SEARCH · Engineering Papers

Results for “Reduce order modeling”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,207 records · Page 67

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Antiferromagnetic transitions of Dirac fermions in three dimensions

We use determinant quantum Monte Carlo simulations to study the role of electron-electron interactions on three-dimensional (3D) Dirac fermions based on the π-flux model on a cubic lattice. We show that the Hubbard interaction drives the 3D Dirac semimetal to an antiferromagnetic (AF) insulator only above a finite critical interaction strength and the long-range AF order persists up to a finite temperature. We evaluate the critical interaction strength and temperatures using finite-size scaling of the spin structure factor. The critical behaviors are consistent with the (3+1)-dimensional Gross-Neveu universality class for the quantum critical point and 3D Heisenberg universality class for the thermal phase transitions. We further investigate correlation effects in the birefringent Dirac fermion system. It is found that the critical interaction strength U c is decreased by reducing the velocity of the Dirac cone, quantifying the effect of velocity on the critical interaction strength in 3D Dirac fermion systems. Our findings unambiguously uncover correlation effects in 3D Dirac fermions and may be observed using ultracold atoms in an optical lattice.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Anthropogenic extremely low volatility organics (ELVOCs) Govern the Growth of Molecular Clusters over the Southern Great Plains during the Springtime

New particle formation (NPF) and growth govern cloud condensation nuclei (CCN) concentrations in many regions. The mechanisms governing the nucleation of molecular clusters vary substantially in different regions of the atmosphere. Additionally, the growth of these clusters from ~2 to 20 nm sizes is often governed by the availability of extremely low volatility organic vapours (ELVOCs). While the pathways to ELVOC formation from the oxidation of biogenic monoterpenes with ozone is better understood, the chemical and mechanistic pathways for ELVOC formation from oxidation of anthropogenic organics are not well understood. We integrate measurements and three-dimensional regional model simulations with the Weather Research and Forecasting Model coupled to chemistry (WRF-Chem) to understand the processes governing new particle formation and growth and secondary organic aerosol (SOA) formation during the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) field campaign at the Southern Great Plains (SGP) observatory in Oklahoma, and contrast it with a site within the Bankhead National Forest (BNF), Alabama in Southeast USA, where 5-year long measurements will begin in 2024. Simulations show that nucleation rates are at least an order of magnitude higher at SGP compared to BNF during the springtime days (April 28 and May 14, 2016), largely due to lower H2SO4 concentrations at BNF, which are needed for nucleation. In addition, the larger CS at BNF (compared to SGP) increase the loss of molecular clusters by coagulation to pre-existing particles. Among the 8 different nucleation mechanisms in WRF-Chem, we find that the amine+H2SO4 nucleation mechanism dominates at the SGP site, while the pure organic ion induced nucleation mechanism dominates over BNF. Through various WRF-Chem sensitivity simulations, we find that anthropogenic ELVOCs are critical for explaining the growth of newly formed particles and the resulting number size distribution observed near the surface at the SGP site during the daytime. In addition, we show that treating organic particles as semisolid, with strong diffusion-limited uptake of organic vapours, brings model predictions into closer agreement with the observed evolution of particle size distribution. Simulations also predict that anthropogenic SOA, formed by the oxidation of aromatic volatile organic compounds (VOCs), is the dominant organic aerosol component at SGP, while biogenic SOA dominates particle composition at the BNF site in Southeast USA on these days.

