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DOE OSTI2025

Glovebox Fires Phase 1: Simulations of Open Burner Experiments

Understanding the duration of a fire required to breach a glovebox via glove ports and/or windows within a facility is important in developing mitigation strategies for safety. To facilitate this understanding, both simulation and experiments are utilized. The approach is to first validate the Computation Fluid Dynamics (CFD) code, Fire Dynamics Simulator (FDS), with experimental data collected by New Mexico Tech (NMT) and then perform simulations of full-scale rooms containing gloveboxes to assess numerous scenarios which would otherwise be cost-prohibitive experimentally. This report provides comparison to first-phase experiments involving a fire without a glovebox. A subsequent report will provide comparison to experiments involving a glovebox. The results indicate good agreement with FDS tending to over predict the pre-mixed and diffusion-mode tests by 6% and 10%, respectively.

42 ENGINEERING↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗

DOE OSTI2023

Four-Stage Multi-Physics Simulations to Assist Temperature Sensor Design for Industrial-Scale Coal-Fired Boiler

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

42 ENGINEERING↗

DOE OSTI2024

Ne viii in the Warm-hot Circumgalactic Medium of FIRE Simulations and in Observations

Abstract The properties of warm-hot gas around ∼ L * galaxies can be studied with absorption lines from highly ionized metals. We predict Ne viii column densities from cosmological zoom-in simulations of halos with masses in ∼10 12 and ∼10 13 M ☉ from the Feedback in Realistic Environments (FIRE) project. Ne viii traces the volume-filling, virial-temperature gas in ∼10 12 M ☉ halos. In ∼10 13 M ☉ halos the Ne viii gas is clumpier, and biased toward the cooler part of the warm-hot phase. We compare the simulations to observations from the COS Absorption Survey of Baryon Harbors (or CASBaH) and COS Ultraviolet Baryon Survey (or CUBS). We show that when inferring halo masses from stellar masses to compare simulated and observed halos, it is important to account for the scatter in the stellar-mass–halo-mass relation, especially at M ⋆ ≳ 10 10.5 M ☉ . Median Ne viii columns in the fiducial FIRE-2 model are about as high as observed upper limits allow, while the simulations analyzed do not reproduce the highest observed columns. This suggests that the median Ne viii profiles predicted by the simulations are consistent with observations, but that the simulations may underpredict the scatter. We find similar agreement with analytical models that assume a product of the halo gas fraction and metallicity (relative to solar) ∼0.1, indicating that observations are consistent with plausible circumgalactic medium temperatures, metallicities, and gas masses. Variants of the FIRE simulations with a modified supernova feedback model and/or active galactic nuclei feedback included (as well as some other cosmological simulations from the literature) more systematically underpredict Ne viii columns. The circumgalactic Ne viii observations therefore provide valuable constraints on simulations that otherwise predict realistic galaxy properties.

Wijers, Nastasha A. (ORCID:0000000163747185)↗

DOE OSTI2024

Fire weather compromises forestation-reliant climate mitigation pathways

Forestation can contribute to climate change mitigation. However, increasing frequency and intensity of climate extremes are posed to have profound impact on forests and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different integrated assessment models (IAMs) by showcasing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently, many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics

Abstract. African and South American (ASA) wildfires account for more than 70 % of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire–climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire–climate relationships in historical and future predictions.

58 GEOSCIENCES↗

DOE OSTI2020

Conceptual Design Study of a Thermal Energy Storage System Using Granular Solids for Coal-fired Boilers

Presentation for the 2020 International Pittsburgh Coal Conference held virtually from September 8-11, 2020.Abstract: As electricity from various renewable energy sources is increasingly supplied to the power grid, the grid’s stability will be challenged. Proposed grid-stability solutions will need to address variability of sources, such as solar and wind energy, in order to alleviate problems such as overgeneration and ramping. In regions where increases in solar energy are prominent, such as California, the net power demand curve often referred to as “the Duck Curve,” has caused problems for energy dispatchers, due to over-generation and fast ramping demands. In this project, we investigated a conceptual design for a Thermal Energy Storage System (TESS) integrated into a coal-fired power plant to support grid-scale storage. Implementing a TESS into power plants can allow for an increase in renewable sources, increase grid resilience, and allow conventional coal-fired plants to operate more efficiently. For this project, a TESS using granular solids as the energy-storage media is integrated into a conventional coal-fired power plant. The purpose of the TESS is to store energy from the flue gas during low demand periods and transfer the energy to steam during peak demand periods. The storage allows the stabilization of combustion and related boiler components at low loads and provides the opportunity to avoid some shutdowns. This study uses thermodynamic properties of several candidate thermal storage materials to estimate the size of storage system required for several operating scenarios.

