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

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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing cathode property prediction via exchange-correlation functionals with and without long-range dispersion corrections

In this work, we benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as positive electrode materials, against available experimental data. Specifically, we examine the accuracy of properties calculated within density functional theory (DFT) using eight different treatments of electron exchange-correlation: the strongly constrained and appropriately normed (SCAN) and Perdew–Burke–Ernzerhof (PBE) density functionals, Hubbard-U-corrected SCAN and PBE (i.e., SCAN+U and PBE+U), and SCAN(+U) and PBE(+U) with added long-range dispersion (D) interactions (i.e., DFT(+U)+D). van der Waals interactions are included respectively via the revised Vydrov-Van Voorhis (rVV10) for SCAN(+U) and the DFT-D3 for PBE(+U). We find that SCAN-based functionals predict larger voltages due to an underestimation of stability of the MO2 systems, while also predicting smaller interlayer spacings compared to their PBE-based counterparts. Furthermore, adding dispersion corrections to PBE has a greater effect on voltage predictions and interlayer spacings than with SCAN, indicating that DFT-SCAN – despite being a ground-state theory – fortuitously captures some short and medium-range dispersion interactions better than PBE. While SCAN-based and PBE-based functionals yield qualitatively similar band gap predictions, there is no significant quantitative improvement of SCAN-based functionals over the corresponding PBE-based versions. Finally, we expect SCAN-based functionals to yield more accurate property predictions than the respective PBE-based functionals for most TMOs, given SCAN's stronger theoretical underpinning and better predictions of systematic trends in interlayer spacings, intercalation voltages, and band gaps obtained in this work.

25 ENERGY STORAGE↗
DOE OSTI2025

Performance Improvements Through Advanced PV Backtracking on Uneven Terrain

The climatic sensitivity of new terrain-aware backtracking algorithms is evaluated across 800 locations in the continental USA on a representative synthetic rolling terrain. We find that a global optimization approach to backtracking results in climate-specific annual energy gains of 2.4%–3.2% relative to a traditional backtracking algorithm baseline. We identify a strong logarithmic correlation between local diffuse fraction and yield improvement, and highlight the effect of seasonal precipitation on performance gains. We also find that a backtracking approach, which approximates the terrain as constant, does not offer significant annual energy gains over the baseline on the synthetic terrain. Our findings suggest that specific yield from backtracking in the USA can be improved by as much as 88 kWh/kW by considering terrain when selecting a backtracking algorithm.

Backtracking↗
DOE OSTI2020

Spirometric traits show quantile-dependent heritability, which may contribute to their gene-environment interactions with smoking and pollution

Background: “Quantile-dependent expressivity” refers to a genetic effect that is dependent upon whether the phenotype (e.g., spirometric data) is high or low relative to its population distribution. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV 1 ), and the FEV 1 /FVC ratio are moderately heritable spirometric traits. The aim of the analyses is to test whether their heritability (h 2 ) is constant over all quantiles of their distribution. Methods: Quantile regression was applied to the mean age, sex, height and smoking-adjusted spirometric data over multiple visits in 9,993 offspring-parent pairs and 1,930 sibships from the Framingham Heart Study to obtain robust estimates of offspring-parent (β OP ), offspring-midparent (β OM ), and full-sib regression slopes (β FS ). Nonparametric significance levels were obtained from 1,000 bootstrap samples. β OP s were used as simple indicators of quantile-specific heritability (i.e.,h 2 = 2β OP /(1+r spouse ), where r spouse was the correlation between spouses). Results: β OP ± standard error (SE) decreased by 0.0009 ± 0.0003 (P = 0.003) with every one-percent increment in the population distribution of FEV 1 /FVC, i.e., β OP ± SE were: 0.182 ± 0.031, 0.152 ± 0.015; 0.136 ± 0.011; 0.121 ± 0.013; and 0.099 ± 0.013 at the 10th, 25th, 50th, 75th, and 90th percentiles of the FEV 1 /FVC distribution, respectively. These correspond to h 2 ± SEs of 0.350 ± 0.060 at the 10th, 0.292 ± 0.029 at the 25th, 0.262 ± 0.020 at the 50th, 0.234 ± 0.025 at the 75th, and 0.191 ± 0.025 at the 90th percentiles of the FEV 1 /FVC ratio. Maximum mid-expiratory flow (MMEF) h 2 ± SEs increased 0.0025 ± 0.0007 (P = 0.0004) with every one-percent increment in its distribution, i.e.: 0.467 ± 0.046, 0.467 ± 0.033, 0.554 ± 0.038, 0.615 ± 0.042, and 0.675 ± 0.060 at the 10th, 25th, 50th, 75th, and 90th percentiles of its distribution. This was due to forced expiratory flow at 75% of FVC (FEF75%), whose quantile-specific h 2 increased an average of 0.0042 ± 0.0008 for every one-percent increment in its distribution. It is speculated that previously reported gene-environment interactions may be partially attributable to quantile-specific h 2 , i.e., greater heritability in individuals with lower FEV 1 /FVC due to smoking or airborne particles exposure vs. nonsmoking, unexposed individuals. Conclusion: Heritabilities of FEV 1 /FVC, MMEF, and FEF75% from quantile-regression of offspring-parent and sibling spirometric data suggest their quantile-dependent expressivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Rate Theory Modeling and Simulations of Silicide Fuel at LWR Conditions

