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At least 199 records · Page 11

Modeling the impact of the fuel injection strategy on the combustion and performance characteristics of a heavy-duty GCI engine

Gasoline compression ignition (GCI) is a promising strategy to achieve high thermal efficiency and low emissions with limited modifications to the conventional diesel engine hardware. It is a partially premixed concept, which derives its superiority from higher volatility and longer ignition delay of gasoline-like fuels combined with higher compression ratio typical of diesel engines. Here, the present study investigates the combustion process in a GCI engine operating with different injection strategies using computational fluid dynamics (CFD). Simulations are carried out on a single cylinder of a multi cylinder heavy-duty compression ignition engine, which operates at a compression ratio of 17:1 and an engine speed of 1038 rev/min. Two different injection strategies viz., late injection (LI), and early pilot injection (EP) are investigated to understand their impact on combustion and performance of the engine. Renormalized group (RNG) k-ε model is used to describe in-cylinder turbulence and KH RT model is used to simulate the fuel spray breakup. The developed CFD methodology is validated against relevant experimental data under a wide range of operating conditions for each injection strategy. The developed CFD methodology was found to capture the engine combustion behavior quite well. Based on the validated CFD model, the differences in the progress of combustion event for the two injection strategies is highlighted. It was found that a larger pilot fuel mass fraction results in a steeper rise in the initial heat release rate which in turn influences the transition to mixing controlled combustion. In line with the experimental data, the study showed that the late pilot injection strategy with three injection pulses, results in higher performance compared to the other conditions.

33 ADVANCED PROPULSION SYSTEMS↗

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DNS Of the ignition process of n-heptane/air premixed combustion with low-temperature chemistry in turbulent boundary layer

In the present work, three-dimensional direct numerical simulation (DNS) of n-heptane/air premixed combustion in turbulent boundary layer was performed to explore the near-wall ignition process with low -temperature chemistry. A reduced chemical mechanism with 58 species and 387 elementary reactions for n-heptane combustion was used in the DNS. The general characteristics of the ignition process near the wall were examined. Here, it was found that low-temperature ignition (LTI) dominates the upstream region, and high -temperature ignition (HTI) appears in the downstream region. The ignition process and the low-temperature chemistry pathways of the DNS are compared with those of a corresponding laminar case. It was found that the ignition process was affected by turbulence, which results in thickened reaction zones. However, the carbon flow analysis of low-temperature chemistry showed that turbulence rarely affects the low-temperature chemistry pathway. The combustion modes of various regions were scrutinized based on the budget terms of species transport equations and the chemical explosion mode analysis (CEMA). It was shown that the reaction term of RO 2 is significant during the LTI process of the upstream region, and the reaction terms of CH 2 O and CO 2 are evident in the downstream region, indicating the occurrence of HTI. It was also shown that auto-ignition is dominant in the upstream region. With increasing streamwise distance, the contribution of flame propagation increases, which takes over that of auto-ignition in the near-wall region.

33 ADVANCED PROPULSION SYSTEMS↗

Violent mergers revisited: The origin of the fastest stars in the Galaxy

Binary systems of two carbon-oxygen white dwarfs are one of the most promising candidates for the progenitor systems of Type Ia supernovae. Violent mergers, where the primary white dwarf ignites when the secondary white dwarf smashes into it while being disrupted on its last orbit, were the first double degenerate merger scenario proposed that ignites dynamically. However, violent mergers likely contribute only a few percent to the total Type Ia supernova rate and do not yield normal Type Ia supernova light curves. Here we revisit the scenario, simulating a violent merger with better methods and, in particular, a more accurate treatment of the detonation. We find good agreement with previous simulations but with one critical difference: The secondary white dwarf being disrupted and accelerated towards the primary white dwarf and impacted by its explosion does not fully burn, and its core survives as a bound object. The explosion leaves behind a 0.16 M ⊙ star travelling 2800 km/s, making it an excellent (and so far the only) candidate to explain the origin of the fastest observed hypervelocity stars. We also show that before the explosion, 5 × 10 −3 M ⊙ of material predominantly consisting of helium, carbon, and oxygen had already been ejected at velocities above 1000 km/s. Finally, we argue that if a violent merger made the hypervelocity stars D6-1 and D6-3 and violent mergers require the most massive primary white dwarfs in binaries of two carbon-oxygen white dwarfs, there has to be a much larger population of white dwarf mergers with slightly lower mass primary white dwarfs. Because this population likely represents ≫10% of the Type Ia supernovae rate, it can essentially only give rise to normal Type Ia supernovae.

