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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Smart manufacturing maturity models and their applicability: a review

The purpose of this paper is to review existing smart manufacturing (SM) maturity models' dimensions and maturity levels to assess their applicability and drawbacks. There are many maturity models available but many of them have not been validated or do not provide a useful guide or tool for applications. This gap creates the need for a review of the existing maturity model's applicability. Nineteen peer-reviewed maturity models related to “Digital Transformation,” “Industry 4.0” or “Smart Manufacturing” were selected based on a systematic literature review and five consulting firm models were selected based on the author's industry knowledge. The chosen models were analyzed to determine 10 categories of dimensions. Then they are assessed on a 1–5 scale for how applicable they are in the 10 categories of dimensions. The five “consulting firm” models have a first-mover advantage, are more widely used in industry and are more applicable, but some require payment, and they lack published details and validation. The 19 “peer reviewed” models are not as widely used, lack awareness in the industry and are not as easy to apply because of no web tool for self-assessment, but they are improving. The categories defined to characterize the models and facilitate comparisons for users include “Information Technology (IT) and Cyber-Physical System (CPS) and Data,” “Strategy and Organization,” “Supply Chain and Logistics,” “Products and Services,” “Culture and Employees,” “Technology and Capabilities,” “Customer and Market,” “Cybersecurity and Risk,” “Leadership and Management” and “Governance and Compliance.” The analyzed maturity models were particularly weak in the areas of cybersecurity, leadership and governance. Researchers and practitioners can use this review with consideration of their specific needs to determine if a maturity model is applicable or if a new model needs to be developed. The review can also aid in the development of maturity models through the discussion of each of the dimension categories. Finally, compared to existing reviews of SM maturity models, this research determines comprehensive dimension categories and focuses on applicability and drawbacks.

42 ENGINEERING↗
NASA NTRS1994

Large Eddy simulation of turbulence: A subgrid scale model including shear, vorticity, rotation, and buoyancy

The Reynolds numbers that characterize geophysical and astrophysical turbulence (Re approximately equals 10(exp 8) for the planetary boundary layer and Re approximately equals 10(exp 14) for the Sun's interior) are too large to allow a direct numerical simulation (DNS) of the fundamental Navier-Stokes and temperature equations. In fact, the spatial number of grid points N approximately Re(exp 9/4) exceeds the computational capability of today's supercomputers. Alternative treatments are the ensemble-time average approach, and/or the volume average approach. Since the first method (Reynolds stress approach) is largely analytical, the resulting turbulence equations entail manageable computational requirements and can thus be linked to a stellar evolutionary code or, in the geophysical case, to general circulation models. In the volume average approach, one carries out a large eddy simulation (LES) which resolves numerically the largest scales, while the unresolved scales must be treated theoretically with a subgrid scale model (SGS). Contrary to the ensemble average approach, the LES+SGS approach has considerable computational requirements. Even if this prevents (for the time being) a LES+SGS model to be linked to stellar or geophysical codes, it is still of the greatest relevance as an 'experimental tool' to be used, inter alia, to improve the parameterizations needed in the ensemble average approach. Such a methodology has been successfully adopted in studies of the convective planetary boundary layer. Experienc e with the LES+SGS approach from different fields has shown that its reliability depends on the healthiness of the SGS model for numerical stability as well as for physical completeness. At present, the most widely used SGS model, the Smagorinsky model, accounts for the effect of the shear induced by the large resolved scales on the unresolved scales but does not account for the effects of buoyancy, anisotropy, rotation, and stable stratification. The latter phenomenon, which affects both geophysical and astrophysical turbulence (e.g., oceanic structure and convective overshooting in stars), has been singularly difficult to account for in turbulence modeling. For example, the widely used model of Deardorff has not been confirmed by recent LES results. As of today, there is no SGS model capable of incorporating buoyancy, rotation, shear, anistropy, and stable stratification (gravity waves). In this paper, we construct such a model which we call CM (complete model). We also present a hierarchy of simpler algebraic models (called AM) of varying complexity. Finally, we present a set of models which are simplified even further (called SM), the simplest of which is the Smagorinsky-Lilly model. The incorporation of these models into the presently available LES codes should begin with the SM, to be followed by the AM and finally by the CM.

