Numerical Propagation Research Articles

Three-dimensional imaging in reflection phase microscopy with minimal axial scanning.

Reflection phase microscopy is a valuable tool for acquiring three-dimensional (3D) images of objects due to its capability of optical sectioning. The conventional method of constructing a 3D map is capturing 2D images at each depth with a mechanical scanning finer than the optical sectioning. This not only compromises sample stability but also slows down the acquisition process, imposing limitations on its practical applications. In this study, we utilized a reflection phase microscope to acquire 2D images at depth locations significantly spaced apart, far beyond the range of optical sectioning. By employing a numerical propagation, we successfully filled the information gap between the acquisition layers, and then constructed complete 3D maps of objects with substantially reduced number of axial scans. Our experimental results also demonstrated the effectiveness of this approach in enhancing imaging speed while maintaining the accuracy of the reconstructed 3D structures. This technique has the potential to improve the applicability of reflection phase microscopy in diverse fields such as bioimaging and material science.

Research on homogeneous information propagation in social network

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users’ interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks.

Uncertainty assessment method for thermal runaway propagation of lithium-ion battery pack

To some extent, the thermal runaway issue pertaining to lithium-ion battery still present challenging problems, to put the safety-related issue under control in high-power and high-energy applications. One of these challenging issues relates to the underpinning stochastic uncertainties associated with thermal runaway propagation. Therefore, this study proposes a methodology to evaluate the uncertainty of thermal runaway propagation in Li-ion battery modules. Firstly, a numerical thermal runaway propagation model is developed for phase change material-based thermal management system and verified by experiments. Subsequently, the Adaptive Kriging High-dimensional model representation method is employed to construct a surrogate model, followed by global sensitivity analysis to evaluate the impact of six different influencing factors on thermal runaway propagation. Finally, the random fluctuation of the critical factors affecting thermal runaway propagation is integrated to analyze and quantify the uncertainty. The results show that the state of charge, the thermal conductivity of phase change material, and inter-cell spacing are critical factors affecting thermal runaway propagation. With the system parameters proposed in this paper, the critical state of charge range that significantly impacts the uncertainty of thermal runaway propagation is approximately 40% to 55%. The analysis of the propagation results and the cell temperature indicates that the highest level of uncertainty occurs at around 45% state of charge. In this case, the thermal runaway propagation is significantly impacted by changes in key factors, and the battery temperature exhibits high fluctuation levels. Furthermore, although there is a 59% likelihood of no propagation, the potential damage to all batteries becomes exceedingly significant once thermal runaway propagation occurs. The method proposed in this study effectively evaluates the uncertainty of thermal runaway propagation in battery systems. It identifies the causes of the uncertainty, which can be used to implement effective suppression measures to enhance safety.

Scalable angular spectrum propagation

Coherent field propagation is an essential computational tool in optics with applications ranging from computational optics and optical design to iterative field reconstructions. An improvement in the computational speed of current propagation methods is therefore highly desired. We describe a scalable angular spectrum (SAS) algorithm with zoom capability for numerical propagation of scalar wave fields in homogeneous media. It allows for propagation models where the destination pixel pitch is larger than the source pixel pitch, requires a computational complexity proportional to the cost of three successive fast Fourier transform operations of the input field, and it is valid for high numerical aperture (NA) propagation geometries. We find that SAS propagation approaches the precision of the computationally far more expensive angular spectrum method in conjunction with zero-padding. This was computationally confirmed by propagation examples. Finally, we discuss the validity of the proposed SAS method, derive practical bandlimit criteria, and state a limit for the propagation distance. The scalability, efficiency, and accuracy at high NA of our proposed wave propagation algorithm yield benefits for a large variety of forward and inverse modeling problems with the ability to apply automatic differentiation.

