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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantum anomalous Hall effect in two-dimensional magnetic insulator heterojunctions

Abstract Recent years have witnessed tremendous success in the discovery of topological states of matter. Particularly, sophisticated theoretical methods in time-reversal-invariant topological phases have been developed, leading to the comprehensive search of crystal database and the prediction of thousands of topological materials. In contrast, the discovery of magnetic topological phases that break time reversal is still limited to several exemplary materials because the coexistence of magnetism and topological electronic band structure is rare in a single compound. To overcome this challenge, we propose an alternative approach to realize the quantum anomalous Hall (QAH) effect, a typical example of magnetic topological phase, via engineering two-dimensional (2D) magnetic van der Waals heterojunctions. Instead of a single magnetic topological material, we search for the combinations of two 2D (typically trivial) magnetic insulator compounds with specific band alignment so that they can together form a type-III broken-gap heterojunction with topologically non-trivial band structure. By combining the data-driven materials search, first-principles calculations, and the symmetry-based analytical models, we identify eight type-III broken-gap heterojunctions consisting of 2D ferromagnetic insulators in the MXY compound family as a set of candidates for the QAH effect. In particular, we directly calculate the topological invariant (Chern number) and chiral edge states in the MnNF/MnNCl heterojunction with ferromagnetic stacking. This work illustrates how data-driven material science can be combined with symmetry-based physical principles to guide the search for heterojunction-based quantum materials hosting the QAH effect and other exotic quantum states in general.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Two-electron bond-orbital model. I

The one-electron bond-orbital model of Harrison (1973) for tetrahedrally coordinated solids is modified to a two-electron model using a generalization of Falicov and Harris' (1969) method for treating the hydrogen molecule. Expressions for nuclear-exchange and pseudodipolar interactions are obtained in terms of bond-orbital model parameters. The eigenfunctions and eigenvalues of the Hamiltonian are employed along with standard perturbation theory to calculate the energy shifts resulting from the application of an external electric field and induced by the magnetic interaction between the nuclear spins and the bond-electron spins. The quantitative predictions of the derived formulas are examined for real semiconductors involving elements and compounds from periodic groups III, IV, and V. It is concluded that the present two-electron model makes it possible to assess the quantitative effects of electron correlation in the anion-cation bond for both nonmagnetic properties (electric susceptibility, dielectric constant) and magnetic properties (nuclear-exchange and pseudodipolar interactions) of various materials.

Huang, C.↗
NASA NTRS1999

Response to CO2 Transient Increase in the GISS Coupled Model: Regional Coolings in a Warming Climate

The (GISS) Goddard Institute for Space Studies coupled atmosphere-ocean model is used to investigate the effect of increased atmospheric CO2 by comparing a compounded 1 percent CO2 increase experiment with a control simulation. After 70 years of integration, the global surface air temperature in the 1 percent CO2 experiment is 1.43 C warmer. In spite of this global warming, there are two distinct regions, the northern Atlantic Ocean and the southern Pacific Ocean, where the surface air temperature is up to 4 C cooler. This situation is maintained by two positive feedbacks: a local effect on convection in the South Pacific and a non-local impact on the meridional circulation in the North Atlantic. The poleward transport of latent energy and dry static energy by the atmosphere is greater in the 1 percent CO2 experiment, caused by warming and therefore increased water vapor and greater greenhouse capacity at lower latitudes. The larger atmospheric transports tend to reduce upward vertical fluxes of heat and moisture from the ocean surface at high latitudes, which has the effect of stabilizing the ocean, reducing both convection and the thermohaline circulation. With less convection, less warm water is brought up from below, and with a reduced North Atlantic thermohaline circulation (by 30 percent at time of CO2 doubling), the poleward energy transport by the oceans decreases. The colder water then leads to further reductions in evaporation, decreases of salinity at high latitudes, continued stabilization of the ocean, and maintenance of reduced convection and meridional overturning. Although sea ice decreases globally, it increases in the cooling regions which reduces the overall climate sensitivity; its effect is most pronounced in the Southern Hemisphere. Tropical warming has been observed over the past several decades; if modeling studies such as this and others which have produced similar effects are valid, these processes may already be beginning.

