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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Understanding and predicting profile structure and parametric scaling of intrinsic rotation

This study reports on a recent advance in developing physical understanding and a first-principles-based model for predicting intrinsic rotation profiles in magnetic fusion experiments. It is shown for the first time that turbulent fluctuation-driven residual stress (a non-diffusive component of momentum flux) along with diffusive momentum flux can account for both the shape and magnitude of the observed intrinsic toroidal rotation profile. Both the turbulence intensity gradient and zonal flow E×B shear are identified as major contributors to the generation of the k ∥ -asymmetry needed for the residual stress generation. The model predictions of core rotation based on global gyrokinetic simulations agree well with the experimental measurements of main ion toroidal rotation for a set of DIII-D ECH discharges. The validated model is further used to investigate the characteristic dependence of residual stress and intrinsic rotation profile structure on the multi-dimensional parametric space covering the turbulence type, q-profile structure, and up-down asymmetry in magnetic geometry with the goal of developing the physics understanding needed for rotation profile control and optimization. It is shown that in the flat-q profile regime, intrinsic rotations driven by ITG and TEM turbulence are in the opposite direction (i.e., intrinsic rotation reverses). The predictive model also produces reversed intrinsic rotation for plasmas with weak and normal shear q-profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Consistent evaluation of GOSAT, SCIAMACHY, carbontracker, and MACC through comparisons to TCCON

Consistent validation of satellite CO 2 estimates is a prerequisite for using multiple satellite CO 2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO 2 data record. We focus on validating model and satellite observation attributes that impact flux estimates and CO 2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry air mole fraction (X CO 2 ) for GOSAT (ACOS b3.5) and SCIAMACHY (BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO 2 mole fraction fields and the MACC CO 2 inversion system (v13.1) and compare these to TCCON observations (GGG2014). We find standard deviations of 0.9 ppm, 0.9, 1.7, and 2.1 ppm versus TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single target errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. When satellite data are averaged and interpreted according to error 2 = a 2 + b 2 / n (where n are the number of observations averaged, a are the systematic (correlated) errors, and b are the random (uncorrelated) errors), we find that the correlated error term a = 0.6 ppm and the uncorrelated error term b = 1.7 ppm for GOSAT and a = 1.0 ppm, b = 1.4 ppm for SCIAMACHY regional averages. Biases at individual stations have year-to-year variability of ~ 0.3 ppm, with biases larger than the TCCON predicted bias uncertainty of 0.4 ppm at many stations. Using fitting software, we find that GOSAT underpredicts the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46–53° N. In the Southern Hemisphere (SH), CT2013b underestimates the seasonal cycle amplitude. Biases are calculated for 3-month intervals and indicate the months that contribute to the observed amplitude differences. The seasonal cycle phase indicates whether a dataset or model lags another dataset in time. We calculate this at a subset of stations where there is adequate satellite data, and find that the GOSAT retrieved phase improves substantially over the prior and the SCIAMACHY retrieved phase improves substantially for 2 of 7 sites. The models reproduce the measured seasonal cycle phase well except for at Lauder125 (CT2013b), Darwin (MACC), and Izana (+ 10 days, CT2013b), as for Bremen and Four Corners, which are highly influenced by local effects. We compare the variability within one day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–100 % the variability of TCCON at different stations (except Bremen and Four Corners which have no variability compared to TCCON) and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g. the SH for models and 45–67° N for GOSAT

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

From deep TLS validation to ensembles of atomic models built from elemental motions. II. Analysis of TLS refinement results by explicit interpretation

TLS modelling was developed by Schomaker and Trueblood to describe atomic displacement parameters through concerted (rigid-body) harmonic motions of an atomic group [Schomaker & Trueblood (1968), Acta Cryst. B24, 63–76]. The results of a TLS refinement are T, L and S matrices that provide individual anisotropic atomic displacement parameters (ADPs) for all atoms belonging to the group. These ADPs can be calculated analytically using a formula that relates the elements of the TLS matrices to atomic parameters. Alternatively, ADPs can be obtained numerically from the parameters of concerted atomic motions corresponding to the TLS matrices. Both procedures are expected to produce the same ADP values and therefore can be used to assess the results of TLS refinement. Here, the implementation of this approach in PHENIX is described and several illustrations, including the use of all models from the PDB that have been subjected to TLS refinement, are provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
DOE OSTI2020