Astronomy and AstroPhysics↗

Characterization of a CMOS camera based film digitization platform for gated x-ray imaging diagnostics at the National Ignition Facility

Hardened gated x-ray detectors use photographic film as the data recording medium due to its low sensitivity to the high-yield neutron environments at the National Ignition Facility (NIF). The photographic film is digitized with a Photometric Data Systems (PDS) microdensitometer, which measures the film’s optical density. The PDS scanner is able to measure a dynamic range of 0–5 OD; however, raster scanning the film is time consuming and maintenance of the instrument is challenging due to legacy technology. Since film usage at NIF is expected to continue in the foreseeable future, a digitization platform that is faster and more maintainable would benefit the NIF’s current and future operations. Here, this work presents the characterization of the digital transitions (DT) atom, a CMOS camera-based digitization platform that records film data in a single image capture very quickly and has widely available user support. The preliminary results suggest that the DT atom is able to reconstruct exposures accurately enough to be a competitive alternative to the PDS Scanner.

47 OTHER INSTRUMENTATION↗

Reduced Petroleum Use Through Easily Reformed Fuels and Dedicated Exhaust Gas Recirculation

This project was proposed to demonstrate a >25% reduction in petroleum usage for a light duty SI vehicle by synergistically combining the Dedicated EGR combustion system developed at SwRI with modified fuel formulations incoroporating biofuel components. Typically, a D-EGR engine yields a 10% gain in fuel efficiency relative to the base engine through a combination of improved thermodynamic efficiency from the EGR application and the significant increase in compression ratio realized through the knock reduction benefits of EGR and hydrogen addition. As the D-EGR performance is strongly linked to hydrogen production, the proposal team anticipated that gasoline formulation changes could enhance the hydrogen production rate relative to a conventional gasoline. This was expected to provide additional efficiency gains to the engine. The final step of incorporating biofuel components would then yield a final result meeting the >25% petroleum reduction target.

33 ADVANCED PROPULSION SYSTEMS↗

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗

Managing Power Systems-Induced Wildfire Risks Using Optimal Scheduled Shutoffs: Preprint

The growing demands for electricity and the increase in extreme weather conditions are putting unprecedented pressure on our electrical grids. Oftentimes, this pressure leads to electrical components failures which might ignite wildfires. This work develops a novel model to balance the reliability of power networks operations and risks of wildfires ignition by optimizing the operational schedule of power transmission networks considering time-varying risk measures that consider exogenous and operational factors. Energy storage systems are considered to deliver power during peak wildfire hours and enable temporal load shifting. The problem is formulated as a mixed-integer linear program that maximizes a weighted sum of the served power demand and the reduction in grid-induced wildfires risk. The results demonstrate the ability of the model to reduce wildfires risks significantly without considerable load shedding.

energy scheduling↗

Experimental Ignition Delay Time Measurements and Chemical Kinetics Modeling of Hydrogen/Ammonia/Natural Gas Fuels

In recent years, hydrogen-carrying compounds have accrued interest as an alternative to traditional fossil fuels due to their function as zero-emission fuels. As such, there is interest in investigating hydrogen-carrying compounds to improve understanding of the fuels' characteristics for use in high pressure systems. In the current study, the oxidation of ammonia/natural gas/hydrogen mixtures was carried out to study carbon monoxide (CO) formation profiles as well as the ignition delay times (IDTs) behind reflected shock waves in order to refine chemical kinetic models. Experiments were carried out in the University of Central Florida's shock tube facility by utilizing chemiluminescence to obtain OH* emission and laser absorption spectroscopy to obtain CO profiles over a temperature range between 1200 K and 1800 K with an average pressure of 2.2 atm. Experimental mixtures included both neat and combination natural gas/hydrogen with ammonia addition, with all mixtures except one having an equivalence ratio of 1. Results were then compared with the GRI 3.0 mechanism, as well as the newly developed UCF 2022 mechanism utilizing chemkin-pro software. In general, both models were able to capture the trend in auto-ignition delay times and CO time histories for natural gas and ammonia mixtures. However, for ammonia–hydrogen mixtures, GRI 3.0 failed to predict ignition delay times, whereas the UCF 2022 mechanism was able to capture the IDTs within the uncertainty limits of the experiments. A sensitivity analysis was conducted for different mixtures to understand the important reactions at the experimental conditions. Lastly, a reaction pathway analysis was carried out to understand important ammonia decomposition pathways in the presence of hydrogen and natural gas.

