Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “particle filtering”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. Furthermore, a series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Development of ML FPGA Filter for Particle Identification and Tracking in Real Time

Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. We report work has started to evaluate an FPGA based Machine Learning (ML) algorithm for a real-time particle identification and tracking with Transition Radiation Detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology. GlueX trigger latency is 3.3 μs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

Portable x-ray fluorescence spectrometer for environmental monitoring of inorganic pollutants

A portable x-ray fluorescence spectrometer has a portable sensor unit containing a battery, a high voltage power supply, an x-ray tube which produces a beam x-ray radiation directed toward a target sample, and a detector for fluorescent x-rays produced by the sample. If a silicon-lithium detector is used, the sensor unit also contains either a thermoelectric or thermochemical cooler, or a small dewar flask containing liquid nitrogen to cool the detector. A pulse height analyzer (PHA) generates a spectrum of data for each sample consisting of the number of fluorescent x-rays detected as a function of their energy level. The PHA can also store spectrum data for a number of samples in the field. A processing unit can be attached to the pulse height analyzer to upload and analyze the stored spectrum data for each sample. The processing unit provides a graphic display of the spectrum data for each sample, and provides qualitative and/or quantitative analysis of the elemental composition of the sample by comparing the peaks in the sample spectrum against known x-ray energies for various chemical elements. An optional filtration enclosure can be used to filter particles from a sample suspension, either in the form of a natural suspension or a chemically created precipitate. The sensor unit is then temporarily attached to the filtration unit to analyze the particles collected by the filter medium.

Thornton, Michael G.↗
NASA NTRS2019

Simultaneous Localization and Mapping for Satellite Rendezvous and Proximity Operations Using Random Finite Sets

Future space missions require that spacecraft have the capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. The goal of this work is to demonstrate the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. The approach is to formulate satellite relative navigation and pose estimation as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. This work utilizes a filter developed using the framework of RFS which are well suited to multi-target SLAM operations, avoiding data association entirely. Relevant RPO scenarios with simulated flash LIDAR measurements are tested with a Probability Hypothesis Density (PHD) RFS filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer. Preliminary results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target. These results demonstrate the feasibility of RFS filtering for spacecraft relative navigation and motivate future studies which may expand to tracking space objects for space situational awareness, as well as relative navigation around small bodies.

Schlenker, Lauren↗
NASA NTRS2019

Simultaneous Localization and Mapping for Satellite Rendezvous and Proximity Operations using Random Finite Sets

Future space missions require that spacecraft have the capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. The goal of this work is to demonstrate the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. The approach is to formulate satellite relative navigation and pose estimation as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. This work utilizes a filter developed using the framework of RFS which are well suited to multi-target SLAM operations, avoiding data association entirely. Relevant RPO scenarios with simulated flash LIDAR measurements are tested with a Probability Hypothesis Density (PHD) RFS filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer. Preliminary results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target. These results demonstrate the feasibility of RFS filtering for spacecraft relative navigation and motivate future studies which may expand to tracking space objects for space situational awareness, as well as relative navigation around small bodies.

Schlenker, Lauren↗
NASA NTRS2025

Simultaneous Localization and Mapping for Spacecraft Rendezvous and Proximity Operations Using Random Finite Sets

Future space missions require spacecraft to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current navigation filters used for RPO can have difficulty when optical sensors are used, due to complications with data association, high measurement uncertainty, and clutter. This paper provides an initial demonstration of the feasibility of using random finite set (RFS) filters for spacecraft relative navigation and pose estimation. Spacecraft relative navigation is formulated as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. Simulated flash LIDAR measurements are processed using a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer over time. Results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario.

David E Gaylor↗
DOE OSTI2025

Studies of optical and chemical properties of aged and fresh biomass burning absorbing aerosols for climate models

