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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2021

A consistent and conservative volume distribution algorithm and its applications to multiphase flows using Phase-Field models

In the present study, the multiphase volume distribution problem, where there can be an arbitrary number of phases, is addressed using a consistent and conservative volume distribution algorithm. The proposed algorithm satisfies the summation constraint, the conservation constraint, and the consistency of reduction. The first application of the volume distribution algorithm is to determine the Lagrange multipliers in multiphase Phase-Field models that enforce the mass conservation, and a multiphase conservative Allen-Cahn model that satisfies the consistency of reduction is developed. A corresponding consistent and conservative numerical scheme is developed for the model. The multiphase conservative Allen-Cahn model has a better ability than the multiphase Cahn-Hilliard model to preserve under-resolved structures. The second application is to develop a numerical procedure, called the boundedness mapping, to map the order parameters, obtained numerically from a multiphase model, into their physical interval, and at the same time to preserve the physical properties of the order parameters. Along with the consistent and conservative schemes for the multiphase Phase-Field models, the numerical solutions of the order parameters are reduction consistent, conservative, and bounded, which are theoretically analyzed and numerically validated. Then, the multiphase Phase-Field models are coupled with the momentum equation by satisfying the consistency of mass conservation and the consistency of mass and momentum transport, thanks to the consistent formulation. Finally, it is demonstrated that the proposed model and scheme converge to the sharp-interface solution and are capable of capturing the complicated multiphase dynamics even when there is a large density and/or viscosity ratio.

42 ENGINEERING↗
DOE OSTI2022

Thermal Aging Effects on the Yield and Tensile Strength of 9Cr-1Mo-V (Grade 91)

The long-term exposure of 9Cr-1Mo-V (Grade 91) steel to elevated temperature can have a significant effect on reducing its yield and tensile strength. The yield and tensile strength changes, in turn, have potential implications to the assurance of the integrity of components constructed with this material over their design or intended lifetime. The ASME Boiler & Pressure Vessel Code (BPVC), Section III, Division 5 (III-5, high temperature reactors) provides tabulated reduction factors for Grade 91 yield and tensile strength as a function of exposure temperature and time up to 300,000 h. ASME BPVC III-5 s intent to extend these factors to an exposure duration of 500,000 h, the lack of available historic information to support the existing factors, and the recent development of a physics-based prediction model for ASME BPVC application are prime motivation for this study. This work describes results of the conventional time-temperature Hollomon–Jaffe parameter, strength reduction ratio prediction method using an updated, extensive Grade 91 unaged and related aged material strength database. The method, previously used by Oak Ridge National Laboratory in its evaluation of Grade 91 and likely used in development of the existing BPVC III-5 reduction factors, provides strength reduction ratio predictions useful for general component fitness-for-service assessments and for computing BPVC III-5 reduction factors as defined. Specific reduction factors to 500,000 h at 650 °C applicable to ASME BPVC III-5 are computed from the strength reduction ratios.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Membrane‐electrode assembly design parameters for optimal CO 2 reduction

Commercial-scale generation of carbon-containing chemicals and fuels by means of electrochemical CO 2 reduction (CO 2 R) requires electrolyzers operating at high current densities and product selectivities. Membrane-electrode assemblies (MEAs) have been shown to be suitable for this purpose. In such devices, the cathode catalyst layer controls both the rate of CO 2 R and the distribution of products. In this study, we investigate how the ionomer-to-catalyst ratio (I:Cat), catalyst loading, and catalyst-layer thickness influence the performance of a cathode catalyst layer containing Ag nanoparticles supported on carbon. In this paper, we explore how these parameters affect the cell performance and establish the role of the exchange solution (water vs. CsHCO 3 ) behind the anode catalyst layer in cell performance. We show that a high total current density is best achieved using an I:Cat ratio of 3 at a Ag loading of 0.01–0.1 mg Ag /cm 2 and with a 1.0 M solution of CsHCO 3 circulated behind the anode catalyst layer. For these conditions, the optimal CO partial current density depends on the voltage applied to the MEA. The work also reveals that the performance of the cathode catalyst layer is limited by a combination of the electrochemically active surface area and the degree to which mass transfer of CO 2 to the surface of the Ag nanoparticles and the transport of OH – anions away from it limit the overall catalyst activity. Hydration of the ionomer in the cathode catalyst layer is found not to be an issue when using an exchange solution. The insights gained allowed for a Ag CO 2 R MEA that operates between 200 mA/cm 2 and 1 A/cm 2 with CO faradaic efficiencies of 78–91%, and the findings and understanding gained herein should be applicable to a broad range of CO 2 R MEA-based devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary’s nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

