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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

EVALUATION OF PRESSURE DROP CORRELATIONS FOR THE WIRE-WRAPPED ROD BUNDLES

The accurate prediction of pressure drop in the fuel rod bundle is of importance for the design and thermal-hydraulic analysis of nuclear reactor system. Due to very complex geometry of wire-wrapped fuel rod bundle geometry for the Sodium Fast Reactor (SFR), a simple method using hydraulic (equivalent) diameter is not sufficient to predict the pressure drop of rod bundle accurately. For several decades, many experimental works have been performed to develop an accurate correlation for wire-wrapped fuel bundle friction factor. In these works, the friction factor correlation of wire-wrapped fuel bundle has been developed as a function of Reynolds number of the flow and the design parameters such as fuel pin diameter, wire diameter, rod pitch, and wire lead length. The Cheng and Todreas correlations are well-known and used in many subchannel analysis code, such as CADET, TRIO-U, MATRA-LMW, SE2-ANL, etc. Novendstern model, Rehme model, Baxi and Dalle Donne model are also well-known and widely referred. Computational Fluid Dynamics is another typical approach to predict the pressure drop of wire-wrapped fuel rod bundle, but it requires the model validation because the accuracy of pressure drop prediction by the CFD simulation depends on what turbulence model is employed. The present work aims at evaluating the existing pressure drop correlations for the wire-wrapped fuel rod bundle and evaluating the pressure drop of 217-pin rod bundle by the CFD analysis.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Martensitic transformation in superlattices of two non-transforming metals

A number of studies have shown that the coherent integration of a second non-transforming phase in martensitic materials results in unprecedented thermo-mechanical properties. We demonstrate the possibility of martensitic transformation by epitaxially combining two non-transforming materials, Mg (hexagonal closed packed) and MgLi (body centered cubic). Density functional theory calculations predict that MgLi/Mg superlattices undergo a transformation crystallographically equivalent to MgSc alloys and can be expected to result in finite temperature shape memory behavior. We predict the zero-temperature energy of the martensite phase to be 9.3 meV/atom lower than austenite for a 50–50 at. % MgLi/Mg nanolaminate, which is expected to result in a martensitic transformation temperature of approximately 150 K. This energy difference can be tuned between 4.9 and 18.1 meV/atom by varying the fraction of the pure Mg phase in the superlattice, potentially enabling the transformation from cryogenic to room and higher temperatures. While Li diffusion might limit the applications of the systems proposed, this work opens the door to a new approach to create martensitic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Shock Tube and Flame Speed Measurements of 2,4,4-Trimethyl-1-Pentene: A Co-Optima Biofuel

Abstract The combustion of 2,4,4-trimethyl-1-pentene (diisobutylene, C8H16), which is a biofuel and a component of surrogate fuels, is examined in this work. Carbon monoxide time–histories and ignition delay times are collected behind reflected shock waves utilizing a shock tube and mid-infrared laser absorption spectroscopy. Measurements were obtained near 10 atm pressure during stoichiometric oxidation of 0.15%C8H16/O2/Ar. Simulated results from chemical kinetic models are provided, and sensitivity analyses are used to discuss differences between models for both ignition delay times and carbon monoxide formation. In addition, laminar burning speeds are obtained at 1 atm, 428 K, and equivalence ratios, phi, between 0.91 and 1.52 inside a spherical chamber facility. Measured burning speeds are found to be less than that of ethanol over the equivalence ratio span. Burning speed measurements are compared to predictions of chemical kinetic mechanisms and are in agreement for the richest conditions; however, at lean conditions, the model predicts a far slower-burning speed. The maximum burning speed occurs at an equivalence ratio of 1.08 with a magnitude of 0.70 m/s. The current work provides the crucial experimental data needed for assessing the feasibility of this biofuel and for the development of future combustion chemical kinetics models.