Canuto, V. M.↗
DOE OSTI2021

A new primer set for Clade I nosZ that recovers genes from a broader range of taxa

Denitrification is an important global N cycle process. The gene encoding NosZ that converts nitrous oxide (N 2 O) to N 2 has been widely used as a biomarker to study denitrifying communities. However, conventional PCR primers target a limited range of the genetically diverse Clade I nosZ, and the amplicons are too long for sequencing on current NGS platforms. To address these issues, here we developed a new PCR primer set that amplifies a 355-bp region of Clade I nosZ and captures broader taxonomic coverage than conventional primers in in silico tests. When compared with the widely used nosZF_nosZR_Rich_2003 set using the same soil samples and the same sequencing depth, the new set retrieved genes from four times more unique species, with consistently higher general diversity-based metrics. The new primer set performed well with different sequencing platforms (Ion Torrent and Illumina), and among a wide variety of soils from polar to tropical, desert to agricultural, and surface to a very low biomass subsoil, with significant differences in denitrifying community diversity and composition. This new primer set for Clade I together with the primers recently reported for Clade II by Chee-Sanford et al. provides a more comprehensive assessment of denitrifier gene hosts, their ecological patterns, and the degree of novelty in retrieved gene sequences.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

A fuzzy set preference model for market share analysis

Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share prediction).

Turksen, I. B.↗
DOE OSTI2024

Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimental spectra and draw physical insights. Recently, there has also been growing interest in building computational XAS databases to enable data analytics and machine learning applications. However, there are non-trivial differences among commonly used XAS simulation codes, both in underlying theoretical formalism and in technical implementation. Reliable and reproducible computational XAS databases require systematic benchmark studies. In this work, we benchmarked Ti K-edge XAS simulations of ten representative Ti-O binary compounds, which we refer to as the Ti-O-10 dataset, using three state-of-the-art codes: XSPECTRA, OCEAN and exciting. We systematically studied the convergence behavior with respect to the input parameters and developed a workflow to automate and standardize the calculations to ensure converged spectra. Our benchmark comparison considers a 35 eV spectral range starting from the K-edge onset, representative of widely used near-edge spectra. Quantitative comparison over this range is based on Spearman’s rank correlation score (r sp ). Our results show: (1) the two Bethe-Salpeter equation (BSE) codes (OCEAN and exciting) have excellent agreement with an average $r$ $sp$ of 0.998; (2) good agreement is obtained between the core-hole potential code (XSPECTRA) and BSE codes (OCEAN and exciting) with an average $r$ $sp$ of 0.990, and this smaller $r$ $sp$ reflects the noticeable differences in the main edge spectral shape that can be primarily attributed to the difference in the strength of the screened core-hole potential; (3) simulations from both methods overall reproduce well the main experimental spectral features of rutile and anatase, and the different treatments of the screened core-hole potential have visible impact on pre-edge intensities and the peak ratio of the main edge; (4) there exist moderate differences in the relative edge alignment of the three codes with a standard deviation of about 0.2 eV, which arise from multiple contributions including the frozen core approximation, final state effects, and different approximations used for the self-energy correction. In conclusion, our benchmark study provides important standards for first-principles XAS simulations with broad impact in data-driven XAS analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Single reader between-cases AUC estimator with nested data

The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.

Health Care Sciences & Services↗
DOE OSTI2020

Understanding and applying coulombic efficiency in lithium metal batteries

Coulombic Efficiency (CE) has been widely used in battery research as a quantifiable monitor to compare the properties and performances of materials, electrolytes, interfaces and the entire electrochemical cells. While CE with ultra high precision effectively predicts the lifespan and health for lithium ion (Li-ion) batteries, it is not necessarily true in rechargeable lithium (Li) metal batteries. A large variation of CE is often seen even from the same materials and electrolytes in Li metal cells. This work discusses the fundamental definition of CE in different configurations of Li metal-based cells. The similarities and differences of CE in Li-ion and Li metal cells have been clarified and used to understand the relevance/irrelevance between CE and cycling. Long-standing goal of this study is to help rational design of cell testing protocols and elaborate electrochemical results. The insights from this work are broadly applicable in many other rechargeable metal cells e.g., Zn, Mg and Na batteries to accelerate reseach innovation.