Laser seeker design with multi-focal diffractive lens

Diffractive optical elements are increasingly favoured due to their ability to provide numerous design freedoms by adjusting the Fresnel regions. The design freedoms make it possible to meet multiple system functions in electro-optical systems simultaneously by using a single optical element. This research introduces a novel laser seeker design with a multi-focal diffractive lens that enhances a laser seeker’s measurement sensitivity and linear measurement range. The development of the multi-focal combined lens, featuring two distinct regions with unique focal lengths, aims to simultaneously fulfill two system functions within the laser seeker. The central region of the lens is intended for adjusting the linear measurement range, while the outer region is utilized to regulate the measurement sensitivity of the seeker. The numerical optical wave propagation method was employed to simulate the behaviour of the laser seeker with a multi-focal diffractive lens, and the combined diffractive lens was compared to an ideal lens in the scope of laser seeker application. The simulation results indicate that the measurement sensitivity of the seeker has been increased between 0° to ±3° angular incidence, and the linear measurement range has been increased from ±18° to ±26°. The findings of this study contribute to the advancement of multi-focal diffractive lenses, which fulfil multiple functions within an electro-optical system concurrently.

Reduced Scaling Real-Time Coupled Cluster Theory.

Real-time coupled cluster (CC) methods have several advantages over their frequency-domain counterparts, namely, response and equation of motion CC theories. Broadband spectra, strong fields, and pulse manipulation allow for the simulation of complex spectroscopies that are unreachable using frequency-domain approaches. Due to the high-order polynomial scaling, the required numerical time propagation of the CC residual expressions is a computationally demanding process. This scaling may be reduced by local correlation schemes, which aim to reduce the size of the (virtual) orbital space by truncation according to user-defined parameters. We present the first application of local correlation to real-time CC. As in previous studies of locally correlated frequency-domain CC, traditional local correlation schemes are of limited utility for field-dependent properties; however, a perturbation-aware scheme proves promising. A detailed analysis of the amplitude dynamics suggests that the main challenge is a strong time dependence of the wave function sparsity.

Acoustic characteristics of the seismic airgun sound propagation in the East Siberian Sea

A passive acoustic monitoring has been conducted by Korea Polar Research Institute (KOPRI) since 2017 in the East Siberian Sea using an autonomous passive acoustic recorder. Seismic airgun sounds, generated from the multichannel seismic survey system of the ice-breaking research vessel Araon, were measured from 18.6 km to 164.2 km in September 2019. During the survey, the R/V Araon maintained the ship’s trajectory at a constant heading and speed to obtain useful acoustic data for investigating the acoustic propagation properties with a distance between the source and receiver in shallow water. The decrease in sound pressure levels as a function of distance was compared with the least-square regression curves and numerical propagation modeling based on the parabolic equation. The broadband sounds in a low-frequency range were propagated over long distances with distinctive characteristics such as precursor arrivals, modal dispersion, and rapid decrease in spectrum level at low frequencies. Our results will be discussed in comparison with the sediment structure and geological history of the measurement region. [This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (20210605, Korea-Arctic Ocean Warming and Response of Ecosystem, KOPRI and 20210632, Survey of Geology and Seabed Environmental Change in the Arctic Seas, KOPRI).]

On coherently processing multiple arrays in underwater environments with spatial coherence losses from random medium effects and sensor position uncertainties

Under ideal circumstances coherently beamforming multiple distributed arrays should provide improvements over the standard approach of processing the arrays individually and combining them incoherently. These include improved directivity and higher gain. However, two major challenges when coherently processing spatially separated arrays in a real ocean are dealing with coherence losses or wave front distortions caused by random medium effects like internal waves and uncertainties in sensor locations. Some of the consequences are degraded beampatterns and poorer array directivity. In this paper, we perform a simulation-based study utilizing numerical propagation models along with theory examining the effects of wave front distortions induced by random medium effects like internal waves and sensor position uncertainties on coherently beamforming distributed arrays in representative ocean waveguides. Realistic realizations of wave front distortions are generated from actual internal wave measurements and models. The sensitivities of coherent processing to these effects are characterized by resultant beampatterns and losses in array directivity along with a quantification of the tolerances.