Energy & Fuels↗
DOE OSTI2021

Prediction of Probabilistic Shock Initiation Thresholds of Energetic Materials Through Evolution of Thermal-Mechanical Dissipation and Reactive Heating

The ignition threshold of an energetic material (EM) quantifies the macroscopic conditions for the onset of self-sustaining chemical reactions. The threshold is an important theoretical and practical measure of material attributes that relate to safety and reliability. Historically, the thresholds are measured experimentally. In this work, we present a new Lagrangian computational framework for establishing the probabilistic ignition thresholds of heterogeneous EM out of the evolutions of coupled mechanical-thermal-chemical processes using mesoscale simulations. Furthermore, the simulations explicitly account for microstructural heterogeneities, constituent properties, and interfacial processes and capture processes responsible for the development of material damage and the formation of hotspots in which chemical reactions initiate. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, heat conduction, reactive chemical heating, gaseous product generation, and convective heat transfer. To determine the ignition threshold, the minimum macroscopic loading required to achieve self-sustaining chemical reactions with a rate of reactive heat generation exceeding the rate of heat loss due to conduction and other dissipative mechanisms is determined. Probabilistic quantification of the processes and the thresholds are obtained via the use of statistically equivalent microstructure sample sets (SEMSS). The predictions are in agreement with available experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data reduction considerations for spherical R-32(CH 2 F 2 )-air flame experiments

The burning velocities of mixtures of refrigerant R-32 (CH 2 F 2 ) with air over a range of equivalence ratios are studied via shadowgraph images of spherically expanding flames (SEFs) in a large, optically accessible spherical chamber at constant pressure. Numerical simulations of the 1-D, unsteady, spherical flames incorporating an optically thin radiation model and detailed kinetics accurately predict experimental results for a range of equivalence ratios. For these low burning velocity flames, the effects of stretch and radiation occur simultaneously and make extraction of the unstretched burning velocity from the experimental data difficult. Different data reduction approaches are shown to have large effects on the burning velocities inferred from the experiments and simulations. A new flame radius tracking approach for the experimental images is shown to provide improved agreement of the burned gas velocity variation with stretch predicted by the simulations and helps to compensate for mild flame distortion due to buoyancy.

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

Dynamics of dark matter misalignment through the Higgs portal

A light singlet scalar field feebly coupled through the super-renormalizable Higgs portal provides a minimal and well-motivated realization of ultra-light bosonic dark matter. We study the cosmological production of dark matter in this model by elucidating the dynamics of two sources of scalar field misalignment generated during the radiation era. For large scalar masses (above $\mathcal{O}$ (10 −3 eV)), dark matter is produced through thermal misalignment, by which the scalar field is driven towards large field values as a result of the finite-temperature effective potential. The dominance of thermal misalignment in this mass range leads to a sharp relic abundance prediction which is, to a significant extent, insensitive to the initial conditions of the scalar field. On the other hand, for low mass scalars (below $\mathcal{O}$ (10 −5 eV)), dark matter is produced via VEV misalignment, which is caused by the induced scalar field vacuum expectation value triggered by the electroweak phase transition. We show that the relic abundance in this low mass range is sensitive to the scalar field initial conditions. In the intermediate mass range, the relic abundance is a consequence of a competition between thermal misalignment and VEV misalignment, which can potentially lead to novel forced resonance effects which cause a recurring enhancement and suppression in the late time oscillation amplitude as a function of the scalar mass. We compare our relic abundance predictions with constraints and projections from equivalence principle and inverse square law tests, stellar cooling, resonant molecular absorption, and observations of extra-galactic background light and diffuse X-ray backgrounds. New experimental ideas are needed to probe most of the cosmologically motivated regions of parameter space.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Nitrate Removal Across Ecogeomorphic Zones in Wax Lake Delta, Louisiana (USA)

