Experiment Data Research Articles

Optimal Sizing, Gear Ratios, and Shifting Schedule of Battery‐Electric Mining Haul Trucks to Enhance Energy Efficiency

At present, mining haul trucks (MHTs) directly deploy the on‐road heavy‐duty trucks’ battery‐electric powertrain, as they can cut down costs and emissions in mining. However, the operating patterns of MHT are different, e.g., ultraduty, low‐speed, and continuous road slopes, resulting in a mismatch between the dynamic and economic performance of mining required and the MHT achieved. The powertrain design and control influence the dynamic and economic performance, which can be quantitatively measured by top speed, gradeability, and energy consumption. This study uses an improved differential evolutionary algorithm to develop an integrated optimization platform to obtain the components sizing, gear ratio, and shifting schedule for the dedicated battery‐electric MHT. Mathematical models are established and validated using on‐site experiment data. An integrated optimization platform is initiated by concurrently formulating the motor sizing, gear ratio, and shifting schedule and solved by the improved differential evolutionary algorithm. Optimization results indicate that the economic performance is enhanced by 10.82%, 11.08%, 11.18%, and 11.20%, respectively, while maintaining or slightly improving the dynamic performance. Besides, the achievable maximum speed at the most common grade is boosted by 11.82%, 6.52%, 7.44%, and 6.52%, respectively. The study provides an approach to developing a battery‐electric powertrain for MHT.

γCygni SNR morphology viewed in the electromagnetic spectrum

AbstractThe investigation of very high energy gamma‐ray sources touches on the problem of the cosmic ray origin and the role of the Galaxy in their generation. The SHALON observations have yielded the results on Cygni SNR Galactic supernova remnant. The observation results are presented with spectral energy distribution and emission map by SHALON in comparison with other experiment data obtained by ROSAT in x‐ray energy range, radio‐data by CGPS, and also observations of GeV–TeV gamma‐rays by Fermi LAT. The overall Cygni SNR characteristics detected in radio, x‐rays, and GeV–TeV gamma‐rays can be a result of the shocks at the interaction of the supernova ejecta and the surrounding medium. The collected experimental data help to make clear the origin of TeV ‐ray emission in the Cygni SNR. The density of target material in the SNR surroundings is enough to produce the observable TeV gamma‐ray flux via the shock acceleration of hadrons in the detected regions. The detection of gamma‐ray emission at 0.8–60 TeV from the North‐West and South‐East shells of Cygni SNR by SHALON would favor the hadronic origin of the gamma‐rays in this supernova remnant.

On the shock wave boundary layer interaction in slightly rarefied gas

The shock wave and boundary layer interaction (SWBLI) plays an important role in the design of hypersonic vehicles. However, discrepancies between the numerical results of high-temperature gas dynamics and experiment data have not been fully addressed. It is believed that the rarefaction effects are important in SWBLI, but the systematic analysis of the temperature-jump boundary conditions and the role of translational/rotational/vibrational heat conductivities are lacking. In this paper, we derive the three-temperature Navier–Stokes–Fourier (NSF) equations from the gas kinetic theory, with special attention paid to the components of heat conductivity. With proper temperature-jump boundary conditions, we simulate the SWBLI in the double cone experiment. Our numerical results show that, when the three heat conductivities are properly recovered, the NSF equations can capture the position and peak value of the surface heat flux, in both low- and high-enthalpy inflow conditions. Moreover, the separation bubble induced by the separated shock and the reattachment point induced by impact between transmitted shock and boundary layer are found to agree with the experimental measurement.

Matching analysis on flow limiting valve of marine diesel engine common rail system

The flow limiting valve (FLV) designed for the high pressure common rail fuel system (CRS) was designed, the simulation model of the FLV and electronic controlled injector is established and the injector’s model was verified by experiment data. Through simulation, the maximum cycle injection quantity (CIQ) and close flow quantity (CFQ) of the CRS matched with the FLV are verified, and the influence of key FLV’s parameters on the matching performance was analysed. The maximum CIQ is 722mm3 and the CFQ is 1194.2mm3, which can satisfy the technical requirements of the CRS under overload conditions. The closing lift and orifice diameter of the FLV has a significant impact on the CFQ, which should be carefully designed according to the technical requirements, and the dimensional tolerance and processing accuracy should be guaranteed in the production and assembly process to ensure the consistency of the CFQ.