Shrivastava, ManishKumar B.↗

DOE OSTI2022

Isotherm Model for Moisture-Controlled CO 2 Sorption

Moisture-controlled sorption of CO 2 , the basis for moisture-swing CO 2 capture from air, is a novel phenomenon observed in strong-base anion exchange materials. Prior research has shown that Langmuir isotherms provide an approximate fit to moisture-controlled CO 2 sorption isotherm data. However, this fit still lacks a governing equation derived from an analytic model. As such, in this paper we derive an analytic form for an isotherm equation from a bottom-up approach, starting with a fundamental theory for an alkali liquid. In the range of interest relevant to CO 2 capture from air, an isotherm equation for an alkali liquid reduces to a simple analytic form with a single parameter, $K_{\text{eq}}$. In the limit $K_{\text{eq}} \gg 1$, a 2nd order approximation simplifies to a Langmuir isotherm that, however, deviates from experimental data. The isotherm theory for an alkali liquid has been generalized to a strong-base anion exchange material. In a strong-base anion exchange material, water concentration inside a sorbent, [H 2 O], is not large enough to be regarded as constant, which allows us to extend $K_{\text{eq}}$ to $K_{\text{eq(AEM)eff}} = K_{\text{eq(AEM)}} \times$ [H 2 O]$^{–n}$ according to the law of mass action. The final isotherm formula has been validated by experimental data from the literature. For a moisture-controlled CO 2 sorbent, $K_{\text{eq(AEM)eff}}$ varies significantly with moisture content of the sorbent. Depending on moisture level, the observed $K_{\text{eq(AEM)eff}}$ in a specific sorbent ranges from a few times to a few thousand times the value of $K_{\text{eq}}$ of a 2 mol L –1 alkali liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Mechanistic Multiscale Uncertainty Propagation in Support of Accelerated Fuel Qualification

Taking a nuclear fuel concept through the research, development, and qualification stages has historically taken on the order of 20 to 25 years because of extensive irradiation tests required for a variety of conditions. The concept of accelerated fuel qualification (AFQ) has been proposed to increase the innovation pace for nuclear fuels. The goal of AFQ is not to replace the traditional qualification approach but rather to reduce the total number of experiments required to ensure approval from the regulatory authority. Of the many AFQ approaches being explored, advanced modeling—and, in particular, mechanistic modeling—is in a uniquely cross-cutting position to reduce the number of required integral tests through the inclusion of separate-effects testing, while helping to extrapolate reactor performance during rare events. We make the case that propagation of uncertainty through various computational length scales helps contextualize mechanistic modeling. We will utilize UO 2 fission gas diffusion predictions from the atomistically informed cluster dynamics code Centipede to inform fuel performance rodlet simulations using the BISON finite element code as the metric for showing how multiscale mechanistic uncertainty quantification can help reduce uncertainty in fuel performance. In conclusion, by quantifying uncertainty and its reduction through multiscale modeling, the qualification process may be accelerated through the reduction of costly irradiation experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Measurements of radial neutral density profiles from Balmer- α emission in Wendelstein 7-X

Radial neutral density profiles are estimated from measurements of passive H α emission in the Wendelstein 7-X stellarator. To parametrize the generally three-dimensional distribution with a low number of degrees of freedom, the neutral density is reduced to a flux surface quantity. Accounting for emission from excitation and recombination processes, neutral density profiles are derived independently for each of the available lines of sight. Density profiles obtained from the different viewing geometries are found to vary within one order of magnitude. Toroidally oriented lines of sight predict systematically lower neutral densities when compared to poloidally oriented ones. This discrepancy is attributed to the simplifications inherent in the imposed model and significant differences in integration volumes across the viewing geometries. In line with expectations, obtained neutral densities are found to decrease with increasing plasma density. Key restrictions of the model include the reduction of the neutral density to a flux surface quantity, uncertainties in the plasma profiles and instrument function, and line integration effects outside the last closed flux surface.

