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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Compressing Hamiltonians with ab initio downfolding for simulating strongly-correlated materials on quantum computers

The accurate first-principles description of strongly correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly correlated materials. The solution of these material-specific models is still exponentially difficult to generate on classical computers, but quantum algorithms allow for a significant speed-up in obtaining the ground states of these compressed Hamiltonians. Here, we demonstrate that using quantum algorithms to obtain the properties of downfolded Hamiltonians can indeed yield high-fidelity solutions. By combining ab initio downfolding and variational quantum eigensolvers, we correctly predict the antiferromagnetic state of one-dimensional cuprate Ca 2 Cu O 3 , the excitonic ground state of monolayer W Te 2 , and the charge-ordered state of correlated metal Sr VO 3 . Numerical simulations using a classical tensor network implementation of variational quantum eigensolvers allow us to simulate large models with up to 54 qubits and encompassing up to four bands in the correlated subspace, which is indicative of the complexity that our framework can address. Through these methods we demonstrate the potential of classical preoptimization and downfolding techniques for enabling efficient materials simulation using quantum algorithms.

Alvertis, Antonios M. [NASA, Ames; LBNL, Berkeley]↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2023

The Predictive Power of Exact Constraints and Appropriate Norms in Density Functional Theory

Ground-state Kohn-Sham density functional theory provides, in principle, the exact ground-state energy and electronic spin densities of real interacting electrons in a static external potential. In practice, the exact density functional for the exchange-correlation (xc) energy must be approximated in a computationally efficient way. About 20 mathematical properties of the exact xc functional are known. In this work, we review and discuss these known constraints on the xc energy and hole. By analyzing a sequence of increasingly sophisticated density functional approximations (DFAs), we argue that (a) the satisfaction of more exact constraints and appropriate norms makes a functional more predictive over the immense space of many-electron systems and (b) fitting to bonded systems yields an interpolative DFA that may not extrapolate well to systems unlike those in the fitting set. We discuss both how the class of well-described systems has grown along with constraint satisfaction and the possibilities for future functional development.

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

Modeling Dust Production, Growth, and Destruction in Reionization-era Galaxies with the CROC Simulations. II. Predicting the Dust Content of High-redshift Galaxies

Abstract We model the interstellar dust content of the reionization era with a suite of cosmological, fluid-dynamical simulations of galaxies with stellar masses ranging from ∼10 5 to 10 9 M ⊙ in the first 1.2 Gyr of the Universe. We use a post-processing method that accounts for dust creation and destruction processes, allowing us to systematically vary the parameters of these processes to test whether dust-dependent observable quantities of galaxies at these epochs could be useful for placing constraints on dust physics. We then forward model observable properties of these galaxies to compare to existing data. We find that we are unable to simultaneously match existing observational constraints with any one set of model parameters. Specifically, the models that predict the largest dust masses D / Z ≳ 0.1 at z = 5—because of high assumed production yields and/or efficient growth via accretion in the interstellar medium—are preferred by constraints on total dust mass and infrared (IR) luminosities, but these models produce far too much attenuation in the ultraviolet (UV), preventing them from matching observations of β UV . To investigate this discrepancy, we analyze the relative spatial distribution of stars and dust as probed by IR and UV emission, which appear to exhibit overly symmetric morphologies compared to existing data, likely due to the limitations of the stellar feedback model used in the simulations. Our results indicate that the observable properties of the dust distribution in high redshift galaxies are a particularly strong test of stellar feedback.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Delay-resolved spectroscopy in terahertz photonic circuits

Photonic integrated circuits incorporating intersubband transitions are ideal for mid-infrared and terahertz nanophotonics. However, the design of epitaxies has long been inhibited by two factors: the modest predictivity of ab initio theory and the absence of absolute intersubband gain and loss measurements under operating conditions. Existing measurements either yield inaccurate gain profiles or do not accurately assess dependence on frequency, bias, and temperature. Here, we present a delay-resolved absolute-referencing method for accurate gain evaluation without these limitations, addressing a long-standing challenge. By creating a photonic circuit that allows broadband pulses to traverse different lengths of a gain medium, we measure the absolute transmission of intersubband structures. Gain profiles match theoretical predictions at lower temperatures, with gain and dispersion clamping after lasing, and faster-than-expected degradation occurs at higher temperatures. Our approach provides a precise experimental evaluation of temperature-dependent gain performance and gives insight into optimizing temperature performance and frequency comb designs.