Uranium silicide (U 3 Si 2 ) fuel has higher thermal conductivity and higher uranium density, making it a promising candidate for the accident-tolerant fuel (ATF) used in light water reactors (LWRs). However, previous studies on the fuel performance of U 3 Si 2 , including both experimental and computational approaches, have been focusing on the irradiation conditions in research reactors, which usually involve low operation temperatures and high fuel burnups. Thus, it is important to examine the fuel performance of U 3 Si 2 at typical LWR conditions so as to evaluate the feasibility of replacing conventional uranium dioxide fuel with this silicide fuel material. As in-reactor irradiation experiments involve significant time and financial cost, it is appropriate to utilize modeling tools to estimate the behavior of U 3 Si 2 in LWRs based on all those available research reactor experimental references and state-of-the-art density functional theory (DFT) calculation capabilities at the early development stage. Hence, in this report, a comprehensive investigation of the fission gas swelling behavior of U 3 Si 2 at LWR conditions is introduced. The modeling efforts mentioned in this report was based on the rate theory (RT) model of fission gas bubble evolution that has been successfully applied for a variety of fuel materials at devious reactor conditions. Both existing experimental data and DFT-calculated results were used for the optimization of the parameters adopted by the RT model. Meanwhile, the fuel-cladding interaction was captured by the coupling of the RT model with simplified mechanical correlations. Therefore, the swelling behavior of U 3 Si 2 fuel and its consequent interaction with cladding in LWRs was predicted by the rate theory modeling, providing valuable information for the development of U 3 Si 2 fuel as an accident-tolerant alternative for uranium dioxide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1989

Redistribution in astrophysically important hydrogen lines

Under typical solar chromospheric conditions for hydrogen radiators, strong collisions due to both electrons and ions are well separated in time, so that a binary collision theory for collisional redistribution is applicable. However, a simple impact approximation may not be used, but rather a unified type theory is required in which frequency dependent line shape parameters are used to describe both impact and quasi-static regions of the spectrum. In addition, correlated terms which describe absorption and emission during a collision are important, and, in fact, without correlated terms describing both transfer of excitation and emission during the same collision unphysical predictions (such as negative intensities) would be obtained. In this paper theory is specifically developed for the coupled Lyman-alpha, Lyman-beta, Hydrogen-alpha system, and equations of statistical equilibrium and absorption and emission coefficients are given. All correlated events are examined and emission during a collision is found to be important in the line wings. Stimulated emission and absorption is also included within a broadband approximation. The major approximation is to ignore lower state interaction. It is found that for Lyman-beta Raman-coupling with Hydrogen-alpha occurs and the overall scattering of radiation in the line wings is mostly coherent. In contrast, for Hydrogen-alpha, incoherent redistribution due to lower state radiative decay (which occurs even in the absence of collisions) is found to dominate the coherent scattering. Finally, in the Lyman series the dominant incoherent contribution is associated with cascade transitions and inelastic collisions between different principal quantum states.

