Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “emergency generator”

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

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

At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Risks to the DOW Mission From Global Advanced Energy Adoption

Advanced energy technologies have the potential to enhance energy security and resilience; however, they can introduce new vulnerabilities even as they mitigate existing ones. This dichotomy highlights that both action and inaction carry strategic risks in a contested and logistically complex operating environment. Regardless of U.S. civilian or military adoption of such technologies, key allies and adversaries are on adoption paths that will impact the U.S. military at the tactical, operational, and strategic level. The global adoption of advanced energy technologies presents the potential for both positive and negative consequences for U.S. Department of Defense (DOD) missions in the next 10-20 years. Three categories of risk drivers are presented in this analysis: infrastructure-related, adoption-related, and response-related. Each risk type can generate consequences for DOD, including power disruptions, suboptimal DOD mission performance and operational effectiveness, higher costs and shortages for both legacy and advanced energy technologies, and loss of U.S. influence and strategic deterrent value. Specific impacts could include tactical impacts, operational impacts, and strategic impacts. Mitigation strategies could include energy resource and technology planning, cybersecurity, supply chain resilience, workforce development, exercises and simulations, investments in commercialized technology, operational testing and pilot programs, research into emerging technologies, partnerships and working groups, common standards, budget planning, and repurposing infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Transforming Energy through Computational Science: Computing for Clean Energy

This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.

advanced computing↗
DOE OSTI2025

Liquid Metals for Advanced Batteries: Recent Progress and Future Perspective

ABSTRACT The shift toward sustainable energy has increased the demand for efficient energy storage systems to complement renewable sources like solar and wind. While lithium‐ion batteries dominate the market, challenges such as safety concerns and limited energy density drive the search for new solutions. Liquid metals (LMs) have emerged as promising materials for advanced batteries due to their unique properties, including low melting points, high electrical conductivity, tunable surface tension, and strong alloying tendency. Enabled by the unique properties of LMs, four key scientific functions of LMs in batteries are highlighted: active materials, self‐healing, interface stabilization, and conductivity enhancement. These applications can improve battery performance, safety, and lifespan. This review also discusses current challenges and future opportunities for using LMs in next‐generation energy storage systems. image

Zheng, Tianrui [Materials Science and Engineering ↗
DOE OSTI2022

Review of Multifunctional Separators: Stabilizing the Cathode and the Anode for Alkali (Li, Na, and K) Metal–Sulfur and Selenium Batteries

Alkali metal batteries based on lithium, sodium, and potassium anodes and sulfur-based cathodes are regarded as key for next-generation energy storage due to their high theoretical energy and potential cost effectiveness. However, metal–sulfur batteries remain challenged by several factors, including polysulfides’ (PSs) dissolution, sluggish sulfur redox kinetics at the cathode, and metallic dendrite growth at the anode. Functional separators and interlayers are an innovative approach to remedying these drawbacks. Here we critically review the state-of-the-art in separators/interlayers for cathode and anode protection, covering the Li–S and the emerging Na–S and K–S systems. The approaches for improving electrochemical performance may be categorized as one or a combination of the following: Immobilization of polysulfides (cathode); catalyzing sulfur redox kinetics (cathode); introduction of protective layers to serve as an artificial solid electrolyte interphase (SEI) (anode); and combined improvement in electrolyte wetting and homogenization of ion flux (anode and cathode). It is demonstrated that while the advances in Li–S are relatively mature, less progress has been made with Na–S and K–S due to the more challenging redox chemistry at the cathode and increased electrochemical instability at the anode. Throughout these sections there is a complementary discussion of functional separators for emerging alkali metal systems based on metal–selenium and the metal–selenium sulfide. The focus then shifts to interlayers and artificial SEI/cathode electrolyte interphase (CEI) layers employed to stabilize solid-state electrolytes (SSEs) in metal–sulfur solid-state batteries (SSBs). The discussion of SSEs focuses on inorganic electrolytes based on Li- and Na-based oxides and sulfides but also touches on some hybrid systems with an inorganic matrix and a minority polymer phase. The review then moves to practical considerations for functional separators, including scaleup issues and Li–S technoeconomics. The review concludes with an outlook section, where we discuss emerging mechanics, spectroscopy, and advanced electron microscopy (e.g. cryo-transmission electron microscopy (cryo-TEM) and cryo-focused ion beam (cryo-FIB))-based approaches for analysis of functional separator structure–battery electrochemical performance interrelations. Finally, throughout the review we identify the outstanding open scientific and technological questions while providing recommendations for future research topics.

