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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Geologic hydrogen: From natural occurrences to anthropogenic generation – A review of fundamentals, potential, challenges and prospects

Growing demand for hydrogen is exposing the environmental and economic limits of reforming-based and carbon-managed supply chains, while the scale-up of electrolytic capacity remains capital-constrained. Geologic hydrogen, defined as molecular H₂ generated and stored within the Earth's crust offers a complementary, potentially lower-cost resource, yet exploration is still ad hoc. This review (1) revisits a global inventory of confirmed hydrogen seeps and subsurface occurrences; (2) analyzes the controlling reactions, migration pathways, and trapping conditions governing these occurrences; (3) proposes a process-based geologic hydrogen system concept analogous to, yet distinct from, the petroleum system; and (4) evaluates potential geologic hydrogen systems within the United States as a representative case study. Here, we contrast natural systems powered by serpentinization, mantle degassing or radiolysis with anthropogenic systems that stimulate the same reactions or convert in-situ hydrocarbons. Stable hydrogen accumulations require generation rates that outpace combined physical, chemical and microbial losses; the Bourakébougou field (Mali) exemplifies a self-recharging, free-gas reservoir sustained by meteoric-water serpentinization beneath an efficient caprock. Prospective geologic hydrogen resources are likely to occur in regions where iron-rich lithologies, deep-seated faults, and low-permeability sealing formations coexist. Applying this principle, we highlight three promising hydrogen play types in U.S. geological terrains: ophiolite belts (Appalachian and Californian regions), the Midcontinent Rift and the Lake Superior banded‑iron formations. Multiphysics numerical models and positive-unlabeled machine-learning workflows help to accelerate play screening and de-risk future production; yet, reaction kinetics, stimulation strategies, and full techno-economic and life-cycle assessments remain pivotal knowledge gaps.

Anthropogenic hydrogen generation↗
DOE OSTI2026

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Applications of explainable artificial intelligence in renewable energy research

Researchers in renewable energy are applying deep learning (DL) to a variety of problems from diverse renewable energy domains, such as biofuels, wind, solar, power systems, buildings, vehicles, and transportation systems. Improvements in accuracy may be demonstrated using DL in laboratory settings. However, the lack of interpretability of DL models poses a practical limitation to their utility in advancing scientific knowledge and in the deployment of DL models in safety-critical energy systems. In this article, we discuss explainable artificial intelligence (XAI) as one pathway toward more interpretable DL models. We explore a brief timeline of U.S. national laboratory interest in XAI, an overview and taxonomy of methods in the field of XAI, and a selection of applications across renewable energy research domains. We conclude by highlighting pivotal areas where XAI can accelerate innovation in artificial intelligence for renewable energy research and other essential future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Mechanistic understanding of pH effects on the oxygen evolution reaction

The oxygen-evolution reaction (OER) is pivotal in many energy-conversion technologies as it is an important counter reaction to others that convert stable chemicals to higher-value products using electrochemistry. The local microenvironment and pH for the anode OER can vary from acidic to neutral to alkaline depending on the system being explored, making definitive mechanistic insights difficult. In this paper, we couple experiments, first-principles calculations based on density functional theory, microkinetics, and transport modeling to explore the entire pH range of the OER. At low current densities, neutral pH values unexpectedly perform better than the acidic and alkaline conditions, and this trend is reversed at higher current densities (> 20 mA cm -2 ). Using multiscale modeling, this switch is rationalized by a change from a dual-reaction mechanism to a single rate-determining step. The model also shows how the alkaline reaction rates dominate in the middle to high pH range. Furthermore, we explore that the local pH for near-neutral conditions is much different (e.g., 2.4 at the reaction surface vs. 9 in the bulk) than the pH extremes, demonstrating the criticality that transport phenomena plays in kinetic activity.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of crystallite size on lithium storage performance of high entropy oxide (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 nanoparticles

