Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “relationships”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Uncovering metallic glasses hidden vacancy-like motifs using machine learning

Vacancies are a ubiquitous type of defect in crystalline materials that can affect their properties and behavior, including atomic diffusivity, deformation processes, thermal conductivity, and electrical conductivity. While well-defined in crystals, it is challenging to identify such topological footprints in the amorphous structure of metallic glasses (MGs). Here, we uncover an unforeseen vacancy-like structural motif named Q7, which refers to an atomic Voronoi polyhedron with seven quadrangular faces, by investigating MG local atomic configurations with the help of machine learning. The Q7 motif exhibits characteristics similar to crystalline vacancies and plays a significant role in short-range structural disorder in MGs. The atoms at the center of the Q7 motif display larger local entropy, atomic volume, and local tension. Additionally, its concentration follows an Arrhenius-like relationship with temperature, accurately indicating the glass transition temperature, and is strongly correlated with the yield and failure of MGs during mechanical deformation. The discovery of the Q7 motif provides new insights into understanding the intricate relationship between local disorder and structural relaxation in MGs, offering a remarkable ability to predict their thermal and mechanical behavior. This potentially paves the way for enhancements in the design and manufacturing processes of MGs and other amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Differentiating supported platinum single atoms, clusters and nanoparticles by styrene hydrogenation

Supported metal catalysts often consist of metal sites ranging from nanoparticles to subnanometer clusters and single atoms. It remains a necessity to differentiate these sites to guide design of optimal catalysts. Here we report a simple method to assess the distribution of metal active sites in catalyst samples. The method takes the advantage of the structure sensitivity of styrene hydrogenation over titania supported platinum (Pt) catalysts with Pt aggregates varied from single atom to ~1.40 nm nanoparticles. The physicochemical properties were characterized by STEM, XPS, XANES, H2-TPR and CO-chemisorption measurements. The reactivity of Pt sites was quantified by styrene hydrogenation at ambient conditions. The nanometer-sized Pt clusters have significantly higher activity than Pt nanoparticles, sub-nanometer clusters or isolated single atoms. Additionally, the relationship between activity and structural/electronic properties of Pt sites influenced by particle sizes was discussed. Similar relationship was found in the carbon supported Pt catalysts.

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

Towards the design of nature-inspired materials: Impact of complex pore morphologies via higher-order homogenization

Even though the development of novel materials that mimic nature is widely used in a variety of engineering and scientific fields, the relationship between effective material properties and underlying, often complex pore morphology is still not fully understood. To address this knowledge gap and accelerate the development of novel nature-inspired materials, this paper adopts a higher-order asymptotic homogenization method to numerically investigate the effect of complex micropore morphology on the effective mechanical properties of a porous system. Specifically, we create unique pore morphologies with varying levels of complexity that serve as a more realistic representation of natural materials. Here, we then use the second-order homogenization method to capture the role of pore size, shape, orientation, and distribution on effective properties. By creating different pore morphologies, we systematically studied the relationship between morphology and effective mechanical properties. The results highlight the necessity of higher-order parameters to fully capture the role of realistic pore morphologies on effective mechanical properties and provide a path forward in the design of nature-inspired materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Control of solvent adsorption in porous liquids through solvent-solvent interactions

Type 3 Porous Liquids (PLs) are a new class of materials that offer transformative potential for gas capture and utilization. These PLs are comprised of a bulky solvent with a suspended empty nanoporous material that enables selective and high-capacity capture from dilute and complex gas steams. The relationship between the nanoporous material, the structure of the solvent molecules, and the time dependence of solvent adsorption into the nanoporous material governs long term adsorption properties. Herein, molecular dynamics (MD) calculations evaluated the solvent dynamics of nine neat solvents with a broad range of chemical identities and experimental density measurements probed the time-dependent adsorption of these solvents into the ZIF-8 metal-organic frameworks (MOFs) to form PL dispersions over three weeks. Two of the nine dispersion, using glyceryl triacetate and 2’-hydroxyacetopheneone as solvents, presented sufficiently low solvent infiltration—characterized by solvent adsorption less than 40% of the ZIF-8 pore volume—to be viable as PLs. The experimental solvent adsorption data for ZIF-8 dispersed in the four aromatic solvents (acetophenone, methylbenzoate, 2-isopropylphenol, and 2’-hydroxyacetophenone) was fit to a kinetic model to quantify rate of solvent adsorption into ZIF-8. The rates of adsorption of these four similar-sized solvents demonstrated that solvent adsorption is slowed and limited by solvent clustering dynamics that are, in turn, driven by intermolecular hydrogen bonding. Ultimately, experimental solvent sorption measurements indicated that glyceryl triacetate and 2’-hydroxyacetophenone with ZIF-8 formed PLs with the most stable porosity and the greatest potential for gas capture. In conclusion, the combined experimental-computational materials exploration framework used here reveals a novel relationship between molecular scale solvent dynamics and macroscale solvent adsorption kinetics critical for the discovery and rapid evaluation of Type 3 PL compositions.