33 ADVANCED PROPULSION SYSTEMS↗

Effects of stratification and charge cooling on combustion in a gasoline direct-injection compression ignition (GDCI) engine

With the development of low temperature engine combustion strategies, performance of gasoline-type fuels under compression ignition conditions has attracted extensive research interest. Meanwhile, for the sake of co-optimization of engines and fuels for future ground transportation, identification and evaluation of general fuel properties should be a core research priority instead of endless testing of specific fuels. In this study, the roles of fuel octane sensitivity in characterizing the ignition performance of gasoline surrogates have been systematically investigated under typical gasoline direct ignition compression ignition (GDCI) engine conditions using 3D combustion CFD simulation, especially considering the subsequent in-cylinder charge stratification and charge cooling. Two different operating conditions, high boost pressure low boost temperature (beyond-RON) case and low boost pressure high boost temperature (beyond-MON) case, were considered. By comparing with our previous zero-dimensional chemical kinetic study of gasoline surrogates in advanced compression ignition (ACI) engines, the effects of stratification and charge cooling on the combustion processes are investigated. It is found that different fuel octane sensitivities lead to slight difference in equivalence ratio stratification and charge cooling due to differences in volatility. However, fuel reactivity is still the more dominant factor than the stratification and charge cooling effects in determining combustion phasing. The present results help to justify the P-T domain framework for engine autoignition analysis of overlapping pressure-temperature trajectory with ignition delay iso-contour. Here, the results also provide useful guidance to the understanding of GCI combustion process, and to the evaluation of controlling fuel properties and the selection of alternative fuels in GCI engines.

33 ADVANCED PROPULSION SYSTEMS↗

Design and Development of a High-Efficiency Single Cylinder Natural Gas-Fueled Jet Ignition Engine

The current energy climate has created a push toward reducing consumption of fossil fuels and lowering emissions output in power generation applications. Combined with the desire for a more distributed energy grid, there is currently a need for small displacement, high efficiency engines for use in stationary power generation. An enabling technology for achieving high efficiencies with spark ignited engines for such applications is the use of jet ignition which enables ultra-lean (λ > ~1.6) combustion via air dilution.This paper provides a comprehensive review of the development of a 390cc, high efficiency single cylinder natural gas-fueled jet ignition engine operating ultra-lean. The engine was developed as part of the Department of Energy’s Advanced Research Projects Agency–Energy (DOE ARPA-E) GENSETS program. Design choices for minimizing friction are highlighted as well as test results showing further friction reduction through downspeeding. Extensive hardware optimization of the combustion system has been performed and results are presented for air-flow path optimization and the jet igniter. The efficiency benefits related to enleanment and downspeeding are analyzed using an efficiency loss breakdown based on the First Law of Thermodynamics. Through optimization efforts a peak brake thermal efficiency in excess of 34% was achieved, representing an increase of greater than 20% over the current state-of-the-industry for comparably sized CNG engines.

Peters, Nathan↗

Managing Power Systems-Induced Wildfire Risks Using Optimal Scheduled Shutoffs

The increasing demands for electricity and the increase in extreme weather conditions are putting unprecedented pressure on our electric grids. Often, this pressure leads to electrical component failures, which might ignite wildfires. This work develops a novel model to balance the reliability of power network operations and the risk of wildfire ignition by opti- mizing the operational schedule of power transmission networks considering time-varying risk measures that include exogenous and operational factors. Energy storage systems are considered to deliver power during peak wildfire hours and enable temporal load shifting. The problem is formulated as a mixed-integer linear program that maximizes a weighted sum of the served power demand and the reduction in grid-induced wildfire risk. The results demonstrate the ability of the model to significantly reduce wildfire risk without considerable load shedding.

power systems operations↗

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Numerical modeling of plasma assisted deflagration to detonation transition in a microscale channel

