Particle Filter Research Articles

Highly Reactive Peroxide Species Promoted Soot Oxidation over an Ordered Macroporous Ce0.8Zr0.2O2 Integrated Catalyzed Diesel Particulate Filter.

Particulate matter, represented by soot particles, poses a significant global environmental threat, necessitating efficient control technology. Here, we innovatively designed and elaborately fabricated ordered hierarchical macroporous catalysts of Ce0.8Zr0.2O2 (OM CZO) integrated on a catalyzed diesel particulate filter (CDPF) using the self-assembly method. An oxygen-vacancy-enriched ordered macroporous Ce0.8Zr0.2O2 catalyst (VO-OM CZO) integrated CDPF was synthesized by subsequent NaBH4 reduction. The VO-OM CZO integrated CDPF exhibited a markedly enhanced soot oxidation activity compared to OM CZO and powder CZO coated CDPFs (T50: 430 vs 490 and 545 °C, respectively). The well-defined OM structure of the VO-OM CZO catalysts effectively improves the contact efficiency between soot and the catalysts. Meanwhile, oxygen vacancies trigger the formation of a large amount of highly reactive peroxide species (O22-) from molecular oxygen (O2) through electron abstraction from the three adjacent Ce3+ (3Ce3+ + Vö + O2 → 3Ce4+ + O22-), contributing to the efficient soot oxidation. This work demonstrates the fabrication of the ordered macroporous CZO integrated CDPF and reveals the importance of structure and surface engineering in soot oxidation, which sheds light on the design of highly efficient PM capture and removal devices.

High throughput clogging free microfluidic particle filter by femtosecond laser micromachining.

In recent decades, driven by the needs of industry and medicine, researchers have been investigating how to remove carefully from the main flow microscopic particles or clusters of them. Among all the approaches proposed, crossflow filtration is one of the most attractive as it provides a non-destructive, label-free and in-flow sorting method. In general, the separation performance shows capture and separation efficiencies ranging from 70% up to 100%. However, the maximum flow rate achievable (µL/min) is still orders of magnitude away from those suitable for clinical or industrial applications mainly due to the low stiffness of the materials typically used. In this work, we propose an innovative hydrodynamic-crossflow hybrid filter geometry, buried in a fused silica substrate by means of the femtosecond laser irradiation followed by chemical etching technique. The material high stiffness combined with the accuracy of our manufacturing technique allows the 3D fabrication of non-deformable channels with higher aspect ratio posts, while keeping the overall device dimensions compact. The filter performance has been validated through experiments with both Newtonian (water-based solution of microbeads) and non-Newtonian fluids (blood), achieving separation efficiencies of up to 94% and large particles recovery rates of 100%, even at very high flow rates (mL/h).

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

Consider a drone that aims to find an unknown number of static targets at unknown positions as quickly as possible. A multi-target particle filter uses imperfect measurements of the target positions to update an intensity function that represents the expected number of targets. We propose a novel receding-horizon planner that selects the next position of the drone by maximizing an objective that combines exploration and target refinement. Confidently localized targets are saved and removed from consideration along with their future measurements. A controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations as well as via a real-robot experiment in which a Parrot Mambo drone searches from a constant altitude for targets located on the floor. Target measurements are obtained on-board the drone using segmentation in the camera image, while planning is done off-board. The sensor model is adapted to the application. Both in the simulations and in the experiments, the novel framework works better than the lawnmower and active-search baselines.

A REVIEW ON THE EFFECTS OF DIETHYL ETHER ON THE CYCLIC VARIATIONS IN DIESEL ENGINES

This review study was compiled from the results of various researches regarding the use of diethyl ether as a fuel or fuel additive in diesel engines. Three techniques are employed for decreasing the hazardous exhaust emissions of diesel engines. The first technique for reducing harmful emissions involves improving combustion by modifying the engine design and fuel injection system; however, this process is both costly and time consuming. The second technique entails using various exhaust gas devices, such as a catalytic converter and diesel particulate filter. However, these devices can have a negative impact on the performance of diesel engines. The last way of reducing emissions and improving diesel engine performance is to use different alternative fuels or fuel additives. Diesel engines primarily release two major pollutants into the atmosphere: oxides of nitrogen (NOx) and particulate matter (PM). It is very difficult to reduce both NOx and PM at the same time in practice. The most research indicates that the most effective way to reduce emissions is to use alternative fuels such as natural gas, biogas, biodiesel, or adding additives to either alternative fuels or conventional diesel fuel. On the other hand, the fuel used has a significant influence on the cycle variations, which in turn has a major effect on the engine performance characteristics, fuel consumption, and exhaust emissions. It is thus essential to comprehensively assess the results of various investigations of alternative fuels or fuel additives before implementation. This study particularly focuses on the use of diethyl ether in diesel engines as fuel or fuel additive in different diesel engine fuels. This review study investigates the effects of the addition of diethyl ether on the cyclic variations, according to the literature.