Hydrogen bonding↗
DOE OSTI2020

High-throughput experimentation for microstructural design in additively manufactured 316L stainless steel

In the present study, a combination of high-throughput (HT) and low-throughput (LT) techniques was used to rapidly determine the processing window and generate processing maps for Selective Laser Melting (SLM) of 316L stainless steel. The HT method includes the fabrication of hundreds of hex nut-shaped specimens, each processed with a unique combination of laser power, scanning speed, and hatch spacing. An easily removable scaffolding permitted rapid sample extraction from the base plate, thus saving machining cost and time. Hardness and immersion density measurements were used for HT characterization to identify a processing window for maximum strength and density. Within the defined processing window, a low-throughput (LT) microstructural interrogation of specimens were performed. The microstructural analysis included quantification at various length scales (i.e., grains size and morphology, texture, primary dendrite arm spacing, and melt pool geometry analysis). Microstructure-based processing maps as a function of volumetric energy density were generated. The combination of HT and LT methods produced a predictive relationship between hardness and primary dendrite arm spacing using a Hall-Petch relationship. A model is proposed to explain the dependence of microstructure on the melt pool geometry. Here, the HT method can be applied for the microstructural design of SLM-fabricated components in other alloys.

316L stainless steel↗
DOE OSTI2023

Response of vibrational properties and thermal conductivity of perovskites to pressure

We study the response of SrTiO 3 and KTaO 3 's vibrational properties and thermal conductivity to pressurization. Our goal is to improve the understanding of the relationship between crystal structure, vibrational dynamics, and thermal conductivity in perovskites. We measure the thermal conductivity of SrTiO 3 and KTaO 3 up to 28 GPa by time-domain thermoreflectance. We also perform Raman scattering and stimulated Brillouin scattering measurements of SrTiO 3 and KTaO 3 to characterize changes in vibrational dynamics with pressure. The thermal conductivity of SrTiO 3 increases under pressure with a slope comparable to that of other perovskites whose thermal conductivity has been measured versus pressure. Alternatively, the thermal conductivity of KTaO 3 has a stronger pressure dependence than that of other materials with similar crystal structure. We correlate pressure-induced changes in Raman and Brillouin spectra with pressure-induced changes in thermal conductivity. We show that pressure-induced changes in phonon lifetimes dominate the pressure dependence of thermal conductivity. This study provides benchmark knowledge of why depends on pressure and improves understanding of structure/thermal-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metacognition, cortical thickness, and tauopathy in aging

We investigated self-rating of cognitive task performance (self-appraisal) and the difference between self-rating and actual task performance (appraisal discrepancy) in cognitively healthy older adults and their relationship with cortical thickness and Alzheimer's disease (AD) biomarkers, amyloid and tau. All participants (N = 151) underwent neuropsychological testing and 1.5T structural magnetic resonance imaging. A subset (N = 66) received amyloid-PET with [11C] PiB and tau-PET with [18F] Flortaucipir. We found that worse performers had lower self-appraisal ratings, but still overestimated their performance, consistent with the Dunning-Kruger effect. Self-appraisal rating and appraisal discrepancy revealed distinct relationships with cortical thickness and AD pathology. Greater appraisal discrepancy, indicating overestimation, was related to thinning of inferior-lateral temporal, fusiform, and rostral anterior cingulate cortices. Lower self-appraisal was associated with higher entorhinal and inferior temporal tau. These results suggest that overestimation could implicate structural atrophy beyond AD pathology, while lower self-appraisal could indicate early behavioral alteration due to AD pathology, supporting the notion of subjective cognitive decline prior to objective deficits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Avalanche gain and its effect on energy resolution in GEM-based detectors