Wendelstein 7-X↗

DOE OSTI2021

Phonon Screening of Excitons in Semiconductors: Halide Perovskites and Beyond

The ab initio Bethe-Salpeter equation (BSE) approach, an established method for the study of excitons in materials, is typically solved in a limit where only static screening from electrons is captured. Here, we generalize this framework to include dynamical screening from phonons at lowest order in the electron-phonon interaction. We apply this generalized BSE approach to a series of inorganic lead halide perovskites, CsPbX 3 , with X=Cl, Br, and I. We find that inclusion of screening from phonons significantly reduces the computed exciton binding energies of these systems. By deriving a simple expression for phonon screening effects, we reveal general trends for their importance in semiconductors and insulators, based on a hydrogenic exciton model. We demonstrate that the magnitude of the phonon screening correction in isotropic materials can be reliably predicted using four material specific parameters: the reduced effective mass, static and optical dielectric constants, and frequency of the most strongly coupled longitudinal-optical phonon mode. Finally, this framework helps to elucidate the importance of phonon screening and its relation to excitonic properties in a broad class of semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Direct statistical simulation of the Lorenz96 system in model reduction approaches

Direct statistical simulation (DSS) of nonlinear dynamical systems bypasses the traditional route of accumulating statistics by lengthy direct numerical simulations by solving the equations that govern the statistics themselves. DSS suffers, however, from the curse of dimensionality as the statistics (such as correlations) generally have higher dimensions than the underlying dynamical variables. Here we investigate two approaches to reduce the dimensionality of DSS, illustrating each method with numerical experiments with the Lorenz96 dynamical system. The forms of DSS chosen here involve approximate closures at second and third order in the equal-time cumulants. We demonstrate significant reduction in computational effort that can be achieved without sacrificing the accuracy of DSS. The methods developed here can be applied to turbulent fluid and magnetohydrodynamical systems. Published by the American Physical Society 2025

Li, Kuan↗

DOE OSTI2021

Performance Modeling of a Variable-Geometry Oscillating Surge Wave Energy Converter on a Raised Foundation: Preprint

This paper analyzes the power capture potential, structural loadings, and costs associated with an oscillating surge wave energy converter (OSWEC) operating on a raised foundation. The raised OSWEC offers opportunities for reduced installation costs, improved energy production, and greater flexibility of deployment when compared with bottom-fixed models. In this investigation, several different foundation geometries were simulated using WEC-Sim to estimate power capture and structural loads. In an effort to maximize power capture, several cases in which flat plates of varying size were attached to the top of the foundation, under and parallel with the OSWEC, were also simulated. These plates were found to enhance power capture by preventing the wave induced pressure from passing underneath the OSWEC, diverting this pressure towards the OSWEC instead. The OSWEC was simulated in the six Wave Energy Prize sea states, which were chosen as a representative sample of U.S. deployment sites. A first-order estimate of structural costs was calculated using the Wave Energy Prize ACE metric, with the foundation comprised predominantly of steel-reinforced concrete and the OSWEC comprised of A36 steel. Influence of foundation geometry on power capture, structural loadings, and ACE are topics of particular interest. This work has been inspired by advances in large scale additive manufacturing techniques that have the potential to dramatically reduce the cost of subsea foundations. These advancements may enable cost effective WEC systems to be deployed on raised foundations.

50 EE - Wind and Water Power Program - Water (EE-4↗

DOE OSTI2024

A crystal-plasticity-informed Gaussian Process Regression model to capture anisotropy in single crystal shape memory alloys

This work presents a machine learning (ML) framework that model the anisotropic actuation responses in a shape memory alloy. A Gaussian Process Regression (GPR) based ML model is trained on a set of different crystal orientations subjected to different actuation conditions. The training employed thermo-mechanical responses from a crystal-plasticity model that captures phase-transformation, stress-induced plasticity, and transformation-induced plasticity. Further, on training the GPR-ML model at fixed stress level for different orientations, it captured the thermo-mechanical responses accounting for the anisotropy, and predicted responses for new orientations with good accuracy. The GPR-ML model is able to capture the transformation temperature variations even when trained using multiple stress levels, and the transformation strain showed significant deviations. The developed GPR-ML model gave reasonable predictions for an unexplored sample set of orientations and loading conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Computational Fluid Dynamics Simulations to Assess Spatial Variability and Optimal Ventilation Scenarios for Biological Laboratory Exposures