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

An Adaptive Tracking Method for Moving Target in Fluctuating Reverberation Environment

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method.

Particle-Filter-Based Fault Diagnosis for the Startup Process of an Open-Cycle Liquid-Propellant Rocket Engine

This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.

Superhydrophobic Materials for Self-cleaning Applications: A Review

Abstract: Since the discovery of the “lotus effect,” the superhydrophobic structure of the research is more and more profound. When the droplets fall on the surface, it will not occur as a wetting phenomenon. Making full use of this feature will achieve the self-cleaning effect of the object surface. In the dust filtration problem, it is possible to spray superhydrophobic materials on the surface of the equipment using superhydrophobic self-cleaning characteristics to change the filtering method and to improve the self-cleaning efficiency. This paper describes the methods of preparing superhydrophobic materials, self-cleaning methods, and the application of superhydrophobic materials in the field of self-cleaning. Based on the summary of patents and papers, this paper focuses on the filtration and self-cleaning effect of superhydrophobic materials on dust particles in humid environments. With a simple structure, high filtration efficiency, and long service life, the super sparse/hydrophilic hybrid mesh can work continuously without manual operation. It can effectively filter dust particles and improve air quality in humid environments. Coal powder condensation experiments in this paper proved that the application of superhydrophobic materials in the field of self-cleaning yielded significant results. When superhydrophobic materials are sprayed on copper-based filters, the hydrophilic line mixes with the hydrophobic region, greatly improving the self-cleaning efficiency.

Jump Clustering, Information Flows, and Stock Price Efficiency

Abstract We study the clustering behavior of stock return jumps modeled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterized by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.

Salience Interest Option: Temporal abstraction with salience interest functions

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.

GNSS Differential Code Bias Determination Using Rao‐Blackwellized Particle Filtering

AbstractThe Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is a near‐real‐time data assimilation model of the high latitude ionosphere, incorporating measurements from many instruments, including slant Total Electron Content measurements from ground‐based Global Navigation Satellite System (GNSS) receivers. These measurements have receiver‐specific Differential Code Biases (DCB) which must be resolved to produce an absolute measurement, which are resolved simultaneously with the ionospheric state using Rao‐Blackwellized particle filtering. These DCBs are compared to published values and to DCBs determined using eight different Global Ionospheric Maps (GIM), which show small but consistent systematic differences. The potential cause of these systematic biases is investigated using multiple experimental A‐CHAIM test runs, including the effect of plasmaspheric electron content. By running tests using the GIM‐derived DCBs, it is shown that using A‐CHAIM DCBs produces the lowest overall error, and that using GIM DCBs causes an overestimation of the topside electron density which can exceed 100% when compared to in situ measurements from DMSP.

Development of Particle Filters for Portable Air Purifiers by Combining Melt-Blown and Polytetrafluoroethylene to Improve Durability and Performance

Improving indoor air quality through the use of air purifiers has become a major focus, with emphasis on developing filters with high efficiency, high holding capacity, and low-pressure drop to improve the clean air delivery rate (CADR) for air purifiers. However, although most studies focused on developing media and evaluating their performance, few studies have reached the employment for a pleated filter. In this study, we newly synthesized flat media and pleated filters by combining polytetrafluoroethylene membrane (PT) and melt-blown (MB) materials (PM) and compared its initial performance to that of other air purifier filters (MB, glass fiber, and PT). Additionally, we analyzed how the performance changed after the particles were loaded. The initial efficiency of the PM filter showed a higher quality factor (QF) than the other filters. Furthermore, when more particles were loaded, the penetration of the PM did not change. These results demonstrate the potential of the PM. However, the CADR and submicron-sized (0.02–0.113 μm) CADR (sCADR) were highest for the MB filter due to the initial pressure drop. Therefore, additional improvements are required to apply the PM in air purifiers. However, the results suggest that the PM can be a new alternative for air purifier filters used in medical centers or facilities with vulnerable populations where a high-efficiency particle air (HEPA) filter must be used.

