Dual Particle Research Articles

Model uncertainty quantification of a degradation model of miter gates using normalizing flow-based likelihood-free inference

Degradation modeling is essential for failure prognostics of miter gates and subsequent risk-informed maintenance or operational decision-making. Typically, degradation models are formulated based on certain assumptions and simplifications and may not fully capture the complexity of underlying degradation mechanisms. Even minor errors in such models may lead to significant discrepancies in failure prognostics due to error accumulation over time. Aiming to improve the accuracy of the degradation model and ensure reliable failure prognostics, this article proposes a degradation model updating framework for miter gates using normalizing flow-based likelihood-free inference, accounting for both model parameter uncertainty and model form uncertainty (i.e., model bias). The proposed work consists of two phases: the offline training phase and the online updating phase. During the offline training phase, the unknown model bias term is modeled as a random noise with uncertain standard deviation. Synthetic data are then generated using a physical model based on prior knowledge of the uncertain model parameters and model bias. The synthetic data are utilized to train an inference model, which has a summary network for data compression and a conditional invertible neural network for parameter estimation. In the online updating phase, the trained inference model uses strain observations to obtain posterior samples. The Gaussian mixture model and dual particle filter techniques are utilized to recursively update posterior samples, thereby improving the estimation accuracy. Subsequently, model bias is inversely estimated using the degradation model, which uses the updated model parameters and the gap length from the inference model. Following that, a regression model is constructed to correct the biases inherent in the simplified degradation model. This process is implemented iteratively over time to perform continuous model updating and failure prognostics. Results of a case study demonstrate the efficacy of the proposed framework in reducing both model parameter uncertainty and recovering model biases and thereby improving the accuracy of failure prognostics of miter gates.

Decoding drivers of carbon flux attenuation in the oceanic biological pump

The biological pump supplies carbon to the oceans’ interior, driving long-term carbon sequestration and providing energy for deep-sea ecosystems1,2. Its efficiency is set by transformations of newly formed particles in the euphotic zone, followed by vertical flux attenuation via mesopelagic processes3. Depth attenuation of the particulate organic carbon (POC) flux is modulated by multiple processes involving zooplankton and/or microbes4,5. Nevertheless, it continues to be mainly parameterized using an empirically derived relationship, the ‘Martin curve’6. The derived power-law exponent is the standard metric used to compare flux attenuation patterns across oceanic provinces7,8. Here we present in situ experimental findings from C-RESPIRE9, a dual particle interceptor and incubator deployed at multiple mesopelagic depths, measuring microbially mediated POC flux attenuation. We find that across six contrasting oceanic regimes, representing a 30-fold range in POC flux, degradation by particle-attached microbes comprised 7–29 per cent of flux attenuation, implying a more influential role for zooplankton in flux attenuation. Microbial remineralization, normalized to POC flux, ranged by 20-fold across sites and depths, with the lowest rates at high POC fluxes. Vertical trends, of up to threefold changes, were linked to strong temperature gradients at low-latitude sites. In contrast, temperature played a lesser role at mid- and high-latitude sites, where vertical trends may be set jointly by particle biochemistry, fragmentation and microbial ecophysiology. This deconstruction of the Martin curve reveals the underpinning mechanisms that drive microbially mediated POC flux attenuation across oceanic provinces.

DMD-based spatiotemporal superresolution measurement of a supersonic jet using dual planar PIV and acoustic data

The present study applies a framework of the spatiotemporal superresolution measurement based on the total-least-squares dynamic mode decomposition, the Kalman filter and the Rauch-Tung-Striebel smoother to an axisymmetric underexpanded supersonic jet of a jet Mach number of 1.35. Dual planar particle image velocimetry was utilized, and paired velocity fields of the flow with a short time interval were obtained at a temporal resolution of 5000 Hz. High-frequency acoustic data of 200,000 Hz were simultaneously obtained. Then, the time-resolved velocity fields of the supersonic jet were reconstructed at a temporal resolution of 200,000 Hz. Also, time coefficients of dynamic modes in high temporal resolution were calculated. The correlation between time coefficients implies that the mixing promotion by screech tone causes the lift-up of the high-velocity fluid from the jet center and accelerates at the downstream side.