A significant amount of uncertainty exists regarding potential human exposure to laboratory biomaterials and organisms in Biosafety Level 2 (BSL-2) research laboratories. Computational fluid dynamics (CFD) modeling is proposed as a way to better understand potential impacts of different combinations of biomaterials, laboratory manipulations, and exposure routes on risks to laboratory workers. Here, in this study, we use CFD models to simulate airborne concentrations of contaminants in an actual BSL-2 laboratory under different configurations. Results show that ventilation configuration, sampling location, and contaminant source location can significantly impact airborne concentrations and exposures. Depending on the source location and airflow patterns, the transient and time-integrated concentrations varied by several orders of magnitude. Contaminant plumes from sources located near a return vent (or exhaust like a fume hood or ventilated biosafety cabinet) are likely to be more contained than sources that are further from the exhaust. Having a direct flow between the source and the exhaust (through-flow condition) may reduce potential exposures to individuals outside the air flow path. Designing a BSL-2 room with ventilation and airflow patterns that maximize through-flow conditions to the return/exhaust vents and minimize dispersion and mixing throughout the room is, therefore, recommended. CFD simulations can also be used to assist in characterizing the impacts of supply and return vent locations, room layout, and source locations on spatial and temporal contaminant concentrations. In addition, proper placement of particle sensors can also be informed by CFD simulations to provide additional characterization and monitoring of potential exposures in BSL-2 facilities.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2021

Performance Modeling of a Variable-Geometry Oscillating Surge Wave Energy Converter on a Raised Foundation

This paper analyzes the power capture potential, structural loadings, and costs associated with an oscillating surge wave energy converter (OSWEC) operating on a raised foundation. The raised OSWEC offers opportunities for reduced installation costs, improved energy production, and greater flexibility of deployment when compared with fixed-bottom models. In this investigation, we simulated several different foundation geometries using WEC-Sim to estimate power capture and structural loads. In an effort to maximize power capture, several cases in which flat plates of varying size were attached to the top of the foundation, under and parallel with the OSWEC, were also simulated. These plates were found to enhance power capture by preventing the wave-induced pressure from passing underneath the OSWEC, diverting this pressure toward the OSWEC instead. The OSWEC was simulated in the six Wave Energy Prize sea states, which were chosen as a representative sample of U.S. deployment sites. A first-order estimate of structural costs was calculated using the Wave Energy Prize ACE metric, with the foundation comprised predominantly of steel-reinforced concrete and the OSWEC comprised of A36 steel. Influence of foundation geometry on power capture, structural loadings, and ACE are topics of particular interest. This work has been inspired by advances in large-scale additive manufacturing techniques that have the potential to dramatically reduce the cost of subsea foundations. These advancements may enable cost-effective WEC systems to be deployed on raised foundations.

cost↗

DOE OSTI2025

The influence of cooling rates on strain phase diagrams and domain structures of ferroelectric thin films: A case study of PbTiO 3

Strain engineering has been established as an effective approach to control phase equilibria, domain configurations, and functional properties of ferroelectric thin films. Temperature-strain phase diagrams have been used as powerful tools for providing insights into strain engineering. However, almost all existing phase diagrams established using the phase-field approach assume quenching conditions without considering actual cooling rates during the post-deposition annealing process of ferroelectric thin films. Within this work, we systematically investigate the influence of cooling rates on domain structures and the strain-phase diagram of ferroelectric thin films using phase-field simulations, taking PbTiO 3 thin films as a model system. We found that both the position of phase boundaries in the strain phase diagrams and the domain morphology are significantly influenced by the cooling rates. It is revealed that while the paraelectric-ferroelectric phase boundary remains invariant, the phase boundaries between single-phase and multi-phase regions tend to shift toward the corresponding multi-phase region as the cool rate reduces. Slow cooling generally leads to more ordered domain structures with increased domain size. Using the obtained equilibrium domain structures, we calculated effective thermal conductivities and found significant variations that can be tuned by the cooling rates. In conclusion, this work reveals an underexplored yet critical impact of cooling rates on phase equilibria and domain structures in ferroelectric thin films, which may inspire further fine-tuning of domains and domain walls in low-dimensional ferroelectrics for multifunctional applications.