High-entropy oxides (HEOs), known for their high theoretical capacity and structural stability, are considered promising anode materials for next-generation lithium-ion batteries (LIBs). In this research, we synthesized a novel spinel-type HEO, (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 , using a solution combustion method. By adjusting the quantity of the combustion agent, we produced samples with varying crystallite sizes. The crystallite size of the HEOs initially enlarges with an increased combustion agent, then diminishes. The enhancement of crystallite size correlates with improved electrochemical performance for lithium storage. Notably, the (Cr 0.2 Mn 0.2 Co 0.2 Ni 0.2 Zn 0.2 ) 3 O 4 nanoparticles, with the largest crystallite size of 36.3 nm, demonstrated a reversible capacity of 343 mA h g -1 after 100 cycles at 100 mA g -1 , a capacity retention to 319 mA h g -1 after 1000 cycles at 1 A g -1 , and a commendable rate capability of 260 mA h g -1 at 2 A g -1 . Furthermore, this study underscores the pivotal role of crystallite size in LIB performance and presents a viable strategy to enhance the lithium storage capabilities of HEOs and other metal oxides.

25 ENERGY STORAGE↗
DOE OSTI2026

Mitigation of polysulfide shuttle effect in Li-S batteries through catalytic disproportionation reaction

Polysulfides are poorly retained within porous cathodes and readily diffuse into the electrolyte over time, leading to the well-known shuttle effect that undermines the reversibility of Li-S batteries. Here, in this study, we demonstrate that catalytic disproportionation of polysulfides provides an effective pathway to suppress this process by rapidly converting dissolved species into solid sulfur and sulfides, thereby preventing their migration into the electrolyte. Fundamentally, the sluggish kinetics of sulfur redox reactions are responsible for the accumulation and redistribution of soluble polysulfides in the bulk electrolyte. By accelerating these kinetics, catalyzed disproportionation not only confines sulfur within the conductive cathode matrix but also promotes the homogeneous precipitation of Li₂S₂/Li₂S, which enhances electrochemical reversibility and cycling stability. Using nitrogen-doped carbon (NC800) as a model catalyst, we reveal its ability to drive a pseudo-16-electron reduction pathway, leading to a single dominant Li₂S product and uniform deposition within the porous framework. In contrast, a non-catalytic carbon (KB) yields multiple polysulfide intermediates and heterogeneous deposition. The mechanistic insights provided here highlight the pivotal role of catalytic disproportionation in reshaping sulfur redox pathways and offer a rational strategy for mitigating polysulfide shuttling in practical Li-S pouch cells.

25 ENERGY STORAGE↗
DOE OSTI2023

Ion-beam radiation-induced Eshelby transformations: The mean and variance in hydrostatic and shear residual stresses

Ion beam plays a pivotal role in ion implantations and the fabrication of nanostructures. However, there lacks a quantitative model to describe the residual stresses associated with the ion-beam radiation. Radiation-induced residual stress/transformation strain have been mostly recognized in the hydrostatic sub strain space. Here, in this study, we use molecular dynamics (MD) simulations to show that the response of a material to irradiation is generally anisotropic that depends on the ion-beam direction, and should be described using tensorial quantities. We demonstrate that accelerator-based ion beam irradiation, combined with the intrinsic lattice anisotropy and externally induced anisotropy (such as anisotropic mechanical loadings), causes radiation-actuated shear transformation strains in addition to hydrostatic expansion. We map out these complex correlations for several materials. Radiation-induced defects are shown to be responsible for residual shear stresses in the manner of Eshelby inclusion transformation. We propose such tensorial response model should be considered for accurate nanoscale fabrication using ion-beam irradiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗
DOE OSTI2021

A review of thermal physics and management inside lithium-ion batteries for high energy density and fast charging

