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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

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

A Package of Momentum and Heat Transfer Coefficients for the Stable Surface Layer Extended by New Coefficients over Sea Ice

The bulk transfer coefficients of momentum, heat, and humidity belong to the main ingredients of numerical weather prediction and climate models. They are needed for the calculation of turbulent fluxes in the surface layer and often rely on the Monin–Obukhov similarity theory requiring universal stability functions. The problem of a derivation of transfer coefficients based on different stability functions has been considered by many researchers over the years but it remains to this day. In this work, dedicated to the memory of S.S. Zilitinkevich, we also address this task, and obtain transfer coefficients from three pairs of theoretically derived stability functions suggested by Zilitinkevich and co-authors for stable conditions. Additionally, we construct non-iterative parametrizations of these transfer coefficients based on earlier work. Results are compared with state-of-the-art coefficients for land, ocean, and sea ice. The combined parametrizations form a package in a universal framework relying on a semi-analytical solution of the Monin-Obukhov similarity theory equations. A comparison with data of the Surface Heat Budget of the Arctic Ocean campaign (SHEBA) over sea ice reveals large differences between the coefficients for land conditions and the measurements over sea ice. However, two schemes of Zilitinkevich and co-authors show, after slight modification, good agreement with SHEBA although they had not been especially developed for sea ice. One pair of the modified transfer coefficients is superior and is compatible to earlier SHEBA-based parametrizations. Finally, an algorithm for practical use of all transfer coefficients in climate models is given.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An accurate perturbative approach to redshift space clustering of biased tracers in modified gravity

We extend the scale-dependent Gaussian Streaming Model (GSM) to produce analytical predictions for the anisotropic redshift-space correlation function for biased tracers in modified gravity models. Employing the Convolution Lagrangian Perturbation Theory (CLPT) re-summation scheme, with a local Lagrangian bias schema provided by the peak-background split formalism, we predict the necessary ingredients that enter the GSM, the real-space halo pairwise velocity and the pairwise velocity dispersion. We further consider effective field theory contributions to the pairwise velocity dispersion in order to model correctly its large scale behavior. We apply our method on two widely-considered modified gravity models, the chameleon-screened ƒ(R) Hu-Sawicki model and the nDGP Vainshtein model and compare our predictions against state-of-the-art N-body simulations for these models. We demonstrate that the GSM approach to predict the monopole and the quadrupole of the redshift-space correlation function for halos, gives very good agreement with the simulation data, for a wide range of screening mechanisms, levels of screening and halo masses at z = 0.5 and z = 1. Our work shows the applicability of the GSM, for cosmologies beyond GR, demonstrating that it can serve as a powerful predictive tool for the next stage of cosmological surveys like DESI, Euclid, LSST and WFIRST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Synthesis of wafer-scale SWCNT forests with remarkably invariant structural properties in a bulk-diffusion-controlled kinetic regime

Robust synthesis of vertically aligned carbon nanotubes (VACNT) at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial applications. To address this need, this work demonstrates that the structural characteristics of single-walled CNTs (SWCNTs) produced in a growth regime dominated by bulk diffusion of the gaseous carbon precursor are remarkably invariant over a broad range of process conditions (precursor concentration increased up to 30-fold, catalyst substrate area from 1 cm 2 to 180 cm 2 , growth pressure from 20 to 790 mbar, and gas flowrates up to 8-fold). A simple growth kinetics model is derived that accounts for both reactant bulk diffusion and competing byproduct formation in the presence of excess hydrogen, which quantitatively reproduces the experimental data in the entire isothermal parameter space. The model enables critical predictions for process scale-up optimization including a potential 6-fold increase in CNT production rate, a ~90% carbon conversion efficiency in select conditions, and elimination of reaction rate decay observed at high pressures by using a hydrogen-free growth environment. This model-guided opportunity to substantially improve synthesis throughput and efficiency, in conjunction with the remarkably invariant CNT properties, is appealing for reliable VACNT device fabrication at both small and industrial scales.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Linearized Pair-Density Functional Theory for Vertical Excitation Energies