An Enhanced Multi-target Multi-Bernoulli Particle Filtering for Direction of Arrival Tracking in the Presence of Impulsive Noise

An Enhanced Multi-target Multi-Bernoulli Particle Filtering for Direction of Arrival Tracking in the Presence of Impulsive Noise

Autonomous underwater vehicle link alignment control in unknown environments using reinforcement learning

AbstractHigh‐speed underwater wireless optical communication holds immense promise in ocean monitoring and surveys, providing crucial support for the real‐time sharing of observational data collected by autonomous underwater vehicles (AUVs). However, due to inaccurate target information and external interference in unknown environments, link alignment is challenging and needs to be addressed. In response to these challenges, we propose a reinforcement learning‐based alignment method to control the AUV to establish an optical link and maintain alignment. Our alignment control system utilizes a combination of sensors, including a depth sensor, Doppler velocity log (DVL), gyroscope, ultra‐short baseline device, and acoustic modem. These sensors are used in conjunction with a particle filter to observe the environment and estimate the AUV's state accurately. The soft actor‐critic algorithm is used to train a reinforcement learning‐based controller in a simulated environment to reduce pointing errors and energy consumption in alignment. After experimental validation in simulation, we deployed the controller on an actual AUV called Tri‐TON. In experiments at sea, Tri‐TON maintained the link and angular pointing errors within 1 m and , respectively. Experimental results demonstrate that the proposed alignment control method can establish underwater optical communication between AUV fleets, thus improving the efficiency of marine surveys.

Reducing Indoor Particulate Air Pollution Improves Student Test Scores: A Randomized Double-Blind Crossover Study.

Short-term exposure to air pollution is associated with a decline in cognitive function. Standardized test scores have been employed to evaluate the effects of air pollution exposure on cognitive performance. Few studies aimed to prove whether air pollution is responsible for reduced test scores; none have implemented a "gold-standard" method for assessing the association such as a randomized, double-blind intervention. This study used a "gold-standard" method─randomized, double-blind crossover─to assess whether reducing short-term indoor particle concentrations results in improved test scores in college students in Tianjin, China. Participants (n = 162) were randomly assigned to one of two similar classrooms and completed a standardized English test on two consecutive weekends. Air purifiers with active or sham (i.e., filter removed) particle filtration were placed in each classroom. The filtration mode was switched between the two test days. Linear mixed-effect models were used to evaluate the effect of the intervention mode on the test scores. The results show that air purification (i.e., reducing PM) was significantly associated with increases in the z score for combined (0.11 [95%CI: 0.02, 0.21]) and reading (0.11 [95%CI: 0.00, 0.22]) components. In conclusion, a short-term reduction in indoor particle concentration led to improved test scores in students, suggesting an improvement in cognitive function.

Reliable urban vehicle localization under faulty satellite navigation signals

Reliable urban navigation using global navigation satellite system requires accurately estimating vehicle position despite measurement faults and monitoring the trustworthiness (or integrity) of the estimated location. However, reflected signals in urban areas introduce biases (or faults) in multiple measurements, while blocked signals reduce the number of available measurements, hindering robust localization and integrity monitoring. This paper presents a novel particle filter framework to address these challenges. First, a Bayesian fault-robust optimization task, formulated through a Gaussian mixture model (GMM) measurement likelihood, is integrated into the particle filter to mitigate faults in multiple measurement for enhanced positioning accuracy. Building on this, a novel test statistic leveraging the particle filter distribution and the GMM likelihood is devised to monitor the integrity of the localization by detecting errors exceeding a safe threshold. The performance of the proposed framework is demonstrated on real-world and simulated urban driving data. Our localization algorithm consistently achieves smaller positioning errors compared to existing filters under multiple faults. Furthermore, the proposed integrity monitor exhibits fewer missed and false alarms in detecting unsafe large localization errors.