Effects of various parameters of different porous transport layers in proton exchange membrane water electrolysis

Porous transport layers (PTLs) play an important role in proton exchange membrane water electrolysis (PEMWE) cells. The PTL facilitates water and gas transport, as well as thermal and electrical conduction, and is required to sustain good contact with adjacent components. It is expected that using PTLs with variations in material properties such as structure, composition, thickness and wettability results in performance changes of the PEMWE. Here, a general mathematical PEMWE model is developed that separates and analyzes the contributions of ohmic, activation, diffusion and Nernst potentials. For model validation, three inherently different anode PTL structures (carbon paper, sintered titanium particles, and titanium felt) are operated over a range of conditions. Additionally, the effects of PTL wettability were used to verify the model using Polytetrafluoroethylene (PTFE) treated Toray papers with PTFE loading ranging from 0% to 20%. The modeling results of both PTFE treated and untreated materials show good agreement with the experimental data. Mass transport or diffusion loss is the primary reason for performance differences between PTFE treated and untreated PTLs. Sintered titanium PTLs with thicknesses above 1 mm suffer from up to 33% increased ohmic losses without indicating any obvious changes in activation and diffusion losses when compared to untreated PTLs. The losses of the cell increase when using PTFE treated Toray paper. Individual contributions are quantified and assigned to increased ohmic, activation, and diffusion losses. In conclusion, the proposed model offers insights into the overpotential contributions of a PEMWE. It is a useful tool for predicting performance of various PTL materials and can be applied for PTL development and optimization efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Fuel properties to enable lifted-flame combustion

The Fuel Properties to Enable Lifted-Flame Combustion project responded directly to solicitation DE-FOA-0000239 AOI 1A, Fuels and Lubricants for Advanced Combustion Regimes. This subtopic was intended to encompass clean and highly-efficient, liquid-fueled combustion engines to achieve extremely low engine-out nitrogen oxides (NOx) and particulate matter (PM) as a target and similar efficiency as state-of-the-art direct injection diesel engines. The intent of this project was to identify how fuel properties can be used to achieve controllable Leaner Lifted Flame Combustion (LLFC) with low NOx and PM emissions. Specifically, this project was expected to identify and test key fuel properties to enable LLFC and their compatibility with current fuel systems and to enhance combustion models to capture the effect of fuel properties on advanced combustion. Successful demonstration of LLFC may reduce the need for after treatment devices, thereby reducing costs and improving thermal efficiency. The project team consisted of key technical personnel from Ford Motor Company (FMC), the University of Wisconsin-Madison (UW), Sandia National Laboratories (SNL) and Lawrence Livermore National Laboratories (LLNL). Each partner had key roles in achieving project objectives. FMC investigated fuel properties relating to LLFC and sooting tendency. Together, FMC and UW developed and integrated 3D combustion models to capture fuel property combustion effects. FMC used these modeling results to develop a combustion system and define fuel properties to support a single-cylinder demonstration of fuel-enabled LLFC. UW investigated modeling the flame characteristics and emissions behavior of different fuels, including those with different cetane number and oxygen content. SNL led spray combustion experiments to quantify the effect of key fuel properties on combustion characteristics critical for LLFC, as well as single cylinder optical engine experiments to improve fundamental understanding of flame lift-off, generate model validation data, and demonstrate LLFC concurrent with FMC efforts. Additionally, LLNL was added to the project during the second year to develop a detailed kinetic mechanism for a key oxygenate to support CFD modeling. Successful completion of this project allowed the team to enhance fundamental understanding of LLFC, improve the state of current combustion models and increase understanding of desired fuel properties. This knowledge also improves our knowledge of how cost effective and environmentally friendly renewable fuels can assist in helping meet future emission and greenhouse gas regulations.