Analytical Workflows for Single-Cell Multiomic Data Using the BD Rhapsody Platform.

The conversion of raw sequencing reads to biologically relevant data in high-throughput single-cell RNA sequencing experiments is a complex and involved process. Drawing meaning from thousands of individual cells to provide biological insight requires ensuring not only that the data are of the highest quality but also that the signal can be separated from noise. In this article, we describe a detailed analytical workflow, including six pipelines, that allows high-quality data analysis in single-cell multiomics. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Image analysis Basic Protocol 2: Sequencing quality control and generation of a gene expression matrix Basic Protocol 3: Gene expression matrix data pre-processing and analysis Basic Protocol 4: Advanced analysis Basic Protocol 5: Conversion to flow cytometry standard (FCS) format Basic Protocol 6: Visualization using graphical interfaces.

Plasma-resonance-assisted filament in a high-pressure microwave discharge

Self-consistent steady-state structure of a thin plasma filament maintained in atmospheric pressure argon by a rotating transverse electric field of ∼1 cm wavelength is simulated numerically under conditions when the local plasma resonance condition is satisfied at some surface inside the filament. The equilibrium radial distributions of the plasma parameters (temperature and density) and the field amplitude inside the filament are calculated based on the solution of the heat equation and the Maxwell equations. It is shown that the occurrence of strong resonant peaks of the radial component of the electric field and the power of Joule losses in the region of critical density at the periphery of the filament provide the energy deposition sufficient to achieve a relatively high gas temperature and a strongly supercritical plasma density in its central region. In relation to the values of these basic parameters of the filament and the lower limit of the external radiation power supporting it, the results obtained are in satisfactory agreement with the data of the earlier experiment.

An iteratively reweighted steered response power approach to multisource localization using a distributed microphone network

The steered response power (SRP) with phase transform algorithm has been demonstrated to be robust against reverberation and noise for single-source localization. However, when this algorithm is applied to multisource localization (MSL), the “peak missing problem” can occur, namely, that some sources dominate over others over short time intervals, resulting in fewer significant SRP peaks being found than the true number of sources. This problem makes it difficult to detect all the sources among the available SRP peaks. We propose an iteratively reweighted steered response power (IR-SRP) approach that effectively solves the “peak missing problem” and achieves robust MSL in reverberant noisy environments. The initial IR-SRP localization function is computed over the time-frequency (T-F) bins selected by a combination of two weighting schemes, one using coherence, and the other using signal-to-noise ratio. When iterating, our method finds the significant SRP peaks for the dominant sources and eliminates the T-F bins contributed by these sources using inter-channel phase difference information. As a result, the remaining sources can be found in subsequent iterations among the remaining T-F bins. The proposed IR-SRP method is demonstrated using both simulated and measured experiment data.

Study of Bragg scattering in the presence of dipole-dipole interaction in plasmonic nanohybrids

