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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.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Evaluation of Algorithms for Compressing Hyperspectral Data

With EO-1 Hyperion in orbit NASA is showing their continued commitment to hyperspectral imaging (HSI). As HSI sensor technology continues to mature, the ever-increasing amounts of sensor data generated will result in a need for more cost effective communication and data handling systems. Lockheed Martin, with considerable experience in spacecraft design and developing special purpose onboard processors, has teamed with Applied Signal & Image Technology (ASIT), who has an extensive heritage in HSI spectral compression and Mapping Science (MSI) for JPEG 2000 spatial compression expertise, to develop a real-time and intelligent onboard processing (OBP) system to reduce HSI sensor downlink requirements. Our goal is to reduce the downlink requirement by a factor > 100, while retaining the necessary spectral and spatial fidelity of the sensor data needed to satisfy the many science, military, and intelligence goals of these systems. Our compression algorithms leverage commercial-off-the-shelf (COTS) spectral and spatial exploitation algorithms. We are currently in the process of evaluating these compression algorithms using statistical analysis and NASA scientists. We are also developing special purpose processors for executing these algorithms onboard a spacecraft.

Cook, Sid↗
DOE OSTI2018

Antarctic surface reflectivity calculations and measurements from the ANITA-4 and HiCal-2 experiments

We report that the balloon-borne HiCal radio-frequency (RF) transmitter, in concert with the ANITA radio-frequency receiver array, is designed to measure the Antarctic surface reflectivity in the RF wavelength regime. The amplitude of surface-reflected transmissions from HiCal, registered as triggered events by ANITA, can be compared with the direct transmissions preceding them by O(10) microseconds, to infer the surface power reflection coefficient R. The first HiCal mission (HiCal-1, Jan. 2015) yielded a sample of 100 such pairs, resulting in estimates of R at highly glancing angles (i.e., zenith angles approaching 90°), with measured reflectivity for those events which exceeded extant calculations [P. W. Gorham et al., Journal of Astronomical Instrumentation, 1740002 (2017)]. The HiCal-2 experiment, flying from December 2016–January 2017, provided an improvement by nearly 2 orders of magnitude in our event statistics, allowing a considerably more precise mapping of the reflectivity over a wider range of incidence angles. We find general agreement between the HiCal-2 reflectivity results and those obtained with the earlier HiCal-1 mission, as well as estimates from Solar reflections in the radio-frequency regime [D. Z. Besson et al., Radio Sci. 50, 1 (2015)]. In parallel, our calculations of expected reflectivity have matured; herein, we use a plane-wave expansion to estimate the reflectivity R from both a flat, smooth surface (and, in so doing, recover the Fresnel reflectivity equations) and also a curved surface. Lastly, multiplying our flat-smooth reflectivity by improved Earth curvature and surface roughness corrections now provides significantly better agreement between theory and the HiCal-2 measurements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Effect of Boundary-Layer Bleed Hole Inclination Angle and Scaling on Flow Coefficient Behavior

Phase II data results of the Fundamental Inlet Bleed Experiments study at NASA Glenn Research Center are presented which include flow coefficient behavior for 21 bleed hole configurations. The bleed configurations are all round holes with hole diameters ranging from 0.795 to 6.35 mm, hole inclination angles from 20deg to 90deg, and thickness-to-diameter ratios from 0.25 to 2.0. All configurations were tested at a unit Reynolds number of 2.46 10(exp 7)/m and at discrete local Mach numbers of 1.33, 1.62, 1.98, 2.46, and 2.92. Interactions between the design parameters of hole diameter, hole inclination angle, and thickness-to-diameter as well as the interactions between the flow parameters of pressure ratio and Mach number upon the flow coefficient are examined, and a preliminary statistical model is proposed. An existing correlation is also examined with respect to this data.