Cooper, J.↗
NASA NTRS1996

Development of airborne eddy-correlation flux measurement capabilities for reactive oxides of nitrogen

This research is aimed at producing a fundamental new research tool for characterizing the source strength of the most important compound controlling the hemispheric and global scale distribution of tropospheric ozone. Specifically, this effort seeks to demonstrate the proof-of-concept of a new general purpose laser-induced fluorescence based spectrometer for making airborne eddy-correlation flux measurements of nitric oxide (NO) and other reactive nitrogen compounds. The new all solid-state laser technology being used in this advanced sensor will produce a forerunner of the type of sensor technology that should eventually result in highly compact operational systems. The proof-of-concept sensor being developed will have over two orders-of-magnitude greater sensitivity than present-day instruments. In addition, this sensor will offer the possibility of eventual extension to airborne eddy-correlation flux measurements of nitrogen dioxide (NO2) and possibly other compounds, such as ammonia (NH3), peroxyradicals (HO2), nitrateradicals (NO3) and several iodine compounds (e.g., I and IO). Demonstration of the new sensor's ability to measure NO fluxes will occur through a series of laboratory and field tests. This proof-of-concept demonstration will show that not only can airborne fluxes of important ultra-trace compounds be made at the few parts-per-trillion level, but that the high accuracy/precision measurements currently needed for predictive models can also. These measurement capabilities will greatly enhance our current ability to quantify the fluxes of reactive nitrogen into the troposphere and significantly impact upon the accuracy of predictive capabilities to model O3's distribution within the remote troposphere. This development effort also offers a timely approach for producing the reactive nitrogen flux measurement capabilities that will be needed by future research programs such as NASA's planned 1999 Amazon Biogeochemistry and Atmospheric Chemistry Experimental portion of LBA.

Bradshaw, John↗
DOE OSTI2023

Prediction and Validation of Flow Properties in Porous Lattice Structures

High-porosity metal foams have been extensively studied as an attractive candidate for efficient and compact heat exchanger design. With the advancements in additive manufacturing, such foams can be manufactured with controlled topology to yield highly tailorable mechanical and transport properties. In this study, a lattice Boltzmann method (LBM)-based pore-scale model is implemented to simulate the fluid flow in additively manufactured (AM) metal foams with unit cell topologies of Cube, Face Diagonal (FD)-Cube, Tetrakaidecahedron (TKD), and Octet lattices. The pressure gradient versus average velocity profiles predicted by the LBM model were validated against in-house measurements on the AM lattice samples with the same unit cell topologies. Based on the simulation results, a novel hybrid model is proposed to accurately predict the volume averaged flow properties (permeability and inertial coefficients) of the four structures. Specifically, the linear LBM (neglecting inertial forces) is first implemented to obtain the intrinsic permeability, and then the standard LBM is applied to obtain the inertial coefficient. Convenient correlations for those flow properties as a function of porosity and fiber diameter are constructed. The effects of the AM print qualities on the flow properties are also discussed. The advantages of the hybrid model compared to the polynomial fitting approach for determining flow properties are discussed and compared quantitatively. The hybrid model and presented results are valuable for flow and thermal transport evaluation when designing new metal foams for specific applications and with different materials and topologies. Finally, the presented correlations based on pore-scale simulations can also be conveniently used in volume-averaged models to predict the macroscale flow behavior in such complex structures.