Here, this work numerically studies the plasma assisted deflagration to detonation transition (DDT) of H 2 /O 2 mixtures in a microscale channel with detailed chemistry and transport. The results show that the DDT onset time is non-monotonically dependent on the discharge pulse number. The DDT is accelerated with small pulse numbers, whereas retarded with large ones. Two different DDT regimes, respectively at a small and large plasma discharge number, via acoustic choking of the burned gas and plasma-enhanced reactivity gradient without acoustic choking, are observed. Without plasma discharge, pronounced pressure and temperature gradients in front of the flame are generated by acoustic compression after the choking of the burned gas, triggering DDT via autoignition. With small plasma pulse numbers, the plasma-generated species enhance the ignition kinetics and lead to an increased reactivity in the boundary layer. After the choking of the burned gas, the plasma-enhanced reactivity advances the sequence of autoignition near the wall, strengthens ignition-shock wave coupling, and accelerates DDT. However, with a large discharge pulse number, a direct autoignition initiating DDT can occur without the acoustic choking of the burned gas due to the strongly accelerated reactivity and elevated temperature. In this case, DDT onset is retarded because the elevated temperature increases sonic velocity and the increased reactivity accelerates fuel oxidation in front of the flame, decelerating the formation of a leading shock and subsequent pressure buildup ahead of the flame. The present modeling reveals that no matter with or without plasma discharge, DDT is initiated by autoignition in thermal, pressure, and reactivity gradient fields via the Zel'dovich gradient mechanism. The acoustic choking of the burned gas may not be the necessary condition of DDT with strong plasma-enhanced reactivity gradient. This work provides an answer to the experimentally observed non-monotonic DDT onset time by plasma, which provides guidance to control DDT in advanced detonation engines and fire safety of hydrogen-fueled catalytic reactors in microchannels by non-equilibrium plasma discharge.

33 ADVANCED PROPULSION SYSTEMS↗

Improving Unit Flexibility Utilizing Plasma Ignitors

Coal fired steam generation operators face increasing market challenges, including competition from low cost generation, renewables, and regulatory pressure. These market conditions are forcing utilities to operate their coal assets in a more flexible mode, including more frequent starts and stops, faster ramp rates, frequent cycling, and extended operation at the lowest possible loads. Without upgrades to firing and control systems, pressure parts, and auxiliary systems, CAPEX and OPEX costs will increase significantly. Typically, expensive support fuels (Oil or Gas) are required to maintain safe, stable coal ignition when operating below 25% to 35% MCR (Maximum Continuous Rating). The exact minimum load without support fuel differs depending upon unit design specifications and fuel being burned. Frequent starts also increase the use of support fuels. The firing and burner control systems must be designed to allow for proper operation over the unit’s load range, ensuring that all mechanical components operate properly, and proper control of air and fuel streams can be maintained. To address the need for frequent starts and the need for stable low load operation on coal fired steam generators, this paper discusses options available, including, Firing Systems upgrades, Digital solutions, and pressure part upgrades. Several case studies are included that highlight options available allowing units to stay in operation and reduce CAPEX and OPEX costs.

01 COAL, LIGNITE, AND PEAT↗

Model-based compositional predictions for a differential scanning calorimetry/thermogravimetric analysis-mass spectrometry system used for heat of vaporization measurements

A Differential Scanning Calorimetry/Thermogravimetric Analysis (DSC/TGA) procedure for determination of Heat of Vaporization (HOV) of fuel and surrogate fuel mixtures has been previously utilized for evaluation of oxygenate impacts on spark ignition fuels. This analysis has been further leveraged with mass spectrometry (MS) to elucidate vapor phase compositional changes during evaporation. In this study we focus on a simple system of three compounds (ethanol, n-hexane, and n-octane) to provide a model-based understanding of the evaporation process under these experimental conditions. An Aspen Plus model was set up to provide liquid and vapor phase concentration predictions during mixture evaporation using the initial mixture composition, and measured temperature and mass loss profiles from DSC/TGA experiments. Agreements between the trends of the MS ion count profiles and model composition predictions provide a degree of validation of this modeling approach that can allow the interpretation and extrapolation of DSC/TGA measurements for more complex fuel mixtures.

33 ADVANCED PROPULSION SYSTEMS↗

Concentration fluctuations and flammability of cryo-compressed hydrogen and methane jets

Compressed hydrogen stored at cryogenic temperatures has a much higher density than room-temperature storage, which enables large-scale hydrogen storage and transport. An understanding of the release of cryogenic hydrogen from pressurized vessels is needed to evaluate the risk and safety concerns with the use of this fuel. Here, the present work extends the analysis of previous experimental studies that measured the gas concentrations of cryo-compressed hydrogen jets and methane jets using a laser Raman scattering diagnostic system. Since the Raman signals are very small, a denoising algorithm was applied to significantly reduce the noise to enable statistical analysis of the data. The transient features of the turbulent jets were characterized by their concentration intermittencies and probability density functions (PDFs). A two-part PDF was developed to predict the bimodal features of the jet concentration distributions. Then, the flammability factors of the cryogenic jets were calculated based on the intermittency and the PDF.

33 ADVANCED PROPULSION SYSTEMS↗