Traditionally it has been assumed that battery thermal management systems should be designed to maintain the battery temperature around room temperature. That is not always true as lithium-ion battery (LIB) R&D is pivoting towards the development of high energy density and fast charging batteries. Therefore, it is necessary to have a comprehensive review of thermal considerations for LIBs targeted for high energy density and fast charging, i.e., the optimal thermal condition, thermal physics (heat transport and generation) inside the battery, and thermal management strategies. As the energy density and charge rate increases, the optimal battery temperature can shift to be higher than room temperature. In the first part of the review various sources of heat generation inside LIBs and various approaches to minimizing battery heat generation are summarized. The importance of heat of mixing due to ion diffusion during fast charging is also highlighted. To improve the temperature uniformity and avoid excessive internal temperature rise, heat transfer inside the battery needs to be enhanced, and reducing the thermal contact resistance between the electrodes and separator can significantly increase the effective thermal conductivity of batteries. In the second part of the review various challenges and latest developments related to thermal transport and properties of LIBs are discussed. Finally, a summary of latest advancement on smart control of internal temperature of LIBs is discussed as depending on the ambient temperature and the optimal temperature; the battery heat needs to be retained or dissipated to elevate or avoid temperature rise.

25 ENERGY STORAGE↗
DOE OSTI2024

Ameliorating the sodium storage performance of hard carbon anode through rational modulation of binder

Hard carbon anodes have emerged as promising candidates for sodium-ion batteries due to their inherent advantages. Nevertheless, the surface imperfections in these materials often culminate in irreversible electrolyte consumption, fostering the development of a heterogeneous and fragile solid electrolyte interface (SEI), thereby compromising the initial Coulombic efficiency (ICE). Here, drawing inspiration from the catalytic potential of C=O (carbonyl) bonds in directing preferential salt reduction, we introduce a novel strategy that leverages the modulation of the binder, a long-term overlooked pivotal components in the electrode process. Specifically, Polymethyl methacrylate (PMMA), abundant in C=O groups, is partially substituted for PVDF, ensuring robust adhesion of the electrode material to the current collector while preserving superior mechanical properties. The accurate combination of two binders with delightful compatibility in the state-of-art electrode process, can promote a uniform formation of the SEI on the hard carbon surface enriched in inorganic components, which can ensure long-term interfacial stability and suppresses excessive solvent decomposition and facilitates Na + transfer at the interface. Consequently, the initial Coulombic efficiency of the hard carbon anode with 70 %PMMA binder achieves 86 %, with prominent cycling stability (88 % capacity retention over 500 cycles) at a high current density of 1.2 A g −1 . When paired with high loading cathodes to assemble the pouch cell, it also demonstrates stable operational scenarios.

25 ENERGY STORAGE↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Scaffolding civic infrastructures: Examining the role of civic technoscience in public engagements with oil and gas pipelines

Oil and gas pipelines bring attention to the importance of public participation in the management of large-scale infrastructure projects. Participation transforms how communities understand benefits and risks, and can result in safer and more resilient projects. However, participation can be hindered by procedural injustices in planning processes and lack of industry transparency. Based on a survey of 103 civil society groups conducted in 2021, this paper investigates how groups mobilize technical projects and expert knowledge to overcome these. We frame these mobilizations as expressions of civic technoscience, and argue their pivotal role in building capacity for effective engagements with pipelines. To this extent, we furthermore suggest that civic technoscience is fundamental to building civic infrastructures for reforming participatory governance gaps. We conclude with recommendations for how civic technoscience can better support the intentions of civic infrastructuring by coordinating activities across coalitions, as well as by shifting the scales at which groups engage with the governance of pipelines.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Insights Into Thermal Runaway Mechanisms: Fast Tomography Analysis of Metal Agglomerates in Lithium-Ion Batteries

Thermal Runaway (TR) in lithium-ion batteries (LIB) is a critical technological and social concern. Whilst such events are rare, TR is characterized by uncontrollable heating leading to catastrophic failures. To deepen the understanding of the failure process and subsequently develop more accurate TR prediction models and as a result safer battery systems, we present in this work high-speed X-ray tomography for in-depth investigations of the copper current collector melting and agglomeration during TR. The melting process presents valuable real-time internal information about heat evolution during TR, previously challenging to access but crucially important for validating TR models. In this work, controlled failure studies combined with high-speed X-ray tomography were performed on two different commercial LIB models, subjecting them to both external heating and nail penetration to induce TR. Through real-time observation via high-speed tomography, followed by segmentation, rendering, and analysis, the formation of copper agglomerates was qualitatively and quantitatively characterized and visualized for the first time. Agglomerates tended to form either from the battery's outermost layers or centrally, depending on the method of TR initiation, and gives an indirect insight into the internal temperature evolution and distribution. Moreover, an initial comparative analysis between the battery models also revealed differences in agglomerate size, which has been linked to the thicker copper current collectors of one of the cell models. We further discuss the impact of larger copper agglomerates on heat distribution and safety. This study not only sheds light on the intricate dynamics of TR in LIBs but also underscores the pivotal role of 'gold-standard' imaging techniques in advancing battery safety, crucial for the robust modeling of TR and the future design of electric vehicle safety systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Dimensionality effects on multicomponent ionic transport and surface complexation in porous media