Advanced optical photothermal infrared spectroscopy for comprehensive characterization of microplastics from intravenous fluid delivery systems

Growing attention is being directed towards exploring the potential harmful effects of microplastic (MP) particles on human health. Previous reports on human exposure to MPs have primarily focused on inhalation, ingestion, transdermal routes, and, potentially, transplacental transfer. The intravenous transfer of MP particles in routine healthcare settings has received limited exploration in existing literature. Standard hospital IV system set up with 0.9 % NaCl in a laminar flow hood with MP contamination precautions. Various volumes of 0.9 % NaCl passed through the system, some with a volumetric pump. Fluid filtered with Anodisc filters washed with isopropyl alcohol. The IV cannula was immersed in Mili-Q water for 72 h to simulate vein conditions. Subsequently, the water was filtered and washed. Optical photothermal infrared (O-PTIR) microspectroscopy is used to examine filters for MP particles. All filters examined from the IV infusion system contained MP particles, including MPs from the polymer materials used in the manufacture of the IV delivery systems (polydimethylsiloxane, polypropylene, polystyrene, and polyvinyl chloride) and MP particles arising from plastic resin additives (epoxy resin, polyamide resin, and polysiloxane-containing MPs). The geometric mean from the extrapolated result data indicated that approximately 0.90 MP particles per mL of 0.9 % NaCl solution can be administered through a conventional IV infusion system in the absence of a volumetric pump. However, with the implementation of a pump, this value may increase to 1.57 particles per mL. Notably, over 72 h, a single cannula was found to release approximately 558 MP particles including polydimethylsiloxane, polysiloxane-containing MPs, polyamide resin, and epoxy resin. Routine IV infusion systems release microplastics. MP particles are also released around IV cannulas, suggesting transfer into the circulatory system during standard IV procedures.

Study of soot dynamic behavior and catalytic regeneration in diesel particulate filters

Catalytic diesel particulate filters (CDPFs) are key technologies for controlling particulate matter emissions. Nevertheless, it is challenging to experimentally observe the internal working states of the CDPF, which hinders comprehensive research on the flow, capture, and regeneration characteristics. In this study, we utilized quartet structure generation set to generate two-dimensional porous walls, lattice Boltzmann method to simulate CDPF flow and diffusion, cellular automata to simulate soot motion, and global reaction kinetics to simulate soot oxidation. The accuracy of the model is validated through flow, combustion, and pressure drop experiments. We propose a new concept of local porosity, where the porosity of a partially porous wall is used to represent the filtering parameters of that part. These findings indicate that porosity dominates filtering. A higher local porosity results in increased near-wall velocity and greater deposition of soot particles. Similarly, alterations in local porosity and deposition mutually influence each other. The velocity distribution and pressure difference at the inlet and outlet are directly influenced by the upstream velocity. Compared with velocity and porosity, particle diameter has less effect on deposition location, but more effect on deposition efficiency. Active regeneration (800 K) occurs approximately 31.17 times more quickly than passive regeneration (600 K) in the absence of a catalyst. The platinum-based catalyst significantly enhanced the soot oxidation rate by oxidizing NO to NO2, resulting in a 3.62-fold increase in passive regeneration and an estimated 1.17-fold increase in active regeneration. These findings are crucial for developing efficient CDPF systems and for reducing particulate emissions.

Bio-based hybrid planar truss: Experimental testing, FE modeling and Bayesian model updating

This paper examines the cyclic behavior of a bio-based truss structure with slotted steel plate bolted connections through a comprehensive approach involving experimental testing, FE modeling, and Bayesian model updating. Experimental tests were performed on both connection and truss levels to determine the cyclic behavior. For the joint level, a total of four distinct joint configurations were tested to determine their axial hysteretic behavior. For the truss level, cyclic tests were carried out on four hybrid planar trusses composed of the four types of truss joints. To define the axial hysteretic behavior of the joints, a robust numerical model is first defined accounting for hysteretic behavior in OpenSees. The proposed model can capture sliding, contact, pinching, strength degradation, and failure behavior. A model reduction via parameter sensitivity analysis is first performed. A Bayesian parameter identification based on Sequential Monte Carlo method was performed based on cyclic truss joints tests. A model of the hybrid truss was developed in OpenSees by integrating the joints and elastic truss elements. The developed model underwent validation using various experimental parameters, including the hysteresis curve, relative displacement at joints, and strain values at each chord. To correct the errors and the bias, a Bayesian model updating framework based on the Sequential Monte Carlo method is proposed herein to deal with model uncertainty. The proposed framework enables the acquisition of highly precise predictions by incorporating global and/or local responses while appropriately accounting for model uncertainties.