Eichorn, Michael B.↗
DOE OSTI2025

Measuring the Muon Neutrino Magnetic Moment in the NOvA Near Detector

Measuring an enhanced neutrino magnetic moment would be a clear indication of physics beyond the Standard Model, shedding light on the correct beyond Standard Model theory or the potential Majorana nature of neutrinos. It would manifest in the NOvA near detector as an excess of neutrino-on-electron elastic scattering interactions at low electron recoil energies. Leveraging an intense and highly pure muon neutrino beam, along with a finely segmented liquid scintillator detector technology specifically designed for electromagnetic shower separation, enables NOvA to achieve a potentially world-leading sensitivity in probing the effective muon neutrino magnetic moment. This analysis, based on neutrino data collected between 2014 and 2021, corresponding to an exposure of $13.8\times10^{20}$ protons-on-target, yields a result consistent with the Standard Model-only hypothesis with a p-value of 0.31. An upper limit on the effective muon neutrino magnetic moment is set at $\mu_{\nu_\mu}<19.1\times 10^{-10}\mu_B$ at $90\%$ confidence level. Despite facing statistical limitations stemming from low cross section of the signal process, systematic uncertainties have a significant impact on this result. To address these challenges, the NOvA Test Beam experiment focuses on mitigating some of the largest systematic uncertainties within NOvA by investigating particle interactions and energy deposition in a small-scale replica NOvA detector. This thesis describes the calibration of the NOvA Test Beam detector, which is a crucial step in analysing the Test Beam data before they can be utilised to reduce NOvA systematic uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Quality quandaries: Statistical detective work to understand the isotopic ratios in Drum 68660 and the radioactive release at WIPP

An incident at the Department of Energy's Waste Isolation Pilot Plant (WIPP) in 2014 resulted in the release of radioactive material into the environment. Initially, it was known that at least one drum in WIPP, identified as drum 68660, was involved. However, questions remained. Could the air-monitor isotopic ratios measured in WIPP at the time of the release be explained by materials in drum 68660 or were other drums involved? Could internal conditions in drum 68660 have caused the breach? What were the implications for 68660's sister drum? These questions needed to be answered as quickly as possible. Here, this analysis, which was completed in three weeks, combined combinatorics and uncertainty analysis to provide scientists with the timely evidence they needed to either answer these important questions or to design experiments to answer them.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Abstract Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Thermal-hydraulic analyses of MIT reactor LEU transition cycles

The Massachusetts Institute of Technology Reactor (MITR) is a research reactor in Cambridge, Massachusetts designed primarily for experiments using neutron beam and in-core irradiation facilities. In the framework of non-proliferation policy, research and test reactors have started a program to convert highly enriched uranium (HEU) fuel to low-enriched uranium (LEU) fuel. A new type of LEU fuel based on a high-density alloy of uranium and molybdenum (U-10Mo) is expected to allow conversion. A transition core plan, from 22 fresh LEU fuel elements to 24 in an equilibrium state, is proposed. This study evaluates the thermal-hydraulic safety margins of each transition cycle and state (i.e. beginning, middle and end). The STAT7 and RELAP5 codes are used in the study. STAT7 provides an integrated platform for statistical propagation of uncertainties in determining steady-state thermal-hydraulic margins. A RELAP5 model was created to verify MITR's STAT7 model, and provide transient analyses. The steady-state analyses determined that, during the entire transition from a fresh to equilibrium core, the Limiting System Safety Settings (LSSS) has a 10.2% margin to the minimum power at which onset of nucleate boiling (ONB) is precluded at 3σ confidence. The loss of flow (LOF) transient, which is the most limiting anticipated transient for the MITR, was simulated. With a conservative initial state of the reactor (operating at μ + 3σ power level and LSSS mass flow rate), the maximum calculated centerline fuel temperature was 115 degrees C, which is a significantly lower value than the blistering limit of 350 degrees C. The cladding wall temperatures did not exceed the ONB temperature throughout the transient. Therefore, during LOF, no nucleate boiling is expected, precluding any critical heat flux trigger. Overall, this study indicates that the proposed LEU transition core specifications have significant margin to thermal-hydraulic limits, during steady-state operation and the LOF transient.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Tunable Nanostructures from Inverse Surfactants

Hierarchical materials in the natural world are often made through the self-assembly of amphiphilic molecules. Achieving similar structural complexity in synthetic materials requires understanding how various molecular parameters affect assembly behavior. In recent years, inverse surfactants─molecules with hydrophobic head groups and hydrophilic macromolecular tails─have been shown to self-assemble into supramolecular assemblies in aqueous solutions that show promise for a number of applications, including drug delivery. Here, we build an understanding of the morphological phase diagram of inverse surfactants using insights from scattering experiments, computer simulations, and statistical mechanics. The scattering and simulation results reveal that changing the headgroup size is an important molecular knob in controlling morphological transitions. The molecular size ratio of the hydrophobic group to the hydrophilic group emerges as a crucial dimensionless quantity in our theory and plays a determining role in setting the micelle structure and the transition from mesoscale to macroscale aggregates. Our minimal theory is able to qualitatively explain the key features of the morphological phase diagram, including the prevalence of fiber-like structures in comparison to spherical and planar micelles. Together, these findings provide a more complete picture of the molecular dependencies of assemblies of inverse surfactants, which we hope may aid in the de novo design of supramolecular structures.