Coulombic interactions between charged species in pore water and at surface/solution interfaces are of pivotal importance for multicomponent reactive transport in porous media. In this study, we investigate the impact of domain dimensionality on electrostatically coupled dispersion and surface-solution reactions during transport of acidic plumes and major ions in porous media. Column and quasi two-dimensional flow-through experiments were performed, with identical silica porous media and under the same advection-dominated conditions. Equal mass fluxes of different electrolyte solutions (i.e., HCl - pH ~ 2.8, NaBr - 100 mM, HCl - pH ~ 2.8 plus NaBr - 100 mM) were continuously injected in the 1-D and 2-D experiments and breakthrough curves of pH and major ions were measured at the outlet of the domains. The presence of pronounced ionic strength gradients in the transverse direction in the 2-D setup caused distinct retardation and transport behaviors of protons and major ions which were not observed in the one-dimensional column experiments. Furthermore, in the cases of salt electrolytes injection, considerably enhanced release of H + (>61%) from the quartz surface was observed in the multidimensional system compared to the one-dimensional setup. Reactive transport modeling was performed to reproduce the experimental outcomes and to analyse the coupling between transport processes, based on the Nernst-Planck formulation of diffusive/dispersive fluxes and on surface complexation reactions at the solid-solution interface. Electrostatic interactions between Na + , B – , and H + , and deprotonation of the quartz surface upon the formation of sodium outer-sphere complexes, are the primary controllers of the spatial and temporal features displayed by the pH and major ions measurements. The reactive transport simulations allowed us to interpret the experimental observations, to visualize the distribution and spatio-temporal evolution of dissolved and solid species, to identify a spatially heterogeneous zonation of Coulombic interactions with distinct behavior at the fringe and core of the injected plumes in the multidimensional setup, and to quantify the different components of the Nerst-Planck fluxes of the charged solutes. This study demonstrates that the domain dimensionality directly affects electrostatic interactions between charged aqueous species in the pore water and surface complexation reactions at the solid-solution interface. The non-trivial effects of dimensionality on multicomponent ionic transport result in a significantly different behavior in 1-D and 2-D systems.

58 GEOSCIENCES↗
DOE OSTI2025

Belowground cross-trophic networks impact CH4 and CO2 emissions in degraded alpine peatlands

Belowground organisms forming complex cross-trophic ecological networks are essential for maintaining peatland carbon stability and energy flow. However, how peatland degradation affects the biodiversity and cross-trophic ecological networks of soil communities remains poorly understood. Here, we examined the degradation effects on soil prokaryotes (i.e., bacteria, archaea), fungi and nematodes in alpine peatlands on the eastern Tibetan Plateau, characterized by varying water table depths (indicating degradation levels). We found that peatland degradation, accompanied by significant shifts in soil moisture and pH (P < 0.05), reduced the taxonomic richness and phylogenetic diversity of prokaryotes, fungi, and nematodes, particularly in deeper soil layers (20–50 cm). Crucially, peatland degradation weakened potential cross-trophic interactions within bipartite networks of prokaryotes-nematodes and fungi-nematodes, resulting in less than 6.5 %–28.8 % of unchanged modules. Degradation-induced changes in soil moisture and pH were identified as primary drivers of biodiversity loss and network restructuring. Furthermore, such changes of belowground cross-trophic networks (particularly prokaryote-nematode) were significantly correlated with greenhouse gas emissions, such as decreased CO2 emissions, maintained CH4 emissions (leading to a higher CH4/CO2 ratio in deep layers), and reduced temperature sensitivity (Q10) of soil respiration. These findings underscore the critical need to protect soil biodiversity and cross-trophic networks in peatlands, particularly under the threat of climate change, to preserve peatland carbon stocks and maintain ecosystem stability. Our findings highlight that belowground cross-trophic networks are pivotal to decipher soil carbon dynamics of degraded peatlands and project the fate of peatland carbon stocks under future climate change scenarios.