Xiao, Jie↗
DOE OSTI2020

A New General Paradigm for Understanding and Preventing Li Metal Penetration through Solid Electrolytes

The use of lithium (Li) or sodium (Na) metal anodes together with highly ion-conductive solid electrolytes (SEs) could provide batteries with a step improvement in volumetric and gravimetric energy densities. Unfortunately, these SEs face significant technical challenges, in large part because Li and Na dendrites can penetrate through SEs, leading to short circuits. The ability of such a soft material (Li or Na metal) to penetrate through ceramic is surprising from the point of view of models widely used in the Li-battery field. We introduce a concept, new to the battery field, for preventing penetration of lithium dendrites through SEs by putting the SE surfaces into a state of residual compressive stress. For a sufficiently high compressive stress, cracks have difficulty forming, and cracks that do form are forced to close, inhibiting dendrite penetration. This approach is widely used to solve commercially important stress corrosion cracking problems in metals and static fatigue problems in ceramics and glasses (e.g., Gorilla Glass). However, the technique will not be useful for SEs if the Li-ion transport rate through a SE is substantially reduced when the SE is under compression. Our molecular dynamics calculations for Li-ion transport through a common SE demonstrate that the introduction of even very high residual compressive stresses (~10 GPa) has only a modest effect on Li-ion transport kinetics, suggesting that this approach is viable and capable of providing a new paradigm for developing high-performance and mechanically stable SEs.

25 ENERGY STORAGE↗
DOE OSTI2026

Fluorescence Signatures of Rare Earth Metals during Precipitation in Various Conditions

Fluorescence spectroscopy is a widely used sensor methodology that analyzes light emitted from a compound or element as it decays from an excited state. This technique is very sensitive and selective, which is ideal to characterize analytes at lower limits of detection. Key example targets of significant industry and research interest include rare earth elements (REEs) such as dysprosium (Dy) and europium (Eu). These are widely used in advanced technologies including semiconductors, electric vehicle motors, lasers, and permanent magnets. Identifying new sources and responsible reutilization of REEs is essential, and new approaches to extract and recycle REEs could be notably enhanced through the integration of on-line sensors. The sensors can support faster process design, informed scale-up, and cost-effective deployment. This study covers the initial exploration of applying fluorescence-based on-line monitoring to REEs within a precipitation process. This study demonstrates the successful scale-up of a fluorescence -based sensing approach, from stationary cuvettes and small-volume microfluidic devices to continuous flow systems operating at the bench scale (10-25mL). This work also provides initial insight into the challenges of signal’s effects and utility within a turbid environment. Using a modular design for monitoring flowing solutions in a flow tube, fluorescence can be characterized for a variety of analytical targets. In this study, detection performance parameters between the cuvette and flow tube system were compared. Additionally, the response of Dy during precipitation by sodium bicarbonate in the two measurement designs was explored. This letter represents a starting point to bridge the gap between traditional fluorescence sensor measurements in a cuvette to future developments that explore the ability to integrate fluorescence sensors into extraction and separation processes at industrially relevant scales.

fluorescence↗
NASA NTRS2019

Diverse Photosynthetic Capacity of Global Ecosystems Mapped by Satellite Chlorophyll Fluorescence Measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V(sub cmax)). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V(sub cmax) is time-consuming and laborious; therefore, the spatiotemporal distribution of V(sub cmax) is still poorly understood due to limited measurements of V(sub cmax). In this study, we used a data assimilation approach to map the spatial variation of V(sub cmax) for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V(sub cmax) map, the mean V(sub cmax) value for each plant function type (PFT) is found to be comparable to a widely used N-derived V(sub cmax) dataset by Kattge et al. (2009). The gradient of V(sub cmax) along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V(sub cmax) are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V(sub cmax) values, is highlighted in this V(sub cmax) map. Legume plants are characterized with high V(sub cmax) values. This V(sub cmax) map also clearly illustrates the emerging soybean revolution in South America where V(sub cmax) is the highest among the world. The gradient of V(sub cmax) in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V(sub cmax), for global climate change studies.

Liming He↗
DOE OSTI2024

fluxfinder: An R Package for Reproducible Calculation and Initial Processing of Greenhouse Gas Fluxes From Static Chamber Measurements

Fluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high-frequency concentration data, there is a growing need for open-source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration-time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.

