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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

The properties of the progenitor supernova, pulsar wind, and neutron star inside PWN G54.1+0.3

The evolution of a pulsar wind nebula (PWN) inside a supernova remnant (SNR) is sensitive to the properties of the central neutron star, pulsar wind, progenitor supernova, and interstellar medium. These properties are both difficult to measure directly and critical for understanding the formation of neutron stars and their interaction with the surrounding medium. In this paper, we determine these properties for PWN G54.1+0.3 by fitting its observed properties with a model for the dynamical and radiative evolution of a PWN inside an SNR. Our modeling suggests that the progenitor of G54.1+0.3 was an isolated ∼15–20 M{sub ⊙} star which exploded inside a massive star cluster, creating a neutron star initially spinning with a period of P{sub 0} ∼ 30–80 ms. We also find that ≳99.9% of the pulsar’s rotational energy is injected into the PWN as relativistic electrons and positrons whose energy spectrum is well characterized by a broken power law. Finally, we propose future observations which can both test the validity of this model and better determine the properties of this source—in particular, its distance and the initial spin period of the central pulsar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Mechanical properties of metal dihydrides

First-principles calculations are used to characterize the bulk elastic properties of cubic and tetragonal phase metal dihydrides, $\text{M}{{\text{H}}_{2}}$ {$\text{M}$ = Sc, Y, Ti, Zr, Hf, lanthanides} to gain insight into the mechanical properties that govern the aging behavior of rare-earth di-tritides as the constituent 3 H, tritium, decays into 3 He. As tritium decays, helium is inserted in the lattice, the helium migrates and collects into bubbles, that then can ultimately create sufficient internal pressure to rupture the material. The elastic properties of the materials are needed to construct effective mesoscale models of the process of bubble growth and fracture. Dihydrides of the scandium column and most of the rare-earths crystalize into a cubic phase, while dihydrides from the next column, Ti, Zr, and Hf, distort instead into the tetragonal phase, indicating incipient instabilities in the phase and potentially significant changes in elastic properties. We report the computed elastic properties of these dihydrides, and also investigate the off-stoichiometric phases as He or vacancies accumulate. As helium builds up in the cubic phase, the shear moduli greatly soften, converting to the tetragonal phase. Conversely, the tetragonal phases convert very quickly to cubic with the removal of H from the lattice, while the cubic phases show little change with removal of H. Finally, the source and magnitude of the numerical and physical uncertainties in the modeling are analyzed and quantified to establish the level of confidence that can be placed in the computational results, and this quantified confidence is used to justify using the results to augment and even supplant experimental measurements.

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

Control of core–shell nanoparticles properties through plasma synthesis: a computational study

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Combined crystal plasticity and grain boundary modeling of creep in ferritic-martensitic steels: II. The effect of stress and temperature on engineering and microstructural properties

This paper describes a series of physically-based crystal plasticity finite element method (CPFEM) simulations of long-term creep and creep rupture of Grade 91 steel. It is Part 2 of a two part series of papers. Part 1 describes the simulation framework; this part focuses on specific simulations and on how the predicted long-term creep properties of Grade 91 compare to the assumptions used in current high temperature design practices. This work extends the model developed in Part 1 to look at creep properties at different temperatures, principal stresses, and multiaxial stress states. The simulations show that empirically extrapolating creep rupture stresses from short-term experimental data may substantially over predict the actual long-term creep properties of Grade 91. Additionally, the CPFEM calculations predict a transition from notch strengthening creep behavior for high values of maximum principal stress and moderate notch severity to notch weakening behavior for low principal stresses and more severe notches. The latter regime better categorizes conditions in engineering components designed for long term elevated temperature use, which implies Grade 91 may be a notch weakening material in actual service. This would have a significant impact on high temperature design practices, though confirmatory test data on long-life, low stress notched specimens is difficult to obtain. Finally, one advantage of the physically-based modeling approach adopted here is that the simulation results also elucidate the microstructural mechanisms causing the macroscopic trends in engineering properties predicted by the simulations. This paper shows that the detailed micromechanical mechanisms predicted by the CPFEM simulations can be abstracted with a simple micromechanical model that can be used to both explain the detailed results and make improved predictions of engineering properties from experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning the properties of inorganic perovskites