Realizing the strength-ductility balance of a warm-rolled 10 Mn steel via preparing dual nano-sized precipitates

A novel strategy involving warm rolling and tempering-annealing was introduced to optimize the strength-ductility balance of Fe-10.2Mn-2.2Al-0.41C-0.6 V (wt. %) steel. The V- and κ-carbide particles produced during the tempering are critical for tailoring the austenite characteristics during the subsequent annealing. Austenite that nucleates on Mn-rich κ-carbides inherit their high Mn content and fine grain size. Quantitative calculations suggest that the pinning effect exerted by the V-carbide particles strongly resists grain boundary motion and recrystallization grain growth, further refining the final microstructure. Moreover, the dual nano-sized particles induce abnormal variations in the grain size and fraction of austenite, influencing the deformation mechanisms. In the absence of deformation twins, the fresh martensite develops into “separating walls” to refine the microstructure and further enhances the work hardening in addition to the complex dislocation mobile. Finally, the tempered-annealed sample demonstrated a simultaneous increase in the yield and tensile strengths from 1166 to 1452 MPa and 1507–1727 MPa, but a slight decrease in the total elongation from 18.6% to 16.8% compared to annealed sample, achieving a good strength-ductility balance.

Enhancing Concurrent Emergency Response: Joint Scheduling of Emergency Vehicles on Freeways with Tailored Heuristic

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk.

Combined Methane Cracking for H2 Production with CO2 Utilization for Catalyst Regeneration Using Dual Functional Nanostructured Particles

Combined Methane Cracking for H₂ Production with CO₂ Utilization for Catalyst Regeneration Using Dual Functional Nanostructured Particles

Duality for the multispecies stirring process with open boundaries

We study the stirring process with N − 1 species on a generic graph G=(V,E) with reservoirs. The multispecies stirring process generalizes the symmetric exclusion process, which is recovered in the case N = 2. We prove the existence of a dual process defined on an extended graph G~=(V~,E~) which includes additional sites in V~∖V where dual particles get absorbed in the long-time limit. We thus obtain a characterization of the non-equilibrium steady state of the boundary-driven system in terms of the absorption probabilities of dual particles. The process is integrable for the case of the one-dimensional chain with two reservoirs at the boundaries and with maximally one particle per site. We compute the absorption probabilities by relying on the underlying gl(N) symmetry and the matrix product ansatz. Thus one gets a closed-formula for (long-ranged) correlations and for the non-equilibrium stationary measure. Extensions beyond this integrable set-up are also discussed.

Investigation of mechanisms of shock wave generation by collapse of cavitation bubbles near particles

The mechanism of generation of shock waves by the collapse of a cavitation bubble near a single particle or dual particles is numerically investigated using OpenFOAM. For the single-particle case, shock waves during bubble inception and jet impacting on the particle surface are revealed in detail. The pressure induced on the particle by the inception shock wave of the bubble decreases with increasing bubble-particle distance, and the pressure is proportional to 1/r1.26 (r being the distance from the center of the shock wave). For the dual particles, the evolution of the neck structure is closely related to the generation mechanism of the shock wave. At extremely close particle–bubble distances, two shock waves propagating in opposite directions are emitted outside and inside the bubble after two necks collide. At long particle–bubble distances, a shock wave is emitted after the neck contracts, and simultaneously the bubble splits into two daughter bubbles. The energy of the shock wave generated by the neck constriction (i.e., the pressure at its generation location) first increases and then decreases with increasing bubble-particle distance. For unequal-sized double particles, the size of the daughter bubble depends on the bubble-particle distance and the particle size. These findings provide new perspectives for understanding the damage sustained by hydro-mechanical components operating in sand-laden water flows.

Jet dynamics of a cavitation bubble near unequal-sized dual particles

The jet dynamics of a cavitation bubble near unequal-sized dual particles is investigated employing OpenFOAM, and the effects of the jets on the particles are quantitatively analyzed in terms of their pressure impacts. Different from single-particle cases, the necks that evolve between dual particles are closely linked to the formation mechanism of the jets. Based on the simulation results, the jet dynamics can be divided into five scenarios: (1) the contraction of the annular depression produced by the collision of the two necks causes the bubble to split into two daughter bubbles and generates a single jet inside each daughter bubble; (2) the annular depression impacts the particle, leading to the bubble to fracture and producing a single jet inside a daughter bubble; (3) the bubble is split by a single neck constriction and produces a single jet; (4) the bubble is split by a single neck constriction and generates two jets; and (5) the bubble is split by the contraction of two necks and produces four jets together with three daughter bubbles. As the bubble–particle distance or the radius ratio of the dual particles increases, the maximum force on the small particle generated by the bubble decreases.