Multiconfiguration pair-density functional theory (MC-PDFT) is a computationally efficient method that computes the energies of electronic states in a state specific or state average framework via an on-top functional. However, MC-PDFT does not include state interaction among these states since the final energies do not come from the diagonalization of an effective model-space Hamiltonian. Recently, multistate extensions such as linearized PDFT (L-PDFT) have been developed to accurately model the potentials near conical intersections and avoided crossings. However, there has not been any systematic study evaluating their performance for predicting vertical excitations at the equilibrium geometry of a molecule, when the excited states are generally well separated. In this paper, we report the performance of L-PDFT on the extensive QUESTDB data set of vertical excitations using a database of automatically selected active spaces. We show that L-PDFT performs well on all these excitations and successfully reproduces the performance of MC-PDFT. These results further demonstrate the potential of L-PDFT, as its scaling is constant with the number of states included in the state-average manifold, whereas MC-PDFT scales linearly in this regard.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicted Fracture Tendency of Naturally Occurring Aluminum Surface Coatings under Tensile Loading

Naturally occurring coatings on aluminum metal, such as its oxide or hydroxide, serve to protect the material from corrosion. Understanding the conditions under which these coatings mechanically fail is therefore expected to be an important aspect of predictive models for aluminum component lifetimes. To this end, we develop and apply a molecular dynamics (MD) modeling framework for conducting tension tests that is capable of isolating factors governing the mechanical strength as a function of coating chemistry, defect morphology, and variables associated with the loading path. We consider two representative materials, including γ-Al 2 O 3 and γ-Al(OH) 3 (i.e., oxide and hydroxide), both of which form readily as aluminum surface coatings. Our results indicate that defects have a significant bearing on the strength of aluminum oxide, with grain boundaries serving to reduce the strain at failure from ε zz = 0.300 to 0.219, relative to perfect single crystal. Our simulations also predict that porosity lowers the elastic stiffness and yield strength of the oxide. Relative to perfect crystal, we find porosity factors of 5%, 10% and 20% decrease the yield stress by 26%, 36% and 53%, respectively. MD predicts that perfect hydroxide and oxide single crystal have respective strains at failure of 0.08 and 0.31 under tensile uniaxial strain loading, and that the corresponding yield stresses are respectively 1.6 and 11.1 GPa. These data indicate that the hydroxide is substantially more susceptible to mechanical failure than the oxide. Our results, coupled with literature findings that indicate hot and humid conditions favor formation of hydroxide and defective oxide coatings, indicate the potential for a complicated dependence of aluminum corrosion susceptibility and stress corrosion cracking on aging history.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring the unknown Lambda-neutron interaction

No published \Lambda Λ n scattering data exist. A relativistic heavy-ion experiment has suggested that a \Lambda Λ nn bound state was seen. However, several theoretical analyses have cast serious doubt on the bound-state assertion. Nevertheless, there could exist a three-body \Lambda Λ nn resonance. Such a resonance could be used to constrain the \Lambda Λ n interaction. We discuss \Lambda Λ nn calculations using nn and \Lambda Λ n pairwise interactions of rank-one, separable form that fit effective range parameters of the nn system and those hypothesized for the as yet unobserved \Lambda Λ n system based upon four different \Lambda Λ N potentials. The use of rank-one separable potentials allows one to analytically continue the \Lambda Λ nn Faddeev equations onto the second complex energy plane in search of resonance poles, by examining the eigenvalue spectrum of the kernel of the Faddeev equations. Although each of the potential models predicts a \Lambda Λ nn sub-threshold resonance pole, scaling of the \Lambda Λ n interaction by as little as \sim ∼ 5% does produce a physical resonance. This suggests that one may use photo-(electro-)production of the \Lambda Λ nn system from tritium as a tool to examine the strength of the \Lambda Λ n interaction.

Gibson, Benjamin↗
DOE OSTI2023

Development of an Advanced Hydrogen Energy Storage System using Aerogel in a Cryogenic Flux Capacitor

