity scintillations, under binary phase shift keying modulation. Numerical calculations reveal that small heliocentric distance, large relative fluctuation factor and spectral index, could induce severe phase fluctuations and high BER. Fortunately, the effects of the plasma irregularities on the BER performance can be mitigated by short optical wavelength under large outer scale.

A constrained 3D gravity inversion for complex density distributions: Application to Brazil rifted continental margin

Gravity inversion is a highly effective method for investigating regional geological structures, and this paper proposes an optimization scheme for constrained three-dimensional (3D) gravity inversion to obtain a 3D density model, utilizing prior geological and geophysics information. Specifically, the proposed method enhances deep structural imaging resolution and minimizes false structures by progressively inverting deep and shallow-density structures using long and short-wavelength signals of gravity anomaly with prior information. The scheme is applied in the southeast passive continental margin of Brazil, and the results show that the density model is consistent with the previous reflection and refraction seismic data. Moreover, the 3D density model reveals several insights: (i) The Abimael Ridge (AR) and the São Paulo Plateau (SPP) exhibit thin crustal thickness (∼5–7 km) indicative of proto-oceanic crust. The SPP and AR area crustal thinning may be related to an aborted opposing rift propagator pair. (ii) The rifting modes of Santos and Campos Basins differ significantly. Campos Basin exhibits a depth-dependent lithospheric stretching model with a relatively intact upper crust. In contrast, Santos Basin shows a highly brittle upper crust that is partially thinned and, in some regions, even absent under the far-field effect of spreading failed rifts, while the lower crust remains relatively intact. Moreover, the upper crustal stretching factor is about five to ten higher than the lower crustal. Thus, the constrained 3D gravity inversion scheme provides a new avenue for continental rifted margin geological structure studies.

Microstructural, optical, and morphological investigations of SnO2 nanomaterials grown by microwave assisted sol gel method

Tin Oxide (SnO2) nanomaterials were grown using the microwave-assisted sol–gel method at different concentrations of tin precursor (namely 0.05, 0.1, 0.2, and 0.3 M). Stannous chloride is used as a Sn precursor. Liquid ammonia was used to maintain the pH in the range of 12–13. Synthesis was carried out in an aqueous medium using a Teflon container in a microwave oven for 1 hour. Precipitate was annealed in ambient air for 600oC. Structural, optical, and morphological investigations were done. XRD reveals the growth of the tetragonal phase of SnO2. The prominent presence of (110), (101), and (211) reflections was noticed at 26.6, 33.7, and 52 two-theta values. Tin oxide is transparent in the visible region of the electromagnetic spectrum. However, several attempts have been made to decrease the visible blindness of tin oxide. The band gap is a property of nanomaterials that can tailor their application in the optoelectronic field. Band gap and crystallite size show a prominent relationship in the nano-domain. Strain was not considered while calculating crystallite size using the Scherrer formula. In this investigation, we have measured the crystallite size and other structural features such as strain, stress, deformation energy, dislocation de\ sity, etc using the W-H plot method. All modified models of the W-H method have been utilized for this measurement. A comparative and comprehensive study of structural features was carried out using the Scherrer method, the Williamson–Hall method, and all its modified models. The crystallite size measured by the Scherrer method and various models of the W-H method shows a peak at 0.2 M concentration. Crystallite size plots of various modified W-H methods show similar trends, followed by the Scherrer plot. Strain calculated by Brag’s theory as well as all modified W-H depicts similar behaviour upon changing the concentration. Globular agglomerated morphology was revealed by scanning electron microscopy (SEM). The presence of tin (Sn) and oxygen (O) was confirmed by energy dispersive x-ray spectroscopy. The band gap was obtained using the Tauc theory, which portrays variation in the range of 3.4 to 3.6 eV.

A Bayesian spatially explicit estimation of daily egg production: application to anchovy in the Bay of Biscay

Biomass estimates of fish resources by the daily egg production method (DEPM) are sensitive to the high variability of the daily egg production ( P0) and egg mortality (Z) in space. This work presents a Bayesian approach to estimate these parameters. A prior distribution of Z based on literature serves to overcome the biologically implausible Z estimates that can result from frequentist approaches. In addition to the classical estimation of a single P0 over the spawning area, the Bayesian framework allows also the modelling of egg densities in space, by including either spatial random effects, smoothing functions, or kriging like models, providing insights into the spatial variability of P0. The Bayesian approach was applied to the Bay of Biscay anchovy DEPM surveys. Results showed that this Bayesian approximation solved the implausible Z problem resulting in tighter credible intervals of both P0 and Z. Overall, spatial models outperformed the non-spatial model in terms of goodness of fit and resulted in slightly different total production estimates across models for each year, with a moderate decrease on uncertainty estimates.

Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning

AbstractThermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low‐Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re‐entry predictions, orbital lifetime analysis, and space object cataloging. In this paper, we investigate the prediction accuracy of empirical density models (e.g., NRLMSISE‐00 and JB‐08) against black‐box machine learning (ML) models trained on precise orbit determination‐derived thermospheric density data (from CHAMP, GOCE, GRACE, SWARM‐A/B satellites). We show that by using the same inputs, the ML models we designed are capable of consistently improving the predictions with respect to state‐of‐the‐art empirical models by reducing the mean absolute percentage error (MAPE) in the thermospheric density estimation from the range of 40%–60% to approximately 20%. As a result of this work, we introduce Karman: an open‐source Python software package developed during this study. Karman provides functionalities to ingest and preprocess thermospheric density, solar irradiance, and geomagnetic input data for ML readiness. Additionally, it facilitates developing and training ML models on the aforementioned data and benchmarking their performance at different altitudes, geographic locations, times, and solar activity conditions. Through this contribution, we offer the scientific community a comprehensive tool for comparing and enhancing thermospheric density models using ML techniques.