Ultrahigh-Speed 3D DNA Walker with Dual Self-Protected DNAzymes for Ultrasensitive Fluorescence Detection and Intracellular Imaging of microRNA.

Herein, a dual self-protected DNAzyme-based 3D DNA walker (dSPD walker), composed of activated dual self-protected walking particles (ac-dSPWPs) and track particles (TPs), was constructed for ultrasensitive and ultrahigh-speed fluorescence detection and imaging of microRNAs (miRNAs) in living cells. Impressively, compared with the defect that "one" target miRNA only initiates "one" walking arm of the conventional single self-protected DNAzyme walker, the dSPD walker benefits from the secondary amplification and spatial confinement effect and could guide "one" target miRNA to generate "n" secondary targets, thereby initiating "n" nearby walking strands immediately, realizing the initial rate over one-magnitude-order faster than that of the conventional one. Moreover, in the process of relative motion between ac-dSPWPs and TPs, the ac-dSPWPs could cleave multiple substrate strands simultaneously to speed up movement and reduce the derailment rate, as well as combine with successive TPs to facilitate a large amount of continuous signal accumulation, achieving an ultrafast detection of miRNA-221 within 10 min in vitro and high sensitivity with a low detection limit of 0.84 pM. In addition, the DNA nanospheres obtained by the rolling circle amplification reaction can capture the Cy5 fluorescence dispersed in liquids, which achieves the high-contrast imaging of miRNA-221, resulting in further ultrasensitive imaging of miRNA-221 in cancer cells. The proposed strategy has made a bold innovation in the rapid and sensitive detection as well as intracellular imaging of low-abundance biomarkers, offering promising application in early diagnosis and relevant research of cancer and tumors.

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively.

Advancing Light Scattering Control Through the Exploration of Temperature‐Sensitive Optical Modifiers

AbstractLight scattering holds immense significance in various applications, including LED lighting, display enhancement, and solar cell optimization, emphasizing the critical role of understanding scattering mechanisms, especially light–matter interactions, achieving effective optical diffusion. Consequently, the influence of stimuli on material properties, including refractive indices and thermal expansion behavior, has emerged as a pivotal consideration, leading to the development of stimuli‐responsive light scattering modifier. Here, a temperature‐sensitive light scattering modifier composed of poly(methyl methacrylate‐co‐styrene) and poly(ethylene‐co‐vinyl acetate) is presented as scattering particles and matrix, respectively, using a melt‐blending process. Optical properties are evaluated across controlled temperatures 0–70 °C. These findings demonstrate an increasing trend in diffuse transmission and haze as temperatures increased across all light scattering modifiers. Significantly, the optimized light scattering modifier, integrating a dual particle system, showcased uniform reduction gaps in parallel transmission and adjustable haze characteristics across the visible spectrum under temperature control. This optical modifier achieved a total transmission of 92% and adjustable haze levels ranging from 35% to 90% under temperature control. Furthermore, this fabrication of three‐layer films incorporating the light scattering modifier exhibited identical characteristics to the developed light scattering modifier, emphasizing their potential for seamless integration across diverse technological domains.

Influence of B4C and ZrB2 reinforcements on microstructural, mechanical and wear behaviour of AA 2014 aluminium matrix hybrid composites

Considering their affordability and high strength-to-weight ratio, lightweight aluminium alloys are the subject of intensive research aimed at improving their properties for use in the aerospace industry. This research effort aims to develop novel hybrid composites based on AA 2014 alloy through the use of liquid metallurgy stir casting to reinforce dual ceramic particles of Zirconium Diboride (ZrB2) and Boron Carbide (B4C). The weight percentage (wt%) of ZrB2 was varied (0, 5, 10, and 15), while a constant 5 wt% of B4C was maintained during this fabrication. The as-cast samples have been assessed using an Optical Microscope (OM) and a Scanning Electron Microscope (SEM) with Energy Dispersive Spectroscopy (EDS). The properties such as hardness, tensile strength, and wear characteristics of stir cast specimens were assessed to examine the impact of varying weight percentages of reinforcements in AA 2014 alloy. In particular, dry sliding wear behaviour was evaluated considering varied loads using a pin-on-disc tribotester. As the weight % of ZrB2 grew and B4C was incorporated, hybrid composites showed higher hardness, tensile strength, and wear resistance. Notably, the incorporation of a cumulative reinforcement consisting of 15 wt% ZrB2 and 5 wt% B4C resulted in a significant 31.86% increase in hardness and a 44.1% increase in tensile strength compared to AA 2014 alloy. In addition, it has been detected that wear resistance of hybrid composite pin (containing 20 wt% cumulative reinforcement) is higher than that of other stir cast wear test pins during the whole range of applied loads. Fractured surfaces of tensile specimens showed ductile fracture in the AA 2014 matrix and mixed mode for hybrid composites. Worn surfaces obtained employing higher applied load indicated abrasive wear with little plastic deformation for hybrid composites and dominant adhesive wear for matrix alloy. Hence, the superior mechanical and tribological performance of hybrid composites can be attributed to dual reinforcement particles being dispersed well and the effective transmission of load at this specific composition.