Johnson, Charles A.↗
DOE OSTI2023

Latent code-based fusion: A Volterra neural network approach

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces. The so-called self-representation embedding of the latent codes leads to a simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter architecture achieved reduction in parameter complexity is primarily due to controlled non-linearities being introduced by the higher-order convolutions in lieu of generalized activation functions. Experimental results on two different datasets have shown a significant improvement in the clustering performance for VNNs auto-encoder over conventional Convolutional Neural Networks (CNNs) auto-encoder. In addition, we also show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a robust classification performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Study of CORC Conductors With Respect to Individual Tape Properties

CEA has been studying the advantages of a conductor based on an assembly of CORC cables in the high field zone of a hybrid Central Solenoid (CS) magnet for EU-DEMO. To this end, the detailed study of geometrical and electrical parameters of a CORC structure are studied in order to evaluate the cable’s electrical performance as well as to determine a number of important parameters (crossing points, contact surface etc…) as function of the cable structure. The paper first presents these geometrical and performance analyses. It will then try to introduce smeared models in order to reduce the cable’s performance to 1D tape scaling law using effective parameters. That reduction is of importance for use in thermohydraulic models. Finally, the paper will present the case study of a particular CORC conductor that is being procured and is foreseen to be tested in SULTAN in 2025. The paper also will try to give some predictive estimate of the cable performance and identify some of the unknowns related to current redistribution and joint resistance.

CORC↗
DOE OSTI2021

Harnessing Photoelectrochemistry for Wastewater Nitrate Treatment Coupled with Resource Recovery

Wastewater is a misplaced resource well suited to recover nutrients, value-added chemicals, energy, and clean water. A photoelectrochemical device is proposed to transform wastewater nitrates to ammonia and nitrous oxide, coupled with water oxidation. Numerical models were developed to quantify the dependence of process efficiencies and nitrogen-removal rates on light absorber band gaps, electrocatalytic kinetic parameters, competing oxygen reduction and hydrogen evolution reactions, and the reacting nitrate species concentrations that affect the mass-transfer limited current densities. With a single light-absorber and state-of-the-art catalysts, optimal solar-to-chemical efficiencies of 7% and 10% and nitrogen-removal rates of 260 and 395 gN m -2 day -1 are predicted for nitrate reduction to ammonia and nitrous oxide, respectively. The influence of competing reactions on the performance depends on the nitrate concentration and band gap of the light absorber modeled. Oxygen reduction is more dominant than hydrogen evolution to compete with the nitrate reduction reaction, but it is mass-transfer limited. Even with kinetic parameters that enhanced the driving forces for the competing reactions, the performance is only minimally affected by these reactions for optimally selected band gaps and nitrate concentrations larger than 100 mM. Here, theoretically predicted peak nitrogen removal rates and specific energy intensities are competitive with reported estimates for electrochemical and Sharon-Anammox processes for ammonia recovery and nitrogen removal, respectively. This result, together with the added benefit of harnessing sunlight to produce value-added products, indicates promise in the photoelectrochemical approach as a tertiary pathway to recover nutrients and energy from wastewater nitrates.

25 ENERGY STORAGE↗
DOE OSTI2022

Local residual stresses and microstructure within recrystallizing grains in iron

The intragranular strains and the microstructure within recrystallizing grains in single phase pure iron have been systematically studied in 3D using synchrotron micro-diffraction. In contradiction to common knowledge we observe that residual elastic strains of up to 1–2 × 10 –3 are present in the recrystallizing grains. The local strain variations within individual grains can be similar in magnitude to those between different grains. The effects of processing parameters, including rolling reduction, recrystallized volume fraction, and cooling rate, as well as microstructural parameters, including grain size, orientation and internal misorientation, on the development of local residual elastic strains and microstructure have been quantified. It is suggested that the difference in local defect density between recrystallized grains and the surrounding deformed matrix, as well as the redistribution of medium to long-range residual stresses in the deformed grains, are the main reasons for the development of such strains in the recrystallized grains. The local residual stress levels are calculated and their possible impact on the mechanical properties and recrystallization processes are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Investigating the Impact of Power-Take-Off System Parameters and Control Law on a Rotational Wave Energy Converter’s Peak-to-Average Power Ratio Reduction