Domain structures↗

DOE OSTI2023

Canonical and noncanonical Hamiltonian operator inference

Here, a method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Thermal conductivity of 3D-printed block-copolymer-inspired structures

This study primarily focuses on examining the impact that geometric structure has on thermal conductivity of multi-phase constructs in different 3D-printed poly(lactic acid), PLA, samples. The investigated structures are inspired by morphologies formed by diblock copolymers: lamellae, hexagonally packed cylinders, and gyroid. This research also investigates how volume percentage and material combination influence the thermal conductivity of these structures. Further, the samples can be tailored to simulate various thermal management structures observed in practical applications, such as thermal interface materials in electronic devices. Thermal conductivity ratio is controlled using air, the least conductive material at 0.026 W/(m K), PLA at 0.136 W/(m K), and thermal paste at 5.11 W/(m K). Different models were tested against thermal conductivity measurements in order to capture the effect of material type (PLA-Air versus PLA-Thermal Paste), volume percentage, structure, and orientation. Simple, effective medium models were good predictions of thermal conductivity in lamellar structures, but it was necessary to develop models for conduction through cylindrical and gyroid structures. Finally, all results were normalized to find a universal model that is independent of structure and material. This approach provides a simple method to predict how to reduce or enhance transport properties and heat management capabilities of 3D printed objects.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A microscopic Kondo lattice model for the heavy fermion antiferromagnet CeIn 3

Electrons at the border of localization generate exotic states of matter across all classes of strongly correlated electron materials and many other quantum materials with emergent functionality. Heavy electron metals are a model example, in which magnetic interactions arise from the opposing limits of localized and itinerant electrons. This remarkable duality is intimately related to the emergence of a plethora of novel quantum matter states such as unconventional superconductivity, electronic-nematic states, hidden order and most recently topological states of matter such as topological Kondo insulators and Kondo semimetals and putative chiral superconductors. The outstanding challenge is that the archetypal Kondo lattice model that captures the underlying electronic dichotomy is notoriously difficult to solve for real materials. Here we show, using the prototypical strongly-correlated antiferromagnet CeIn 3 , that a multi-orbital periodic Anderson model embedded with input from ab initio bandstructure calculations can be reduced to a simple Kondo-Heisenberg model, which captures the magnetic interactions quantitatively. We validate this tractable Hamiltonian via high-resolution neutron spectroscopy that reproduces accurately the magnetic soft modes in CeIn 3 , which are believed to mediate unconventional superconductivity. Our study paves the way for a quantitative understanding of metallic quantum states such as unconventional superconductivity.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slitting method require an inverse analysis to solve the problem. The accuracy of the result as well as an uncertainty component (the model uncertainty) depends on the basis functions used in the inverse solution. The use of a series expansion as the basis functions for the inverse solution was analysed in a previous work for the particular case where functions orders grew consecutively. The present work presents a new estimation of the model uncertainty and a new improved methodology to select the final basis functions for the case where the basis is composed of polynomials. Including nonconsecutive polynomial orders in the basis generates a larger space of possible solutions to be evaluated and allows the possibility to include higher-order polynomials. The paper includes a comparison with two other inverse analyses methodologies applied to synthetically generated data. With the new methodology, the final error is reduced and the uncertainty estimation improved.