Russell, Gary L.↗
DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗
NASA NTRS2005

Sensitivity of Ozone to Bromine in the Lower Stratosphere

Measurements of BrO suggest that inorganic bromine (Br(sub y)) at and above the tropopause is 4 to 8 ppt greater than assumed in models used in past ozone trend assessment studies. This additional bromine is likely carried to the stratosphere by short-lived biogenic compounds and their decomposition products, including tropospheric BrO. Including this additional bromine in an ozone trend simulation increases the computed ozone depletion over the past approx.25 years, leading to better agreement between measured and modeled ozone trends. This additional Br(sub y) (assumed constant over time) causes more ozone depletion because associated BrO provides a reaction partner for ClO, which increases due to anthropogenic sources. Enhanced Br(sub y) causes photochemical loss of ozone below approx.14 km to change from being controlled by HO(sub x) catalytic cycles (primarily HO2+O3) to a situation where loss by the BrO+HO2 cycle is also important.

ozone trends↗
DOE OSTI2024

Comparing plant litter molecular diversity assessed from proximate analysis and 13 C NMR spectroscopy

Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using 13 C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Impact of Gender on Pharmocokinetics of Intranasal Scopolamine

Introduction: An intranasal gel dosage formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness (SMS), which is commonly experienced by astronauts during space missions. The bioavailability and pharmacokinetics (PK) were evaluated under IND guidelines. Since information is lacking on the effect of gender on the PK of Scopolamine, we examined gender differences in PK parameters of INSCOP at three dose levels of 0.1, 0.2 and 0.4 mg. Methods: Plasma scopolamine concentrations as a function of time data were collected from twelve normal healthy human subjects (6 male/6 female) who participated in a fully randomized double blind crossover study. The PK parameters were derived using WinNonlin. Covariate analysis of PK profiles was performed using NONMEN and statistically compared using a likelihood ratio test on the difference of objective function value (OFV). Statistical significance for covariate analysis was set at P<0.05(ΔOFV=3.84). Results: No significant difference in PK parameters between male and female subjects was observed with 0.1 and 0.2 mg doses. However, CL and Vd were significantly different between male and female subjects at the 0.4 mg dose. Results from population covariate modeling analysis indicate that a onecompartment PK model with first-order elimination rate offers best fit for describing INSCOP concentration-time profiles. The inclusion of sex as a covariate enhanced the model fitting (ΔOFV=-4.1) owing to the genderdependent CL and Vd differences after the 0.4 mg dose. Conclusion: Statistical modeling of scopolamine concentration-time data suggests gender-dependent pharmacokinetics of scopolamine at the high dose level of 0.4 mg. Clearance of the parent compound was significantly faster and the volume of distribution was significantly higher in males than in females, As a result, including gender as a covariate to the pharmacokinetic model of scopolamine offers the best fit for PK modeling of the drug at dose of 0.4 mg or higher.

Putcha, L.↗
DOE OSTI2020

Carbon isotope evidence for the substrates and mechanisms of prebiotic synthesis in the early solar system

Meteorites contain prebiotic, bio-relevant organic compounds including amino acids. Their syntheses could result from diverse sources and mechanisms and provide a window on the conditions and materials present in the early solar system. Here we constrain alanine’s synthetic history in the Murchison meteorite using site-specific 13 C/ 12 C measurements, reported relative to the VPDB standard. The δ 13 C VPDB values of -29 ± 10‰, 142 ± 20‰, and -36 ± 20‰ for the carboxyl, amine-bound, and methyl carbons, respectively, are consistent with Strecker synthesis of interstellar-medium-derived aldehydes, ammonia, and low-δ 13 C nebular or interstellar-medium-derived CN. We report experimentally measured isotope effects associated with Strecker synthesis, and use them to constrain the δ 13 C values of the alanine precursors, which we then use to construct a model that predicts the molecular-average δ 13 C values of 19 other organic compounds of prebiotic significance found in Murchison if they were made by our proposed synthetic network. Finally, most of these predictions agree with previous measurements, suggesting that interstellar-medium-derived aldehydes and nebular and/or pre-solar CN could have served as substrates for synthesis of a wide range of prebiotic compounds in the early solar system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Multi-scale modeling of wastage layer formation in metallic fuel cladding