Due to the irregular nature of real waves, the power captured in a wave energy converter (WEC) system is highly variable. This is an important barrier to the effective use of WECs. To address this challenge, this study focuses on a rotational WEC power-take-off system in which high-speed and high-efficiency generators along with a torque/power smoothing inertia element can be effectively utilized. In the first phase of this study, the U.S. Department of Energy’s reference model 3 (WEC-Sim RM3; two-body point absorber), along with a slider-crank WEC, were integrated for linear to rotational conversion. Relative motion between the float and spar in RM3 was the driving force for this slider-crank WEC, which is connected to a motor/generator set through a gearbox. RM3 geometry was scaled down by 25 times to work within the limits of the physical motor/generator set used in the experimentation. Once the integration in a hardware-in-the-loop simulation environment was successfully completed, data on the peak-to-average power ratio was collected for various wave conditions including regular and irregular waves. The control algorithm designed to keep the system in resonance with waves was able to maintain relatively high speed depending on the specific gear ratio and wave period. Initial results with hardware-in-the-loop simulations reveal that gear ratio and crank radius have a strong impact on the peak-to-average power ratio. In addition, it was found that output power from the generator was maximized at a larger gear ratio, as the crank radius was increased.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2021

Constraints on Λ CDM extensions from the SPT-3G 2018 $EE$ and $TE$ power spectra

Here, we present constraints on extensions to the Λ CDM cosmological model from measurements of the E-mode polarization autopower spectrum and the temperature-E-mode cross-power spectrum of the cosmic microwave background (CMB) made using 2018 SPT-3G data. The extensions considered vary the primordial helium abundance, the effective number of relativistic degrees of freedom, the sum of neutrino masses, the relativistic energy density and mass of a sterile neutrino, and the mean spatial curvature. We do not find clear evidence for any of these extensions, from either the SPT-3G 2018 dataset alone or in combination with baryon acoustic oscillation and Planck data. None of these model extensions significantly relax the tension between Hubble-constant, H 0 , constraints from the CMB and from distance-ladder measurements using Cepheids and supernovae. The addition of the SPT-3G 2018 data to Planck reduces the square-root of the determinants of the parameter covariance matrices by factors of 1.3–2.0 across these models, signaling a substantial reduction in the allowed parameter volume. We also explore CMB-based constraints on H 0 from combined SPT, Planck, and ACT DR4 datasets. While individual experiments see some indications of different H 0 values between the TT, TE, and EE spectra, the combined H 0 constraints are consistent between the three spectra. For the full combined datasets, we report H 0 = 67.49 ± 0.53 km s -1 Mpc -1 , which is the tightest constraint on H0 from CMB power spectra to date and in 4.1σ tension with the most precise distance-ladder-based measurement of H 0 . The SPT-3G survey is planned to continue through at least 2023, with existing maps of combined 2019 and 2020 data already having ~ 3.5 x lower noise than the maps used in this analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

42 ENGINEERING↗
DOE OSTI2023

Enhancing the activity of Fe-N-C oxygen reduction reaction electrocatalysts by high-throughput exploration of synthesis parameters

Here, the most active class of platinum group metal-free (PGM-free) oxygen reduction reaction (ORR) electrocatalysts in acidic electrolytes are those synthesized by heat treatment of iron, carbon, nitrogen precursors (Fe-N-C). Due to the large number of possible precursor compounds, a small fraction of the synthesis variable space has been explored. Correlation of synthesis variables with Fe speciation and ORR activity has been limited. In this work, an automation platform and a multi-port ball-milling were utilized to evaluate the effects of synthesis variables, such as identity of iron precursor, iron loading, and carbon and nitrogen sources on the ORR activity of iron -nitrogen-carbon catalysts in acidic electrolyte. The ORR activity is correlated with catalyst Fe speciation determined using Fe K-edge X-ray absorption spectroscopy (XAFS).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