Kurtz, Eric↗
DOE OSTI2018

FY-2018 FES Joint Research Target

Fusion alphas and injected energetic neutral particle beams provide an important source of heating and current drive in advanced tokamak operating scenarios and burning plasma regimes. Alfvén eigenmode instabilities can cause the redistribution or loss of fast ions and driven currents, as well as potentially decreasing fusion performance and leading to localized losses. Measured fast ion fluxes in DIII-D and NSTX-U plasmas with different levels of Alfvén eigenmode activity will be used to determine the threshold for significant fast ion transport, assess mechanisms and models for such transport, and quantify the impact on beam power deposition and current drive. Measurements will be compared with theoretical predictions, including quantitative fluctuation data and fast ion density, in order to validate models and improve understanding of underlying mechanisms. Model predictions will guide the development of attractive operating regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Benchmarking Initiatives

Benchmarking has been recognized as an efficient tool for model validation as well as scientific collaboration. Several workshop series have been established to foster the benchmarking idea: setting up test cases with increasing complexity for method development and code comparison. In addition to representing model complexity, one of the key efforts is to develop codes that are suitable for modern HPC platforms such as PetaFlop supercomputers. It has been realized that those challenges are beyond single team capabilities. The Subsurface Environmental Simulation Benchmarking (SSBench) working group was initiated to develop, publish, and make publicly available well-described benchmark problems that could be used to demonstrate simulator conformance with norms established by the subsurface science and engineering community. SSBench supports the development of environmentally-relevant benchmark problem sets that provide rigorous tests for process model conceptualization, numerical implementation, process coupling, and accuracy. The initial SSBench focus on multicomponent reactive transport modeling reflects the expanding range of mechanisms and increasing complexity needed to address issues relevant to climate change, nuclear waste management, contaminant remediation, and pollution prevention.

benchmarking↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2022

Continuum Model to Define the Chemistry and Mass Transfer in a Bicarbonate Electrolyzer

Bicarbonate electrolyzers are devices designed to convert CO 2 captured from point sources or the atmosphere into chemicals and fuels without needing to first isolate pure CO 2 gas. In this work, we report here an experimentally validated model that quantifies the reaction chemistry and mass transfer processes within the catalyst layer and cation exchange membrane layer of a bicarbonate electrolyzer. Our results demonstrate that two distinct chemical microenvironments are key to forming CO at high rates: an acidic membrane layer that promotes in situ CO 2 formation and a basic catalyst layer that suppresses the hydrogen evolution reaction. We show that the rate of CO product formation can be increased by modulating the catalyst and membrane layer properties to increase the rate of in situ CO 2 generation and transport to the cathode. These insights serve to inform the design of bicarbonate and BPM-based CO 2 electrolyzers while demonstrating the value of modeling for resolving rate-determining processes in electrochemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Modeling and Simulation Tool to Improve Urban Search Planning (NA22 Final Report)

The venture project Tool to Improve Urban Search Planning (LL14-V-Urban Search Planning ToolPD3RS) supported two major types of effort: 1) development of a software prototype search planning tool, with the goal of transitioning the tool to operational users for evaluation and possible adoption; 2) enabling research to better understand the radiation characteristics of an urban environment, with implications for deployment of radiation detection systems in that environment. The first effort has resulted in prototype software, Optimization Planning Tool for Urban Search (OPTUS), that has been distributed to a limited set of potential users, including users within the NNSA emergency response community. The second effort supported various research topics including high fidelity radiation transport modeling in urban environments, measurement campaigns for model validation, development of vehicle-based high purity germanium (HPGe) detector systems and supporting data analysis, and computational capabilities for archiving and managing large data sets. While some of these related efforts have informed the models and software design of the OPTUS tool, in large part they pursued more basic research, some of which has evolved into separate DNN R&D projects. Project performers included Oak Ridge National Laboratory (ORNL), Lawrence Berkeley National Laboratory (LBNL) and the Remote Sensing Laboratory (RSL), in addition to Lawrence Livermore Laboratory (LLNL), the project lead. The project also supported university research, including several internships at the laboratories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

