Intensive Computation Research Articles

Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor

<p><span>Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.</span></p>

CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, several previous studies have found that such models may be affected by miscalibration, an issue that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the C ontrastive L earning and A daptive KNN for P hotometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and $k$-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data, bypassing the intensive computation required for KNN. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, a deeper investigation on miscalibration for conventional deep learning is presented. We point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP.

A Methodological Framework for High-Resolution Surface Urban Heat Island Mapping: Integration of UAS Remote Sensing, GIS, and the Local Climate Zoning Concept

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria’s capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024.

Fault3DNnet: A lightweight 3D seismic fault detection network with bidirectional decoding

Fault detection from seismic data is a critical component of hydrocarbon exploration and production. In recent years, deep-learning techniques have made tremendous achievements and breakthroughs in seismic fault detection. Especially U-nets, represented by FaultSeg3D and its variants, are a dominant method for 3D deep-learning-based seismic fault interpretation. The incorporation of state-of-the-art network modules or training strategies with U-nets continuously improves the performance of fault detection for field seismic data, usually at the cost of increased computational intensity and hardware consumption. We design a lightweight bidirectional decoding network, Fault3DNnet, for fault detection from 3D seismic data volume. To make it simple and concise, our Fault3DNnet is equipped with basic conventional convolution and regular operation modules. The two decoding branches in Fault3DNnet could provide complementary information and improve fault detection performance. The superiority of Fault3DNnet is verified using publicly available synthetic and four field seismic data sets. The experimental results indicate that the method predicts fault structures with improved completeness and continuity compared with FaultSeg3D. Meanwhile, the parameter and computational quantity of the network are only 1/7 and 1/5 of FaultSeg3D, respectively. With the same graphics processing unit memory consumption, Fault3DNnet can handle the size of approximately six times the volume of 3D seismic data during inferencing as that of FaultSeg3D. It helps to improve prediction stability and decrease discontinuous artifacts at the stitching boundaries when predicting the large field data in chunks. In general, our Fault3DNnet delivers superior fault detection results while greatly reducing computational intensity and memory usage.

Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning

The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.

Two-Step Fifth-Order Efficient Jacobian-Free Iterative Method for Solving Nonlinear Systems

This article introduces a novel two-step fifth-order Jacobian-free iterative method aimed at efficiently solving systems of nonlinear equations. The method leverages the benefits of Jacobian-free approaches, utilizing divided differences to circumvent the computationally intensive calculation of Jacobian matrices. This adaptation significantly reduces computational overhead and simplifies the implementation process while maintaining high convergence rates. We demonstrate that this method achieves fifth-order convergence under specific parameter settings, with broad applicability across various types of nonlinear systems. The effectiveness of the proposed method is validated through a series of numerical experiments that confirm its superior performance in terms of accuracy and computational efficiency compared to existing methods.

Hourly electricity CO[formula omitted] intensity profiles based on the real operation of large-scale natural gas combined cycle cogeneration plants

The decarbonization of energy systems is a major challenge that requires complex cross-sectoral strategies that need to be supported by energy modeling. As many technologies rely on electricity, an accurate estimation of its CO2 intensity is of utmost importance for the reliability of modeling results. When electricity is generated from fossil sources, its CO2 intensity depends on several parameters. This research paper presents an hourly calculation of the CO2 intensity of power generation from natural gas combined cycles, based on real data from several years of operation of three plants. As these plants are also operated in combined heat and power mode, two alternative allocation methods are compared. The results confirm the variability of CO2 intensity based on the different operation strategies of the plants and the share of heat and electricity generated. The hourly analysis shows average values in the range of 230–250 gCO2/kWh in winter, rising to around 330–370 gCO2/kWh in summer. The real CO2 intensity profiles presented in this paper can be integrated into energy planning models to improve their ability to estimate the potential benefits of different decarbonization solutions by including the effect of the operational profiles over the day and the year.