A simplified approach for efficiently simulating submarine slump generated tsunamis

A simplified approach was proposed to efficiently simulate the tsunamis generated by a submarine slump. The landslide tsunami generation process was simulated using a long-wave model, simplifying the wave generation problem as a slump traveling down a plane slope with a prescribed trajectory. As a result, the landslide tsunami generation process was parameterized by 11 input parameters. The wave profile at the end of the wave generation process can then be specified as the initial conditions in any numerical tsunami propagation model to study the subsequent tsunami propagation. To demonstrate the capability of this new landslide tsunami generation approach, we used it in combination with an existing Boussinesq wave solver to simulate the 1998 Papua New Guinea landslide tsunami. The results based on the newly calculated physics-based wave profile compare reasonably well with field measurements and the results based on an existing tuning-based wave profile. Sensitivity tests were performed to examine the sensitivity of the runup results to each of the 11 input parameters. Six sets of 100-case Monte Carlo experiments were conducted to investigate the propagation of uncertainty from the input parameters to the runup results. Runup uncertainty was found to be approximately 1.5 times the parameter uncertainty, highlighting the uncertain nature of landslide tsunamis.

Temporal dispersion correction for wave‐propagation modelling with a series approach

AbstractTemporal dispersion correction of second‐order finite‐difference time stepping for numerical wave propagation modelling exploits the fact that the discrete operator is exact but for the wrong frequencies. Mapping recorded traces to the correct frequencies removes the numerical error. Most of the implementations employ forward and inverse Fourier transforms. Here, it is noted that these can be replaced by a series expansion involving higher time derivatives of the data. Its implementation by higher‐order finite differencing can be sensitive to numerical noise, but this can be suppressed by enlarging the stencil. Tests with the finite‐element method on a homogeneous acoustic problem with an exact solution show that the method can achieve the same accuracy as higher‐order time stepping, similar to that obtained with Fourier transforms. The same holds for an inhomogeneous problem with topography where the solution on a very fine mesh is used as reference. The series approach costs less than dispersion correction with the Fourier method and can be used on the fly during the time stepping. It does, however, require a wavelet that is sufficiently many times differentiable in time.

Prediction of the internal structure of a lithium-ion battery using a single ultrasound wave response

This paper describes a means to predict the internal structure of a lithium-ion battery from the response of an ultrasonic pulse, using a genetic algorithm. Lithium-ion batteries are sealed components and the internal states of the cell such as charge, health, and presence of structural defects are difficult to measure. Ultrasonic inspection of lithium-ion batteries is a recent and growing area of research. Reflected and transmitted ultrasound pulses are proposed as a non-invasive means of gaining insights into the internal structure and changes within the closed body of a cell. However, the multiple layers present in a lithium-ion cell are problematic when attempting to interpret waveforms as many internal reflections superimpose. Attributing specific features of a cell to wave characteristics is challenging.In this work a genetic algorithm has been developed as a means to reverse engineer a single ultrasound wave response to predict the internal layered structure of a lithium-ion battery cell. A first randomised guess at the layered structure is made. A numerical wave propagation model is used to predict the ultrasound waveform associated with that structure. This waveform is then compared with a measured or reference waveform to establish its fitness. The layered structure is generationally mutated until the predicted waveforms converge on the reference signal. As this occurs the predicted layered body reveals insights into the cell structure under inspection.Initially, the algorithm was tested against an idealised model battery and its predicted waveform, giving a model-model verification. Further, experimental ultrasonic reflection signals were captured from small capacity lithium-ion cells. Estimations of layer structure predicted by the model were compared with CT-scans of the cells to assess performance. The genetic algorithm was found to be effective in converging the predicted wave response to the reference signal and creating accurate battery structures. It was shown that only part of the waveform was required to generate accurate predictions, which is helpful in avoiding parts of the signal contaminated by near field transducer effects.It was demonstrated that the genetic algorithm can predict material wave speed to 40–1100 m/s (3–29 %) accuracy when battery layer geometry is provided; and thicknesses to within approximately 0.2–7.5 μm (1–13 %) when material properties are provided. Providing the genetic algorithm with parameter constraints; either the layer topology and/or the material properties, substantially improved predictions to estimate the wave speeds on average to approximately ±50 m/s (3–4 %) and the layer thicknesses ±5 μm (7–8 %).This raises the possibility of the use of this approach to predict state of charge when the battery construction is known, or the presence of internal defects and damage to a known battery material composition.