The Cryogenic Flux Capacitor (CFC) is a cold, dense fluid storage core with integrated design features that afford the designer flexibility and provide new possibilities for the storage and discharge of energy. The stored energy, in this case, is represented by hydrogen physically bonded within the nanoscale pores within the aerogel composite blanket material, and the process of bonding or debonding is governed by the principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (~1,000 m 2 /g) allows for storage densities close to, or in some cases exceeding, that of normal boiling point liquids. Its performance easily exceeds what can be achieved via ambient temperature and high-pressure gas storage for an equivalent volume. CFC storage is predicted to be easily scalable, constructed from readily available commercial materials, and lends itself to a range of pressure applications. The project team designed and manufactured the CFC, integrating it into a set of temperature-controlled flow loops. The CFC test setup was validated against existing data from previous NASA work. Tests used bottled hydrogen gas for test validation. A cryogenic fluid, nitrogen, flowed through a metered control system. Hydrogen temperature, pressure, and flow rates, as well as the temperature control targets, provided all necessary control targets. The team analyzed the recorded data and updated the Technology Maturation Plan based on the results of the test, making recommendations for a follow-up test, scale-up, and maturation pathway. For the engineering development and maturation of CFC hydrogen storage technology, a Techno-Economic Assessment (TEA) and Commercialization Plan for the integration of this technology with various power generation assets were produced. A companion analytical model was developed, experimentally validated, compared against performance metric, and used to tune the model and scale up the technology.

08 HYDROGEN↗
DOE OSTI2022

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Good, the Bad, and the Ugly: Pseudopotential Inconsistency Errors in Molecular Applications of Density Functional Theory

The pseudopotential (PP) approximation is one of the most common techniques in computational chemistry. Despite its long history, the development of custom PPs has not tracked with the explosion of different density functional approximations (DFAs). As a result, the use of PPs with exchange/correlation models for which they were not developed is widespread, although this practice is known to be theoretically unsound. The extent of PP inconsistency errors (PPIEs) associated with this practice has not been systematically explored across the types of energy differences commonly evaluated in chemical applications. Here, we evaluate PPIEs for a number of PPs and DFAs across 196 chemically relevant systems of both transition-metal and main-group elements, as represented by the W4-11, TMC34, and S22 data sets. Near the complete basis set limit, these PPs are found to cleanly approach all-electron (AE) results for noncovalent interactions but introduce root-mean-squared errors (RMSEs) upwards of 15 kcal mol –1 into predictions of covalent bond energies for a number of popular DFAs. We achieve significant improvements through the use of empirical atom- and DFA-specific PP corrections, indicating considerable systematicity of the PPIEs. The results of this work have implications for chemical modeling in both molecular contexts and for DFA design, which we discuss.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing

In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For AraC family regulators (AFRs) represented in these data, we posited AFR/promoter/inducer groupings. Twelve promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in Escherichia coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled the further diversification of these inducible plasmids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A data-driven perspective on the colours of metal–organic frameworks

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structure and Magnetic Properties of Homoleptic Trivalent Tris(alkyl)lanthanides

Six new solvent-free, homoleptic paramagnetic tris(alkyl)lanthanides Ln{C(SiHMe 2 ) 3 } 3 (1Ln) and Ln{C(SiHMe 2 ) 2 Ph} 3 (2Ln) (Ln = Gd, Dy, and Er) were synthesized to investigate the magnetic properties of 4f organometallic compounds stabilized by secondary Ln←H–Si and benzylic interactions. The unit cell of 1Gd contains one independent molecule (Z = 2), while 1Dy and 1Er crystallize with four independent isostructural molecules per unit cell (Z = 16). In all molecules, as in other 1Ln compounds, the three tris(dimethylsilyl)methyl ligands form a trigonal planar LnC 3 core, and six secondary interactions involving Ln←H–Si bonding in Ln{C(SiHMe 2 ) 3 } 3 form above and below the equatorial plane. Two and five crystallographically independent molecules of each 2Ln (2Gd, Z = 8; 2Dy, Z = 20) form with three π-coordinated phenyl groups in addition to either one or two secondary Ln←H–Si interactions per molecule. The packing of these midseries organolanthanide compounds contrasts the single crystallographically unique molecules in previously reported La{C(SiHMe 2 ) 3 } 3 (1La, Z = 2, Z' = 1) and La{C(SiHMe 2 ) 2 Ph} 3 (2La, Z = 2, Z' = 1/3). 2La doped with 2Dy can adopt the crystallographic structure of 2La, which promotes magnetic properties, namely a higher χ m T value at low temperatures as well as stronger magnetic anisotropy. The ac susceptibility data for 10% 2Dy doped into 2La suggests slow relaxation at low temperatures with a relaxation barrier of ~45 K. The computed saturated magnetization of 1Er (M ≈ 4.5 μ B ) and 1Dy (M ≈ 6 μ B ) matches the experimental values, while the computed value for 2Dy better matches the value measured for 2Dy diluted in 2La (M ≈ 5 μ B ). Gas-phase calculations predict that the ground-state and first excited-state multiplet separations are larger for 1Er than 2Er, while the ordering for dysprosium is 1Dy > 2Dy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Experimental and computational study of particle formation kinetics in UF6 hydrolysis