Wilson, Stephanie J.↗
NASA NTRS1990

Visualization of yeast chromosomal DNA

The DNA molecule is the most significant life molecule since it codes the blue print for other structural and functional molecules of all living organisms. Agarose gel electrophoresis is now being widely used to separate DNA of virus, bacteria, and lower eukaryotes. The task was undertaken of reviewing the existing methods of DNA fractionation and microscopic visualization of individual chromosonal DNA molecules by gel electrophoresis as a basis for a proposed study to investigate the feasibility of separating DNA molecules in free fluids as an alternative to gel electrophoresis. Various techniques were studied. On the molecular level, agarose gel electrophoresis is being widely used to separate chromosomal DNA according to molecular weight. Carl and Olson separate and characterized the entire karyotype of a lab strain of Saccharomyces cerevisiae. Smith et al. and Schwartz and Koval independently reported the visualization of individual DNA molecules migrating through agarose gel matrix during electrophoresis. The techniques used by these researchers are being reviewed in the lab as a basis for the proposed studies.

Lubega, Seth↗
DOE OSTI2023

Improving Mathematical Exposition of an Industrial-Scale Linear Program

Industrial-scale models require considerable setup time; hence, once built, they are used in myriad ways to consider closely related cases. In practice, the code for these models frequently evolves without appropriate notational choices, largely as a result of the lengthy development time of, and the number of individuals contributing to, their formulation. This leads to inefficiencies and obfuscates model structures that might be leveraged to expedite solutions. In this paper, we advocate for an emerging literature on model formulation “best practices” and present the reformulation of a widely used industrial-scale linear program. The efficient mathematical expression of this linear program, used to plan capacity expansion in the energy sector, allows for greater transparency of model structures and enhanced ability to identify computational performance improvements, as well as a lucid interpretation of its solutions. This type of formulation is employed in several mathematical programming courses at our university as an example of the advantages of best practices; the model more broadly is used widely to inform policy in the U.S. energy sector.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

A Multi-Level Parallelization Concept for High-Fidelity Multi-Block Solvers

The integration of high-fidelity Computational Fluid Dynamics (CFD) analysis tools with the industrial design process benefits greatly from the robust implementations that are transportable across a wide range of computer architectures. In the present work, a hybrid domain-decomposition and parallelization concept was developed and implemented into the widely-used NASA multi-block Computational Fluid Dynamics (CFD) packages implemented in ENSAERO and OVERFLOW. The new parallel solver concept, PENS (Parallel Euler Navier-Stokes Solver), employs both fine and coarse granularity in data partitioning as well as data coalescing to obtain the desired load-balance characteristics on the available computer platforms. This multi-level parallelism implementation itself introduces no changes to the numerical results, hence the original fidelity of the packages are identically preserved. The present implementation uses the Message Passing Interface (MPI) library for interprocessor message passing and memory accessing. By choosing an appropriate combination of the available partitioning and coalescing capabilities only during the execution stage, the PENS solver becomes adaptable to different computer architectures from shared-memory to distributed-memory platforms with varying degrees of parallelism. The PENS implementation on the IBM SP2 distributed memory environment at the NASA Ames Research Center obtains 85 percent scalable parallel performance using fine-grain partitioning of single-block CFD domains using up to 128 wide computational nodes. Multi-block CFD simulations of complete aircraft simulations achieve 75 percent perfect load-balanced executions using data coalescing and the two levels of parallelism. SGI PowerChallenge, SGI Origin 2000, and a cluster of workstations are the other platforms where the robustness of the implementation is tested. The performance behavior on the other computer platforms with a variety of realistic problems will be included as this on-going study progresses.

Hatay, Ferhat F.↗
DOE OSTI2020

Materials and technological processes for High-Gradient accelerating structures: new results from mechanical tests of an innovative braze-free cavity.

Pure oxygen-free high-conductivity copper is a widely used material for manufacturing accelerating cavities working at room temperature. Several studies attempted to explain limitations associated with the maximum allowed field gradients and the behaviour of vacuum RF breakdown in copper accelerating structures through generation and movement of dislocations under stresses associated with RF electric and magnetic fields. Pure copper and also copper alloys undergo mechanical and thermal treatments to be hardened and strengthened during manufacturing, although their mechanical properties significantly change after heating above 590°C. High temperature brazing and diffusion bonding are assembly methods widely used to manufacture ultra-high vacuum accelerating devices. However, these processes, occurring at about 800–1000°C, significantly affect the mechanical properties of copper and copper alloys. We introduce here a novel Tungsten Inert Gas welding procedure, which is fast and keeps the high-gradient surfaces of the cavity and other components well below the copper annealing temperature. This process may be successfully used to manufacture copper-based accelerating components. This technology further preserves the hardness and cleanliness of copper in order to achieve the maximum accelerating gradient.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Suppressing ion migration in metal halide perovskite via interstitial doping with a trace amount of multivalent cations