SCALE 6.2.4 Validation: Nuclear Criticality Safety

The computational bias of criticality safety computer codes must be established through the validation of the codes to critical experiments. A large collection of suitable experiments has been vetted by the International Criticality Safety Benchmark Evaluation Project (ICSBEP) and made available in the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook). More than 600 cases from this handbook have been prepared and reviewed within the Verified, Archived Library of Inputs and Data (VALID), which is maintained by the Reactor and Nuclear Systems Division at Oak Ridge National Laboratory. The performance of the KENO V.a and KENO-VI Monte Carlo codes within the SCALE 6.2.4 code system is assessed using the VALID models of benchmark experiments. A range of nuclear cross section libraries based on Evaluated Nuclear Data File (ENDF)/B-VII.1 in both multigroup (MG) and continuous energy (CE) formats is considered. The critical experiments available to validate the KENO V.a code cover 15 broad categories of systems. These systems use a range of fissile materials, including a range of uranium enrichments, various plutonium isotopic vectors, and some mixed uranium/plutonium oxides. The physical forms of the fissile material also vary and are represented as metal, solutions, or arrays of rods or plates in a water moderator. The neutron energy spectra of the systems also vary and cover fast, intermediate, mixed, and thermal spectra. Over 550 of the total cases use the KENO V.a code for the four nuclear data libraries considered in this report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R 2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R 2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Chu, Yufei [Stony Brook Univ., NY (United States)]↗
DOE OSTI2016

Full core LOCA analysis for BWR/6 - Methodology and first results

As part of collaboration between the Paul Scherrer Institute (PSI) and the Swiss regulator ENSI, a study aimed at core-wide estimates of the fuel rod behaviour during large-break loss-of-coolant-accidents (LB-LOCA) in BWR/6 plants was recently conducted on the basis of a Swiss reactor. To perform such full core LOCA analysis, a methodology consisting of a multi-physics sequence of interfaced best-estimate models and codes was established: CASMO-4E/SIMULATE-3 (C4/S3) for the core modelling, TRACE for the plant system analysis and FALCON for the fuel thermomechanical calculations during base irradiation (BI) as well as for the transient analyses (TA). As starting point, reference C4/S3 models validated for all operated cycles of the given plant were used to select as situation target, a recent cycle representative of modern BWR core loadings and fuel assembly designs. Based on the selected C4/S3 cycle model, PSI in-house computational modules were developed or updated in order to set up the TRACE full core model with regards to assembly geometries, fuel rods thermophysical properties and initial 3-D power distributions. Also, an interface to FALCON BI models was established for the transfer of the irradiation history, including primarily linear heat generation rates (LHGR), for an equivalent fuel pin. For each assembly, the power of the equivalent pin is defined with the maximum power of all pins at each axial position. However, in order to reduce the computational time of the fuel performance analyses, the whole core was subdivided into groups of assemblies, each represented by the most powerful assembly within the group. On this basis, the FALCON BI analyses were conducted with coupling to the PSI advanced model for gas release and swelling (GRWS-A) and an interface was developed to transfer the calculated fuel rod characteristics at end of irradiation to initialize the core fuel rods heat structures in the TRACE model. Subsequently, double-ended recirculation line rupture scenarios at hot-full-power (HFP) conditions were investigated with TRACE in order to determine a conservative transient case as function of cycle burnup, single failure criterion and availability of safety systems. For the most conservative case of this study, an interface was set-up to transfer the TRACE results as boundary conditions to the FALCON TA models, including thus primarily time and axial distributions of the clad outer temperatures. As final step, the FALCON TA were conducted for the representative assemblies in order to infer core-wide estimates of the fuel rod behaviour with specific focus on equivalent cladding reacted levels, hoop strains as well as margins to failure criteria. The objective of this paper is to present this methodology along with the obtained first results. Regarding the latter, it is found that principally, even for the most conservative LOCA scenario of this study, the clad temperature excursion remains mild enough at all core locations such that no significant rod ballooning and no cladding failure is predicted, something in line with corresponding analyses conducted earlier for BWR/4 plants. (authors)