Environmental life-cycle analysis of hydrogen technology pathways in the United States

Hydrogen is a zero-carbon energy carrier with potential to decarbonize industrial and transportation sectors, but its life-cycle greenhouse gas (GHG) emissions depend on its energy supply chain and carbon management measures (e.g., carbon capture and storage). Global support for clean hydrogen production and use has recently intensified. In the United States, Congress passed several laws that incentivize the production and use of renewable and low-carbon hydrogen, such as the Bipartisan Infrastructure Law (BIL) in 2021 and the Inflation Reduction Act (IRA) in 2022, which provides tax credits of up to $3/kg depending on the carbon intensity of the produced hydrogen. A comprehensive life-cycle accounting of GHG emissions associated with hydrogen production is needed to determine the carbon intensity of hydrogen throughout its value chain. In the United States, Argonne’s R&D GREET® (Greenhouse Gases, Regulated emissions, and Energy use in Technologies) model has been widely used for hydrogen carbon intensity calculations. This paper describes the major hydrogen technology pathways considered in the United States and provides data sources and carbon intensity results for each of the hydrogen production and delivery pathways using consistent system boundaries and most recent technology performance and supply chain data.

Risk factors, timing and frequency of occurrence of post-COVID syndrome in workers with intensive computer workload

According to leading experts of the World Health Organization, the threat of a new strain of SARS-CoV-2 coronavirus remains, which will lead to a sharp increase in morbidity and mortality. The severity of complications after COVID-19 depends not only on how severe the disease was, but also on other factors: genetic predisposition, age, concomitant diseases, and working conditions. This makes it urgent to conduct further scientific research on the ranking of the factors of the production environment that accelerate the spread of COVID-19. The study aims to determine the degree of occupational risk, taking into account the timing and frequency of manifestations of post-COVID syndrome (U09.9; U10.9 according to ICD–X) in specialists with intensive computer workload. In the period 2020–2024, the researchers conducted a survey at the workplaces of Kislovodsk enterprises and examined 556 people with an intensive computer load associated with servicing the flows of mass consumers during the pandemic and in the post-pandemic period. We have studied the timing and frequency of the occurrence of post-COVID syndrome, objectified by the degree of health risk factors in various categories of workers with intensive computer workload. The timing and frequency of seven post-COVID syndrome manifestations ranked (according to the degree of occupational health risk factors) (U09.9; U10.9 according to ICD-X) that occur in workers with intensive computer workload in the first two weeks after discharge from the hospital during hospitalization for COVID-19 (occupational health risk factors category A) or arising within three to four weeks after discharge from the hospital during hospitalization for COVID-19 (occupational health risk factors of category B). Based on statistical and clinical and hygienic data obtained in the period from 2020 to 2024, post-COVID manifestations in the gradations of "high" and "moderately high" occupational health risk for employees (n=556) with intensive computer load were ranked by the timing and frequency of occurrence of the named syndrome, as well as by the degree of risk of potential COVID-19 infections of the named groups of specialists associated with servicing mass consumer flows infected with constantly mutating representatives of enveloped RNA viruses. According to the degree of occupational risk to the health of persons with intensive computer workload, six groups of specialists associated with servicing mass consumer flows, potentially infected with COVID-19 and other representatives of enveloped RNA viruses, were ranked. Limitations. Post-COVID syndromes that occur within one to two and three to four weeks after discharge from the hospital have been studied. Ethics. Scientists have conducted research in accordance with the principles of biomedical ethics and approved by the local Ethics Committee of Izmerov Research Institute of Occupation Health, Moscow (Protocol No. 4 dated 04/14/2021). Each participant of the study submitted a voluntary written informed consent signed by him after explaining to him the potential risks and benefits.

Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model

Abstract Understanding the relationship between structural connectivity (SC) and functional connectivity (FC) of the human brain is an important goal of neuroscience. Highly detailed mathematical models of neural masses exist that can simulate the interactions between functional activity and structural wiring. These models are often complex and require intensive computation. Most importantly, they do not provide a direct or intuitive interpretation of this structure–function relationship. In this study, we employ the emerging concepts of spectral graph theory to obtain this mapping in terms of graph harmonics, which are eigenvectors of the structural graph’s Laplacian matrix. In order to imbue these harmonics with biophysical underpinnings, we leverage recent advances in parsimonious spectral graph modeling (SGM) of brain activity. Here, we show that such a model can indeed be cast in terms of graph harmonics, and can provide a closed-form prediction of FC in an arbitrary frequency band. The model requires only three global, spatially invariant parameters, yet is capable of generating rich FC patterns in different frequency bands. Only a few harmonics are sufficient to reproduce realistic FC patterns. We applied the method to predict FC obtained from pairwise magnitude coherence of source-reconstructed resting-state magnetoencephalography (MEG) recordings of 36 healthy subjects. To enable efficient model inference, we adopted a deep neural network-based Bayesian procedure called simulation-based inference. Using this tool, we were able to speedily infer not only the single most likely model parameters, but also their full posterior distributions. We also implemented several other benchmark methods relating SC to FC, including graph diffusion and coupled neural mass models. The present method was shown to give the best performance overall. Notably, we discovered that a single biophysical parameterization is capable of fitting FCs from all relevant frequency bands simultaneously, an aspect that did not receive adequate attention in prior computational studies.

Fast neural network inverse model to maximize throughput in ultra-wideband WDM systems

Ultra-wideband systems expand the optical bandwidth in wavelength-division multiplexed (WDM) systems to provide increased capacity using the existing fiber infrastructure. In ultra-wideband transmission, power is transferred from shorter-wavelength WDM channels to longer-wavelength WDM channels due to inelastic inter-channel stimulated Raman scattering. Thus, managing launch power is necessary to improve the overall data throughput. While the launch power optimization problem can be solved by the particle swarm optimization method it is sensitive to the objective value and requires intensive objective calculations. Hence, we first propose a fast and accurate data-driven deep neural network-based physical layer in this paper which can achieve 99%−100% throughput compared to the semi-analytical approach with more than 2 orders of magnitude improvement in computational time. To further reduce the computational time, we propose an iterative greedy algorithm enabled by the inverse model to well approximate a sub-optimal solution with less than 6% performance degradation but almost 3 orders of magnitude reduction in computational time.

Accurate nuclear quantum statistics on machine-learned classical effective potentials.

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD). In particular, we employ a machine-learned potential to accurately represent corrections to classical potentials, thereby significantly reducing the computational cost of simulating NQEs. We validate our approach using four distinct systems: Morse potential, Zundel cation, single water molecule, and bulk water. Our framework allows us to accurately compute position-dependent static properties, as demonstrated by the excellent agreement obtained between the machine-learned potential and computationally intensive PIMD calculations, even in the presence of strong NQEs. This approach opens the way to the development of transferable machine-learned potentials capable of accurately reproducing NQEs in a wide range of molecular systems.

AN OPTIMIZED BIDIRECTIONAL CONVOLUTIONAL RECURRENT NEURAL NETWORK ARCHITECTURE WITH GROUP-WISE ENHANCEMENT MECHANISM OF SENTIMENTS FOR THE PERSPECTIVE OF CUSTOMER REVIEW SUMMARIZATION

Customer reviews play pivotal roles in consumers’ purchase decisions, but the sheer volume of text data can be overwhelming. In existing system, while ensemble methods can enhance performance, the associated computational complexity and resource intensiveness should be carefully considered, and appropriate measures should be taken to address these challenges in the context of Customer Review Summarization. This study introduces a Particle Swarm Optimization (PSO) based optimized architecture for a Bidirectional Convolutional Recurrent Neural Network (BiCRNN) with a group-wise enhancement mechanism tailored for Customer Review Summarization and named as OBiCRNN. This model is designed for the perspective of customer review summarization, aiming to effectively capture sentiments and generate concise summaries. The integration of PSO optimizes the network parameters, enhancing the learning process. Feature extraction is done by Modified Principal Component Analysis (MPCA) which uses correlated feature sets and extracts most informative features for given datasets. BiCRNN utilizes bidirectional LSTM and GRU layers for comprehensive context understanding, while the group-wise enhancement mechanism categorizes sentiment-related features, amplifying essential sentiments and attenuating less relevant ones. With this novel approach, the architecture leverages both PSO and BiCRNN for an advanced framework in customer review summarization where outcomes demonstrate the effectiveness of the deep learning (DL) model in producing coherent and informative summaries, enhancing the accessibility of customer feedback for both consumers and businesses. The study contributes to the field of natural language processing (NLP) and customer sentiment analysis, offering a scalable solution for managing the wealth of information present in online customer reviews.

