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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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Influence of functional groups on low-temperature combustion chemistry of biofuels
Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to produce a diverse array of advanced biofuels with complex molecular structure and functional groups. In order to integrate biofuels into existing combustion systems, and to optimize the design of next-generation combustion systems, understanding connections between molecular structure and ignition at low-temperature conditions (< 1000 K) remains a priority that is addressed in part using chemical kinetics modeling. The development of predictive models relies on detailed information, derived from experimental and theoretical studies, on molecular structure and chemical reactivity, both of which influence the balance of chain reactions that occur during combustion – propagation, termination, and branching. In broad context, three main categories of reactions affect ignition behavior: (i) initiation reactions that generate a distribution of organic radicals, ; (ii) competing unimolecular decomposition of and bimolecular reaction of with O2; (iii) decomposition mechanisms of peroxy radical adducts (RO), including isomerization via RO ⇌ OOH. Furthermore, all three categories are influenced by functional groups in different ways, which causes a shift in the balance of chain reactions that unfold over complex temperature- and pressure-dependent mechanisms.
A sophisticated model to predict ash inhibition during combustion of pulverized char particles
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An intrinsic kinetics model to predict complex ash effects (ash film, dilution, and vaporization) on pulverized coal char burnout in air (O2/N2) and oxy-fuel (O2/CO2) atmospheres
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Application of machine learning modeling in prediction of solar still performance: A comprehensive survey
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Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona
Here, this study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R 2 : 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.
Predictive performance modeling framework for a novel enclosed particle receiver configuration and application for thermochemical energy storage
Concentrating solar power (CSP) plants can provide dispatchable power with a thermal energy storage capability for increased renewable-energy grid penetration. Particle-based CSP systems permit higher temperatures, and thus, potentially higher solar-to-electric efficiency than state-of-the-art molten-salt heat-transfer systems. This paper describes a detailed numerical analysis framework for estimating the performance of a novel, geometrically complex, enclosed particle receiver design. The receiver configuration uses arrays of small tubular absorbers to collect and subsequently transfer solar energy to a flowing particulate medium. The enclosed nature of the receiver design renders it amenable to either an inert heat-transfer medium, or a reactive heat-transfer medium that requires a controllable ambient environment. The numerical analysis framework described in this study is demonstrated for the case of thermal reduction of CaCr 0.1 Mn 0.9 O 3-$\delta$ for thermochemical energy storage. The modeling strategy consists of Monte Carlo ray tracing for absorbed solar-energy distributions from a surround heliostat field, computational fluid dynamics modeling of small-scale local tubular arrays, surrogate response surfaces that approximately capture simulated tubular array performance, a quasi-two-dimensional reduced-order description of counter-flow reactive solids and purge gas, and a radiative exchange model applied to embedded-cavity structures at the size scale of the full receiver. In this work we apply the numerical analysis strategy to a single receiver configuration, but the framework can be generically applicable to alternative enclosed designs. In conclusion, we assess sensitivity of receiver performance to surface optical properties, heat-transfer coefficients, solids outlet temperature, and purge-gas feed rates, and discuss the significance of model assumptions and results for future receiver development.
A fast analytical model for predicting battery performance under mixed kinetic control
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Quantitative Structure–Activity Relationship Models to Predict Cardiac Adverse Effects
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Greenhouse Gas Emissions and Decarbonization Potential of Global Fired Clay Brick Production
Fired clay bricks (FCBs) are a dominant building material globally due to their low cost and simplicity of production, especially in low- and middle-income countries. With a projected rising housing demand, commensurate growth in brick demand is anticipated, the production of which could result in significant greenhouse gas (GHG) emissions. Robust models are needed to estimate brick demand and emissions to systematically address decarbonization pathways. Few sources report production values; hence, we present two novel proxy models: (i) a consumption prediction model, relying on country-specific clay extraction data, dynamic building stock modeling, and average material intensity use allowing for projections to 2050; and (ii) a GHG emissions model, using literature-based data and production technology-specific inputs. Based on these models, the current global FCB consumption is estimated as 2.18 Gt annually, resulting in approximately 500 million tCO2e (1% of current global GHG emissions). If unaddressed, this fraction could increase to 3.5-5% in 2050 considering a moderate SSP 2-4.5 climate change mitigation scenario. Consequently, we explored three potential decarbonization pathways: (i) improving energy efficiency; (ii) shifting production to best practices; and (iii) replacing half of FCB demand with hollow concrete blocks, resulting in 27%, 49%, and 51% reduction in GHG emissions, respectively.
Application of Surface Tension Model for Prediction of Interfacial Speciation of CO 2 -Loaded Aqueous Solutions of Monoethanolamine
A thermodynamic platform in combination with experimental surface tension data is used to predict the surface phase speciation of CO 2 -loaded aqueous solutions of 30 weight percent (%) monoethanolamine (MEA) at 20, 40, and 60 °C from their corresponding bulk phase speciation. The osmotic coefficient dependency of the surface tension of an electrolyte solution has been employed to estimate the surface tension. In order to calculate the osmotic coefficients, the Pitzer equation for the excess Gibbs energy was applied. According to the results of this study, molecular MEA, and to some extent protonated MEA and the MEA-carbamate have surface propensity and will accumulate at the surface phase. The carbonate ion shows nearly even distribution between bulk and surface phases, and bicarbonate displays a tendency to remain in the bulk phase that increases with CO 2 loading. The differences in the behaviors of protonated MEA, MEA-carbamate, carbonate and bicarbonate are due to differences among their sizes and polarizabilities. Using first principles calculations, the static isotropic dipole polarizabilities for bicarbonate, carbonate, MEA carbamate and protonated MEA were calculated. The bulk and surface phases’ ionic strengths reasonably represent this behavior of ionic species at the surface phase.
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
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