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Results for “SURFACE ENERGY”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Durable low surface-energy surfaces

A formulation for forming a low surface-energy surface on a substrate having (i) a fluoroalkyl silane having a low surface energy part, (ii) a liquid crystal silane operable for enhancing the orientation of the molecules of the fluoroalkyl silane and for crosslinking with the fluoroalkyl silane, and, (iii) a transport medium for applying the fluoroalkyl silane and the liquid crystal silane to the surface of a substrate. In one embodiment the formulation can includes a crosslinking agent for crosslinking the fluoroalkyl silane. In another embodiment the formulation has a condensation catalyst for enhancing chemical bonding of the fluoroalkyl silane to the substrate. The transport medium can be an alcohol such as methanol or ethanol.

Willis, Paul B.↗
NASA NTRS2014

Highly Accurate Potential Energy Surface, Dipole Moment Surface, Rovibrational Energy Levels, and Infrared Line List for (32)S(16)O2 up to 8000 cm(exp -1)

A purely ab initio potential energy surface (PES) was refined with selected (32)S(16)O2 HITRAN data. Compared to HITRAN, the root-mean-squares error (RMS) error for all J=0-80 rovibrational energy levels computed on the refined PES (denoted Ames-1) is 0.013 cm(exp -1). Combined with a CCSD(T)/aug-cc-pV(Q+d)Z dipole moment surface (DMS), an infrared (IR) line list (denoted Ames-296K) has been computed at 296K and covers up to 8,000 cm(exp -1). Compared to the HITRAN and CDMS databases, the intensity agreement for most vibrational bands is better than 85-90%. Our predictions for (34)S(16)O2 band origins, higher energy (32)S(16)O2 band origins and missing (32)S(16)O2 IR bands have been verified by most recent experiments and available HITRAN data. We conclude that the Ames-1 PES is able to predict (32/34)S(16)O2 band origins below 5500 cm(exp -1) with 0.01-0.03 cm(exp -1) uncertainties, and the Ames-296K line list provides continuous, reliable and accurate IR simulations. The Ka-dependence of both line position and line intensity errors is discussed. The line list will greatly facilitate SO2 IR spectral experimental analysis, as well as elimination of SO2 lines in high-resolution astronomical observations.

Sulfur Dioxide (SO2)↗
NASA NTRS1975

Surface energy and surface tension at holes and cracks

The concept of surface tension and surface energy of solids was used by Griffith to obtain a criterion for the extension of cracks in brittle materials. Griffith, however, neglected the stresses due to the normal traction at the crack implied by the surface tension. A complete solution to the problem of an elliptic hole in an infinite plate with surface tension loading at the hole is given. Complex potentials are given in closed form in terms of elliptic integrals of the first, second, and third kinds. Stress distributions are studied. For a flat crack, the nature of the singularity at the tip is shown to be radically different from that usually encountered in fracture mechanics. The implications of our analysis for theories of fracture in brittle materials are discussed.

Rajapakse, Y. D. S.↗
NASA NTRS1994

Computed Potential Energy Surfaces and Minimum Energy Pathways for Chemical Reactions

Computed potential energy surfaces are often required for computation of such parameters as rate constants as a function of temperature, product branching ratios, and other detailed properties. For some dynamics methods, global potential energy surfaces are required. In this case, it is necessary to obtain the energy at a complete sampling of all the possible arrangements of the nuclei, which are energetically accessible, and then a fitting function must be obtained to interpolate between the computed points. In other cases, characterization of the stationary points and the reaction pathway connecting them is sufficient. These properties may be readily obtained using analytical derivative methods. We have found that computation of the stationary points/reaction pathways using CASSCF/derivative methods, followed by use of the internally contracted CI method to obtain accurate energetics, gives usefull results for a number of chemically important systems. The talk will focus on a number of applications including global potential energy surfaces, H + O2, H + N2, O(3p) + H2, and reaction pathways for complex reactions, including reactions leading to NO and soot formation in hydrocarbon combustion.

Walch, Stephen P.↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
NASA NTRS1991

Theoretical characterization of the potential energy surface for the reversible reaction H + O2 yields HO2(asterisk) yields OH + O. III - Computed points to define a global potential energy surface

Computed energies and geometries are reported which, combined with previously published calculations, permit a global representation of the potential energy surface for the reaction H + O2 yields HO2(asterisk) yields OH + O. These new calculations characterize the potential energy surface (PES) for all H atom angles of approach to O2 and for the region of the inner repulsive wall. The region of the T-shaped H-O2 exchange saddle point is connected with the constrained energy minimum (CEM) path, and a new collinear H-O2 exchange saddle point is characterized which lies only 9 kcal/mol above the H + O2 asymptote. A vibrational analysis which utilizes local cubic and quartic polynomial representations of the PES along the CEM path has been carried out. Optimal geometries, energies, and harmonic frequencies are reported along with anharmonic analyses for the O2 and OH asymptotes and for the HO2 minimum region of the PES.

Walch, Stephen P.↗
NASA NTRS1994

Multilayer Relaxation and Surface Energies of Metallic Surfaces

The perpendicular and parallel multilayer relaxations of fcc (210) surfaces are studied using equivalent crystal theory (ECT). A comparison with experimental and theoretical results is made for AI(210). The effect of uncertainties in the input parameters on the magnitudes and ordering of surface relaxations for this semiempirical method is estimated. A new measure of surface roughness is proposed. Predictions for the multilayer relaxations and surface energies of the (210) face of Cu and Ni are also included.