36 MATERIALS SCIENCE↗

DOE OSTI2022

NIR Dataset and Models (Near-Infrared Spectroscopy Calibration Dataset for Butanediol Fermentation and Multivariate Calibration Models) [SWR-22-60]

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be used in a variety of chemical feedstocks, liquid fuels, and biosynthetic building blocks. While 2,3-BDO can be efficiently produced by fermentation, the fermentation requires continuous monitoring and control to maximize 2,3-BDO yields and minimize inhibitory coproducts. Because of the time required for sampling and at-line measurement of fermentation samples with high pressure liquid chromatography (HPLC), the ability for operators to perform real-time modification to fermentation conditions is limited. To overcome this challenge, researchers from the National Renewable Energy Laboratory (NREL) have developed a calibration model which can predict the concentration of several analytes in real-time using near-infrared (NIR) spectra of the filtered fermentation broth. While significantly reducing the need for off-line sampling, NREL expects this technology to play a critical role in maximizing 2,3-BDO production. 2,3-butanediol (2,3-BDO) is a useful chemical platform that can be used to create a variety of products. For instance, 2,3-BDO can be (1) dehydrated and converted into methyl ethyl ketone, a liquid fuel additive or (2) deoxydehydrated into 1,3-butadiene for synthetic rubber, which can also be oligomerized in high yields to gasoline, diesel, and jet fuel. In order to maximize 2,3-BDO production, frequent measurement of fermentation samples is needed, as small changes in oxygen concentration can drive the fermentation to undesired products. For example, oxygen-deficient conditions result in glycerol production, while excess oxygen concentrations result in acetoin production. This results in the need for measuring dissolved oxygen, glucose, and xylose concentrations in order to optimize the aeration rate of the fermentation. Traditional monitoring methods occurs off-line and can take up to 30 minutes per sample. With multiple fermenters and high-pressure liquid chromatography (HPLC) injectors, resulting in the need for multiple samples, the sampling process can take hours to complete. NREL’s calibration model can predict the glucose, xylose, 2,3-BDO, acetoin, and glycerol concentrations from NIR spectra of filtered fermentation liquor samples. Using a partial least-squares (PLS) calibration model, NREL’s model can monitor the concentration of these analytes during subsequent fermentations at bench- and pilot-scale, demonstrating the utility of NIR spectroscopy combined with chemometrics for real-time, at-line monitoring of 2,3-BDO fermentations.

Wolfrum, Edward↗

DOE OSTI2020

Insights into the Interaction of Redox Active Organic Molecules and Solvents with the Pristine and Defective Graphene Surfaces from Density Functional Theory

A systematic study is reported on the interaction of two representative redox active organic molecules and two solvent molecules with pristine and defective graphene surfaces as a model of an electrode surface of a redox flow battery (RFB). The redox active molecules include a catholyte, 2,5-di-tert-butyl-1,4-dimethoxybenzene (DDB), and an anolyte molecule, benzothiadiazole (BTZ), and the solvent molecules include acetonitrile (MeCN) and ethylene carbonate (EC). The graphene defects investigated include a single vacancy, double vacancy, zigzag step edge, and armchair step edge. Computations suggest that the interactions of all molecules with a pristine graphene surface are relatively weak (0.2 to 0.8 eV) and dominated by van der Waals effects; therefore, these molecules are chemically stable upon interacting with pristine nondefective graphene. The BTZ, DDB, MeCN, and EC molecules interact strongly (1.5 to 5.5 eV) with the single vacancy and zigzag step edges of graphene that leads to the possible decomposition of the molecules with strength of interaction in the order of MeCN > BTZ > DDB > EC and MeCN > EC > BTZ > DDB, respectively. Calculations show that the BTZ, DDB, MeCN, and EC molecules interact less strongly (0.2 to 1.4 eV) to the double vacancy and armchair step edge than to the single vacancy and zigzag step edge. The binding energies of the molecules were significantly reduced when interacting with the passivated defects, suggesting that the passivation of the defects could help prevent unwanted chemical interactions between the neutral molecules and the electrode surface. In all organic RFBs, one of the crucial bottlenecks is the stability of the constituent molecules with the electrode material, and this study provides insights into the chemical interaction of selected candidate species with a model carbon electrode.

Howard, Jason D.↗