Optical spectroscopy↗
DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗
DOE OSTI2023

Predictive models of long COVID

Background: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models — logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the ‘long COVID’ label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). Findings: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. Interpretation: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

$\overline{\Sigma }^{\pm }$ production in $\text {pp}$ and $\text {p}{-}\text{Pb}$ collisions at $\sqrt{s_{\textrm{NN}}} = 5.02$ TeV with ALICE

The transverse momentum spectra and integrated yields of anti-$Σ$ hyperons ($\overline{\Sigma}^{\pm}$) have been measured in and collisions at $\sqrt{s_{\textrm{NN}}} = 5.02$ TeV with the ALICE experiment. Measurements are performed via the newly accessed decay channel $\overline{\Sigma}^{\pm}$ → $\bar{\textrm{n}}π^±$. A new method of antineutron reconstruction with the PHOS electromagnetic spectrometer is developed and applied to this analysis. The p T spectra of $\overline{\Sigma}^{\pm}$ are measured in the range 0.5 < p T < 3 GeV/c and compared to predictions of the PYTHIA 8, DPMJET, PHOJET, EPOS LHC and EPOS4 models. The EPOS LHC and EPOS4 models provide the best descriptions of the measured spectra both in pp and p-Pb collisions, while models which do not account for multiparton interactions provide a considerably worse description at high p T . The total yields of $\overline{\Sigma }^{\pm }$ in both pp and p-Pb collisions are compared to predictions of the Thermal-FIST model and dynamical models PYTHIA 8, DPMJET, PHOJET, EPOS LHC and EPOS4. All models reproduce the total yields in both colliding systems within uncertainties. The nuclear modification factors R pPb for both $\overline{\Sigma}^{+}$ and $\overline{\Sigma}^{-}$ are evaluated and compared to those of protons, $Λ$ and $Ξ$ hyperons, and predictions of EPOS LHC and EPOS4 models. No deviations of R pPb for $\overline{\Sigma}^{\pm}$ from the model predictions or measurements for other hadrons are found within uncertainties.

Abualrob, I. J. [University of Houston] (ORCID:000↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2021

Evaluating the grain-scale deformation behavior of a single-phase FCC high entropy alloy using synchrotron high energy diffraction microscopy

We report although the deformation behavior of high-entropy alloys (HEAs) has been extensively studied at the macroscale, many important properties have yet to be explored for these alloys at the microscale, thus hampering accurate prediction of damage and failure processes. Synchrotron high-energy diffraction microscopy (HEDM) and fast-Fourier transform-based crystal plasticity modeling was conducted to investigate the three-dimensional (3D) grain-resolved micromechanical response for approximately 1,900 constituent grains within a single-phase FCC HEA up to 1% applied strain. The evolution of grain-resolved elastic strains, lattice reorientations, and maximum resolved shear stresses (mRSS) were evaluated to quantify elastic, yield, and fully plastic behavior. Overall, the initial critical resolved shear stress (CRSS), determined via in situ HEDM and companion modeling, was found to be > 20% higher than estimated using the classical polycrystalline Taylor factor (M = 3.06). However, a descriptive parameter based on the average grain-resolved Taylor factor ($\overline{M}$) was found to show excellent agreement with plastic yielding behavior observed within HEDM datasets. Noticeable deviations in HEDM lattice reorientations compared to both EVP-FFT simulations and classical predictions for FCC polycrystals were discovered, highlighting the complexity in correlating local lattice reorientations, Taylor, and Schmid factors with plastic response for this material at the grain-scale. Therefore, it is anticipated that the overall trends and parameter identification of 3D grain-resolved properties in this study can serve as an important foundation for continued mesoscale investigation on both well-established and newly developed Cantor-like HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Technical and Economic Evaluation of the First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope via Machine Assisted History Matching