Christakopoulos, Panagiotis [ORNL] (ORCID:00090004↗
DOE OSTI2025

A New Measurement of the Neutron Electric Form Factor with the Super Bigbite Spectrometer Apparatus

Protons and neutrons, collectively known as nucleons, make up the nuclei at the core of atoms which form our world. The nucleon has been under intensive study for over 100 years, and yet we still do not fully understand the internal dynamics which govern properties like its spin or its mass-which contributes to almost all of the visible mass in the universe. These dynamics are governed by quantum chromodynamics(QCD), the predictions of which are experimentally tested at high energy accelerator facilities such as Jefferson Lab. TheGEN-II experiment (E12-09-016) is one such experiment. GEN-II is part of the Super Bigbite Spectrometer (SBS) experimental form factor pro gramme taking place in Hall A at Jefferson Lab, which aims to make precision measurements of the nucleon electromagnetic form factors(EMFFs)at record high values of squared four momentum transfer ¿2. EMFFs describe the electric and magnetic moment distributions within the nucleon. They can be measured through elastic electron scattering off the nucleon, and describe the recoil response of the target nucleon at a given energy scale. GEN-II is a double polarized semi-exclusive beam target asymmetry (BTA) experiment, seeking to measure the electric form factor of the neutron,¿¿ ¿,at three new values of squared four-momentum transfer¿2 =2.92,6.74and9.82GeV². The latter two points being at record high ¿2. The form factor is determined through measuring the BTA of quasielliptical scattering of a neutron from a polarized nuclear target. The experiment utilized the CEBAF accelerator to produce longitudinally polarized electrons up to ~85% polarization, which were scattered off neutrons within a novel polarized helium-3 (³He) target. This new polarized ³He target was employed by building on the technology of its precursors which existed in similar preceding experiments. This target was designed to operate at the high luminosities typical of Hall A, and reached a record breaking combination of polarization and beam intensity known as figure of merit, three times larger than those predecessors. The SBS collaboration designed and constructed two brand new high acceptance spectrometers for these experiments, an electron arm named Bigbite (BB) and a hadron arm named Super Bigbite. Both spectrometers featured a large acceptance EM dipole magnet, and complementary detector systems. The electron arm contained gaseous electronmultipliers(GEMs) which were used for high precision tracking of the scattered electrons, a heavy gas cherenkov (GRINCH) which was used for PID between electrons and pions, a plastic i ii scintillator timing hodoscope to provide high resolution timing of the start of events, and a pair of EM calorimeters (BBCal) which provided energy measurements of detected particles, and provided the experimental trigger. The hadron arm also contained a system of GEMs which will be utilized for future SBS experiments, and a hadron calorimeter designed to provide position, timing and energy measurements of the recoiling nucleon. The calibration of all detector subsystems, beam and target data is discussed, with a focus on novel timing calibrations to the hodoscope and hadron calorimeter. An analysis of selecting quasielliptical events and suppressing background contributions from a number of sources which contaminate the final event sample is given. The largest irreducible backgrounds are found to be from misidentified protons, timing accidentals and inelastic events. The physical asymmetry is measured and used to extract a value for the form factor ratio ¿¿ ¿/¿¿ ¿. High precision ¿2 data for ¿¿ ¿ is used to then extract ¿¿ ¿. This work finds at ¿2 = 2.92 GeV2 that ¿¿ ¿ = 0.0129+0.0019 -0.0020. This result is in statistical agreement with existing fits to world data, and predictions from the constituent quark model and Dyson–Schwinger equations, in this region of ¿2.

Penman, Gary [Univ. of Glasgow, Scotland (United K↗
NASA NTRS2019

Evaluation of the Impacts of Biomass Burning Aerosols on the NASA GEOS Sub-Seasonal Climate Forecasts