We have developed a theory of the Bragg scattering for metallic nanohybrid made of an ensemble of metallic nanorods doped in a substrate. The substrate can gas, liquid or solid. An external laser field is applied to study the Bragg scattered light. The photons from the incident laser interact with the surface plasmons od nanorods and produce surface plasmon polaritons (SPPs). The incident laser field also induced dipoles in the ensemble of nanorods and they interact with each other via the dipole-dipole interaction (DDI). We have developed a theory for Bragg scattering for metallic nanohybrids using the coupled-mode formulism based on Maxwell’s equation in the presence of SPP and DDI fields. It is found that the theory of Bragg scattered depends on the susceptibility induced by the SPP and DDI fields. We used the quantum mechanical density matrix method to calculate the susceptibility. An analytical expression of the Bragg scattered light intensity is obtained. These expressions can be useful for experimental scientists and engineers who can used them to compare their experiments and make new types of plasmonic devices. Next, we have compared our theory with the experiment data for a nanohybrid made of ensemble of Au-nanoris doped in water. We found a good agreement between theory and experiments. We have also performed the numerical simulations to study the effect of SPP and DDI fields on the Bragg intensity. We have predicted an enhancement the Brag intensity due to the SPP and DDI couplings. The enhancement is due to the two extra scattering mechanisms of the SPP and DDI polaritons with acoustic phonons. We have also found that the one peak in the Bragg intensity can be split int many peaks due the SPP coupling, DDI coupling and phase factor. The splitting is due the Bragg factor appearing in the theory, and it includes the coupling of the incident laser, SPP and DDI electric fields with of acoustic phonons. The enhancement effect can be used to fabricate new types of nanosensors. Similarity, splitting phenomenon can be used to fabricate new types nanoswitches where one peak can be considered as the OFF position and many peaks can be considered as the ON position.

Multi-Particle Tracking in Complex Plasmas Using a Simplified and Compact U-Net.

Detecting micron-sized particles is an essential task for the analysis of complex plasmas because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches. This work presents an approach for tracking micron-sized particles in a dense cloud of particles in a dusty plasma at Plasmakristall-Experiment 4 using a U-Net. The U-net is a convolutional network architecture for the fast and precise segmentation of images that was developed at the Computer Science Department of the University of Freiburg. The U-Net architecture, with its intricate design and skip connections, has been a powerhouse in achieving precise object delineation. However, as experiments are to be conducted in resource-constrained environments, such as parabolic flights, preferably with real-time applications, there is growing interest in exploring less complex U-net architectures that balance efficiency and effectiveness. We compare the full-size neural network, three optimized neural networks, the well-known StarDist and trackpy, in terms of accuracy in artificial data analysis. Finally, we determine which of the compact U-net architectures provides the best balance between efficiency and effectiveness. We also apply the full-size neural network and the the most effective compact network to the data of the PK-4 experiment. The experimental data were generated under laboratory conditions.

Preharvest Durum Wheat Yield, Protein Content, and Protein Yield Estimation Using Unmanned Aerial Vehicle Imagery and Pléiades Satellite Data in Field Breeding Experiments

Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source for phenotyping. In this study, we separately evaluated pan-sharpened Pléiades satellite imagery (50 cm) and UAV imagery (2.5 cm) to phenotype durum wheat in small-plot (12 m × 1.10 m) breeding trials. The Gaussian process regression (GPR) algorithm, which provides predictions with uncertainty estimates, was trained with spectral bands and а selected set of vegetation indexes (VIs) as independent variables. Grain protein content (GPC) was better predicted with Pléiades data at the growth stage of 20% of inflorescence emerged but with only moderate accuracy (validation R2: 0.58). The grain yield (GY) and protein yield (PY) were better predicted using UAV data at the late milk and watery ripe growth stages, respectively (validation: R2 0.67 and 0.62, respectively). The cumulative VIs (the sum of VIs over the available images within the growing season) did not increase the accuracy of the models for either sensor. When mapping the estimated parameters, the spatial resolution of Pléiades revealed certain limitations. Nevertheless, our findings regarding GPC suggested that the usefulness of pan-sharpened Pléiades images for phenotyping should not be dismissed and warrants further exploration, particularly for breeding experiments with larger plot sizes.