Uncertainty analysis of gamma-ray densitometry applied for gas flow modulation technique in bubble columns

The gas flow modulation technique is a recently proposed approach for measuring the axial gas dispersion coefficient in bubble columns. This study presents a quantitative analysis of the experimental uncertainty associated with gamma-ray densitometry and ensemble-averaging of the data. The considered uncertainty sources are the statistics of the photon counting process, a mismatch between the modelled and the real radiation propagation due to the spatial extent of the detector, and a potential mismatch between modulation and sampling frequencies. The analysis is based on a numerical gamma-ray propagation model and a Monte Carlo approach to account for statistical uncertainty. The proposed algorithm supports the selection of an optimal total scanning time based on detector size, modulation parameters, involved fluids and column and source parameters. The analysis reveals that a mismatch between the modulation and sampling frequencies is most critical while the impact of the other considered uncertainty sources is rather marginal.

Flash-profilometry: fullfield lensless acquisition of spectral holograms for coherence scanning profilometry.

Flash-profilometry is a novel measurement approach based on the fullfield lensless acquisition of spectral holograms. It is based on spectral sampling of the mutual coherence function and the subsequent calculation of its propagation along the optical axis several times the depth-of-field. Numerical propagation of the entire coherence function, rather than solely the complex amplitude, allows to digitally reproduce a complete scanning white-light interferometric (WLI) measurement. Hence, the corresponding 3D surface profiling system presented here achieves precision in the low nanometer range along an axial measurement range of several hundred micrometers. Due to the lensless setup, it is compact, immune against dispersion effects and lightweight. Additionally, because of the spectral sampling approach, it is faster than conventional coherence scanning WLI and robust against mechanical distortions, such as vibrations and rigid body movements. Flash-profilometry is therefore suitable for a wide range of applications, such as surface metrology, optical inspection, and material science and appears to be particularly suitable for a direct integration into production environments.

Alarm reduction and root cause inference based on association mining in communication network

With the growing demand for data computation and communication, the size and complexity of communication networks have grown significantly. However, due to hardware and software problems, in a large-scale communication network (e.g., telecommunication network), the daily alarm events are massive, e.g., millions of alarms occur in a serious failure, which contains crucial information such as the time, content, and device of exceptions. With the expansion of the communication network, the number of components and their interactions become more complex, leading to numerous alarm events and complex alarm propagation. Moreover, these alarm events are redundant and consume much effort to resolve. To reduce alarms and pinpoint root causes from them, we propose a data-driven and unsupervised alarm analysis framework, which can effectively compress massive alarm events and improve the efficiency of root cause localization. In our framework, an offline learning procedure obtains results of association reduction based on a period of historical alarms. Then, an online analysis procedure matches and compresses real-time alarms and generates root cause groups. The evaluation is based on real communication network alarms from telecom operators, and the results show that our method can associate and reduce communication network alarms with an accuracy of more than 91%, reducing more than 62% of redundant alarms. In addition, we validate it on fault data coming from a microservices system, and it achieves an accuracy of 95% in root cause location. Compared with existing methods, the proposed method is more suitable for operation and maintenance analysis in communication networks.