25 ENERGY STORAGE↗
DOE OSTI2026

A tutorial on high-order harmonic generation in atoms, molecules, and condensed matter

This tutorial introduces strong-field-driven high-order harmonics, their experimental generation and characterization techniques, and their main applications including attosecond pulse generation and ultrafast spectroscopy of the target material. We begin from the use of atomic targets, where the first high-order harmonic generation (HHG) experiments were realized in the late 1980s. Then, we briefly discuss the basics of the microscopic generation mechanism and how various steps of the mechanism were exploited in applications such as generating isolated attosecond pulses and probing molecular orbitals. We introduce and describe the standard experimental approaches for condensed phase HHG, where we discuss unique technical challenges of the use of solid-state materials, such as the mitigation of plasma formation and laser damage. We cover the fundamentals of high-harmonic spectroscopy in condensed matter systems, such as wide bandgap dielectrics, semiconductors, liquid media, and 2D-crystals. We provide some examples of rapidly emerging spectroscopic capabilities, such as for probing crystal symmetries, Berry phases, and associated non-trivial topological properties of the source material. Finally, we provide an overview of the research field, including some of the challenges, opportunities, and open questions.

Attosecond pulses↗
DOE OSTI2022

Nonclassical light from finite-range interactions in a two-dimensional quantum mirror

Excitons in a semiconductor monolayer form a collective resonance that can reflect resonant light with extraordinarily high efficiency. We investigate the nonlinear optical properties of such atomistically thin mirrors and show that finite-range interactions between excitons can lead to the generation of highly nonclassical light. We describe two scenarios, in which optical nonlinearities arise either from direct photon coupling to excitons in excited Rydberg states or from resonant two-photon excitation of Rydberg excitons with finite-range interactions. The latter case yields conditions of electromagnetically induced transparency and thereby provides an efficient mechanism for single-photon switching between high transmission and reflectance of the monolayer, with a tunable dynamical timescale of the emerging photon-photon interactions. Remarkably, it turns out that the resulting high degree of photon correlations remains virtually unaffected by Rydberg-state decoherence, in excess of nonradiative decoherence observed for ground-state excitons in two-dimensional semiconductors. This robustness to imperfections suggests a promising approach to quantum photonics at the level of individual photons.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Makah Tribe Strategic Energy Plan

The U.S. Department of Energy’s (DOE) Energy Transitions Initiative Partnership Project (ETIPP) connects remote and island communities, regional partners, and the DOE national laboratories to support communities as they seek to build resilience in their energy systems. The Makah Tribe faces several energy challenges, including frequent power outages and the potential for an extended outage due to an earthquake or tsunami. The Tribe joined ETIPP in 2022 to address those challenges, seeking to build energy resilience and sovereignty in the community. The Makah Tribe, Spark Northwest, the Pacific Northwest National Laboratory (PNNL), and the National Renewable Energy Laboratory (NREL) collaborated to develop a strategic energy plan as part of the second cohort of ETIPP communities. The long-term energy vision of the Makah Tribe includes increasing energy efficiency in the community, improving energy management capacity, and developing the renewable energy generation and storage sufficient to independently power the Reservation for one year. Additionally, the ETIPP team worked with Makah leadership, staff, and community members to identify a set of community priorities, values, and goals to guide energy development as the Tribe takes the incremental steps toward their vision for energy sovereignty. Those energy values include ecosystem-based management, energy sovereignty and project ownership, workforce development and capacity, economic opportunity, community wellbeing and priorities, and emergency disaster resilience. To understand what would be needed for a year for energy independence, the PNNL team conducted an assessment to determine the current energy usage of the Tribe and also modeled several scenarios for future energy use. Using the energy usage values, the team estimated how two types of renewable energy technology, specifically locally deployed solar and small-scale wind, could contribute towards the energy independence goal. The energy baseline and resource assessment produced the following key findings:

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Extracting Critical Metals from Authentic Bauxite Residue

Securing domestic sources of critical minerals (CM) is paramount to ensuring a robust and self-sustaining technology infrastructure. Utilizing untapped non-conventional sources, like acid mine drainage, coal ash, bauxite residue/red mud, and more is an emerging approach that addresses this national concern while identifying new value-added pathways originating from waste streams. Red mud is a byproduct of the Bayer process that produces alumina and contains a wealth of CM – 50 µg/g Ga, 2.9 mg/g total rare earth elements plus yttrium, 12 mg/g Mn, 58 mg/g Al, and others. About 170 MM tonnes of red mud are co-produced annually alongside the 142 MM tonnes of alumina generated, highlighting the abundancy of this feedstock. In this work, different strategies were explored for extracting CM from authentic bauxite residue that involve thermal heating, microwave heating, and sonication all with different acids and buffers. CM recovery was then explored one step further by testing the adsorption of the extracted metals onto NETL’s Multi-functional Sorbent Technology (MUST). Microwave treatment of red mud proved the most effective CM extraction, whereas sonication was less desirable due to scale-up concerns. Successful recovery of CM from this leachate with MUST supports further studies toward optimizing the overall process.