42 ENGINEERING↗
NASA NTRS1985

The casting of semi-permeable membranes in a microgravity environment

The National Electric Company of Venezuela, C.A.D.A.F.E., is sponsoring the development of this experiment which represents Venezuela's first scientific experiment in space. The experience can be classified as an educational one which could add knowledge to the fundamental study of polymeric membranes. Presently, semi-permeable membranes are being manufactured from several different kind of polymers all over the world and specific applications have been identified in fluid separation processes such as reverse osmosis, ultrafiltration and electrodyalisis. Although, the ultrastructure of asymetric and composite membranes have been under intensive study, still there are many questions about the factors affecting this structure and their degree of correlation. Nevertheless, there is indication that the entire morphological structure of polymeric membranes could be affected by the differences in specific gravity between the cast solution and the coagulation liquid, normally used in the membranes preparation process. The casting of semi-permeable membranes in space might help to identify the effect of gravity upon the structure of these membranes. It is important to recognize that the casting process involves changes of states (liquid-gas, liquid-solid) and that in a micro gravity environment, there will be a reduction on buoyancy-driven natural convection and density gradients

Ivan Vera↗
DOE OSTI2017

Monte Carlo sampling and correlation coefficients of critical experiments

We study the influence of calculation parameters on Monte-Carlo generated correlation coefficients of k{sub eff} values for a data set of critical experiments. The correlation coefficients can be used e.g. to reduce conservatism in bias estimations, but generally must be treated correctly if a data set includes correlations. We find that the actual value of the correlation coefficient for a given data set depends on the number of samples and the ratio of the sampling uncertainty to the Monte-Carlo uncertainty of the individual k{sub eff} calculation. We also discuss that the number of input parameters of the model to calculate the k{sub eff} values do not necessarily impact the convergence of the correlation coefficient with increasing sample number. However, we explain how varying the aforementioned parameters lead to different converged results for the correlation coefficients. The conclusion is that a correlation coefficient should not be discussed isolated but with the underlying uncertainties of both, the k{sub eff} estimation and the Monte Carlo sampling approach. The underlying modelling approach of the experimental data has to be described in detail. This should include a sensitivity analysis of the impact of input parameters on the k{sub eff} uncertainty with respect to the modelling approach.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Computational and empirical studies predict Mycobacterium tuberculosis -specific T cells as a biomarker for infection outcome

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2-year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis . We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb -specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlations between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Statistical measures of Planck scale signal correlations in interferometers

Here, a model-independent statistical framework is presented to interpret data from systems where the mean time derivative of positional cross correlation between world lines, a measure of spreading in a quantum geometrical wave function, is measured with a precision smaller than the Planck time. The framework provides a general way to constrain possible departures from perfect independence of classical world lines, associated with Planck scale bounds on positional information. A parametrized candidate set of possible correlation functions is shown to be consistent with the known causal structure of the classical geometry measured by an apparatus, and the holographic scaling of information suggested by gravity. Frequency-domain power spectra are derived that can be compared with interferometer data. As a result, simple projections of sensitivity for specific experimental set-ups suggests that measurements will directly yield constraints on a universal time derivative of the correlation function, and thereby confirm or rule out a class of Planck scale departures from classical geometry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Genome-centric view of carbon processing in thawing permafrost

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. However, accurate prediction of carbon gas emissions produced from thawing permafrost is limited by our understanding of the resident microbial communities and their associated carbon metabolism. Here, metagenomic sequencing of 214 samples from intact, thawing and thawed sites collected over three years enabled the recovery of 1,529 metagenome-assembled genomes (MAGs), including many from phyla with poor genomic representation. These genomes were shown to broadly reflect the diversity of this complex ecosystem with genus-level representatives recovered for >60% of the community, constituting a two orders of magnitude increase in the number of genomes available for understanding carbon processing in this environment. Metabolic reconstruction, supported by metatranscriptomic and metaproteomic data, revealed key populations involved in organic matter degradation, including bacteria encoding a pathway for xylose degradation only previously identified in fungi. Combined analysis of the microbial communities and geochemical data highlighted lineages correlated with the production of greenhouse gases and suggest novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in each stage of carbon processing, providing key information for predicting the impact of climate change on these systems.