Propagation from meteorological to hydrological drought in the Horn of Africa using both standardized and threshold-based indices

Abstract. There have been numerous drought propagation studies in data-rich countries, but not much has been done for data-poor regions (such as the Horn of Africa, HOA). In this study, we characterize meteorological, soil moisture and hydrological drought and the propagation from one to the other for 318 catchments in the HOA to improve understanding of the spatial variability in the drought hazard. We calculate the standardized precipitation index (SPI), standardized soil moisture index (SSMI) and standardized streamflow index (SSI). In addition, we use the variable threshold method to calculate the duration of drought below a predefined percentile threshold for precipitation, soil moisture and discharge. The relationship between meteorological and soil moisture drought is investigated by finding the SPI accumulation period that has the highest correlation between SPI and SSMI, and the relationship between meteorological and hydrological drought is analysed by the SPI accumulation period that has the highest correlation between SPI and SSI time series. Additionally, we calculated these relationships with the ratio between the threshold-based meteorological-drought duration and soil moisture drought duration and the relation between threshold-based meteorological-drought duration and streamflow drought duration. Finally, we investigate the influence of climate and catchment characteristics on these propagation metrics. The results show that (1) the propagation from SPI to SSMI and the mean drought duration ratio of meteorological to soil moisture drought (P / SM) are mainly influenced by soil properties and vegetation, with the short accumulation periods (1 to 4 months) of SPI in catchments with arable land, high mean annual precipitation, and low sand and silt content, while longer accumulations (5 to 7 months) are in catchments with low mean annual upstream precipitation and shrub vegetation; (2) the propagation from SPI to SSI and precipitation-to-streamflow duration ratio are highly influenced by the climate and catchment control, i.e. geology, elevation and land cover, with the short accumulation times in catchments with high annual precipitation, volcanic permeable geology and cropland and the longer accumulations in catchments with low annual precipitation, sedimentary rocks and shrubland; and (3) the influence of mean annual upstream precipitation is more important for the propagation from SPI to SSI than from SPI to SSMI. Additionally, precipitation accumulation periods of approximately 1 to 4 months in wet western areas of the HOA and of approximately 5 to 7 months in the dryland regions are found. This can guide forecasting and management efforts as different drought metrics are thus of importance in different regions.

Research on Enhanced Orbit Prediction Techniques Utilizing Multiple Sets of Two-Line Element

Acquiring accurate space object orbits is crucial for many applications such as satellite tracking, space debris detection, and collision avoidance. The widely used two-line element (TLE) method estimates the position and velocity of objects in space, but its accuracy can be limited by various factors. A combination of multiple TLEs and advanced modeling techniques such as batch least squares differential correction and high-precision numerical propagators can significantly improve TLE accuracy and reliability, ensuring better space object surveillance. Previous studies analyzed additional factors that may influence TLE accuracy and evaluated the accuracy of Starlink TLE using precise ephemeris data from SpaceX. The results indicate that utilizing multiple TLEs for precise orbit determination can significantly enhance the performance of orbit prediction methods, particularly when compared to SGP4. By leveraging 10-day Starlink TLEs, the accuracy of 5-day predictions can be improved by approximately twofold. Additionally, producing two pseudo-observations within an orbital period near the TLE epoch yields the greatest effect on prediction accuracy, with this distribution of pseudo-observations increasing accuracy by approximately 10% compared to a uniform distribution. Further research can explore more data fusion and machine learning approaches to optimize operations in space.

Experimental and Modeled Output Characteristics of a Compact, Passively Q-Switched Tm:YLF Laser

We measure the output characteristics of a compact, laser diode end-pumped Tm:YLF laser, operated in continuous wave (CW) mode, or passively Q-switched (PQS) using various Cr2+:ZnS saturable absorbers. A numerical Finite-Difference Beam Propagation Method (FD-BPM) is developed and used to model the laser CW and pulsed performance. Good agreement between the experiment and simulated output is demonstrated. The 39 mm-long laser resonator utilizes a 20.2 mm-long Tm:YLF crystal, a 1908 nm volume Bragg grating (VBG) reflector to restrict the emission wavelength, and an intra-cavity negative lens to reduce the energy fluence in the saturable absorber. A laser utilizing a Cr2+:ZnS saturable absorber with 91.9% unsaturated transmission generates 9.8 mJ PQS pulses with 8.7 ns full-width-half-maximum (FWHM) pulse duration. Maximum average output powers of 6.1 W and 6.5 W are generated in PQS and CW operation, respectively.