The formation and growth of UO 2 F 2 particles by gas-phase UF 6 hydrolysis remains of interest to actinide chemistry researchers. The total number concentration of the UO 2 F 2 aerosol particles that can be produced in the reaction is regulated primarily by the availability of water molecules under our reactor conditions. An increase in water molecule concentration corresponds with a higher amount and larger size of UO 2 F 2 aerosol particles produced. The growth rates of aerosol particles appear to approach a single number in the range of [0.05 ± 0.03–0.08 ± 0.04] (nm s -1 ), as the molar ratio of water to UF 6 decreases below 1. The size of primary particles produced from the UF 6 hydrolysis under water-deprived conditions was estimated to be 3.6 ± 0.4 nm. As the molar ratio became greater than 1.7, the size of primary particles increased with increased availability of water molecules. The primary particle model developed in this work predicted a size range for the UO 2 F 2 primary particles similar to that estimated based on the data from gas-phase UF 6 hydrolysis experiments. This result suggests that the volume-driven coalescence process assumption used in the derivation of the primary particle model was reasonable. The ability to precisely control the availability of water molecules and reaction time could lead to the production of nearly monodispersed aerosol particles. This finding has significant implications in the engineering and manufacturing of fuel powder materials and possibly the future development and deployment of environmental sampling apparatus.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Non-Boltzmann Effects in Chain Branching and Pathway Branching for Diethyl Ether Oxidation

Low-temperature (LT) engine applications have several potential benefits, including reduced emissions and increased efficiency. Attaining these benefits requires accurate kinetic modeling of LT chain branching, which depends heavily on ketohydroperoxide (KHP) decomposition. For diethyl ether (DEE), a promising biofuel, current estimates of the KHP decomposition rate constant are largely based on empirical fits to data. In this study, we investigate the most important KHP isomer in DEE LT oxidation by applying variable reaction coordinate transition state theory to the main pathway for KHP decomposition: OO bond fission to produce •OH and a keto-alkoxy radical, •OQ'O. We also use ab initio kinetics methods to investigate the decomposition of •OQ'O, where we find dominant branching to acetic acid, with the remaining flux going to CH 3 C(O)OCHO. Additionally, new time-resolved measurements of DEE and acetic acid concentrations during LT (450–600 K) DEE oxidation are obtained in a laser photolysis flow reactor coupled with multiplexed photoionization mass spectrometry. These new experimental data, along with jet-stirred reactor data in the literature, are compared with the predictions of a recent DEE mechanism (Tran et al. Proc. Comb. Inst. 2019, 37, 511-519) that was modified with the newly calculated ab initio rate constants for KHP and •OQ'O decomposition. The predictions of the modified mechanism are quite poor when compared to the experimental data; this is primarily due to the new KHP ⇌ •OQ'O + •OH rate constant, which is 1–2 orders of magnitude slower than empirical values employed in recent mechanisms. To reconcile the new KHP rate constant and the experimental data, we explore and quantify the possible role of non-Boltzmann (nB) reaction sequences. The nB reactions have a substantial effect on both the overall mechanism reactivity and the •OQ'O branching to acetic acid. We also provide guidance on the proper implementation of nB reactions in kinetic mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Comprehensive Assessment of the Accuracy of the Ideal Adsorbed Solution Theory for Predicting Binary Adsorption of Gas Mixtures in Porous Materials

Quantifying the adsorption of chemical mixtures in porous adsorbents is critical to developing these materials for useful separation applications. The ideal adsorbed solution theory (IAST) is the most widely applied mixing theory for predicting mixture adsorption using single-component adsorption data, but a perceived lack of experimental data has limited previous efforts to explore the accuracy of IAST in a systematic way. In this paper, we take advantage of a large collection of binary experimental data for gas adsorption that became available recently to tackle this issue. Specifically, we identify more than 400 examples in which binary adsorption data and single-component data are available in the same publication and apply IAST to all these examples. This analysis includes experimental data from 63 gas mixtures of 37 different molecular species and 174 different adsorbents. In addition to being the most systematic evaluation to date of the accuracy of IAST for gas adsorption, these data will be valuable for future efforts to test or develop mixing theories that improve upon IAST.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