Woodcroft, Ben J.↗
DOE OSTI2020

Machine-Learning-Driven, Site-Specific Weather Forecasting for Grid-Interactive Efficient Buildings: Preprint

Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Use of the Wilshire Equations to Correlate and Extrapolate Creep Data of Inconel 617 and Nimonic 105

Advanced power plant alloys must endure high temperatures and pressures for durations at which creep data are often not available, necessitating the extrapolation of creep life. A recently developed creep life extrapolation method is the Wilshire equations, with which multiple approaches can be used to increase the goodness of fit of available experimental data and improve the confidence level of calculating long-term creep strength at times well beyond the available experimental data. In this article, the Wilshire equation is used to extrapolate the creep life of Inconel 617 and Nimonic 105 to 100,000 h. The use of (a) different methods to determine creep activation energy, (b) region splitting, (c) heat- and processing-specific tensile strength data, and (d) short-duration test data were investigated to determine their effects on correlation and extrapolation. For Inconel 617, using the activation energy of lattice self-diffusion as Q C * resulted in a poor fit with the experimental data. Additionally, the error of calculated rupture times worsened when splitting regions. For Nimonic 105, the error was reduced when heat- and processing-specific tensile strengths were used. Extrapolating Inconel 617 creep strength to 100,000 h life gave conservative results when compared to values calculated by the European Creep Collaborative Committee.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Structural Basis for Molecular Discrimination by a 3',3'-cGAMP Sensing Riboswitch

Cyclic dinucleotides are second messengers that target the adaptor STING and stimulate the innate immune response in mammals. Besides protein receptors, there are bacterial riboswitches that selectively recognize cyclic dinucleotides. We recently discovered a natural riboswitch that targets 3',3'-cGAMP, which is distinguished from the endogenous mammalian signal 2',3'-cGAMP by its backbone connectivity. Here, we report on structures of the aptamer domain of the 3',3'-cGAMP riboswitch from Geobacter in the 3',3'-cGAMP and c-di-GMP bound states. The riboswitch adopts a tuning forklike architecture with a junctional ligand-binding pocket and different orientations of the arms are correlated with the identity of the bound cyclic dinucleotide. Subsequent biochemical experiments revealed that specificity of ligand recognition can be affected by point mutations outside of the binding pocket, which has implications for both the assignment and reengineering of riboswitches in this structural class.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Direct numerical simulations of turbulent premixed cool flames: Global and local flame dynamics analysis

The cool flame dynamics, especially in turbulent flows, is of great interest for both practical application and fundamental research. Here, in this study, a series of direct numerical simulations of turbulent premixed n-C 7 H 16 /O 2 /O 3 /N 2 cool flames are performed, with the focus on the influence of turbulence intensity (u'/S L , where S L is the laminar flame speed) on the flame structure as well as the global and local cool flame dynamics. It is found that the cool flame front is considerably wrinkled by turbulence at high u'/S L , leading to significantly thickened turbulent cool flame brush and largely altered local reactivity compared with the reference laminar flame. However, the turbulent flame structure in the temperature space is found to be insensitive to u'/S L . Besides, with increasing u'/S L , the normalized turbulent cool flame speed (S L /S L ) is monotonically increased, attributed to substantial augmentation on the flame surface area (A T /A L ), while the stretching factor (I 0 ) remains almost constant and is smaller than 1. The underlying mechanisms for such variations are revealed through local flame dynamics analysis. Specifically, the local flame displacement speed S d is found to be strongly negatively correlated with flame curvature; meanwhile, such negative correlation and the probability distribution function (PDF) of flame curvature are barely influenced by u'/S L , leading to a weak dependence of I 0 on u'/S L . In contrast, the PDF of the tangential strain rate is found to span a much wider range and shift to the positive side as u'/S L increases, suggesting that the enhanced tangential strain rate is the main cause for the increase in surface area of the turbulent premixed cool flame. Finally, the influence of equivalence ratio on above findings is found to be insignificant, indicating that although the local reactivity of turbulent premixed cool flames is altered due to the differential diffusion, the resultant flame- stretch interaction is insensitive to the equivalence ratio. This study presents some unique cool flame dynamics that are distinct from hot flames, which can help improve the understanding and modeling of turbulent cool flames.

Cool flames↗