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At least 109 records · Page 6

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DOE OSTI2026

Upcycling Real‐World Post‐Consumer Polyolefins Plastics Into Light Olefins Via Microwave‐Assisted Processing

The rapid accumulation of plastic waste, particularly post-consumer polyolefins (POs) pose severe environmental and economic challenges worldwide. Recycling of post-consumer POs remains inefficient due to difficulties in separating mixed plastics, complex additives compositions, and high processing costs, resulting in recycling rates of less than 9%. To address these critical issues, this study utilized an innovative microwave-assisted catalytic upcycling approach for the efficient upcycling of complex post-consumer POs mixtures into valuable light olefins. Using the microwave-assisted catalytic upcycling approach, gas yields reached up to 80 wt.% from post-consumer POs mixtures, accompanied by a high selectivity (>70 wt.%) toward valuable light olefins. The upcycling of POs under microwave conditions is fully invested, including additives in real-word plastics, mixtures of different POs, reusability of catalyst, and more. The microwave-assisted catalytic upcycling approach offers an efficient, scalable, and cost-effective solution for upcycling post-consumer plastic mixtures, thereby advancing the principles of a circular economy.

42 ENGINEERING↗

DOE OSTI2024

Vacuum-assisted extrusion to reduce internal porosity in large-format additive manufacturing

Large-scale 3D printing of polymer composite structures has gained popularity and seen extensive use over the last decade. Much of the research related to improving the mechanical properties of 3D-printed parts has focused on exploring new materials and optimizing print parameters to improve geometric control and minimize voids between printed beads. However, porosity at the microstructural level (within the printed bead) has been much less studied although it is almost universally observed at levels of 4 %-10 % when using fiber reinforced materials. This study introduces a vacuum-assist approach that minimizes internal porosity by removing ambient air from the interstitial space between pellets in the hopper and acts as a negative pressure vent for gases that evolve during the initial stages of single-screw extrusion. Vacuum-assisted extrusion was able to reduce porosity below 2 % across a wide range of processing parameters, moisture content, fiber reinforcements, and printing platforms. Specifically, when printing on a large-format extruder (Strangpresse Model-30), the vacuum-assisted extrusion reduced internal porosity by 35–75 % compared to conventional non-vacuum extrusion, and only pores with length scale > 2 microns are affected. The success of this approach prompted the design of a patent-pending continuous vacuum hopper relevant for large-scale 3D printing on commercial systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Effect of surfactant assisted ultrasonic pretreatment on production of volatile fatty acids from mixed food waste

In this study, the potential effect of surfactant assisted ultrasonic pretreatment on mixed food waste was investigated. Surfactants, such as Rhamnolipid, Sodium dodecyl sulfate; Glucopon and Triton X 100 were evaluated in this work. Among them, the maximum solubilization of chemical oxygen demand of 45.5 % and the highest release of soluble COD of 31 g/L were observed for ultrasonication assisted by Triton X 100 at a dose of 0.01 g/g TS in 30 min. The presence of a surfactant also reduced 27.5 % of energy demand when compared to ultrasonic pretreatment alone. Compared to the non-pretreated samples after anaerobic digestion, ultrasonication assisted by Triton X 100 led to 95 % increase of volatile fatty acid titers and 83 % increase of carbon conversion efficiency. Furthermore, sonication with the addition of Triton X 100 was proven to be highly effective toward increasing digestibility of and yield of volatile fatty acid from mixed food waste.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗

DOE OSTI2022

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Bimetallic-Derived Catalytic Structures for CO 2 -Assisted Ethane Activation