Here, we present avalanche gain and associated resolution measurements recorded with a 4 He:CO 2 (70:30) gas mixture and pure SF 6 , a Negative Ion (NI) gas. SF 6 is of particular interest to the directional dark matter detection community, as its low thermal diffusion helps to retain recoil ionization track features over long drift lengths. With the aid of a general form of the reduced first Townsend coefficient (RFTC), multiple GEM-based detector data sets are used to study the high-gain behavior of the 4 He:CO 2 gas mixture. The high-gain data is well described purely in terms of the reduced electric field strength and the number of GEMs, and the robust relationship between the RFTC and the average, reduced, electric field strength across the GEMs is emphasized. The associated (pulse-height) resolution measurements are used to discuss the variance of the avalanche distribution and to describe and estimate the lower limits of energy resolution one should expect to measure using a simple relationship with the RFTC. In the end, a description of avalanche gain, its effect on energy resolution, and the contributing experimental parameters in GEM-based detectors is developed over a broad parameter space for further use.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. Here, we demonstrate improved processing and laser control performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Model for determining rupture area in Zircaloy cladding under LOCA conditions

The dispersal of nuclear fuel through a cladding rupture opening during a transient event is the primary safety concern preventing the United States nuclear industry from extending burnup beyond a peak rod average burnup of 62 GWd/tU. The majority of research thus far has prioritized understanding, or at a minimum prioritized quantifying, fuel fragmentation and pulverization as a function of burnup and to a lesser extent onset temperature. Early high burnup loss-of-coolant accident tests performed at the Halden Boiling Water Reactor evaluated the impact of relocation on cladding temperature, whereas historical and recent cladding burst tests focused primarily on generating data related to cladding balloon and burst timing. However, very little effort has been put forth to develop a relationship for determining the rupture opening dimensions for Zircaloy cladding tubes bursting during a simulated light water reactor loss-of-coolant accident. This is significant because high burnup loss-of-coolant accident data suggest dispersal could be limited by the size of the rupture opening or rupture dimensions, thereby minimizing the safety concern. Therefore, the purpose of this research is to build a database composed of all simulated loss-of-coolant accident data to develop an empirical model that conservatively captures the rupture opening dimensions and translates those dimensions to rupture area. Analysis of the experimental data indicates the rupture dimensions are strongly correlated to the peak balloon strain. This relationship is leveraged to develop a rupture length empirical correlation as a function of peak balloon strain and a rupture width empirical correlation as a function of rupture length. Outliers in the experimental data are identified and discussed. Finally, the rupture opening empirical model is presented in such a way for general implementation into high fidelity fuel performance codes or fuel performance codes used for safety analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Chain flexibility and glass transition temperatures of poly(n-alkyl (meth)acrylate)s: Implications of tacticity and chain dynamics

The relationship between chain microstructure and glass transition temperature (T g ) is complicated for poly(alkyl methacrylate)s and poly(alkyl acrylate)s. Despite intensive studies, relationships between the structures of these polymers and their properties, including solution characteristics and T g , are still controversial. Solution properties, chain conformations, including Flory's characteristic ratio (C ∞ ), persistence length (l p ), and chain diameters, and T g are reported for series of poly(n-alkyl acrylate)s and poly(n-alkyl methacrylate)s having alkyl side chain lengths (n) ranging from 1 to 10 carbons in length, with uniform and well-characterized tacticities. Chain flexibilities of both series of polymers decrease as n increases, reflecting increased hindrances to rotation about backbone bonds as side chains become longer. Conversely, the T g s for both series of polymers decrease substantially as n increases, reflecting the greater side chain mobilities of long alkyl substituents. For shorter alkyl chain lengths, T g s for the poly(n-alkyl acrylate)s are much lower than for the corresponding poly(n-alkyl methacrylate)s, a difference which has been attributed in the past to the presumed reduced chain flexibility of polymethacrylates due to the presence of the α-methyl substituent. However, contrary to such expectations, C ∞ and l p values for these two series of polymers are nearly identical at a given n value, except for the longest n-alkyl substituents. Instead, the differences in T g may be attributed to the differences in tacticity of the two series – almost ideally atactic for the poly(n-alkyl acrylate)s but high in syndiotacticity for the poly(n-alkyl methacrylate)s. The T g s for the two series of polymers approach that of polyethylene at longer alkyl chain lengths. In addition, the effects of tacticity and chain dynamics on C ∞ , l p , and T g of poly(methyl methacrylate) is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effects of 2D filler on rheology of additive manufacturing polymers: Simulation and experiment on polyetherketoneketone-mica composites