Strong donor-acceptor interaction of difluoroboron β-diketonates with polyvinylcarbazole: Deeply red-shifted emission of charge-transfer complexes and exciplexes

Donor-acceptor systems exhibiting exciplex luminescence attract much attention due to the wide prospects for their application in the field of OLEDs, nonlinear optics and lasers. Difluoroboron β-diketonates (BF2bdks) are a well-known class of organic dyes, which are able to form effective exciplex fluorescence, but this process has not received due attention for practical application. Strong donor-acceptor interaction in polyvinylcarbazole (donor) and BF2bdks derivatives (acceptor) binary system induces deeply red-shifted emission of charge-transfer complexes and exciplexes with a large bathochromic shift up to ∼170 nm relative to conventional monomeric luminescence. The results of quantum chemistry simulation are in agreement with the experiment data. Luminescence color tuning of the film are capable by the dye concentration, the donor-acceptor properties of the dye substituents and even the excitation wavelength. White light broad spectrum of a dual luminescence of the investigated binary systems has CIE chromaticity coordinates (0.35; 0.33) close to the standard neutral white light CIE coordinates (0.33; 0.33). This study will facilitate photophysical research and industrial applications of donor-acceptor binary systems based on polymer materials.

An efficient parameterized simulation framework for 3D scarf-repaired composite laminates

This paper proposes a parameterized simulation framework for predicting the load-bearing capacity of 3D scarf-repaired composite laminates which remarkably reduces the modeling time from several hours to just a few seconds. The specific implementation is demonstrated through a detailed step-by-step explanation. The main contribution lies in: (1) By introducing a reusable geometric partitioning method for composite layers and implementing a partitioning method-based numbering scheme for element and node, the effective management of element and node IDs of elements and nodes in such complex 3D models is greatly facilitated; (2) By ingeniously combining open-source meshing tools and text rewriting techniques, a highly efficient approach has been developed to construct complete models for such structures, showcasing the remarkable potential of these existing techniques in this field. The reliability of the efficient parameterized simulation framework proposed in this study is verified through the comparison between strength values acquired from progressive damage analysis (PDA) and experiment data reported in the existing literature.

Experimental Investigation of Water Infiltration Law in Loess with Black Locust (Robinia pseudoacacia) Roots

Physical model experiments are increasingly applied in the study of the water infiltration law in loess with roots. In the past, due to differences in study objects and the limitations of measuring techniques, the infiltration law in loess with roots is rarely evaluated by using appropriate indoor physical model experimental data. In order to investigate the law of water infiltration in loess with roots, we designed a new soil column experimental device that can automatically collect data and images. By comparing the soil column experiment data of loess, we analyzed variables in root contents (the ratio of root mass to dry soil mass) and root types. Roots with diameters of 0–2 mm, 2–5 mm, and 5–10 mm are defined as type I, type II, and type III, respectively. It was found that the water infiltration rate, water-holding capacity, and saturated permeability coefficient increase with the increase in root content. In loess containing different root types, the root types were found to improve the rate of water infiltration, water-holding capacity, and saturated permeability coefficient in the soil. The root types were ranked in descending order in terms of their impact: root type II had the highest improvement, followed by root type III, and then root type I. The phenomenon of circumferential flow existed when water infiltrated loess with roots. Root content and root type would affect the radius of circumferential flow, infiltration path, and cross-section. When calculating the saturated permeability coefficient of loess with roots, ignoring the effect of circumferential flow would lead to a higher result.

An improved particle swarm algorithm-based method for kinetic modeling study of ammonia/air laminar flame speed

In recent years, ammonia has garnered increasing attention as a promising carbon-free fuel. Laminar flame speed is a critical property of ammonia fuel that has been extensively studied by researchers using chemical kinetics mechanisms. However, some deviations still remain in the numerical predictions. To further improve the prediction accuracy of the laminar flame speed for ammonia/air flame, an ammonia kinetic mechanism is developed in this work. Reactions from Gri-Mech 3.0, Li et al. mechanism, and Han et al. mechanism that have significant impact on laminar flame speed were first assembled to the Okafor et al. mechanism, and H/OH sub-mechanisms were merged to establish a kinetic mechanism containing 43 species and 142 reactions. A particle swarm algorithm with improved inertia weights is proposed and the fitness function for outputting laminar flame speed prediction error is customized based on Cantera codes, then forming the algorithm framework used to optimize the kinetic mechanism. The pre-exponential factor A, the temperature exponent n and the activation energy Ea of the four reactions in the merged mechanism that have high sensitivity to the laminar flame speed are selected as the independent variables for optimization, and the reactions rate constants corresponding to the high prediction accuracy of the laminar flame speed is finally obtained. The numerical prediction indicates a reduction in mean absolute percentage error of laminar flame speed from 22.563 % to 10.649 % using the optimization mechanism. The results of the sensitivity and reaction pathways analyses demonstrated that more H-related reactions were considered in the optimized mechanism, and the relative ROP of H-related reactions were adjusted at different equivalence ratios, resulting in higher or lower predictions of laminar flame speed compared with the unoptimized Okafor et al. mechanism. Ignition delay time as well as the species distribution of NH3, NO, and N2O was studied. The ignition delay time predictions using the optimized mechanism are in better agreement with the experiment data at P = 11 atm and 30 atm. The optimization mechanism demonstrated accurate predictions for NH3 and NO while exhibiting an overestimation for N2O, but the N2O prediction error is smaller compared to the Okafor et al. mechanism.