Huckaby, E. David↗

DOE OSTI2021

Impact on Cycle Efficiency of Small CHP Plants from Increasing Firing Temperature Enabled by AM of Turbine Blades

Combined Heat and Power (CHP) systems are gaining popularity because of their potential for high overall thermodynamic efficiency and increased need for distributed power generation. Most CHP systems include a gas turbine for electricity generation and a heat recovery steam generator (HRSG) for steam generation. The steam can be used to power a steam turbine for additional electricity generation or to drive rotating equipment, for space heating, for absorption chillers, etc. A CHP cycle configuration is often driven by the ‘principal’ utility for the facility that it services; steam or electricity. In either case, performance improvements in the gas turbine have the potential to increase the steam and power output from the cycle, which is a direct result of improvements in the gas turbine efficiency and power output. One possible opportunity to improve an existing gas turbine’s performance is to increase the firing temperature with improved turbine cooling and increased compression ratio. In this study, the impact on CHP cycle performance from increasing the turbine firing temperature by 100 °C and improving the turbine blade cooling for a 6-MW scale gas turbine is estimated using an analytical cooled gas turbine model and a steam cycle model. A sensitivity analysis was performed to understand the impact of increasing the internal cooling effectiveness, thermal barrier coating performance and blade material upgrades on gas turbine and CHP cycle efficiency. The impacts of turbine blade cooling improvements were studied for three common CHP cycle configurations identified from the literature. Various definitions for CHP cycle efficiency from the literature are used in the comparisons. The results showed that a 100 °C increase in firing temperature can increase the gas turbine efficiency by 1 percentage point without improving cooling effectiveness and add 2 additional percentage points in efficiency by using advanced turbine blades with higher internal cooling efficiency. Studied engine upgrades showed potential for increasing the CHP cycle efficiency by 3 percentage points while increasing the steam generation rate by 8%.

Uysal, Selcuk Can↗

DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents the techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which are lacking in public literature. To identify the optimum plant configuration, the study considered multiple cases with varying levels of thermal integration with the plant air separation unit (ASU). Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE) for each case. The optimization design variables include the sCO2 cooler outlet temperatures, recuperator approach temperatures, and pressure drops. High fidelity models for recuperators, coolers, and turbines were developed and used to capture the impact of design variables on plant efficiency and capital costs. The optimization was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms available under NETL’s Framework for Optimization and Quantification of Uncertainty and Sensitivity platform. The optimized direct sCO2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on the F-class gas turbine with CCS. The LCOE of the optimized direct sCO2 plants is 13 to 17% higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to the high uncertainty in component capital costs for these new plants. Recuperators make up over 50% of the sCO2 power block costs. Consequently, any research and development efforts to reduce the recuperator capital costs will benefit the technology’s commercialization. The study also presents preliminary results showing the impact of co-firing landfill gas and natural gas on plant efficiency, LCOE, and CO2 emissions.

Pidaparti, Sandeep↗

DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Pidaparti, Sandeep↗

DOE OSTI2024

High-Temperature Oxidation Behavior of Wrought and Additive Manufactured Ni-based Alloys in Direct-Fired Supercritical CO2 Power Cycle Environments