The Kβ X-ray emission spectrum of 3 d transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, and has become crucial for understanding their spin and oxidation states. The spectrum is commonly treated using crystal-field multiplet theory, a semi-empirical theory that uses tunable parameters to control the strength of the effects present in X-ray emission spectroscopy (XES). However, determining the experimental values of these parameters remains a challenge. We present a methodology that applies Bayesian optimization to crystal-field multiplet theory to determine parameter values. The algorithm is tested on the X-ray emission spectra of a collection of Mn, Co, and Ni oxides. We are able to find optimal values for the four most impactful parameters: Slater−Condon reduction factors F dd , F pd , and G pd , and crystal field splitting 10 Dq . The algorithm produces significantly improved accuracy compared to current analysis methods, and probes interparameter dependencies by modeling the error landscape. This advancement enhances XES analysis by offering an approach of obtaining quantitative electronic structural information on 3 d transition metal valence shells, facilitating applications across various scientific fields.

Bayesian optimization↗
DOE OSTI2025

Rate expressions and kinetic parameters for metal ferrites in relation to applications of fossil fuel conversion to hydrogen: Part 1 of 2

Here, the goal of the present work was to provide the necessary reaction emulation information to enable detailed process simulation of a chemical looping H 2 production system from fossil fuels using CaFe 2 O 4 . This specifically pertained to the necessary kinetic data, reaction model development, and model rate parameters required for reaction emulation in both reducing and oxidizing environments. A logical methodology was defined, which included discretization of the reaction network, establishing a core model for reaction emulation that could be adapted based on the system phenomena, and development of a rate parameter regression tool designed around the core model. An extensive array of data sets was acquired by which parametric regressions were performed. The work presented and tabulated a comprehensive set of rate parameters for the reduction and oxidation reactions of CaFe 2 O 4 and descendent phases of Ca 2 Fe 2 O 5 , FeO, Fe 3 O 4 , Fe, and CaO to emulate reaction behavior in a looping-based process environment. This included direct reduction using CH 4 , H 2 , and CO, and direct oxidation reactions with steam, CO 2 and O 2 . Dynamic equilibrium was quantified for reactions that could utilize H 2 O and CO 2 as soft oxidants to re-saturate lattice oxygen in the depleted structure/phases. The kinetics associated with the oxidative mechanisms with the soft oxidants were quantified and compared to those of the reducing counterparts. The analysis provided critical insight to emulate reactions for a process that seeks to use natural gas (NG) or other fossil fuels as a direct reductant for the end goal of H 2 production.

calcium ferrite oxygen carriers↗
DOE OSTI2020

Life-cycle energy use and greenhouse gas emissions of palm fatty acid distillate derived renewable diesel

This study aims to quantify life-cycle fossil energy use and greenhouse gas (GHG) emissions for palm fatty acid distillate (PFAD) derived renewable diesel (RD) taking into consideration different feedstock classifications that are applicable to PFAD (residue, byproduct, or coproduct), and incorporating updated data for key processes. Under the three classifications, the PFAD to RD pathway was modeled using the Greenhouse gases, Regulated Emissions, and Energy Use in Technologies (GREET®) model. PFAD-derived RD could reduce fossil energy consumption by 77%–88%, relative to petroleum diesel. GHG emissions are very sensitive to PFAD classification and coproduct handling methods. Considering the production of palm oil and PFAD and economic value, we maintain that PFAD should be treated as a byproduct in palm oil refineries. With this treatment, PFAD-derived RD could achieve 84% GHG emissions reductions, compared to the emissions of petroleum diesel. We also employed a substitution method to address the substitution of PFAD by other materials in the marketplace. Compared to coproduct allocation results, we found substituting PFAD by tallow, soy oil, barley, and canola oil results in lower GHG emissions. Due to high induced land-use change emissions associated with palm farming, if PFAD is treated as a coproduct with refined palm oil, PFAD-derived RD may not deliver GHG reductions. A sensitivity analysis identified key parameters such as palm fruit yield, oil extraction efficiency in oil mills, and energy use intensity for RD production affects LCA results significantly; future efforts to improve these parameters could result in further GHG reductions.

09 BIOMASS FUELS↗
DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