In recent years, the simultaneous upgrading of CO 2 and ethane has emerged as a promising approach for generating valuable gaseous (CO, H 2 , and ethylene) and liquid (aromatics and C 3 oxygenates) chemicals from the greenhouse gas CO 2 and large-reserved shale gas. The key challenges for controlling product selectivity lie in the selective C-H and C-C bond cleavage of ethane with the assistance of CO 2 . Bimetallic-derived catalysts likely undergo alloying or oxygen-induced segregation under reaction conditions, thus providing diverse types of interfacial sites, e.g., metal/support (M/M'O x ) interface and metal oxide/metal (M'O x /M) inverse interface, that are beneficial for selective CO 2 -assisted ethane upgrading. The alloying extent can be initially predicted by cohesive energy and atomic radius (or Wigner-Seitz radius), while the preference for segregation to form the on-top suboxide can be approximated using the work function, electronegativity, and binding strength of adsorbed oxygen. Furthermore, bimetallic-derived catalysts are typically supported on high surface area oxides. Modifying the reducibility and acidity/basicity of the oxide supports and introducing surface defects facilitate CO 2 activation and oxygen supplies for ethane activation. Using in situ synchrotron characterization and density functional theory (DFT) calculations, we found that the electronic properties of oxygen species influence the selective cleavage of C-H/C-C bonds in ethane, with electron-deficient oxygen over the metal (or alloy) surface promoting nonselective bond scission to produce syngas and electron-enriched oxygen over the metal oxide/metal interface enhancing selective C-H scission to yield ethylene. Here, we further demonstrate that the preferred structures of the catalyst surfaces, either alloy surfaces or metal oxide/metal inverse interfaces, can be controlled through the appropriate choice of metal combinations and their atomic ratios. Through a comprehensive comparison of experimental results and DFT calculations, the selectivity of C-C/C-H bond scission is correlated with the thermodynamically favorable bimetallic-derived structures (i.e., alloy surfaces or metal oxide/metal inverse interfaces) under reaction conditions over a wide range of bimetallic catalysts. These findings not only offer structural and mechanistic insights into bimetallic-derived catalysts but also provide design principles for selective catalysts for CO 2 -assisted activation of ethane and other light alkanes. This Account concludes by discussing challenges and opportunities in designing advanced bimetallic-derived catalysts, incorporating new reaction chemistries for other products, employing precise synthesis strategies for well-defined structures with optimized site densities, and leveraging time/spatial/energy-resolved in situ spectroscopy/scattering/microscopy techniques for comprehensive structural analysis. The research methodologies established here are helpful for the investigation of dynamic alloy and interfacial structures and should inspire more efforts toward the simultaneous upgrading of CO 2 and shale gas.

03 NATURAL GAS↗

DOE OSTI2023

Dislocation-Assisted Quasi-Two-Dimensional Semiconducting Nanochannels Embedded in Perovskite Thin Films

Defect engineering in perovskite thin films has attracted extensive attention recently due to the films’ atomic-scale modification, allowing for remarkable flexibility to design novel nanostructures for next generation nanodevices. However, the defect-assisted three-dimensional nanostructures in thin film matrices usually has large misfit strain and thus causes unstable thin film structures. In contrast, defect-assisted one- or two-dimensional nanostructures embedded in thin films can sustain large misfit strains without relaxation, which make them suitable for defect engineering in perovskite thin films. Here, we reported the fabrication and characterization of edge-type misfit dislocation-assisted two-dimensional BiMnO x nanochannels embedded in SrTiO 3 /La 0.7 Sr 0.3 MnO 3 /TbScO 3 perovskite thin films. The nanochannels are epitaxially grown from the surrounding films without noticeable misfit strain. Diode-like current rectification was spatially observed at nanochannels due to the formation of Schottky junctions between BiMnO x nanochannels and conducting La 0.7 Sr 0.3 MnO 3 thin films. Such atomically scaled heterostructures constitute more flexible ultimate functional units for nanoscale electronic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Swelling-Assisted Sequential Infiltration Synthesis of Nanoporous ZnO Films with Highly Accessible Pores and Their Sensing Potential for Ethanol

Here, we report a swelling-assisted sequential infiltration synthesis (SIS) approach for the design of highly porous zinc oxide (ZnO) films by infiltration of block copolymer templates such as polystyrene-block- polyvinyl pyridine with inorganic precursors followed by UV ozone-assisted removal of the polymer template. We show that porous ZnO coatings with the thickness in the range between 140 and 420 nm can be obtained using only five cycles of SIS. The pores in ZnO fabricated via swelling-assisted SIS are highly accessible, and up to 98% of pores are available for solvent penetration. The XPS data indicate that the surface of nanoporous ZnO films is terminated with -OH groups. Density functional theory calculations show a lower energy barrier for ethanol-induced release of the oxygen restricted depletion layer in the case of the presence of -OH groups at the ZnO surface, and hence, it can lead to higher sensitivity in sensing of ethanol. We monitored the response of ZnO porous coatings with different thicknesses and porosities to ethanol vapors using combined mass-based and chemiresistive approaches at room temperature and 90 °C. The porous ZnO conformal coatings reveal a promising sensitivity toward detection of ethanol at low temperatures. In conclusion our results suggest the excellent potential of the SIS approach for the design of conformal ZnO coatings with controlled porosity, thickness, and composition that can be adapted for sensing applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗

DOE OSTI2024

Suppressing strain propagation in ultrahigh-Ni cathodes during fast charging via epitaxial entropy-assisted coating

Surface reconstruction and the associated severe strain propagation have long been reported as the major cause of cathode failure during fast charging and long-term cycling. Despite tremendous attempts, no known strategies can simultaneously address the electro-chemomechanical instability without sacrificing energy and power density. Here we report an epitaxial entropy-assisted coating strategy for ultrahigh-Ni LiNi x Co y Mn 1-x-y O 2 (x ≥ 0.9) cathodes via an oriented attachment-driven reaction between Wadsley-Roth phase-based oxides and the layered-oxide cathodes. Further, the high anti-cracking and anti-corrosion tolerances as well as the fast ionic transport of the entropy-assisted surface effectively improved the fast charging/discharging capability, wide temperature tolerance and thermal stability of the ultrahigh-Ni cathodes. Comprehensive analysis from the primary and secondary particle level to the electrode level using multi-scale in situ synchrotron X-ray probes reveals greatly reduced lattice dislocations, anisotropic lattice strain and oxygen release as well as improved bulk/local structural stability, even when charging beyond the threshold state of charge (75%) of layered cathodes. Layered Ni-rich oxide cathodes are susceptible to challenges with surface reconstruction and strain propagation, limiting their cyclability. The authors propose a solution involving oriented attachment-driven reactions, utilizing Wadsley-Roth nanocrystals and layered oxide to induce an epitaxial entropy-assisted coating, effectively addressing these issues.

25 ENERGY STORAGE↗

DOE OSTI2021

Double bonus: surfactant-assisted biomass pelleting benefits both the pelleting process and subsequent enzymatic saccharification of the pretreated pellets

Pelleting overcomes the utilization limitation of lignocellulosic biomass due to its low density and related processing costs. Efficiently combining biomass pelleting and enzymatic saccharification is a challenge for applying biomass with low density in a biorefinery. In this study, we propose a novel biomass processing approach involving the addition of surfactant during pelleting and using the pellets for pretreatment and enzymatic saccharification. Here, we investigated the effects of polyethylene glycol (PEG) 6000 assisted pelleting on pelleting specific energy consumption and friction, and on subsequent pretreatment as well as on enzymatic saccharification of the pretreated pellets. The results showed that PEG 6000 assisted pelleting decreased the total pelleting specific energy consumption by about 14% for wheat straw and 18% for pine, and reduced the maximum friction during ejection of pellets by about 34% for wheat straw and 29% for pine. PEG 6000 assisted pelleting enhanced subsequent enzymatic sugar yield effectively for both acid and alkaline pretreated wheat straw and pine, especially for acid-treated pine pellets, where a 256% increase in glucose yield was obtained. The results suggest that PEG 6000 decreases the cleavage of β-O-4' linkages during pretreatment and thus hinders the formation of phenolic hydroxyl groups, contributing to the enhanced sugar yield in enzymatic saccharification. As surfactants were found to have beneficial effects on both biomass pelleting and the sugar yield obtained from the enzymatic saccharification of the pretreated pellets, it would be advantageous to add surfactants during pelletizing when the pellets are intended for application in a biorefinery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Tracking the reaction networks of acetaldehyde oxide and glyoxal oxide Criegee intermediates in the ozone-assisted oxidation reaction of crotonaldehyde