Physics-data coupling-driven method to predict the penetration depth into concrete targets

The projectile penetration process into concrete target is a nonlinear complex problem. With the increase of experiment data, the data-driven paradigm has exhibited a new feasible method to solve such complex problem. However, due to poor quality of experimental data, the traditional machine learning (ML) methods, which are driven only by experimental data, have poor generalization capabilities and limited prediction accuracy. Therefore, this study intends to exhibit a ML method fusing the prior knowledge with experiment data. The new ML method can constrain the fitting to experimental data, improve the generalization ability and the prediction accuracy. Experimental results show that integrating domain prior knowledge can effectively improve the performance of the prediction model for penetration depth into concrete targets.

Spectroscopic Distance, Mass, and Age Estimations for APOGEE DR17

We derive distances and masses of stars from the Sloan Digital Sky Survey (SDSS) Apache Point Observatory Galactic Evolution Experiment Data Release 17 using simple neural networks. Training data for distances comes from Gaia EDR3, supplemented by literature distances for star clusters. For masses, the network is trained using asteroseismic masses for evolved stars and isochrone masses for main-sequence stars. The models are trained on effective temperature, surface gravity, metallicity, and carbon and nitrogen abundances. We found that our distance predictions have median fractional errors that range from ≈20% at low log g and ≈10% at higher log g with a standard deviation of ≈11%. The mass predictions have a standard deviation of ±12%. Using the masses, we derive ages for evolved stars based on the correspondence between mass and age for giant stars given by isochrones. The results are compiled into a Value Added Catalog called DistMass that contains distances and masses for 733,901 independent spectra, plus ages for 396,548 evolved stars.

Few-shot meta transfer learning-based damage detection of composite structures

Damage detection and localization using data-driven approaches in carbon fiber reinforced plastics (CFRP) composite structures is becoming increasingly important. However, the performance of conventional data-driven methods degrades greatly under little amount of data. In addition, the scarcity of data corresponding to defect/damage conditions of CFRP structures lead to extreme data imbalance, which make this problem even more challenging. To address these challenges of few training data and the scarcity of damage samples, this paper proposes a few-shot meta transfer learning (FMTL)-based approach for damage detection in CFRP composite structures. This method leverages knowledge learnt from an unbalanced data domain generated from a single CFRP composite sample and adapts the knowledge to be applied for other data domains generated by CFRP samples with different structural properties. The contributions of this research include demonstrating the feasibility of harnessing knowledge from notably limited experiment data, designing an algorithm for configuring hyperparameters based on a specific FMTL task, and identifying the impacts of hyperparameters on learning performances. Results show that FMTL can improve the recall rate by at least 15% while preserving the ability to identify health conditions. This method can be extremely useful when we need to monitor health condition of critical CFRP structures, like airplanes, because they can rarely generate data under damage conditions for model training. FMTL enables us to build new models based on unbalanced source domain data with the cost of a minimal set of samples from the target domain.