Fuel-cladding chemical interaction (FCCI) is a major concern for U-Zr metallic fuels' performance, primarily due to the formation of a brittle layer (wastage) in the cladding. This brittle layer, resulting from intermetallic compounds between cladding constituents Fe, Cr, and lanthanide fission products, significantly impacts the cladding's mechanical integrity. Recent efforts focus on developing a mechanistic modeling framework to understand lanthanide production, transport to the fuel-cladding interface, and phase transformation to intermetallic phases. A multi-scale computational approach has been used to calculate lanthanide transport rates, with atomistic calculations determining Nd diffusivities through the solid fuel matrix and along pore surfaces. These diffusivities inform a mesoscale model to determine an effective diffusion coefficient, accounting for porosity and infiltration with bond sodium. This effective diffusivity is used in engineering-scale simulations via the BISON fuel performance code, which has been validated against EBR-II and FFTF reactor experiments.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Experimental and modeling studies of PR and ND oxalate solubility to high ionic strengths: Insight into actinide(III) oxalates

Actinide oxalates are chemical compounds important to nuclear industry, ranging from actinide separation in waste reprocessing, to production of specialty actinides, and to disposal of high level nuclear waste (HLW) and spent nuclear fuel (SNF). In this study, the solubility constants for Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O by performing solubility experiments in HNO 3 and mixtures of HNO 3 and H 2 C 2 O 4 at 23.0 ± 0.2 °C have been determined. The targeted starting materials, Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O, were successfully synthesized at room temperature using PrCl 3 , NdCl 3 and oxalic acid as the source metrials. Then, we utilized the targeted solubility-controlling phases to conduct solubility measurements. There was no phase change over the entire periods of experiments, demonstrating that Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O were the solubility-controlling phases in our respective experiments. Based on our experimental data, we have developed a thermodynamic model for Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O in the mixtures of HNO 3 and H 2 C 2 O 4 to high ionic strengths. The model for Pr 2 (C 2 O 4 ) 3 •10H 2 O reproduces well the reported experimental data for Pu 2 (C 2 O 4 ) 3 •10H 2 O, which are not utilized for the model development, demonstrating that Pr(III) is an excellent analog for Pu(III). Similarly, the model for Nd 2 (C 2 O 4 ) 3 •10H 2 O reproduces the solubility of Am 2 (C 2 O 4 ) 3 •10H 2 O and Cm 2 (C 2 O 4 ) 3 •10H 2 O. The Pitzer model was used for the calculation of activity coefficients. Based on the published, well established model for dissociation constants for oxalic acid and stability constants for actinide-oxalate complexes [i.e., AmC 2 O 4 + , and Am(C 2 O 4 ) 2 - ] to high ionic strengths, we have obtained the solubility constants (log 10 K 0 ) for the following reactions at 25 °C, Pr 2 (C 2 O 4 ) 3 •10H 2 O ⇌ 2Pr 3+ + 3C 2 O 4 2- + 10H 2 O(l). Nd 2 (C 2 O 4 ) 3 •10H 2 O ⇌ 2Nd 3+ + 3C 2 O 4 2- + 10H 2 O(l). to be -30.82 ± 0.30 (2σ), and - 31.14 ± 0.35 (2σ), respectively. These values for can be directly applied to Pu 2 (C 2 O 4 ) 3 •10H 2 O, Am 2 (C 2 O 4 ) 3 •10H 2 O and Cm 2 (C 2 O 4 ) 3 •10H 2 O. The model established for actinide oxalates by this study provides the needed knowledge with regard to solubilities of actinide/REE oxalates at various ionic strengths, and is expected to find applications in many fields, including the geological disposal of nuclear waste and the mobility of REE under the surface conditions, as Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O can be regarded as the pure Pr and Nd end-members of deveroite, a recently discovered natural REE oxalate with the following stoichiometry, (Ce 1.01 Nd 0.33 La 0.32 Pr 0.11 Y 0.11 Sm 0.01 Pb 0.04 U 0.03 Th 0.01 Ca 0.04 ) 2.01 (C 2 O 4 ) 2.99 •9.99H 2 O. Regarding its importance in the geological disposal of nuclear waste, Am 2 (C 2 O 4 ) 3 •10H 2 O/Pu 2 (C 2 O 4 ) 3 •10H 2 O/Cm 2 (C 2 O 4 ) 3 •10H 2 O can be the source-term phase for actinides, as demonstrated by the instance in the disposal in clay/shale formations. This is exemplified by the stability of Am 2 (C 2 O 4 ) 3 •10H 2 O in comparison with Am(OH) 3 (am), Am(OH) 3 (s) and AmCO 3 (OH)(s) under the relevant geological repository conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Effective point-charge analysis of crystal fields: Application to rare-earth pyrochlores and tripod kagome magnets 𝑅 ⁢3⁢ Mg⁢ 2 ⁢Sb⁢ 3⁢ O⁢ 14