Particulate emissions from burning fuels from sub-Saharan Africa is currently poorly characterized. With increasing population and industrialization, emissions are expected to grow. Optical methods for measuring light absorption in the ultraviolet (UV) and visible (vis) regions of the spectrum are crucial for understanding the optical properties of BB aerosols (Bond and Bergstrom, 2006; Lack and Cappa, 2010; Moschos et al., 2021). One such instrument is the dual-spot AE33 model aethalometer is a common filter-based instrument that can be used to measure aerosol absorption properties (Hansen et al., 1984). This instrument has many figures of merit; however, the method's limitations include potential biases from scattering within the filter material, multiple scattering effects of the particles, filter loading (i.e. particle shadowing), and the need for wavelength-specific correction factors (Cλ), which depend on accurate intercomparison with other in situ techniques (Bond et al., 1999; Collaud Coen et al., 2010). This work aims to: 1. Optically characterize BB aerosol generated from African fuel 2. Correct the aethalometer at multiple wavelengths 3. Parametrize these correction factors. 4. Combusting the same fuels with two difference tube furnace systems assess the general applicability of this flexible means of producing BB aerosol. This was accomplished by conducting laboratory studies at Los Alamos National Laboratory (LANL) and North Carolina A&T State University (NCA&T), where aethalometer measurements were compared to in situ optical measurements. Mass cross sections of BB aerosol from these under-studied fuels will be determined along with their wavelength dependence.

09 BIOMASS FUELS↗
NASA NTRS2016

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

Prognostics↗
NASA NTRSDate not supplied

Testing of the ISS’s Charcoal HEPA Integrated Particle Scrubber (CHIPS) Filters to Ascertain an Efficiency Curve and Most Penetrating Particle Size

High air quality is a top priority in hermetically sealed environments, such as the International Space Station (ISS). Aerosol particulate filtration on the ISS is achieved via 21 HEPA filters. Traditional HEPA filters have an efficiency of 99.97%, but HEPA filters aboard the ISS have been found to achieve 99.99% efficiency. This may be due to the flight-grade HEPA filters chosen or, less likely, that they are combined with activated charcoal filters for siloxane removal in a combination referred to as Charcoal HEPA Integrated Particle Scrubbers (CHIPS) filters. All filters have a Most Penetrating Particle Size (MPPS), but, until now, the MPPS and efficiency curve of the CHIPS filters was not known. The efficiency curve of the CHIPS filters was ascertained at NASA Glenn Research Center through standard salt crystal aerosolization. This method was selected over aerosolized oil droplets due to a desire to preserve the filter’s function for further testing. The CHIPS filters were tested as they are oriented on the ISS (activated charcoal filter followed by a flight-grade HEPA filter), with the HEPA portion alone, and with the activated charcoal portion alone. The results of this experiment are generally useful but can be especially useful for the microbiology and Environmental Control and Life Support Systems (ECLSS). This is due to microbial contamination being a potential concern in the air revitalization systems on the ISS. Knowing the MMPS of the CHIPS filters, in conjunction with levels of airborne microbes, is useful for predicting which and how many microbes are likely to get through the HEPA filters that sit in the beginning of the ISS airstream. It may also be useful to other groups at NASA to know how many particulates are able to pass through high-efficiency filtration, and even for healthcare cleanrooms and other air purification pursuits.

A Nicolas Whitlock↗
NASA NTRS2006

An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Monte Carlo↗
NASA NTRS1972

Regenerative particulate filter development

Development, design, and fabrication of a prototype filter regeneration unit for regenerating clean fluid particle filter elements by using a backflush/jet impingement technique are reported. Development tests were also conducted on a vortex particle separator designed for use in zero gravity environment. A maintainable filter was designed, fabricated and tested that allows filter element replacement without any leakage or spillage of system fluid. Also described are spacecraft fluid system design and filter maintenance techniques with respect to inflight maintenance for the space shuttle and space station.

Descamp, V. A.↗
DOE OSTI2023

Diesel Particulate Filter Durability Performance Comparison Using Metals Doped B20 vs. Conventional Diesel Part II: Chemical and Microscopic Characterization of Aged DPFs

This project's objective was to generate experimental data to evaluate the impact of metals doped B20 on diesel particle filter (DPF) ash loading and performance compared to that of conventional petrodiesel. The effect of metals doped B20 vs. conventional diesel on a DPF was quantified in a laboratory controlled accelerated ash loading study. The ash loading was conducted on two DPFs - one using ULSD fuel and the other on B20 containing metals dopants equivalent to 4 ppm B100 total metals. Engine oil consumption and B20 metals levels were accelerated by a factor of 5, with DPFs loaded to 30 g/L of ash. Details of the ash loading experiment and on-engine DPF performance evaluations are presented in the companion paper (Part I). The DPFs were cleaned, and ash samples were taken from the cleaned material. X-ray Fluorescence (XRF), X-Ray Photoelectron Spectroscopy (XPS) and X-Ray Diffraction (XRD) were conducted on the ash samples. Core samples were taken from the cleaned DPF and were subjected to scanning electron microscope energy dispersive x-ray spectroscopy (SEM-EDS) and XRF analysis. A comparison of the data from the two DPFs is presented. The XRD and XPS analysis showed that the compounds present in the ash from the two DPFs were nearly identical, though differing in concentrations. CaSO4 was the biggest component of the ash from both DPFs. The metals doped B20 fuel resulted in ash with similar characteristics to that deposited by the lube oil and did not appear to have any deleterious physical effects on the DPF substrate (did not penetrate the substrate).