Materials selection is a key concern for corrosion resistance in elevated temperature and pressure direct-fired supercritical CO2 (sCO2) power cycles. Herein, the wrought product form of alloy H282, as well as several additional nickel-based commercial alloys (230, 263, 617, 625, 740H), were exposed to direct-fired supercritical CO2 conditions (95CO2 – 4H2O – 1O2) at 750 °C and 20 MPa for over 2,000 h. Additionally, additive manufactured (AM) 282 produced by laser powder bed fusion (LPBF) were also included in the exposures. This paper focuses primarily on the differences observed in the degradation behavior of the wrought and additively manufactured materials. AM samples in the as-printed state were found to form less protective oxide scales resulting in higher oxidation rates compared to wrought samples. However, applying a 600 grit surface finish to the AM samples modified the oxidation behavior to closely resemble that of the wrought material. The AM samples formed Mo-rich carbides in the alloy beneath the oxide following sCO2 exposure, implying potentially higher levels of carbon uptake relative to the wrought material. Conversely, the AM samples seemed to be less affected by water-assisted oxide volatilization compared to the wrought material. The results are discussed in terms of the potential compatibility issues that may arise when using Ni-based alloys in the hot portions of direct-fired sCO2 power cycles, particularly in the case of thin-walled components.

Carney, Casey↗

DOE OSTI2026

AI/ML-Enhanced Wind Forecasts for Reducing Uncertainty in Prescribed Fire Planning

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Electric Vehicle Fire Primer for Fleet Managers

The U.S. Department of Energy's Vehicle Technologies Office provides project assistance through Clean Cities, technical expertise, and funding to help stake-holders implement alternative fuels and electric vehicles (EVs). Fleet managers considering EVs can learn about the potential for EV fires and measures to help reduce fire risk.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Simulations of flaming combustion and flaming-to-smoldering transition in wildland fire spread at flame scale

Our objective in the present study is to provide basic insights into the coupling between external-gas and solid biomass vegetation processes that control the dynamics of flame spread in wildland fire problems. Here we focus on a modeling approach that resolves processes occurring at vegetation and flame scales, i.e., the formation of flammable vapors due to the thermal degradation of the solid biomass, the subsequent combustion in ambient air, the thermal feedback to the biomass through radiative and convective heat transfer, and the possible transition from flaming combustion (taking place outside of the solid biomass) to smoldering combustion (taking place inside the solid biomass). The capability uses a multiphase combustion framework and treats external-gas processes through a Large Eddy Simulation solver and solid biomass processes through a discrete particle model. The discrete particle model adopts a one-dimensional porous medium formulation, includes descriptions of drying, thermal pyrolysis, oxidative pyrolysis, and char oxidation, as well as a description of the external-gas-to-solid-biomass diffusion of oxygen mass; the discrete particle model thereby provides a treatment of in-depth oxidative processes and allows the simulation of smoldering combustion. The modeling capability is applied to the simulation of fire spread across a surrogate biomass vegetation bed corresponding to a discrete array of cylindrical-shaped, vertically-oriented, pine wood sticks, characterized by a monomodal size distribution, in horizontal flat terrain and under wind-aided conditions. The numerical results demonstrate that the model can simulate successful flaming-to-smoldering transition followed by complete biomass consumption.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Effect of soot concentration on radiative transport in a 2 m JP-8 pool fire

Hubbard et al. (2024) investigated conjugate heat transfer in a 7.9 m JP-8 pool fire, utilizing both heavily sooting and non-sooting fuel models. Their findings indicated that all models produced pool radiative heat fluxes within the measurement uncertainty for large-scale outdoor experiments. This study aims to further investigate the influence of soot concentration on radiative transport. To this end, Large Eddy Simulations (LES) of a 2 m diameter JP-8 pool fire were conducted, employing four turbulent combustion chemistry models: the Eddy Dissipation Concept (EDC) and three variants of flamelet model. Additionally, three variations of the one-equation subgrid scale turbulence kinetic energy model, and multiple mesh resolutions were utilized. The primary validation metrics were pool radiative heat flux and plume radiative fraction, with simulation data compared to experimental results published by Blanchat et al. (2010). The EDC model produced high soot concentrations near the pool surface, resulting in a significant shielding effect. In contrast, the flamelet combustion model has different kinetic limiters for soot production, leading to soot formation higher in the flame, where it can be more readily oxidized. Flamelet models incorporating both acetylene and benzene contributions to soot growth demonstrated better agreement with experimental data. To assess the sensitivity of pool radiative heat flux to soot mass concentration, we used a one-dimensional approximation over a wide range of soot concentrations and compared it to three-dimensional solutions obtained using Discrete Ordinates. Furthermore, this analysis illustrates the impacts of soot shielding and Turbulence Radiation Interactions (TRI), with TRI emission enhancement factors predicted to be in the range of 3–5 near the edges of the plume.