The reaction of unsaturated compounds with ozone (O 3 ) is recognized to lead to the formation of Criegee intermediates (CIs), which play a key role in controlling the atmospheric budget of hydroxyl radicals and secondary organic aerosols. The reaction network of two CIs with different functionality, i.e. acetaldehyde oxide (CH 3 CHOO) and glyoxal oxide (CHOCHOO) formed in the ozone-assisted oxidation reaction of crotanaldehyde (CA), is investigated over a temperature range between 390 K and 840 K in an atmospheric pressure jet-stirred reactor (JSR) at a residence time of 1.3 s, stoichiometry of 0.5 with a mixture of 1% crotonaldehyde, 10% O 2 , at an fixed ozone concentration of 1000 ppm and 89% Ar dilution. Molecular-beam mass spectrometry in conjunction with single photon tunable synchrotron vacuum-ultraviolet (VUV) radiation is used to identify elusive intermediates by means of experimental photoionization energy scans and ab initio threshold energy calculations for isomer identification. Addition of ozone (1000 ppm) is observed to trigger the oxidation of CA already at 390 K, which is below the temperature where the oxidation reaction of CA was observed in the absence of ozone. The observed CA + O 3 product, C 4 H 6 O 4 , is found to be linked to a ketohydroperoxide (2-hydroperoxy-3-oxobutanal) resulting from the isomerization of the primary ozonide. Products corresponding to the CIs uni- and bi-molecular reactions were observed and identified. A network of CI reactions is identified in the temperature region below 600 K, characterized by CIs bimolecular reactions with species like aldehydes, i.e., formaldehyde, acetaldehyde, and crotonaldehyde and alkenes, i.e., ethene and propene. The region below 600 K is also characterized by the formation of important amounts of typical low-temperature oxidation products, such as hydrogen peroxide (H 2 O 2 ), methyl hydroperoxide (CH 3 OOH), and ethyl hydroperoxide (C 2 H 5 OOH). Detection of additional oxygenated species such as alcohols, ketene, and aldehydes are indicative of multiple active oxidation routes. This study provides important information about the initial step involved in the CIs assisted oligomerization reactions in complex reactive environments where CIs with different functionalities are reacting simultaneously. It provides new mechanistic insights into ozone-assisted oxidation reactions of unsaturated aldehydes, which is critical for the development of improved atmospheric and combustion kinetics models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Modeling of the spray-induced wall stress acting on the ignition assistance device

This research introduces a novel wall-stress model called the Spray-Induced Wall Stress (SIWS) model, which considers the effects of spray-wall impingement and the resulting formation of wall stress within the Lagrangian spray modeling framework. The primary objective of this paper is to provide a mathematical description of the fundamental physics underlying the model. Subsequently, the proposed model is validated using existing experimental data. Here, the remainder of the study focuses on the practical application of the model to an ignition assistance device. Specifically, this device is installed in a compression ignition engine and designed to enhance ignition in aviation-fueled high-altitude aircraft propulsion systems. The research sheds light on the mechanical impulse caused by the high-speed impact of the spray jet, leading to the accumulation of mechanical stress on the rigid body of the ignition assistance device. Previous studies on fluid-structure interaction have only considered the interaction between the gas phase and the solid wall. However, the SIWS model incorporates the additional impact of the impinging liquid spray jet. Consequently, the simulated stress distribution on the ignition assistance device can be estimated by considering both the gas-phase-induced term and the spray-induced term simultaneously.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Interplay of vibration- and environment-assisted energy transfer

Abstract We study the interplay between two environmental influences on excited state energy transfer in photosynthetic light harvesting complexes, namely, vibrationally assisted energy transfer (VAET) and environment-assisted quantum transport (ENAQT), considering a dimeric chromophore donor–acceptor model as a prototype for larger systems. We demonstrate how the basic features of the excitonic energy transfer are influenced by these two environments, both separately and together, with the environment being fully quantum in the case of VAET and treated in the Haken–Strobl–Reineker classical limit in the case of ENAQT. Our results reveal that in the weak noise regime, the presence of a classical noise source is detrimental to the energy transfer that is resonantly assisted by the exciton-vibration interactions intrinsic to VAET. In the strong noise regime we reproduce all the features of ENAQT, including the turnover into a Zeno regime where energy transfer is suppressed, and VAET is insignificant.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Sensor-Assisted Fault Mitigation in Quantum Computation

We propose a method for assisting fault mitigation in quantum computation through the use of sensors, co-located near physical qubits. Specifically, we consider using transition edge sensors co-located on silicon substrates hosting superconducting qubits to monitor for energy injection from ionizing radiation that has been demonstrated to increase decoherence in transmon qubits. We generalize from these two physical device concepts and explore the potential advantages of co-located sensors to assist fault mitigation in quantum computation. In the simplest scheme, co-located sensors beneficially assist rejection of calculations potentially affected by environmental disturbances. Investigating the potential computational advantage further required development of an extension to the standard formulation of quantum error correction. In a specific case the standard three-qubit, bit flip quantum correction code, we show that given a 20% overall error probability per qubit, approximately 90% of repeated calculation attempts are correctable. However, when sensor-detectable errors account for 45% of overall error probability, the use of colocated sensors uniquely associated with independent qubits, boosts the fraction of correct final-state calculations to 96% at the cost of rejecting 7% of repeated calculation attempts.