Quantum computing dataset of maximum independent set problem on king lattice of over hundred Rydberg atoms

Finding the maximum independent set (MIS) of a large-size graph is a nondeterministic polynomial-time (NP)-complete problem not efficiently solvable with classical computations. Here, we present a set of quantum adiabatic computing data of Rydberg-atom experiments performed to solve the MIS problem of up to 141 atoms randomly arranged on the king lattice. A total of 582,916 events of Rydberg-atom measurements are collected for experimental MIS solutions of 733,853 different graphs. We provide the raw image data along with the entire binary determinations of the measured many-body ground states and the classified graph data, to offer bench-mark testing and advanced data-driven analyses for validation of the performance and system improvements of the Rydberg-atom approach.

Investigation on the yield and failure criterion of foamed concrete

This study systematically investigated the yielding and failure criterions of foamed concrete through the proposed control equations. Firstly, four loading-path experiments including uniaxial tension, uniaxial compression, triaxial shear, and isotropic compression were conducted to comprehensively analyze the mechanical behaviour of foamed concretes. The results indicated that foamed concretes experienced elastic stage, softening or hardening stage, and strength stability stage subjected to the uniaxial loading. During the loading process, some cemented binders within foamed concretes would be broken into particles and then moved to fill the internal pores. Afterwards, the failure criterion of foamed concretes was explicated based on the critical state theory, where two control equations regarding stress and deformation aspects were proposed. Finally, a yield function containing tension, compression, and shear loading paths was proposed to describe the yielding performance of foamed concretes. Compared with the experiment data, the prediction accuracy of the proposed yield function has reached more than 95 %. This research provides theorical basis for the practical application of foamed concretes.

Release Control of Biologically Active Substances from Simulation Model of Silicone Liners

Introduction. In modern medicine, the requirements for the quality of used materials are getting stricter. There is serious concern about bacterial and fungal contamination related to the use of silicone polymeric products, especially those that are in direct contact with human tissues and body fluids. In this regard, the issue of impregnating silicone medical products with various biologically active substances (BAS), particularly with antimicrobial properties, appears relevant.Aim. The research studies the relevance of adding BAS into silicone medical devices to improve their quality and prevent negative consequences of their use. The authors of the article aimed at proving prolonged release of the chosen BAS from silicone products during their contact with skin and the bacteriostatic effect emerging as a result of the BAS release. That required verifying the existence of BAS in the washouts from silicone disks, which, in turn, proves the fact that BAS initially distributed evenly in the volume of a silicone disk are capable of diffusing to the surface and then releasing from it under mechanical action, as well as when treating it with alcohol or alcohol-containing solutions.Materials and methods. Symdiol and bisabolol were selected as the BAS for this study due to their proved antibacterial, anti-inflammatory and moisturizing properties. Bisabolol and symdiol were used in the form of Dragosantol 100 and SymDiol 68T preparations correspondingly. Silicone disks (SDs), impregnated with BAS (0,2 % of the volume) were used as a simulation model of silicone liners. The BAS release from silicone liner models was assessed using highly sensitive chromatographic methods of thin-layer and gas-liquid chromatography.Results and discussion. The method of impregnating SDs with BAS was worked out, the optimal concentration of these substances to add them into the silicone base was selected. The release of the impregnated additives, as well as prolonged stable releasing effect, were confirmed. The data obtained during the experiment allows saying with confidence that the impregnated preparation (symdiol and bisabolol) releases from a SD even upon short and low-intensity mechanical contact with skin, which produces stable bacteriostatic effect on a wide range of microorganisms. The process of BAS release from SDs is also facilitated when treating the product periodically with alcohol-containing solutions, which is necessary according to the operating rules. The obtained results of the chromatographic research quite correlate with the data of the previous microbiological experiments regarding the studied topic. Release of the studied BAS from polymer products during their contact with the skin within the period of not less than 3 months justifies feasibility of adding these BAS into the silicone base.Conclusions. The study confirmed the release of biologically active substances from silicone medical products. Adding BAS into silicone liners undoubtedly improves the quality of these medical products, which can be applied in prosthetics and orthoses.