Polymeric composites have attracted increasing interest in fundamental studies and industrial applications due to their often-superior characteristics over conventional pure polymers. In this work, we studied the relationship between the rheological properties, critical in processing methods such as additive manufacturing, and the structure of a polymer-2D filler system by experiment and simulation methods. Considering the high performance polymer polyetherketoneketone (PEKK) with 2D filler (mica) composite as an example, the effects of filler size, filler content, surface chemistry and shear rate were systematically investigated. Here, it is demonstrated that the interaction between the polymer and 2D filler brings two different consequences on tuning the viscosity of polymer-filler composite. The stronger interaction increases the shear stress of the polymer-2D filler composite system, leading to higher viscosity. On the other hand, the stronger interaction can promote more absorption of polymer chains on the surface, which makes the local polymer network disentangled and decreases the continuous viscosity from the polymer network. This work provides a fundamental understanding on the relationship between macroscopic rheological properties and the structural properties and energetics at molecular level of polymer-2D filler composite.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Investigation of structural effects of aromatic compounds on sooting tendency with mechanistic insight into ethylphenol isomers

Small aromatic molecules with oxygen-containing functional groups are a promising class of fuel additives, as they can be readily sourced from depolymerized lignin. These oxygenated aromatic compounds (OACs) show a lower sooting tendency than aromatic hydrocarbons, but OACs having alkyl groups such as ethylphenol show a higher sooting tendency than other OACs such as phenol and anisole, despite the oxygen moiety. In this study, we investigate the relationship between chemical structure and soot precursor formation to explain observed differences in the sooting tendency of OACs and to gain insight into how alkyl or oxygenated substituents on the aromatic ring affect soot precursor formation. The weakest bond for 15 aromatic compounds was identified and cleavage of these bonds was shown to generate either benzyl or phenoxy radicals. A linear relationship between standard enthalpy of formation (ΔH f o ) of these radicals and the yield sooting index (YSI) was found, and thus ΔH f ° can be applied as a metric to estimate YSIs of various aromatic compounds; higher ΔH f o of a radical indicates an increase in the radical reactivity and leads to more soot precursor formation. Flow reactor experiments were performed for 2-ethylphenol and 3-ethylphenol to elucidate how ortho and meta substitution effects the sooting tendency. Soot precursors were identified from the experiment and their formation pathways were investigated computationally. 2-ethylphenol produces more oxygenated products than 3-ethylphenol since the ortho position has increased resonance stabilization of radical intermediates, which leads to lower YSI. Overall, these results further inform the selection of potential biomass-derived fuel blendstocks that have favorable sooting tendencies.

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

Generation and validation of comprehensive synthetic weather histories using auto-regressive moving-average models

As energy system design moves to more complex methods of optimization including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilized to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilizing both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. Here, it is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.

42 ENGINEERING↗
DOE OSTI2023

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIF total ) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIF obs ) through radiative transfer modeling. However, the current satellite SIF obs and thus SIF total are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, in this work, we proposed an algorithm to generate both global high-resolution SIF total (HSIF total ) and high-resolution SIF obs (HSIF obs ) at 1 km from low-resolution SIF obs (LSIF obs ) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIF total using the Orbiting Carbon Observatory-2 SIF (R 2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIF total and GPP. We also compared HSIF total to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIF total was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIF total product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

54 ENVIRONMENTAL SCIENCES↗