Orrell, John L.↗

DOE OSTI2022

Deep Learning-Assisted Real-Time Forward Modeling of Electromagnetic Logging in Complex Formations

Higher dimensional (i.e., 2-D and 3-D) modeling is indispensable to correctly evaluate the responses of electromagnetic logging tools in complex formation environments. However, limited by the high computational cost of rigorous modeling such as finite difference method and the finite element method, the real-time applications in the well logging industry primarily rely on the 1-D forward solver, which would result in erroneous formation evaluation for complex scenarios. As a result, aiming at realizing fast modeling for electromagnetic logging tools in complex formations, this paper proposes a general framework assisted by deep neural networks (DNNs). The framework consists of three modules: earth model classification, parameter extraction, and surrogate construction. Separate DNNs are trained and tested for different modules. The accuracy and efficiency of the DNN assisted fast modeling are validated by several experiments. Here, this study finds that the fast modeling assisted by DNNs is able to calculate the tool responses and reconstruct the subsurface formations in real-time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Assisted migration in a warmer and drier climate: less climate buffering capacity, less facilitation and more fires at temperate latitudes?

Assisted tree migration has been proposed as a conceptual solution to mitigate lags in biotic responses to anthropogenic climate change. The rationale behind this concept is that tree species currently growing under warmer and drier climates will be more resistant and resilient to the new climatic conditions than tree species naturally growing in currently wetter and colder climates. However, we hypothesize that, by being more stress‐tolerant to warmer and drier conditions, translocated species should exhibit different functional attributes, which could induce important ecological and societal costs and overcome the desired benefits of maintaining wood production and other ecosystem services. We used principal component analysis (PCA) to analyze variation in seven traits of 106 tree and tall shrub species from contrasting latitudinal distributions in western North America and Europe to predict the potential functional changes of forest ecosystems due to the translocation of tree species from low to high latitudes. We show that species from both continents differed primarily by their position on the leaf economy spectrum (LES) and their size traits. Even though, in Europe, differences in LES were significantly correlated to species southern latitudinal positions, in both continents differences in size traits were significantly correlated to latitude. These results suggest that assisted migration by translocating more conservative species of shorter stature in currently cooler climates should decrease the buffering capacity of forest canopies, decrease facilitation for understory species, and increase wildfire risks, whose effects have the potential to accelerate climate warming through negative atmospheric feedback processes. As an alternative solution to assisted migration that may accelerate rather than mitigate climate change, we recommend that foresters gradually diversify the vertical structure and layering of the existing forest canopy to maintain a sustainable water cycle and energy balance between the soil, the tree and the atmosphere without increasing the wildfire risk.

Environmental Sciences & Ecology↗

DOE OSTI2021

Large Eddy Simulation of Lean Mixed-Mode Combustion Assisted by Partial Fuel Stratification in a Spark-Ignition Engine

Partial fuel stratification (PFS) is a promising fuel injection strategy to improve the stability of lean combustion by applying a small amount of pilot injection right before spark timing. Mixed-mode combustion, which makes use of end-gas autoignition following conventional deflagration-based combustion, can be further utilized to speed up the overall combustion. In this study, PFS-assisted mixed-mode combustion in a lean-burn direct injection spark-ignition (DISI) engine is numerically investigated using multi-cycle large eddy simulation (LES). A previously developed hybrid G-equation/well-stirred reactor combustion model for the well-mixed operation is extended to the PFS-assisted operation. The experimental spray morphology is employed to derive spray model parameters for the pilot injection. The LES-based model is validated against experimental data and is further compared with the Reynolds-averaged Navier–Stokes (RANS)-based model. Overall, both RANS and LES predict the mean pressure and heat release rate traces well, while LES outperforms RANS in capturing the cycle-to-cycle variation (CCV) and the combustion phasing in the mass burned space. Liquid and vapor penetrations obtained from the simulations agree reasonably well with the experiment. Detailed flame structures predicted from the simulations reveal the transition from a sooting diffusion flame to a lean premixed flame, which is consistent with experimental findings. LES captures more wrinkled and stretched flames than RANS. Finally, the LES model is employed to investigate the impacts of fuel properties, including heat of vaporization (HoV) and laminar burning speed (SL). Combustion phasing is found more sensitive to SL than to HoV, with a larger fuel property sensitivity of the heat release rate from autoignition than that from deflagration. Moreover, the combustion phasing in the PFS-assisted operation is shown to be less sensitive to SL compared with the well-mixed operation.

fuel property sensitivity↗