Cross-trophic networks↗
DOE OSTI2022

Dynamics and growth rate implications of ribosomes and mRNAs interaction in E. coli

Understanding how cells grow and adapt under various nutrient conditions is pivotal in the study of biological stoichiometry. Recent studies provide empirical evidence that cells use multiple strategies to maintain an optimal protein production rate under different nutrient conditions. Mathematical models can provide a solid theoretical foundation that can explain experimental observations and generate testable hypotheses to further our understanding of the growth process. In this study, we generalize a modeling framework that centers on the translation process and study its asymptotic behaviors to validate algebraic manipulations involving the steady states. Using experimental results on the growth of E. coli under C-, N-, and P-limited environments, we simulate the expected quantitative measurements to show the feasibility of using the model to explain empirical evidence. Our results support the findings that cells employ multiple strategies to maintain a similar protein production rate across different nutrient limitations. Moreover, we find that the previous study underestimates the significance of certain biological rates, such as the binding rate of ribosomes to mRNA and the transition rate between different ribosomal stages. Furthermore, our simulation shows that the strategies used by cells under C- and P-limitations result in a faster overall growth dynamics than under N-limitation. In conclusion, the general modeling framework provides a valuable platform to study cell growth under different nutrient supply conditions, which also allows straightforward extensions to the coupling of transcription, translation, and energetics to deepen our understanding of the growth process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Synthesis of porous organic cage CC3 via solvent modulated evaporation

We demonstrate a Humidity Modulated Solvent Evaporation (HMSE) approach to promote the nucleation and growth of CC3 crystals. This approach relies on the gradual evaporation of dichloromethane (solvent) from a diluted concentration of CC3 precursors deposited on aluminum foil. The slow solvent evaporation allowed enough time for the organization and formation of CC3 porous organic cage. The solvent diffusion rate was modulated by the relative humidity in the system. The slow kinetics implied during the development of CC3 phase, allowed the formation of CC3 crystals. HRTEM, SAED, SEM, and XRD patterns were used as pivotal characterization techniques to follow and confirm the formation of CC3 crystals.

porous organic cages, modulated evaporation, cryst↗
DOE OSTI2023

On falling film evaporator – A review of mechanisms and critical assessment of correlation on a horizontal tube bundle with updated development

Optimization of the energy efficiency and reduction of the greenhouse gas emission for the heat pump system is imperative to meet the net-zero goals at 2050. Chillers with falling film evaporator design not only possess better system performance but also contain less refrigerant inventory. Hence, accurate prediction of the evaporator performance is pivotal especially when charged with low-GWP refrigerant. Here, the study reviews the correlations for falling film evaporator with a horizontal tube bundle configuration. The major efforts of this study include four tasks: (a) literature review of the experimental studies and available empirical correlations; (b) comprehensive discussion of the falling film evaporation heat transfer mechanism; (c) development of a new rationally based correlation based on available literature; and (d) comparison of different correlations based on the existing data. The collected data includes 4114 data points from 8 sources, 6 refrigerants (R-600a, R-290, R-245fa, R-134a, R-1234ze(E), R-123), 5 types of the tubes (Plain, Turbo-GII-HP, GEWA-B, Low-fin, High-Flux), liquid Weber number from 2.2 × 10 –6 to 0.7, imposed vapor Weber number from 0 to 37.2, heat flux from 2.5 to 151.5 kW/m 2 , and film Reynolds number from 1 to 3159.8. The new correlation gives a MAD of 28.3%, and an R 2 of 0.86. Yet, the developed correlation considers various heat transfer mechanisms, including the transition point from falling film evaporation to the nucleate boiling, local evaporation, dry-out, mist flow, imposed flow, and enhanced tube effects.

42 ENGINEERING↗