Polymer flooding has become globally established as a potential enhanced oil recovery method for heavy oils. To determine whether this technology may be useful in developing the substantial heavy oil resources on the Alaska North Slope, a polymer flood field pilot commenced at the Milne Point Unit in August 2018. This study seeks to evaluate the results of the field pilot on a technical and economic basis. A reservoir simulation model is constructed and calibrated to predict the oil recovery performance of the pilot through machine-assisted reservoir simulation techniques. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are introduced into the simulation model, forcing viscous fingering effects. In the ensuing polymer flood, these transmissibility contrasts are reduced to replicate the restoration of injection conformance during polymer flooding. Transmissibility contrasts are later reinstated to replicate fracture overextension interpreted in one of the producing wells. The calibrated simulation models produced at each stage of the history matching process are used to forecast oil recovery. These forecasts are used as input for economic analysis, incremental to waterflooding expectations. The simulation forecasts indicate that polymer flooding significantly increases the heavy oil production for this field pilot compared to waterflooding alone, yielding attractive project economics. However, meaningful variations between simulation scenarios demonstrate that a simulation model is only valid for prediction if flow behavior in the reservoir remains consistent with that observed during the history matched period. Critically, this means that a simulation model calibrated for waterflooding may not fully capture the technical and economic benefits of an enhanced oil recovery process such as polymer flooding. Subsequently, the simulation model and economic model are used in conjunction to conduct a sensitivity analysis for polymer flood design parameters, from which recommendations are provided for both the continued operation of the current field pilot and future polymer flood designs. The results demonstrate that a higher polymer concentration can be injected due to the development of fractures in the reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter demonstrates the decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding.

Keith, Cody↗
DOE OSTI2020

Favorable Redox Thermodynamics of SrTi 0.5 Mn 0.5 O 3–δ in Solar Thermochemical Water Splitting

Two-step, solar thermochemical splitting of water using nonstoichiometric redox-active metal oxides has emerged as an intriguing approach for large-scale hydrogen production. Perovskites have been proposed as alternatives to state-of-the-art fluorite CeO 2–δ because of their potential for lowering reduction temperature while maintaining high fuel productivity. Guided by computational insights, we explore the thermodynamic properties and water splitting efficacy of the cubic perovskite SrTi 0.5 Mn 0.5 O 3–δ (STM55). Thermogravimetric analysis is performed under controlled oxygen partial pressures (pO 2 ) and temperatures up to 1500 °C, from which both the enthalpy and entropy of reduction as a function of oxygen nonstoichiometry are determined. STM55 provides an attractive combination of moderate enthalpy, 200–250 kJ (mol-O) –1 , and high entropy, with unusual δ dependence. Using a water splitting cycle in which the material is thermally reduced at 1350 °C (pO 2 , ~10 –5 atm) and subsequently exposed to steam at 1100 °C (steam partial pressure of pH 2 O = 0.4 atm), we demonstrate a hydrogen yield of 7.4 mL g –1 . Through both half-cycles, the material remains largely in quasi-equilibrium with the gas phase, as reflected in the agreement of the measured data with predicted profiles based on the thermodynamic data. This behavior indicates rapid surface and bulk diffusion kinetics. Cyclic operation showed the material to be free of degradation and always resulted in a 2:1 yield of H 2 /O 2 . Altogether, STM55 provides outstanding performance characteristics for thermochemical hydrogen production.

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

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
DOE OSTI2026

Energetics of the nucleation and glide of disconnection modes in symmetric tilt grain boundaries

Grain boundaries (GBs) evolve by the nucleation and glide of disconnections, which are dislocations with a step character. In this work, motivated by recent success in predicting GB properties such as the shear coupling factor and mobility from the intrinsic properties of disconnections, we develop a systematic method to calculate the energy barriers for the nucleation and glide of individual disconnection modes under arbitrary driving forces and a quasi-2D setting. This method combines tools from bicrystallography to enumerate disconnection modes and the Nudged elastic band (NEB) method to calculate their energetics, yielding minimum energy paths and atomistic mechanisms for the nucleation and glide of each disconnection mode. We apply the method to accurately predict shear coupling factors of $[001]$ symmetric tilt grain boundaries in Cu. Particular attention is paid to the boundaries where the dislocation-based disconnection nucleation model produces incorrect nucleation barriers. We demonstrate that the method can accurately compute energy barriers and predict shear-coupling factors in the low-temperature regime. For certain disconnection modes in which the assumptions underlying our method do not hold, we report upper bounds on the energy barriers for disconnection nucleation and glide. In addition, the NEB trajectories reveal interesting phenomena such as the dissociation of a higher energy mode into lower energy modes, and in some cases, shear coupling being mediated by partial disconnections, wherein the GB structure temporarily changes to a metastable state before reverting back to its original structure. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2020