Cations with suitable sizes to occupy an interstitial site of perovskite crystals have been widely used to inhibit ion migration and promote the performance and stability of perovskite optoelectronics. However, such interstitial doping inevitably leads to lattice microstrain that impairs the long-range ordering and stability of the crystals, causing a sacrificial trade-off. Here, we unravel the evident influence of the valence states of the interstitial cations on their efficacy to suppress the ion migration. Incorporation of a trivalent neodymium cation (Nd 3+ ) effectively mitigates the ion migration in the perovskite lattice with a reduced dosage (0.08%) compared to a widely used monovalent cation dopant (Na + , 0.45%). As a result, the photovoltaic performances and operational stability of the prototypical perovskite solar cells are enhanced with a trace amount of Nd 3+ doping while minimizing the sacrificial trade-off.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Fabrication of Turbine Disk Materials by Additive Manufacturing

Precipitation-strengthened, nickel-based superalloys are widely used in the aerospace and energy industries due to their excellent environmental resistance and outstanding mechanical properties under extreme conditions. Powder-bed additive manufacturing (AM) technologies offer the potential to revolutionize the processing of superalloy turbine components by eliminating the need for extensive inventory or expensive legacy tooling. Like selective laser melting (SLM), electron beam melting (EBM) constructs three-dimensional dense components layer-by-layer by melting and solidification of atomized, pre-alloyed powder feedstock within 50-200 micron layers. While SLM has been more widely used for AM of nickel alloys like 718, EBM offers several distinct advantages, such as less retained residual stress, lower risk of contamination, and faster build rates with multiple-electron-beam configurations. These advantages are particularly attractive for turbine disks, for which excessive residual stress and contamination can shorten disk life during high-temperature operation. In this presentation, we will discuss the feasibility of fabricating disk superalloy components using EBM AM. Originally developed using powder metallurgy forging processing, disk superalloys contain a higher refractory content and precipitate volume fraction than alloy 718, thus making them more prone to thermal cracking during AM. This and other challenges to produce homogeneous builds with desired properties will be presented. In particular, the quality of lab-scale samples fabricated via a design of experiments, in which the beam current, build temperature, and beam velocity were varied, will be summarized. The relationship between processing parameters, microstructure, grain orientation, and mechanical response will be discussed.

oxidation↗
DOE OSTI2020

Gasoline Compression Ignition (GCI) on a Light-Duty Multi-Cylinder Engine Using a Wide Range of Fuel Reactivities and Heavy Fuel Stratification

Many research studies have focused on utilizing gasoline in modern compression ignition engines to reduce emissions and improve efficiency. Collectively, this combustion mode has become known as gasoline compression ignition (GCI). One of the biggest challenges with GCI operation is maintaining control over the combustion process through the fuel injection strategy, such that the engine can be controlled on a cycle-by-cycle basis. Research studies have investigated a wide variety of GCI injection strategies (i.e., fuel stratification levels) to maintain control over the heat release rate while achieving low temperature combustion (LTC). This work shows that at loads relevant to light-duty engines, partial fuel stratification (PFS) with gasoline provides very little controllability over the timing of combustion. On the contrary, heavy fuel stratification (HFS) provides very linear and pronounced control over the timing of combustion. However, the HFS strategy has challenges achieving LTC operation due to the air handling burdens associated with the high EGR rates that are required to reduce NOx emissions to near zero levels. In this work, a wide variety of gasoline fuel reactivities (octane numbers ranging from < 40 to 87) were investigated to understand the engine performance and emissions of HFS-GCI operation on a multi-cylinder light-duty engine. The results indicate that over an EGR sweep at 4 bar BMEP, the gasoline fuels can achieve LTC operation with ultra-low NOx and soot emissions, while conventional diesel combustion (CDC) is unable to simultaneously achieve low NOx and soot. At 10 bar BMEP, all the gasoline fuels were compared to diesel, but using mixing controlled combustion and not LTC.

Dempsey, Adam↗