Computational fluid dynamics↗

DOE OSTI2023

Creating an Advanced Sensor Network to calculate real-time, mass-weighted flue gas composition and air heater leakage of a coal-fired utility boiler under dynamic operating conditions

Utilization of renewable energy sources to minimize the environmental impact of energy production has changed the way utility boilers operate, requiring frequent load cycling between full load and partial loads as low as 30%. Dynamic operation of coal-fired utility boilers significantly reduces boiler efficiency when compared to steady state at full load. Data-driven plant optimization has shown success with coal-fired utility boilers under dynamic operating conditions. The purpose of this work was to create an Advanced Sensor Network (ASN) to provide more extensive real-time data to inform dynamic plant optimization of Net Unit Heat Rate (NUHR). The ASN consists of gas sampling grids in the convective pass of the boiler and downstream of the air heater. These sampling grids allow for quantification of spatial variation of flue gas within the boiler and calculation of mass-weighted composition of flue gas through the combination of composition, velocity, and temperature measurements. The comparison of O 2 between the inlet and outlet of the air heater is used to calculate air leakage in real time. Flue gas composition and air heater leakage are both important factors in boiler efficiency and NUHR. Further, the results of this work support the value of mass-weighted averages for determining flue gas composition accurately. The measurements from the ASN show increased composition stratification during dynamic operation, with an average standard deviation 38% higher than observed during steady-state operation. Air heater leakage was also observed to increase from 2.8% to 5.1% following a load change. Prior to the installation of the ASN, these data would not have been available for dynamic control. These real-time data will be leveraged to calculate and optimize for NUHR during dynamic operation in future work.

42 ENGINEERING↗

DOE OSTI2024

Intense formation of secondary ultrafine particles from Amazonian vegetation fires and their invigoration of deep clouds and precipitation

New particle formation (NPF) in fire smoke is thought to be unlikely due to large condensation and coagulation sinks that scavenge molecular clusters. We analyze aircraft measurements over the Amazon and find that fires significantly enhance NPF and ultrafine particle (UFP < 50 nm diameter) numbers compared to background conditions, contrary to previous understanding. We identify that the nucleation of dimethylamine with sulfuric acid, which is aided by the formation of extremely low volatility organics in biomass-burning smoke, can overcome the large condensation and coagulation sinks and explain aircraft observations. We show that freshly formed clusters rapidly grow to UFP sizes through biomass-burning secondary organic aerosol formation, leading to a 10-fold increase in UFP number concentrations. Here, we find a contrasting effect of UFPs on deep convective clouds compared to the larger particles from primary emissions for the case investigated here. UFPs intensify the deep convective clouds and precipitation due to increased condensational heating, while larger particles delay and reduce precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Enzymatic Biocontrol of Fire Blight ( Erwinia amylovora ) Using an Engineered Glycosyl Hydrolase

Current management of fire blight, caused by Erwinia amylovora , relies heavily on streptomycin a practice that has contributed to the emergence of antibiotic-resistant strains and raised environmental and regulatory concerns. Enzyme-based biocontrol agents offer a promising antibiotic-free approach that combines target specificity with environmental compatibility. This study evaluates CAase, a bacteriophage-derived glycosyl hydrolase, for its ability to disrupt E. amylovora biofilms and reduce disease severity. Biochemical assays and scanning electron microscopy confirmed that CAase efficiently degraded the extracellular polysaccharide (EPS) matrix, releasing cells from biofilms. Gas chromatography–mass spectrometry (GC–MS) linkage analysis of EPS isolated from two Erwinia amylovora strains demonstrated that CAase preferentially cleaves galactan-rich amylovoran produced by strain EA273, while exhibiting only limited activity toward the levan-rich EPS from strain EA1430. Functional assays revealed that CAase reduced bacterial viability by nearly 2 orders of magnitude at higher enzyme concentrations, strongly suppressed surface motility, and induced ultrastructural damage visible by transmission electron microscopy. Importantly, field trials showed that CAase significantly lowered blossom and shoot blight incidence under orchard conditions. These results highlight CAase as a potent enzyme-based strategy for reducing the virulence of E. amylovora and demonstrate its potential as a sustainable alternative to antibiotics in fire blight management.

antibiotic resistance↗