Programmable Anisotropy and Percolation in Supramolecular Patchy Particle Gels

Patchy particle interactions are predicted to facilitate the controlled self-assembly and arrest of particles into phase-stable and morphologically tunable "equilibrium" gels, which avoids the arrested phase separation and subsequent aging that is typically observed in traditional particle gels with isotropic interactions. Despite these promising traits of patchy particle interactions, such tunable equilibrium gels have yet to be realized in the laboratory due to experimental limitations associated with synthesizing patchy particles in high yield. Here, we introduce a supramolecular metal-coordination platform consisting of metallic nanoparticles linked by telechelic polymer chains, which validates the predictions associated with patchy particle interactions and facilitates the design of equilibrium particle hydrogels through limited valency interactions. We demonstrate that the interaction valency and self-assembly of the particles can be effectively controlled by adjusting the relative concentration of polymeric linkers to nanoparticles, which enables the gelation of patchy particle hydrogels with programmable local anisotropy, morphology, and low mechanical percolation thresholds. Moreover, by crowding the local environment around the patchy particles with competing interactions, we introduce an independent method to control the self-assembly of the nanoparticles, thereby enabling the design of highly anisotropic particle hydrogels with substantially reduced percolation thresholds. We thus establish a canonical platform that facilitates multifaceted control of the self-assembly of the patchy nanoparticles en route to the design of patchy particle gels with tunable valencies, morphologies, and percolation thresholds. These advances lay important foundations for further fundamental studies of patchy particle systems and for designing tunable gel materials that address a wide range of engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Yield Estimation of the August 2020 Beirut Explosion by Using Physics‐Based Propagation Simulations of Regional Infrasound

Abstract The August 2020 Beirut explosion is the largest single‐fired ammonium nitrate explosion documented in history. The massive explosion excited loud infrasound in the atmosphere, and clear waveforms were recorded by a regional infrasound array at an epicentral distance of 100 km, allowing for accurate measurements of explosion energy. We estimate the explosion size based on the infrasound waveform inversion. Unlike conventional inversions using empirical models, we perform full 3‐D finite‐difference simulations to obtain a physics‐based propagation model for the inversion. Accurate numerical modeling of infrasound is challenging as the propagation is substantially affected by the turbulent atmosphere. Instead of a single deterministic prediction, we provide a range of waveform predictions by running multiple simulations with stochastic weather forecast models, which allows for comprehensive uncertainty analysis of numerical modeling and estimated yields. Finally, we expand the yield estimation technique for seismoacoustic analysis and demonstrate the substantial advantage of the joint approach.

58 GEOSCIENCES↗
DOE OSTI2024

Critical Shear Stresses for Erosion from Yield Stresses

The critical shear stress for erosion is important in many fields of engineering and science that involve suspension of particles from settled particle beds. However, determining the critical shear stress for erosion remains intricate, particularly where interparticle forces play a key role. Because these forces vary widely (Pease, et al., 2019), a more convenient means of estimating the critical shear stress for erosion would be advantageous. Here we evaluate the assertion that critical shear stresses for erosion may be derived from yield stresses for nominally cohesive particle beds across the full range of particle Reynolds numbers. The critical shear stress for erosion for non-cohesive particle beds may be estimated from the Shields diagrams or the analytical expressions from conservation of force about a particle at the interface. However, equivalent diagrams for cohesive particle beds have remained elusive. In 1992, pioneering analysis by Dade included the influence of the van der Waals force in particle forces balances to show that the critical shear stresses for erosion may be related to yield stresses subject to the limitations of small particle Reynolds numbers, flat particle beds, uniform particle diameters, and negligible electrostatic forces. Here we relax these restrictions. We find that the critical shear stress for erosion does relate to the yield stresses in a linear fashion for sufficiently large yield stresses. A criterion is developed to determine when the yield stresses may be important. Pease LF, AJ Fuher, JA Bamberger, and MJ Minette. 2024. Shields Diagrams for Cohesive Particle Beds. Manuscript. Dade WB, ARM Nowell and PA Jumars. 1992. “Predicting erosion resistance of muds.” Marine Geology, 105 (1992) 285-297.

shear stress, erosion, yield stress, slurry, settl↗