Human activities have increased nitrate export from rivers, degrading coastal water quality. At deltaic river mouths, the flow of water through wetlands increases nitrate removal, and the spatial organization of removal rates influences coastal water quality. To understand the spatial distribution of nitrate removal in a river-dominated delta, in this study we deployed 23 benthic chambers across ecogeomorphic zones with varying elevation, vegetation, and sediment properties in Wax Lake Delta (Louisiana, USA) in June 2018. Regression analyses indicate that normalized difference vegetation index is a useful predictor of summertime nitrate removal. Mass transfer velocity were approximately three times greater on a vegetated submerged levee (13 mm hr –1 ), where normalized difference vegetation index was greatest, compared to other locations (4.6 mm hr –1 ). Two methods were developed to upscale nitrate removal across the delta. The flooded-delta method integrates spatially explicit potential removal rates across submerged portions of the delta and suggests that intermediate elevations on the delta—including submerged levees—are responsible for 70% of potential nitrate removal despite covering only 33% of the flooded area. The channel network method treats the delta as a network of river channels and suggests that although secondary channels are more efficient than primary channels at removing received nitrate, primary channels collectively contribute more to overall removal because they convey more of the total nitrate load. The two upscaling methods predict similar rates of nitrate removal, equivalent to less than 4% of nitrate entering the delta. To protect coastal waters against high nitrate loads, management policies should aim to reduce upstream nutrient loads.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Greybox Thermal Parameter Identification of Electric Machine Stators

The parameters of electric machine thermal equivalent circuit networks are difficult to predict due to material and manufacturing uncertainties. In this paper, a Greybox system identification approach is used to identify parameters of electric machine stator lumped parameter thermal networks (LPTNs). LPTNs provide a low order, computationally efficient, dynamic model of temperatures at specific locations. Second and third order LPTN model structures are defined as state space equations with stator thermal parameters to be identified. To test the Greybox electric machine stator thermal system identification, five stator motorette prototypes were constructed with controlled variations in slot fill and slot liner thickness. The variation in the motorette thermal parameters and thermal time constants are detected using the Greybox identification. Special attention is given to the impact of sampling rate and Greybox data record length on parameter estimation accuracy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Ion Transport in Concentrated Crosslinked Solid Polymer Electrolytes

Crosslinking polymers is a common approach to create mechanically stable solid materials such as polymer electrolytes for lithium batteries. In conventional liquid electrolytes, the solvent molecules move freely to accommodate the field-induced motion of ions. However, in crosslinked polymer electrolytes, the rearrangement of polymer segments is constrained by the deformation limits of the network. Herein, we develop a new transport model that accounts for both the formation of concentration gradients and the elasticity of the electrolyte. The elasticity is incorporated by adding an additional term related to the entropy of crosslinked strands to the electrochemical potential of the salt. The resulting Crosslink Model contains two adjustable parameters: $\mathcal{N}$, the average number of monomers in a strand, and λ crit , the maximum strain the network can sustain. These solid-like constraints produce singularities in the governing transport equations, fundamentally altering the concentration profiles. Plateaus in salt concentrations emerge near the electrodes, and network elasticity introduces a strain overpotential. When compared to a Baseline Model ($\mathcal{N}$ → ∞, equivalent to concentrated solution theory), which predicts steepest gradients near the electrodes, both models yield similar current–voltage relationships. Model predictions are compared to electrochemical data for a poly(ethylene oxide)-based crosslinked polymer electrolyte.