Slick: Modeling a Universe of Molecular Line Luminosities in Hydrodynamical Simulations

We present slick (the Scalable Line Intensity Computation Kit), a software package that calculates realistic CO, [C i], and [C ii] luminosities for clouds and galaxies formed in hydrodynamic simulations. Built on the radiative transfer code despotic, slick computes the thermal, radiative, and statistical equilibrium in concentric zones of model clouds, based on their physical properties and individual environments. We validate our results by applying slick to the high-resolution run of the Simba simulations, testing the derived luminosities against empirical and theoretical/analytic relations. To simulate the line emission from a universe of emitting clouds, we have incorporated random forest machine learning (ML) methods into our approach, allowing us to predict cosmologically evolving properties of CO, [C i], and [C ii] emission from galaxies such as luminosity functions. We tested this model in 100,000 gas particles, and 2500 galaxies, reaching an average accuracy of ∼99.8% for all lines. Finally, we present the first model light cones created with realistic and ML-predicted CO, [C i], and [C ii] luminosities in cosmological hydrodynamical simulations, from z = 0 to z = 10.

Numerical study on infrared radiation intensity and suppression methods of nozzle considering multi-component effects

Improving the accuracy of infrared intensity calculation in exhaust system and expanding infrared suppression methods are of great significance for enhancing aircraft stealth performanc. In this paper, the infrared radiation (IR) intensity (3–5 µm) of nozzle considering the influence of turbine and afterburner are numerically studied. Initially, the flow field characteristics are computed using commercial software, followed by ray tracing simulations using the reverse Monte Carlo method (RMCM) and transmission calculations using the multi-scale multi-group wideband k distribution model (MSMGWB). The wall emissivity is determined experimentally. This paper quantitatively analyzes the difference between infrared intensity obtained through traditional computational methods and that of the actual model, revealing the impact of low-emissivity coating and turbine cooling on the infrared intensity of the integrated model. It emphasizes the suppression effects and physical mechanisms of the heat shield flanging structure on the infrared characteristics of the integrated model. The results indicate significant differences between infrared intensity derived from traditional computational methods and that from actual models. The influence of the afterburner and turbine infrared characteristics must be considered in infrared numerical studies. When the computation model is a standalone nozzle, the infrared intensity can increase by up to 541.78 % and decrease by a maximum of 14.02 % compared to the integrated model compose of the turbine, afterburner, and nozzle. For the model composed of nozzle and afterburner, the infrared intensity can decrease by up to 10.5 % under non-swirl flow condition, without any increase observed. Compared with turbine wall, changing the emissivity of afterburner wall has more significant effect on infrared suppression of integrated calculation model. Reducing the emissivity of all afterburner walls from 0.7 to 0.1 significantly reduces the infrared characteristics in the range of 0° to 12.5°, while increasing it in the range of 12.5° to 45°. Applying a low-emissivity coating only to the high-temperature walls of the afterburner can inhibit the increase in emitted radiation on the wall, thereby eliminating the increase in infrared intensity mentioned above and reducing the infrared intensity by up to 39.14 %. Compared to low-emissivity coating technology for turbine, implementing cooling measures on turbine blades can effectively reduce the infrared intensity of the integrated model, achieving a maximum reduction of 19.7 % when the blade temperature is lowered by 240 K. Adding flanging structure to the heat shield in the convergent section alters the flow field structure near the nozzle walls, lowering wall temperature and thereby suppressing the infrared characteristics of the integrated model between 10° and 80°. This results in a maximum reduction in infrared intensity of 14.75 % with negligible impact on thrust coefficients. This is the angle range where afterburner low emissivity coatings and turbine cooling cannot achieve infrared suppression. This is of great significance for infrared stealth in large angle range. Combined with the above effective infrared suppression measures, the infrared intensity can be reduced by up to 49.42 %.