Bozzolo, Guillermo↗
NASA NTRS1994

Computed Potential Energy Surfaces and Minimum Energy Pathway for Chemical Reactions

Computed potential energy surfaces are often required for computation of such observables as rate constants as a function of temperature, product branching ratios, and other detailed properties. We have found that computation of the stationary points/reaction pathways using CASSCF/derivative methods, followed by use of the internally contracted CI method with the Dunning correlation consistent basis sets to obtain accurate energetics, gives useful results for a number of chemically important systems. Applications to complex reactions leading to NO and soot formation in hydrocarbon combustion are discussed.

Walch, Stephen P.↗
NASA NTRS1997

Rotational Energy Transfer of N2 Gas Determined Using a New Ab Initio Potential Energy Surface

Rotational energy transfer between two N2 molecules is a fundamental process of some importance. Exchange is expected to play a role, but its importance is somewhat uncertain. Rotational energy transfer cross sections of N2 also have applications in many other fields including modeling of aerodynamic flows, laser operations, and linewidth analysis in nonintrusive laser diagnostics. A number of N2-N2 rigid rotor potential energy surface (PES) has been reported in the literature.

Huo, Winifred M.↗
NASA NTRS2015

Modification of Surface Energy via Direct Laser Ablative Surface Patterning

Surface energy of a substrate is changed without the need for any template, mask, or additional coating medium applied to the substrate. At least one beam of energy directly ablates a substrate surface to form a predefined topographical pattern at the surface. Each beam of energy has a width of approximately 25 micrometers and an energy of approximately 1-500 microJoules. Features in the topographical pattern have a width of approximately 1-500 micrometers and a height of approximately 1.4-100 micrometers.

Wohl, Christopher J., Jr.↗
NASA NTRS1993

Surface energy fluxes at Central Florida during the convection and precipitation electrification experiment

One of the objectives of CaPE is to better understand the convective process in central and south Florida during the warm season. The energy and moisture exchanges between the surface and the atmosphere are closely related to this process. Some recent studies have shown that the surface energy balance plays an important role in the climatic fields (Shukla and Mintz, 1982; Sud and Smith, 1985; Sato et. al, 1989). Surface energy fluxes and related surface processes such as evapotranspiration and sensible heat transfer directly effect the temperature, humidity, cloud formation and precipitation. For example, mesoscale circulation around a discontinuity in vegetation type were shown to be stronger with wet soil than with dry soil using an evapotranspiration model (Pinty et. al, 1989). In order to better describe the processes in the atmosphere at various scales and improve our ability of modeling and predicting weather related events, it is crucial to understand the mechanism of surface energy transfer in relation to atmospheric events. Surface energy flux measurements are required to fully understand the interactions between the atmosphere and the surface.

Nie, D.↗
DOE OSTI2025

Surface Energy Balance System (SEBS) Instrument Handbook

A Surface Energy Balance System (SEBS) has been installed collocated with each deployed ECOR system at the Southern Great Plains (SGP), North Slope of Alaska (NSA), Tropical Western Pacific (TWP), ARM Mobile Facility 1 (AMF1), and ARM Mobile Facility 2 (AMF2). The surface energy balance system consists of upwelling and downwelling solar and infrared radiometers within one net radiometer, a wetness sensor, and soil measurements. The SEBS measurements allow the comparison of ECOR sensible and latent heat fluxes with the energy balance determined from the SEBS and provide information on wetting of the sensors for data quality purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2026

The two radiative states of the Arctic atmosphere and their impacts on the surface energy budget of sea ice

The surface energy budget (SEB) is a central regulator of Arctic climate and sea ice evolution, yet its processes remain poorly constrained due to sparse observations and complex, coupled surface-atmosphere interactions. This study leverages year-long measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to provide the most comprehensive assessment to date of the central Arctic SEB and its modulation by atmospheric variability. Ship- and ice-based observations from October 2019 to September 2020 were used to directly measure or tightly constrain each term of the SEB, leading to exceptional energetic closure with the seasonal snow and ice mass balance. The analysis reveals strong seasonal transitions in atmosphere-surface energy transfer that are modulated by the atmospheric state and constrained by the ability of the surface temperature to respond. Classification of the atmosphere into its two dominant radiative states—the semi-transparent (ST) and opaque (OP)—highlights the central role of synoptic-scale variability in clouds. The ST atmospheric state dominated the long winter ice growth season, with limited cloudiness supporting persistent surface radiative cooling and ice growth. The OP state, associated with liquid-containing or thick ice clouds, became dominant in spring, with the combination of increased solar heating and cloud surface longwave warming driving ice and snow melt. Eddy covariance versus bulk approaches for deriving surface turbulent heat fluxes provide vastly different perspectives on the role of turbulence in modulating the SEB. These results establish a high-quality benchmark dataset for Arctic SEB studies and demonstrate how the balance of atmospheric radiative states exerts a first-order control on the annual evolution of the sea ice. The findings have broad implications for advancing observing technologies, understanding Arctic amplification, improving climate models, and predicting future sea ice change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