Patel, Vivaan [University of California, Berkeley,↗
DOE OSTI2022

OC6 Phase Ia: CFD Simulations of the Free-Decay Motion of the DeepCwind Semisubmersible

Currently, the design of floating offshore wind systems is primarily based on mid-fidelity models with empirical drag forces. The tuning of the model coefficients requires data from either experiments or high-fidelity simulations. As part of the OC6 (Offshore Code Comparison Collaboration, Continued, with Correlation, and unCertainty (OC6) is a project under the International Energy Agency Wind Task 30 framework) project, the present investigation explores the latter option. A verification and validation study of computational fluid dynamics (CFD) models of the DeepCwind semisubmersible undergoing free-decay motion is performed. Several institutions provided CFD results for validation against the OC6 experimental campaign. The objective is to evaluate whether the CFD setups of the participants can provide valid estimates of the hydrodynamic damping coefficients needed by mid-fidelity models. The linear and quadratic damping coefficients and the equivalent damping ratio are chosen as metrics for validation. Large numerical uncertainties are estimated for the linear and quadratic damping coefficients; however, the equivalent damping ratios are more consistently predicted with lower uncertainty. Some difference is observed between the experimental and CFD surge-decay motion, which is caused by mechanical damping not considered in the simulations that likely originated from the mooring setup, including a Coulomb-friction-type force. Overall, the simulations and the experiment show reasonable agreement, thus demonstrating the feasibility of using CFD simulations to tune mid-fidelity models.

17 WIND ENERGY↗
DOE OSTI2024

Revealing the role of redox reaction selectivity and mass transfer in current–voltage predictions for ensembles of photocatalysts

Photocatalysts are conceptually simple reaction units where nanoscale semiconductors integrated with catalysts drive a pair of redox reactions on illumination. However, the proximity of reaction sites performing cathodic and anodic reactions poses dire challenges to realize large light-to-fuel conversion efficiencies. In this study, a powerful, yet straightforward, equivalent-circuit detail-balance modeling framework is developed and applied to evaluate the performance of photocatalytic systems featuring multiple light absorbers. Specifically, low bandgap iridium-doped strontium titanate is modeled as a Z-scheme photocatalyst to achieve desirable hydrogen evolution and iron-based redox shuttle oxidation reactions. Our model has unique capabilities to simulate competing redox reactions and address mass-transfer limitations. In a significant departure from state-of-the-art circuit models, our study develops tools to perform load-line analyses by incorporating a net electrochemical load curve that includes both desired and competing redox reactions. Consequently, reaction selectivity is predicted from equivalent circuit models for photocatalytic and photoelectrochemical systems. Our investigation into ensembles comprised of multiple, semi-transparent light absorbers reveals their potential to outperform a single, optically thick light absorber, particularly when operated under mass-transfer-limited conditions. However, this outcome hinges on minimizing mass-transfer rates of select redox species to prevent undesired reactions of hydrogen oxidation and/or redox shuttle reduction. Our findings demonstrate that reaction selectivity can be achieved by tuning asymmetry in redox species mass-transfer even with perfectly symmetric electrocatalytic charge-transfer coefficients. The influences of various kinetic, mass-transfer, and thermodynamic parameters are explored to offer crucial insights for synthesis of the next-generation of photocatalysts and selective coatings, and reactor designs.

25 ENERGY STORAGE↗
DOE OSTI2020

Do Programmers Prefer Predictable Expressions in Code?

Source code is a form of human communication, albeit one where the information shared between the programmers reading and writing the code is constrained by the requirement that the code executes correctly. Programming languages are more syntactically constrained than natural languages, but they are also very expressive, allowing a great many different ways to express even very simple computations. Still, code written by developers is highly predictable, and many programming tools have taken advantage of this phenomenon, relying on language model surprisal as a guiding mechanism. Additionally, while surprisal has been validated as a measure of cognitive load in natural language, its relation to human cognitive processes in code is still poorly understood. In this paper, we explore the relationship between surprisal and programmer preference at a small granularity—do programmers prefer more predictable expressions in code? Using meaning-preserving transformations, we produce equivalent alternatives to developer-written code expressions and run a corpus study on Java and Python projects. In general, language models rate the code expressions developers choose to write as more predictable than these transformed alternatives. Then, we perform two human subject studies asking participants to choose between two equivalent snippets of Java code with different surprisal scores (one original and transformed). We find that programmers do prefer more predictable variants, and that stronger language models like the transformer align more often and more consistently with these preferences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat

Abstract. Humboldt Glacier, northern Greenland, has retreated and accelerated through the 21st century, raising concerns that it could be a significant contributor to future sea-level rise. We use a data-constrained ensemble of three-dimensional higher-order ice sheet model simulations to estimate the likely range of sea-level rise from the continued retreat of Humboldt Glacier. We first solve for basal traction using observed ice thickness, bed topography, and ice surface velocity from the year 2007 in a PDE-constrained (partial differential equation) optimization. Next, we impose calving rates to match mean observed retreat rates from winter 2007–2008 to winter 2017–2018 in a transient calibration of the exponent in the power-law basal friction relationship. We find that power-law exponents in the range of 1/7–1/5 – rather than the commonly used 1/3–1 – are necessary to reproduce the observed speedup over this period. We then tune an iceberg calving parameterization based on the von Mises stress yield criterion in another transient-calibration step to approximate both observed ice velocities and terminus position in 2017–2018. Finally, we use the range of basal friction relationship exponents and calving parameter values to generate the ensemble of model simulations from 2007–2100 under three climate forcing scenarios from CMIP5 (two RCP8.5 forcings, Representative Concentration Pathway) and CMIP6 (one SSP5-8.5 forcing, Shared Socioeconomic Pathway). Our simulations predict 5.2–8.7 mm of sea-level rise from Humboldt Glacier, significantly higher than a previous estimate (∼ 3.5 mm) and equivalent to a substantial fraction of the 40–140 mm predicted by ISMIP6 from the whole Greenland Ice Sheet. Our larger future sea-level rise prediction results from the transient calibration of our basal friction law to match the observed speedup, which requires a semi-plastic bed rheology. In many simulations, our model predicts the growth of a sizable ice shelf in the middle of the 21st century. Thus, atmospheric warming could lead to more retreat than predicted here if increased surface melt promotes hydrofracture of the ice shelf. Our data-constrained simulations of Humboldt Glacier underscore the sensitivity of model predictions of Greenland outlet glacier response to warming to choices of basal shear stress and iceberg calving parameterizations. Further, transient calibration of these parameterizations, which has not typically been performed, is necessary to reproduce observed behavior. Current estimates of future sea-level rise from the Greenland Ice Sheet could, therefore, contain significant biases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Integration of the Biot–Gassmann Fluid Substitution Method and Machine Learning-Based Velocity–Stress Relationship for Estimating In Situ Stresses

Recent advancements have shown that in situ stresses can be reliably estimated through an integrated machine/deep learning (ML/DL)-based framework, which relies on models trained and validated using true triaxial ultrasonic velocity (TUV) experimental data that involve measurements of ultrasonic velocity in saturated rocks under varying stress configurations. However, when the goal is to interpret lower frequency measurements, it may be more appropriate to run experiments on dry rocks and then obtain Biot–Gassmann-derived equivalent saturated velocities (low-frequency approximation) and employ these quantities for training ML/DL models to predict in situ stress. Whether the dispersion effect of frequency on the velocity–stress relationship substantially impacts in situ stress prediction is an important and unresolved question. This work presents an enhancement of ML/DL-based workflow by training and implementing ML/DL models using equivalent saturated acoustic velocities (low-frequency) obtained by applying Biot–Gassmann fluid substitution on the ultrasonic velocities of dry cores. The models were trained on TUV data sets derived from three subsurface cores extracted from the geothermal well 16B(78)-32 at the Utah FORGE site. Each core was subjected to 75 unique stress configurations for velocity measurement in the dry state. The ML/DL trained on the TUV data set with equivalent saturated velocities demonstrated promising performance to predict in situ stress in subsurface geological rocks using velocity–stress relationships with R 2 of 0.86, 0.971, and 0.975 and root mean squared error (RMSE) of 2.59, 1.92, and 1.80 for validation/testing phases of vertical, minimum horizontal, and maximum horizontal stress models, respectively. Additionally, interpretation and explanation by Shapley additive explanations (SHAP) analysis further improved scientific validation and model reliability for estimating in situ stresses.