Integrating modeled environmental variability into neural network training for underwater source localization.

Supervised machine learning (ML) is a powerful tool that has been applied to many fields of underwater acoustics, including acoustic inversion. ML algorithms depend on the existence of extensive labeled datasets, which are difficult to obtain for the task of underwater source localization. A feed-forward neural network (FNN) trained on imbalanced or biased data may end up suffering from a problem analogous to model mismatch in matched field processing (MFP), that is, producing incorrect results due to a difference between the environment sampled by the training data and the actual environment. To overcome this issue, physical and numerical propagation models can act as data augmentation tools to compensate for the lack of comprehensive acoustic data. This paper examines how modeled data can be effectively used for training FNNs. Mismatch tests compare the output from a FNN and MFP and show that the network becomes more robust to various kinds of mismatches when trained on diverse environments. A systematic analysis of how the training dataset's variability impacts a FNN's localization performance on experimental data is carried out. Results show that networks trained with synthetic data achieve better and more robust performance than regular MFP when environment variability is taken into account.

Time‐domain scaled boundary perfectly matched layer for elastic wave propagation

AbstractA novel artificial boundary method called the scaled boundary perfectly matched layer (SBPML) is proposed for the transient analysis of elastic wave propagation in unbounded domains. This method constructs a generalized perfectly matched layer (PML) around the finite interior subdomain based on a coordinate transformation inspired by the scaled boundary finite element method (SBFEM). It can model the infinite exterior subdomains of heterogeneous materials, featuring both parallel and radial straight‐line physical surfaces and interfaces. In addition, generally‐shaped (even nonconvex‐shape) artificial boundaries can be used to provide high flexibility in choosing the geometry of the finite interior subdomain. Two coordinate mapping technologies, namely the modified scaled boundary coordinate transformation from the SBFEM and the complex coordinate stretching from the PML, are successively utilized to construct the proposed method. By introducing the integral of stress as an auxiliary variable, the resulting SBPML subdomain is represented in the time domain using a mixed displacement‐stress unsplit‐field formulation. This has the added benefit of allowing for easy incorporation into standard dynamic finite element methods. The performance of the SBPML is demonstrated through several numerical examples, including P‐SV and SH wave propagation problems occurring in unbounded domains with complex geometries and heterogeneous material properties. The numerical results are evaluated in comparison to both reference solution and existing artificial boundary methods, ultimately demonstrating the flexibility, robustness, accuracy, and stability of the proposed approach.

A method of using back propagation neural network to estimate orbital lifetime of LEO satellites

The “IADC space debris mitigation guidelines” requires that the post-mission orbit lifetime of low Earth orbiting (LEO) satellites should not be longer than 25 years, which is the key to debris mitigation. To analyze the degree of global compliance to this 25-year guideline, a fast and relatively accurate orbit life estimation algorithm is required. The traditional method uses numerical orbit propagation to accomplish the task, although its accuracy is high when a high-precision force model is used, the calculation is time-consuming. This paper proposes a new method for orbit lifetime estimation without relying on orbit propagation. A back propagation (BP) neural network is used to directly fit the relationship from the satellite parameters to the orbit lifetime. The space objects that have reentered the Earth’s atmosphere are selected from the catalog database of the US Space Surveillance Network (SSN), and are used to generate samples for network training. The genetic algorithm is combined with the traditional back propagation algorithm for the training of the neural network. The results show that for the specific application background of identifying whether a satellite complies with the 25-year guideline, the orbit lifetime estimation accuracy of the BP network meets the demand to some extent. Moreover, because it does not involve orbit propagation, the BP network has a high computational efficiency in estimating orbit lifetime, showing a good application prospect.