High-speed turbulent flows towards the exascale: STREAmS-2 porting and performance

Exascale High Performance Computing (HPC) represents a tremendous opportunity to push the boundaries of Computational Fluid Dynamics (CFD), but despite the consolidated trend towards the use of Graphics Processing Units (GPUs), programmability is still an issue. STREAmS-2 (Bernardini et al. Comput. Phys. Commun. 285 (2023) 108644) is a compressible solver for canonical wall-bounded turbulent flows capable of harvesting the potential of NVIDIA GPUs. Here we extend the already available CUDA Fortran backend with a novel HIP backend targeting AMD GPU architectures. The main implementation strategies are discussed along with a novel Python tool that can generate the HIP and CPU code versions allowing developers to focus their attention only on the CUDA Fortran backend. Single GPU performance is analysed focusing on NVIDIA A100 and AMD MI250x cards which are currently at the core of several HPC clusters. The gap between peak GPU performance and STREAmS-2 performance is found to be generally smaller for NVIDIA cards. Roofline analysis allows tracing this behavior to unexpectedly different computational intensities of the same kernel using the two cards. Additional single-GPU comparisons are performed to assess the impact of grid size, number of parallelized loops, thread masking and thread divergence. Parallel performance is measured on the two largest EuroHPC pre-exascale systems, LUMI (AMD GPUs) and Leonardo (NVIDIA GPUs). Strong scalability reveals more than 80% efficiency up to 16 nodes for Leonardo and up to 32 for LUMI. Weak scalability shows an impressive efficiency of over 95% up to the maximum number of nodes tested (256 for LUMI and 512 for Leonardo). This analysis shows that STREAmS-2 is the perfect candidate to fully exploit the power of current pre-exascale HPC systems in Europe, allowing users to simulate flows with over a trillion mesh points, thus reducing the gap between the Reynolds numbers achievable in high-fidelity simulations and those of real engineering applications.

Speech recognition inspired features for acoustic emission

For the application of acoustic emission methods in the field of condition monitoring, extracting meaningful information out of signals is an essential step in signal processing and mandatory for machine learning. However, when dealing with acoustic emission signals, the computational intensity of extracting features is becoming more important due to its large amount of data. The methodology in this study, hence, investigates established methods from descriptive statistics as typically used in acoustic emission and techniques commonly employed in speech recognition. Thereby, the objective is to improve the feature extraction of acoustic emission signals, emphasizing the synergies between the methods of both approaches. Extracting traditional acoustic emission features based on descriptive statistics alone usually yields robust features capturing the significant patterns within the time and frequency domain. However, traditional acoustic emission features may neglect more detailed information present in the signals due to averaging or mere evaluation of extreme values. To address this limitation, the methodology is extended by incorporating techniques from the field of speech recognition, such as applying window functions and digital filter banks to calculate the so-called cepstral coefficients. When comparing machine learning models that incorporate these cepstral coefficients alongside those that do not, this approach demonstrates that speech recognition techniques can result in more effective models. Here, the basic concept for speech recognition-based feature extraction and its implementation are presented, and necessary considerations and techniques are discussed, which are crucial for performant signal processing, particularly for real-time monitoring applications. The methodology is demonstrated, using acoustic emission signals derived from industrial-scale processes , such as e.g. milling, drilling or friction stir welding.