colloids↗
DOE OSTI2025

A Demand Bidding Model for Multi-Product Industrial Plants

The growing contribution of renewable energy sources has increased volatility and uncertainty in electricity markets, challenging traditional grid operation paradigms. Demand bidding (DB), a market participation model where (large) electricity users communicate their willingness to pay for electricity to the grid operator, was shown in previous work to enhance grid stability and lower generation cost. We present a DB model for multi-product industrial plants, based on an extended optimal power flow problem where the plant dynamics are represented using autoregressive with extra inputs (ARX) models. We compare DB to price-based demand-side management, showing that, under certain assumptions, the two approaches are equivalent, while DB provides more transparency and predictability to the grid operator. A case study based on an industrial air separation unit is discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Production of β-ketoadipic acid from glucose in Pseudomonas putida KT2440 for use in performance-advantaged nylons

Biomass-derived chemicals can offer unique chemical functionality relative to petroleum-derived building blocks. To this end, here we report that β-ketoadipic acid (βKA), a C6 diacid with a β-ketone group, can be used as a performance-advantaged replacement for adipic acid in a nylon-6,6 analog. Building on our previous efforts to produce shikimate-derived products from carbohydrates, Pseudomonas putida KT2440 is engineered to produce βKA from glucose, achieving a 26 g/L titer. Following purification, βKA imparts an increase of 69 degrees C above the nylon-6,6 glass transition temperature and 20% reduced water permeability, equivalent to nylon-6,10. Molecular simulations predict that the enhanced thermal properties result from rigidity introduced by the β-ketone. Process analysis predicts that βKA can be produced for US$1.94/kg from sugars, requiring 63% less energy and emitting 43% less greenhouse gases than fossil-based adipic acid. Overall, this study illustrates the potential for βKA to serve as a useful building block for bio-based polymers.

09 BIOMASS FUELS↗
DOE OSTI2026

J -Resolved Molecular Fingerprinting by Parahydrogen Hyperpolarized Low-Field NMR

A J-resolved spectroscopy that depends on homonuclear scalar coupling in the strong-coupling regime and heteronuclear coupling in the weak regime expands complex peak patterns to a second axis. Hyperpolarization by Signal Amplification by Reversible Exchange (SABRE) enables the spectroscopy at a low magnetic field of 0.82 mT. Overlapping peaks of molecules such as 3-fluoropyridine and 3,5-difluoropyridine are resolved. Density matrix simulations of the 1 H and 19 F spins indicate a strong dependence on the signs and values of the J-coupling constants, including the homonuclear couplings that are not directly observable. The best matching peak positions and intensities predict coupling constants, including couplings between chemically equivalent nuclear spins, ranging in magnitude from 0.4 to 9.0 Hz for the two molecules. Simulations of other spin systems show unique patterns for molecules containing 1 H and 19 F or 13 C. The dependence of the J-resolved peak patterns on all coupling constants in a spin system presents a new modality for portable and inexpensive identification of molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

SolVap_StarTer (Solvent Evaporation Graphical User Interface) [SWR-26-029]

SolVap_StarTer is an open-source solvent evaporation GUI. The software combines previously published (peer-reviewed scientific journals) evaporation and crystallization physics into an easy-to-use graphical interface. The software will allow the user to specify a coating composition and drying environment, and then predict the evolution of the film as it dries. Potential applications include tracking the change in multi-solvent composition and solute concentration in solution-processed perovskites films, predicting the point of crystallization, and calculating equivalent processing parameters for transferring a process between different coating and drying methods. Github repo: https://github.com/NatLabRockies/SolVap Streamlit app: https://solvap.streamlit.app/

Starger, Jesse [National Laboratory of the Rockies↗