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS↗
DOE OSTI2022

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection

Here, we present an approach for characterizing complex temporal behavior in the sensor measurements of a system in order to support detection of anomalies in that system. We first characterize typical behavior by extending a hidden Markov model-based approach to time series alignment. We then use a trace of that learned behavior to develop a particle filter that enables efficient estimation of the filtering distribution on the state space. This produces filtered residuals that can then be used in an anomaly detection framework. Our motivating example is the daily behavior of a building’s heating, ventilation, and air conditioning (HVAC) system, using sensor measurements that arrive every minute and induce a state space with 15,120 states. We provide an end-to-end demonstration of our approach showing improved performance of anomaly detection after application of alignment and filtering compared to the unaligned data. The proposed model is implemented as a computationally efficient R package alignts (align time series) built with R and Fortran 95 with OpenMP support.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Real-time ensemble microalgae growth forecasting with data assimilation

Accurate short-range (e.g., 7-day) microalgae growth forecasts will be beneficial for both production and harvesting of microalgae. This study developed an operational microalgae growth forecasting system with ensemble data assimilation (DA). The forecasting system was validated against observed Monoraphidium minutum 26B-AM growth in two outdoor pond cultures located in Mesa, Arizona, U.S. We first examined the relative roles of uncertainty in the meteorological forecast and initial conditions (i.e., algal concentration at the time of forecast) in the microalgae 7-day forecast and found initial conditions dominated the microalgae forecasting skill, suggesting the importance of implementing DA to improve initial condition characterization. To correct the systematic bias in biomass simulations, we developed a particle filter with bias estimation (PFBE) DA method to estimate biases and correct the model forecast. We found the DA forecasting system could improve the 7-day microalgae forecasting skill by about 85% on average compared to model forecasts without DA. These results suggest the potential accuracy of biomass growth forecasts may be sufficient to inform real-time operational decisions, such as harvesting planning, for commercial-scale microalgae production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Research to Address Technical Barriers to Expanded Markets for Biodiesel and Biodiesel Blends (CRADA Final Report)

NREL and the Clean Fuels Alliance America (CFAA) will work cooperatively to assess the effects of biodiesel blends on the performance of modern diesel engines. This work will include research to understand the impact of biodiesel blends on the operation and durability of particle filters and NO x control sorbents/catalysts, to quantify the effect on emission control systems performance, and to understand effects on engine component durability. This research was performed at NREL Renewable Fuels and Lubricants (REFUEL) laboratory (an engine testing laboratory) as well as at third party labs paid directly by CFAA with NREL as part of the project management team. Also, research to develop appropriate ASTM standards for biodiesel quality and stability was conducted in NREL’s bench scale fuel chemistry laboratory. The cooperative project involved laboratory testing and research at NREL using biodiesel from a variety of sources and in collaboration with a broad range of other stakeholders. In addition, NREL will work with NBB to set up an Industrial Steering Committee to design the scope for the various tasks and to provide technical oversight to these projects. NREL and NBB will cooperatively communicate the study results to as broad an audience as possible. This research benefits the public by expanding markets for a domestically produced low-carbon intensity fuel for use in diesel engines.

09 BIOMASS FUELS↗
DOE OSTI2024

Orbitrap LC-MS Analysis of Nanoparticle Composition at the EPCAPE Mount Soledad site between 04 18 2023 and 06 14 2023

Weekly peak lists containing m/z, intensity, and assigned formula for filter samples, size selected for sub-100 nm particles. Filters were collected daily between 4/18/23 and 6/14/23, grouped based on calendar week for extraction, and analyzed via Thermo Scientific Q Exactive Plus Orbitrap LC-MS. Formulas were assigned to background-corrected peak lists and restricted to CHONS/CHONSNa atoms for the negative and positive modes respectively. Filters were grouped into calendar weeks 0-8 with dates provided in README text file.

54 ENVIRONMENTAL SCIENCES↗