Emission sources of trace gases and aerosol particles in the South American (SA)and African (Af) continents have a strong seasonal and space variability associated with the extensive vegetation fires activities. In both continents, during the austral winter, the fires affect mainly tropical forest and savannah-type biomes and are mostly associated with deforestation and agricultural/pasture land management. Smoke aerosol particles, on average, contribute to at least 90% of the total aerosol optical depth (AOD) in the visible spectrum in the case of the South America regional smoke. Smoke aerosols also act as cloud condensation nuclei affecting cloud microphysics properties and therefore, changing the radiation budget, hydrological cycle and global circulation patterns over disturbed areas (Kaufman, 1995; Rosenfeld, 1999; Andreae,et al., 2004; Koren et al., 2004, Zhang, 2008; Ott et al., 2010; Randles et al., 2013). This study aims to evaluate and quantify the impact of including a comprehensive emission field of biomass burning aerosol on the performance of a seasonal climate forecast system, not only regarding the AOD itself but mainly on the meteorological state variable (e.g., precipitation and temperature). To address the questions put above, we designed two numerical experiments: 1- named"AERO_CTL" which applies the Quick Fire Emissions Dataset (QFED) emissions estimated with intra-diurnal variation (hereafter, BBE), and 2- named "AERO_CLM" where the sourcee mission is based on a climatology of the QFED emissions, with only monthly variation(hereafter, BBCLIM). Hindcast simulations were produced using the Goddard Earth ObservingSystem global circulation model, version 5, sub-seasonal to seasonal (GEOS5-S2S) system with a nominal spatial resolution of 56km (Rienecker et al., 2008). In both experiments, the aerosol feedbacks from cloud developments and radiation interactions were accounted. The two experiments consisted of 4 members each and ran from June to November spanning over the years 2000 to 2015. Model performance was evaluated by calculating statistical metrics on the mean area of SA and Af. Our results demonstrated that the skill model in predicting AOD is significantly improve when BBE source emission is applied over SA, but not over the Afcontinent. Over SA, the correlation between the AERO_CTL model configuration and MERRA-2 is 0.93 (R2= 0.86, RMS=0.02, BIAS=0.01), while the AERO_CLM model presents a value of0.81 (R2= 0.65, RMS=0.04, BIAS=0.06). However, the AERO_CTL experiment better represents the inter-annual variability of the AOS in both regions. The gain of the skill in predicting the AOD by the AERO_CTL experiment is also seen in some meteorological variables. We observed an increase in the model skill in predicting the 2-meter temperature and precipitation of up to 0.3 for the AERO_CTL experiment in comparison to the AERO_CLM. AERO_CLM. According to the analyzed hindcast, we inferred that representing the BBE more realistically implies in a significant gain of skills in the seasonal climate forecasting over SA and Af continents.

Aerosol↗
DOE OSTI2016

Ordered Self-Similar Patterns in Anisotropic Stochastic Growth

Here, we propose an anisotropic stochastic growth model to rationalize the anisotropic self-assembly of supramolecules to form elongated two-dimensional ribbon structures in a recent experiment. The model exhibits distinct growth scenarios that are critically controlled by the ratio of the transverse and the longitudinal growth rate. In the regime of suppressed transverse growth, the model generates the experimentally observed elongated structures through layer-by-layer growing. We further observe the convergence of rough clusters toward smooth regular elliptic patterns by averaging over a number of independent growth processes. Remarkably, these resulting elliptic clusters are self-similar in each instantaneous moment in the growth process. Statistical analysis suggests that the realization of such ordered patterns does not rely on the delicate coordination of different parts in the cluster growth. The self-similarity phenomenon derived from this idealized model may have wider implications, notably in the designed clustering of various elementary building blocks with anisotropic interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1982

Geometrical aspects of the tribological properties of graphite fiber reinforced polymide composites

A latin square statistical experimental test design was used to evaluate the effect of temperature, load and sliding speed on the tribological properties of graphite fiber reinforced polymide (GFRPI) composite specimens. Hemispherically tipped composite riders were slid against 440 C HT stainless steel disks. comparisons were made to previous studies in which hemispherically tipped 440 C HT stainless steel riders were slid against GFRPI composite disks and to studies in which GFRPI was used as a liner in plain spherical bearings. The results indicate that sliding surface geometry is especially important, in that different geometries can give completely different friction and wear results. Load, temperature, and sliding distance were found to influence the friction and wear results but sliding speed was found to have little effect. Experiments on GFRPI riders with 10 weight percent additions of graphite fluoride showed that this addition has no effect on friction and wear.