bauxite residue↗
DOE OSTI2025

Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030

The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

ARGO↗
DOE OSTI2022

Data and Multistage Optimization for the New Grid

Three essential capabilities for using exascale computing resources on next generation power grid applications are: generating large renewable energy datasets, modeling damage and operations during and after extreme events, and employing multi-stage optimization for infrastructure planning. Powerscenarios, developed as part of the ExaSGD project, addresses these capabilities by enabling users to build synthetic wind farms for large test systems using numerical weather prediction-based data sources. We share examples of using Powerscenarios to build out wind farms on the ACTIVSg2000 test system, to enable simulated operations and emergency asset allocation during a hurricane strike, and to compute multi-stage infrastructure buildouts.

economic dispatch↗
DOE OSTI2026

Quantitative characterization of gradient microstructures: A study on friction stir spot processing of pure cobalt

Heterogeneous microstructures in polycrystalline metals can enhance the strength and ductility, outperforming homogeneous structures of similar composition. This study investigates deformed cobalt via friction stir spot processing with varying dwell times to uncover the effects of plastic deformation and heat generation on the formation of morphological, phase, and grain boundary character gradients. A new approach to quantify the morphological gradients in materials, which describes grain morphology in terms of density followed by parametric regression, enables direct quantification of processing depth and gradient sharpness. Results show that longer processing times increase the steepness of morphological gradients and reduce the deformation depth for friction stir spot processing with low plunge depths and high tool rotational speeds. The amount of retained FCC is increased in the shorter processing conditions, primarily due to refined grain size, increased defect content, and reduced heat generation. Crystallographic texture analysis of the HCP phase indicated a dominant B-fiber described by (0001) ∥ shear plane normal in the extreme processing conditions and the formation of a P-fiber, shear direction ∥ ⟨11$\bar2$0⟩ for intermediate dwell times. The texture of the FCC phase for low processing times was a C texture {100}⟨011⟩ where longer processing times were dominated by a [001] fiber texture with a main {110}⟨100⟩ orientation and emergence of a slight [111] fiber in the longest processing condition. The approaches outlined in this work give insight into quantifying gradients and improve the understanding of highly deformed cobalt.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Detecting ionizing radiation using halide perovskite semiconductors processed through solution and alternative methods

We report the direct detection of high-energy radiation such as X-rays and.-rays by semiconductors at room temperature is a challenging proposition that requires remarkably pure and nearly perfect crystals. The emergence of metal halide perovskites, defect-tolerant semiconductors, is reviving hope for new materials in this field after an almost 20 year hiatus. Metal halide perovskites, which combine exceptional optoelectronic properties, versatile chemistry and simple synthesis, are challenging traditional approaches for the development of novel semiconductors for detecting hard radiation. We discuss the relevant physical properties, promising materials, fabrication techniques and device architectures for high-performance, low-cost detectors by targeting next-generation semiconductors for radiation detection. We also present a perspective on the impact of such advances in future medical imaging applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗
DOE OSTI2021

Numerical Evidence for Many-Body Localization in Two and Three Dimensions

Disorder and interactions can lead to the breakdown of statistical mechanics in certain quantum systems, a phenomenon known as many-body localization (MBL). Much of the phenomenology of MBL emerges from the existence of localized-bits, or l-bits, a set of conserved quantities that are spatially localized and binary (i.e., possess only ±1 eigenvalues). While MBL and l-bits are known to exist in one-dimensional systems, their existence in dimensions greater than one is a key open question. To tackle this question, we develop an algorithm that can find approximate binary l-bits in arbitrary dimensions by adaptively generating a basis of operators in which to represent the l-bit. We use the algorithm to study four models: the one-, two-, and three-dimensional disordered Heisenberg models and the two-dimensional disordered hard-core Bose-Hubbard model. For all four of the models studied, our algorithm finds high-quality l-bits at large disorder strength and rapid qualitative changes in the distributions of l-bits in particular ranges of disorder strengths, suggesting the existence of MBL transitions. Furthermore, these transitions in the one-dimensional Heisenberg model and two-dimensional Bose-Hubbard model coincide well with past estimates of the critical disorder strengths in these models which further validates the evidence of MBL-like behavior in the other two and three-dimensional models we examine. In addition to finding MBL-like behavior in higher dimensions, our algorithm can be used to probe MBL in various geometries and dimensionality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Surface heterogeneity propagation and homogenization for pouch cell-scale Li metal anodes