Spectroscopy, crystal-field, and transition intensity analyses of the C[formula omitted](O[formula omitted]) centre in Er[formula omitted] doped CaF2 crystals

Erbium ions in crystals show considerable promise for the technologies that will form the backbone of future networked quantum information technology. Despite advances in leveraging erbium’s fibre-compatible infrared transition for classical and quantum applications, the transitions are, in general, not well understood. We present detailed absorption and laser site-selective spectroscopy of the C3v(O2−) centre in CaF2:Er3+ as an interesting erbium site case study. The 4I15/2Z1→4I13/2Y1 transition has a low-temperature inhomogeneous linewidth of 1 GHz with hyperfine structure observable from the 167Er isotope. A parametrized crystal-field Hamiltonian is fitted to 34 energy levels and the two ground state magnetic splitting factors. The wavefunctions are used to perform a transition intensity analysis and electric-dipole parameters are fitted to absorption oscillator strengths. Simulated spectra for the 4I11/2→4I15/2 and 4I13/2→4I15/2 inter-multiplet transitions are in excellent agreement with the experimentally measured spectra. The 4I13/2 excited state lifetime is 25.0ms and the intensity calculation is in excellent agreement with this value.

The influence of the rotor spacing distance and rotating speed ratio on the power production of dual rotor wind turbines using a modified two-way interaction blade element momentum method

The present study aims to develop an accurate and fast improved blade element method (BEM) with two-way rotor-rotor interaction model which can account for two-way rotor-rotor interaction and the influence of rotor spacing while most previous BEM models for co-axis dual rotor wind turbines (DRWT) ignore the rear to front rotor interaction effect. A modified BEM model distinct from computationally intensive Computation Fluids Dynamics (CFD) model, is proposed for DRWTs. Validation of the present model is conducted by comparing it with experimental data from referenced literature for both co- and counter-rotating configuration. In our case study, for both co- and counter-rotating configurations, the power coefficient (CP) maps exhibit a double-hump shape instead of a single peak on the power output -λ curve observed in a single rotor wind turbine system. This characteristic may lead the DRWT to a local CPmax condition rather than the global CPmax while pursing the optimum operational condition. Although the counter-rotating DRWT can achieve a higher CPmax for most rotor spacing ratio (S/R) cases, it exhibits inferior power output than the single rotor for the S/R = 0.1 case. The present model can serve as a rapid and accurate model for configuring and control strategy evaluating DRWTs.

Model simplification to simulate groundwater recharge from a perched gravel-bed river

Gravel-bed rivers are an important source of groundwater recharge in some regions of the world. Their interactions with groundwater are complex and highly variable in space and time, with considerable water storage in the riverbed sediments. In losing river sections, where most of the groundwater recharge occurs, the river can be separated from the regional groundwater system by an unsaturated zone (i.e., perched). The complexity of groundwater–surface water interactions in these environments calls for the use of 3D fully integrated hydrological models to represent them, but their computational intensity limits their practicality for parameter inference, uncertainty quantification and regional scale problems. On the other hand, the simple groundwater–surface water exchange functions currently implemented in regional scale groundwater models are not suited to represent complex gravel-bed river systems such as braided rivers. There is therefore a need for developing groundwater–surface water exchange functions tailored to gravel-bed rivers that can be used in regional scale models.To address this issue, we developed a model simplification framework that combines a 3D integrated surface and subsurface hydrological model, a 2D cross-sectional river-aquifer model and a 1D conductance-based analytical model. We aim at broadly simplifying the 3D model while ensuring the appropriate simulation of groundwater recharge. We demonstrate our modelling approach on the Selwyn River (New Zealand) using piezometric data and groundwater recharge estimates derived from field observations and satellite imagery.Our results indicates that groundwater recharge from this river can be simulated using a simple 1D analytical model, which can easily be implemented in regional groundwater models (e.g., MODFLOW models). However, to represent properly the time variability of groundwater recharge, it is essential to use the groundwater level in the shallow aquifer associated with the river as input to the regional groundwater model. Our approach is generally transferable to other gravel-bed rivers but requires some observations of river losses for proper calibration.