Numerical Investigations on the Jet Dynamics during Cavitation Bubble Collapsing between Dual Particles

The jet dynamics during cavitation bubble collapsing between unequal-sized dual particles are investigated utilizing a numerical model that combines the finite volume approach alongside the volume of fluid approach. The model incorporates the compressibility of the two-phase fluid and accounts for mass and heat transfer between two phases. The computational model utilizes an axisymmetric model, where the axis of symmetry is defined as the line that connects the centers of the particles and the bubble. A comprehensive analysis is presented on the influence of the particle radius and bubble–particle distance on the jet behavior. Furthermore, the variations of surface pressure on the particles induced by jet impingement are quantitatively analyzed. Four distinct jet behaviors are categorized, depending on the formation mechanism, as well as the number and the direction of the jets. For case 1, the bubble produces a single jet directed toward a small particle; for case 2, the bubble fragments produces double jets receding from each other; for case 3, the bubble produces double jets approaching each other; and for case 4, the bubble produces a single jet directed toward a large particle. The pressure perturbations induced by jet impingement upon the particles exceed those caused by shock wave impacts. The larger the bubble volume at the moment of jet formation, the longer the duration of the pressure variation caused by the jet impinging on the particles.

Dual particle imaging: Applications in security and environmental imaging

Radiation imaging detectors have been applied in a variety of fields for detection, localization and characterization of radioactive sources. In this work, we describe a handheld detection system (H2DPI) that can simultaneously image neutrons and gamma rays, and reconstruct the energy spectra of the incident particles. This system was applied to the detection and characterization of a plutonium metal source in experiments performed at the National Criticality Experiments Research Center, where the 4.5 kg plutonium source was placed approximately 60 cm from the H2DPI. Neutron and gamma-ray images were measured that show good agreement with the known location of the source. Furthermore, the H2DPI was also used to measure the neutron spectrum, which shows the expected shape of a Watt fission spectrum, and the gamma-ray energy spectrum, which shows the energies common to plutonium metal. Lastly, we describe the application of the H2DPI in augmented reality, whereby real-time neutron and gamma-ray imaging data can be streamed to the Microsoft HoloLens to allow the user to visualize the source locations.

An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework

Lithium-ion battery state-of-charge (SOC) estimation plays an indispensable role in the battery management system functionalities. To achieve SOC estimation with high accuracy, a precise battery model is required. Among various battery models, electrochemical model (EM) stands out for its ability to provide insights into the electrochemical mechanisms of the battery. However, the model parameters will vary with the battery running and aging, besides, the model-based methods cannot well handle the model errors. Therefore, to ensure estimation accuracy, selecting a more insightful battery model and a more effective estimation algorithm becomes crucial. This paper proposes an electrochemical model-based SOC estimation method with online parameter adjustment and an optimized algorithm. Firstly, a single particle model (SPM) with electrolyte and thermal dynamics is introduced and simplified by an approximate solution. Secondly, the particle swarm optimization (PSO) method is used to solve the problem of the particle filter (PF) algorithm, such as particle degradation and particle diversity decrease. Finally, during SOC estimating, a double-scale dual particle filter (D-PF) is utilized to adjust parameters as battery aging and temperature changes. The battery tests are applied under different working conditions and temperatures. Experimental results show that the error of SOC estimation reduces significantly compared to the previous electrochemical model-based method, and the proposed method has a higher accuracy and effectiveness.

Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algorithm, the DDPSO approach demonstrated superior search capabilities and stability of optimization results. The maximum fitness value improved by 4.2%, the average value increased by 12.74%, and the concentration level of optimization results rose by 72.91% on average. The new optimization method pioneers fresh perspectives for optimizing the stability of RFSW, providing significant grounds for the process optimization and offline programming of complex spatial curve weldments.