An indispensable step to understand collective magnetic phenomena in rare-earth compounds is the determination of spatially anisotropic single-ion properties resulting from spin-orbit coupling and crystal field (CF). The CF Hamiltonian has a discrete energy spectrum—accessible to spectroscopic probes such as neutron scattering—controlled by a number of independent parameters reflecting the point symmetry of the magnetic sites. Determining these parameters in low-symmetry systems is often challenging. Here, we describe a general method to analyze CF excitation spectra using adjustable effective point-charges. We benchmark our method to existing neutron-scattering measurements on pyrochlore rare-earth oxides and obtain a universal point-charge model that describes a large family of related materials. We adapt this model to the newly discovered tripod kagome magnets (𝑅 3 ⁢Mg 2 ⁢Sb 3 ⁢O 14 , 𝑅 = Tb, Ho, Er, Yb) for which we report broadband inelastic neutron-scattering spectra. Analysis of these data using adjustable point-charges yields the CF wave functions for each compound. From this, we calculate thermomagnetic properties that accurately reflect our measurements on powder samples and predict the effective gyromagnetic tensor for pseudospin degrees of freedom—a crucial step to understand the exotic collective properties of these kagome magnets at low temperature. We present further applications of our method to other tripod kagome materials and triangular rare-earth compounds 𝑅⁢MgGaO 4 (𝑅 =Yb, Tm). Overall, this study establishes a widely applicable methodology to predict CF and single-ion properties of rare-earth compounds based on interpretable and adjustable models of effective point charges.

Crystal-field theory↗
DOE OSTI2022

Differing behavioral changes in crayfish and bluegill under short- and long-chain PFAS exposures: Field study in Northern Michigan, USA

The emergent contaminant family, per- and poly-fluorinated alkyl substances (PFAS) has gained research attention due to their widespread detection and stability within the environment. Despite the growing amount of research on perfluorooctanesulfonic acid (PFOS) and perfluoro-n-octanoic acid (PFOA) in aquatic organisms, investigations detailing behavioral and physiological effects of aquatic organisms exposed to a mixture of PFAS analytes in the wild have been limited. The objective of this study was to evaluate the potential behavioral and histological effects of environmental exposure to PFAS compounds within multiple trophic levels of aquatic ecosystems. The current study investigates effects of environmentally relevant PFAS concentration exposures in crayfish (Faxonius immunis, F. rusticus, F. virilis) and bluegill (Lepomis macrochirus) sourced from four water bodies in Northern Michigan. Antipredator response and foraging behavioral assays were used to investigate potential effects on crayfish; a swimming speed behavioral assay and liver and gill histology analysis were used to investigate potential effects on fish. Linear mixed model and multiple regression analyses resulted in significant relationships between tissue accumulation levels of long chain PFAS compounds and crayfish foraging and fish critical swimming speed responses. Crayfish foraging decreased and fish critical swim speeds increased with PFAS exposure which may lead to energetic and population concerns. Antipredator response in crayfish and liver and gill histology in fish were not significantly related to PFAS tissue or water concentrations. The sensitivity of crayfish and bluegill behavior contributes to the growing body of research regarding the differential toxicity of short-chain and long-chain PFAS compounds. The sensitivity of some aquatic organism behaviors to PFAS accumulated in tissue may have implications for PFAS transfer and alterations to ecosystem functioning; based on the results of this field study, further laboratory research is recommended to further evaluate these relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine Learning for Joint Quality Control

The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