42 ENGINEERING↗
DOE OSTI2015

SU-E-T-252: Developing a Pencil Beam Dose Calculation Algorithm for CyberKnife System

Purpose: Currently there are two dose calculation algorithms available in the Cyberknife planning system: ray-tracing and Monte Carlo, which is either not accurate or time-consuming for irregular field shaped by the MLC that was recently introduced. The purpose of this study is to develop a fast and accurate pencil beam dose calculation algorithm which can handle irregular field. Methods: A pencil beam dose calculation algorithm widely used in Linac system is modified. The algorithm models both primary (short range) and scatter (long range) components with a single input parameter: TPR{sub 20}/{sub 10}. The TPR{sub 20}/{sub 20}/{sub 10} value was first estimated to derive an initial set of pencil beam model parameters (PBMP). The agreement between predicted and measured TPRs for all cones were evaluated using the root mean square of the difference (RMSTPR), which was then minimized by adjusting PBMPs. PBMPs are further tuned to minimize OCR RMS (RMSocr) by focusing at the outfield region. Finally, an arbitrary intensity profile is optimized by minimizing RMSocr difference at infield region. To test model validity, the PBMPs were obtained by fitting to only a subset of cones (4) and applied to all cones (12) for evaluation. Results: With RMS values normalized to the dmax and all cones combined, the average RMSTPR at build-up and descending region is 2.3% and 0.4%, respectively. The RMSocr at infield, penumbra and outfield region is 1.5%, 7.8% and 0.6%, respectively. Average DTA in penumbra region is 0.5mm. There is no trend found in TPR or OCR agreement among cones or depths. Conclusion: We have developed a pencil beam algorithm for Cyberknife system. The prediction agrees well with commissioning data. Only a subset of measurements is needed to derive the model. Further improvements are needed for TPR buildup region and OCR penumbra. Experimental validations on MLC shaped irregular field needs to be performed. This work was partially supported by the National Natural Science Foundation of China (61171005) and the China Scholarship Council (CSC)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Revealing the critical role of radical-involved pathways in high temperature cyclopentanone pyrolysis

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. Understanding CPO decomposition is crucial for building a high-temperature combustion model. Here we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time- histories of carbon monoxide (CO), ethylene (C 2 H 4 ), and CPO absorbances over the temperature range of 1156-1416 K and pressure range of 8.53-10.06 atm were measured during current experiments. A corresponding detailed kinetic model was generated using the Reaction Mechanism Generator (RMG) with dominant unimolecular/radical-involved decomposition pathways from either previous studies or quantum calculations within the current work. The obtained model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the absorbance ratio between C 2 H 4 and CO was used as an important factor to validate models and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed H radicals play a major role in the decomposition, and the whole decomposition process could be divided into three stages based on the H radical concentration. Finally, the insights from present work can be used to generate a better CPO combustion model and help evaluate CPO as an advanced biofuel.

09 BIOMASS FUELS↗
DOE OSTI2026

WINDPROF: Merged Best-Estimate Wind Profile Data – Nantucket (WFIP3 Campaign)

WINDPROF provides 10-minute wind and turbulence profiles, integrating Doppler lidars, wind profiling radars, and sonic anemometers across Northeast U.S. coastal/offshore sites during the WFIP3 campaign. Key data include wind speed, direction, vertical velocity, and turbulence parameters, with standardized quality control (e.g., instrument-specific thresholds and inter-instrument validation). Profiles are interpolated to a height grid (20 m spacing below 100 m; 30 m above) and include comprehensive uncertainty estimates. The Nantucket dataset covers February 2024–September 2025, offering reproducible methods for atmospheric research, model validation, and wind energy studies.

17 WIND ENERGY↗