Li metal anode is a promising candidate for next-generation energy storage systems and is widely explored in Li-ion and solid-state batteries. Despite its potential, Li metal anodes face instabilities during long-term cycling, especially when paired with NMC or sulfur-based cathodes, where Li is cycled at high capacities of 6-8 mAh/cm2, equivalent to a thickness of 30-40 µm. During such extensive utilization, degradation mechanisms such as excessive solid electrolyte interphase (SEI), dendrites, and dead Li emerge, and ultimately lead to sudden failure and reduced cycle life. The origin of the degradation mechanisms stem from surface heterogeneities introduced during Li foil manufacturing and storage [1, 2]. As cycling progresses, the local heterogeneities propagate, resulting in uneven Li utilization and degradation-prone regions across the electrode [3], which consequently induce performance variations at the cell level. This heterogeneity propagation is particularly pronounced in large-format pouch cells in practical applications, where the surface effects are magnified. Without a clear understanding of the multi-scale heterogeneities and the development of surface homogenization methods, the performance consistency will be compromised, hindering the commercialization of Li metal batteries. In this presentation, we investigate the evolution of surface heterogeneity propagation on commercially available Li foils. We discover that inhomogeneous Li utilization appears as early as the first half-cycle of formation, manifesting as localized clusters and pits. In pouch cell configurations, these features exhibit areal density variations across the electrode on a scale of millimeters. To improve Li utilization homogeneity, a scalable mechanical brushing method is introduced to remove the chemically heterogeneous surface passivation layer. Furtherore, the influence of utilization homogeneity on cell-to-cell consistency is evaluated using 32 Li-NMC811 pouch cells divided into as-received and brushed Li groups. On the brushed Li, clusters and pits are no longer observable, and the cells exhibit significantly improved consistency in discharge capacity trajectories and cycle lifetime. Overall, this study highlights the role of Li surface utilization homogeneity on long-term cycling performance. Our research provides a pathway for improving large-area electrode uniformity and establishing evaluation methods for cell-to-cell consistency, both are key steps toward the commercialization of Li metal batteries and beyond. [1] Otto, Svenja-K., et al. "In-depth characterization of lithium-metal surfaces with XPS and ToF-SIMS: toward better understanding of the passivation layer." Chemistry of Materials 33.3 (2021): 859-867. [2] Hatzell, Kelsey, et al. "Aligning lithium metal battery research and development across academia and industry." Joule (2024). [3] Kim, Sangwook, et al. "Calendar life of lithium metal batteries: Accelerated aging and failure analysis." Energy Storage Materials 65 (2024): 103147.

25 - ENERGY STORAGE↗
DOE OSTI2021

Tonal Emergence: An agent-based model of tonal coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Computational analysis of flame initiation, quenching, and re-ignition in a prechamber natural gas engine under varying EGR-dilution levels

The on-road natural-gas (NG) fueled transportation relies on stoichiometric spark-ignition engines for the advantages of simple after-treatment system despite the efficiency penalty relative to lean-burn combustion strategies. Exhaust gas recirculation (EGR) has the potential to reduce this efficiency gap at low to moderate loads without the need for complex lean-exhaust aftertreatment systems. However, EGR dilution leads to reduced combustion stability and increased cycle-to-cycle variability. A promising technology that has the potential to achieve reliable operation under diluted conditions is the prechamber ignition (or turbulent jet ignition) which uses chemically active turbulent jets generated from combustion inside a prechamber to initiate, stabilize and accelerate combustion of the mixture inside the main chamber. The present work focusses on developing a RANS-based CFD approach to accurately reproduce in-cylinder phenomena in a stoichiometric NG prechamber-assisted heavy-duty engine without relying on complex combustion models that account for turbulence-chemistry interactions. This is necessary because reactive prechamber jets at high EGR dilution tend to extinguish while emerging into the main chamber, which is followed by a phase of re-ignition — a phenomenon that conventional G-equation or well-stirred reactor combustion models cannot reproduce. With addition of a damping multiplier to the well-stirred reactor model, the predictions are seen to show good agreement with experimental pressure evolution and combustion images acquired from a single cylinder Cummins N-14 optical diesel engine retrofitted with a prechamber ignition system. Model predictions of local heat release in the flame and temperature evolution inside the flame are used to investigate combustion dynamics in the prechamber and the main chamber. It is seen that the well-stirred reactor model with the inclusion of damping is able to reproduce the temporary reduction in heat release within the flame, which can be considered equivalent to quenching of jets, and the subsequent re-ignition of the flame inside the main chamber. The delay between quenching and re-ignition depends on the amount of dilution, as explained by an illustration of flame evolution in a Borghi diagram.

Prechamber ignition↗