Mechanical‐wear behavior and microstructure analysis of Al2214 alloy with B4C and graphite particles hybrid composites

AbstractIn the present work, hybrid composites made of Al2214 alloy with B4C and graphite were produced by using a liquid metallurgical process. Al2214 alloy was utilized to create hybrid composites that had 1.5–6 wt% of B4C particles and a constant 3 wt% of graphite particles. Microstructural analysis using scanning electron microscope (SEM), energy dispersion spectroscopy (EDS), and X‐ray diffraction (XRD) was done on the produced composites. The density, hardness, ultimate, yield strength, and elongation as a percentage were carried out using ASTM E8 for tensile and E10 standard for hardness test. The wear behavior of Al2214‐B4C and graphite composites was examined as per ASTM G99 standard using a wear testing device under a variety of loads and rotation speeds. Graphite and boron carbide particles were equally dispersed throughout the Al2214 alloy, according to SEM photographs. Graphite and B4C particles were detected in the Al2214 alloy by EDS and XRD analyses. The density of Al alloy composites was decreased by adding dual particles to the matrix. The Al2214 alloy's hardness, ultimate strength, yield strength, and wear resistance were all enhanced by the inclusion of dual particles, which increased these properties by 15.4%, 40.4%, and 46.7%, respectively. The presence of hybrid particles in the Al2214 alloy was revealed by EDS and XRD patterns. The density of Al alloy composites was decreased by adding dual particles to the matrix. Tensile force micrographs provided further evidence of the unique fracture behaviors shown by the Al2214 alloy and its composites. In order to examine the wear mechanisms and different morphologies of worn surfaces, scanning electron micrographs were taken.

Experimental study of interactions between dual porous particles released side by side during settling

The settling of porous particles is relevant to many fields of research, which is essential for many natural phenomena and industrial processes. However, due to the complex particle–particle and particle–fluid interactions, the mechanism of porous particles settling has not been fully understood yet. In this study, the settling behaviors and interactions of dual porous particles released side by side are experimentally investigated. The Reynolds number varied from 12 to 258, which is within the transition zone. Particle Tracking Velocimetry and Particle Image Velocimetry were utilized to capture the particles' trajectories and velocities and provide insight into the flow fields around the particles, respectively. The influences of particle porosity ɛ, initial spacing between particles l0*, and Reynolds number Re on the settling process are systematically studied. In this study, ɛ varies from 0 to 0.858 and l0* varies from 0 to 5. The experimental results reveal that there was repulsion between porous particles during settling, which was negatively correlated with l0* and ɛ. When ɛ > 0.3 and l0* < 0.5, the magnitude of the repulsion between porous particles is significantly different from that of the solid particles, and thus, the effect of porosity is important. For other cases, the interaction between porous particles is essentially the same as that between solid particles. The repulsion between the two particles is a result of flow fields asymmetry, and this repulsion vanishes when l0* > 5. The settling process can be divided into three stages depending on the repulsion. Compared to solid particles, the interactions between porous particles are weaker. The reason is that the pore reduces the duration of particle interactions in the repulsion phase, which contributes to the particles to rapid approaching to reach the stable phase.

3D-Printing of Hierarchical Porous Copper-Based Metal-Organic-Framework Structures for Efficient Fixed-Bed Catalysts.

Metallic structures with hierarchical open pores that span several orders of magnitude are ideal candidates for various catalyst applications. However, porous metal materials prepared using alloy/dealloy methods still struggle to achieve continuous pore distribution across a broad size range. Herein, we report a printable copper (Cu)/iron (Fe) composite ink that produces a hierarchical porous Cu material with pores spanning over 4 orders of magnitude. The manufacturing process involves four steps: 3D-printing, annealing, dealloying, and reannealing. Because of the unique annealing process, the resulting hierarchical pore surface becomes coated with a layer of Cu-Fe alloy. This feature imparts remarkable catalytic ability and versatile functionality within fixed bed reactors for 4-nitrophenol (4-NP) reduction and Friedländer cyclization. Specifically, for 4-NP reduction, the porous Cu catalyst demonstrates an excellent reaction rate constant (kapp = 86.5 × 10-3 s-1) and a wide adaptability of the substrate (up to 1.26 mM), whilst for Friedländer cyclization, a conversion over 95% within a retention time of only 20 min can be achieved by metal-organic-framework-decorated porous Cu catalyst. The utilization of dual metallic particles as printable inks offers valuable insights for fabricating hierarchical porous metallic structures for applications, such as advanced fixed-bed catalysts.