Fusaro, R. L.↗
NASA NTRS2001

UNIPASS for AvSP? A Broader View

UNIPASS is a general-purpose probabilistic computer program consisting of three major modules, including preprocessor, solver and postprocessor. UNIPASS contains a user-friendly Graphical User Interface (GUI), numerous state-of-the-art probabilistic analysis techniques, a large library of statistical distributions and a function module with a large library of support functions that can easily define any complex limit-state function in a scripting FORTRAN-like syntax format. Its inverse probability analysis and sensitivities analysis capabilities make it a powerful design aid in any product cycle. Its precise numerical analysis engine is accurate enough to push the failure probabilities of a design to well below 10 (exp -50). UNIPASS is equipped with advanced artificial intelligence that is designed to handle systems with an essentially unlimited number of random variables with ease and efficiency. Its modular arrangement allows you to tailor an analysis to the desired level of accuracy and efficiency. The depth and comprehensiveness of UNIPASS are built upon the decades of experience and expertise of industry leaders including Boeing Aircraft, NASA and the DoD. Its rich content also makes UNIPASS a valuable instructional tool for random processes and probabilistic mechanics. The topics include: 1) Reliability in AvSP; 2) Role of UNIPASS in AvSP; and 3) Examples. This paper is in viewgraph form.

Wu, N. Eva↗
DOE OSTI2014

Statistical modelling of discharge behavior of atmospheric pressure dielectric barrier discharge

In this work, stochastic behavior of atmospheric pressure dielectric barrier discharge (DBD) has been investigated. The experiment is performed in a DBD reactor consisting of a pair of stainless steel parallel plate electrodes powered by a 50 Hz ac high voltage source. Current pulse amplitude distributions for different space gaps and the time separation between consecutive current pulses are studied. A probability distribution function is proposed to predict the experimental distribution function for the current pulse amplitudes and the occurrence of the transition regime of the pulse distribution. Breakdown voltage at different positions on the dielectric surface is suggested to be stochastic in nature. The simulated results based on the proposed distribution function agreed well with the experimental results and able to predict the regime of transition voltage. This model would be useful for the understanding of stochastic behaviors of DBD and the design of DBD device for effective operation and applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2016

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Model ensembles↗
DOE OSTI2015

EXTENDING LEAN AND EGR-DILUTE OPERATING LIMITS OF A MODERN GDI ENGINE USING A LOW-ENERGY TRANSIENT PLASMA IGNITION SYSTEM

The efficiency improvement and emissions reduction potential of lean and EGR dilute operation of spark-ignition gasoline engines is well understood and documented. However, dilute operation is generally limited by deteriorating combustion stability with increasing inert gas levels. The combustion stability decreases due to reduced mixture flame speeds resulting in significantly increased combustion initiation periods and burn durations. A study was designed and executed to evaluate the potential to extend lean and EGR-dilute limits using a low-energy transient plasma ignition system. The low-energy transient plasma was generated by nano-second pulses and its performance compared to a conventional transistorized coil ignition system operated on an automotive, gasoline direct injection (GDI) single-cylinder research engine. The experimental assessment was focused on steady-state experiments at the part load condition of 1500 rpm 5.6 bar IMEP, where dilution tolerance is particularly critical to improving efficiency and emissions performance. Experimental results suggest that the energy delivery process of the low-energy transient plasma ignition system significantly improves part load dilution tolerance by reducing the early flame development period. Statistical analysis of relevant combustion metrics was performed in order to further investigate the effects of the advanced ignition system on combustion stability. Results confirm that at select operating conditions EGR tolerance and lean limit could be improved by as much as 20% (from 22.7 to 27.1% EGR) and nearly 10% (from λ=1.55 to 1.7) with the low-energy transient plasma ignition system.

Sevik, James Michael↗
DOE OSTI2024

Reversible Electron-Beam Patterning of Colloidal Nanoparticles at Fluid Interfaces

The directed self-assembly of colloidal nanoparticles (NPs) using external fields guides the formation of sophisticated hierarchical materials but becomes less effective with decreasing particle size. As an alternative, electron-beam-driven assembly offers a potential avenue for targeted nanoscale manipulation, yet remains poorly controlled due to the variety and complexity of beam interaction mechanisms. Here, we investigate the beam–particle interaction of silica NPs pinned to the fluid–vacuum interface of ionic liquid droplets. In these experiments, scanning electron microscopy of the droplet surface resolves NP trajectories over space and time while simultaneously driving their reorganization. With this platform, we demonstrate the ability to direct particle transport and create transient, reversible colloidal patterns on the droplet surface. By tuning the beam voltage, we achieve precise control over both the strength and sign of the beam–particle interaction, with low voltages repelling particles and high voltages attracting them. This response stems from the formation of well-defined solvent flow fields generated from trace radiolysis of the ionic liquid, as determined through statistical analysis of single-particle trajectories under varying solvent composition. Altogether, electron-beam-guided assembly introduces a versatile strategy for nanoscale colloidal manipulation, offering new possibilities for the design of dynamic, reconfigurable systems with applications in adaptive photonics and catalysis.

36 MATERIALS SCIENCE↗