We report the ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of CH 4 addition on the electron properties and electric field dynamics in a Ar nanosecond-pulsed dielectric barrier discharge

Non-equilibrium plasmas derive their low temperature reactivity from producing and driving energetic electrons and active species under large electric fields. Therefore, the impact of reactants on the plasma properties including electron number density, electric field, and electron temperature is critical for applications such as plasma CH 4 reforming. Due to experimental complexity, electron properties and the electric field are rarely measured together in the same discharge. Here, we combine time-resolved Thomson scattering and electric field induced second harmonic generation (EFISH) to probe electron temperature, electron density, and electric field strength in a 60 Torr CH 4 /Ar nanosecond-pulsed dielectric barrier discharge (ns-DBD) while varying the CH 4 mole fraction from 0 to 8%. These measurements are compared to a 1-D numerical model to benchmark its predictions and identify areas of uncertainty. Nonlinear coupling between CH 4 addition, electron temperature, electron density, and the electric field was directly observed. Contrary to previous measurements in He, the electron temperature increased with CH 4 mole fraction. This rise in electron temperature is identified as electron heating by residual electric fields that increased with larger CH 4 mole fraction. Moreover, the electron number density has been found to decrease rapidly with the increase of methane mole fraction. Comparison of these measurements with the model yielded better agreement at higher CH 4 mole fractions and with the usage of ab initio calculated Ar electron-impact cross-sections from the B-spline R-matrix (BSR) database. Furthermore, the calculated plasma properties are shown to be sensitive to the residual surface charge implanted on the quartz dielectric surfaces. Without considering surface charge in the simulations, the calculated electric field profiles agreed well with the measurements, but the electron properties were underpredicted by more than a factor of three. Therefore, measurements of either the electric field or electron properties measurements alone are insufficient to fully validate modelling predictions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Observational measures of halo properties beyond mass

Different properties of dark matter haloes, including growth rate, concentration, interaction history, and spin, correlate with environment in unique, scale-dependent ways. While these halo properties are not directly observable, galaxies will inherit their host haloes’ correlations with environment. In this paper, we show how these characteristic environmental signatures allow using measurements of galaxy environment to constrain which dark matter halo properties are most tightly connected to observable galaxy properties. Here, we show that different halo properties beyond mass imprint distinct scale-dependent signatures in both the galaxy two-point correlation function and the distribution of distances to galaxies’ kth nearest neighbours, with features strong enough to be accessible even with low-resolution (e.g. grism) spectroscopy at higher redshifts. As an application, we compute observed two-point correlation functions for galaxies binned by half-mass radius at $z$ = 0 from the Sloan Digital Sky Survey, showing that classic galaxy size models (i.e. galaxy size being proportional to halo spin) as well as other recent proposals show significant tensions with observational data. We show that the agreement with observed clustering can be improved with a simple empirical model in which galaxy size correlates with halo growth.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

No redshift evolution in the rest-frame ultraviolet emission line properties of quasars from z = 1.5 to z = 4.0

We analyse the rest-frame ultraviolet (UV) spectra of 2531 high-redshift (3.5 < z < 4.0) quasars from the Sloan Digital Sky Survey DR16Q catalogue. In combination with previous work, we study the redshift evolution of the rest-frame UV line properties across the entire redshift range, 1.5 < z < 4.0. We improve the systemic redshift estimates at z > 3.5 using a cross-correlation algorithm that employs high signal-to-noise template spectra spanning the full range in UV emission line properties. We then quantify the evolution of C iv and He ii emission line properties with redshift. The increase in C iv blueshifts with cosmological redshift can be fully explained by the higher luminosities of quasars observed at high redshifts. We recover broadly similar trends between the He ii equivalent width and C iv blueshift at both 1.5 < z < 2.65 and 3.5 < z < 4.0 suggesting that the blueshift depends systematically on the spectral energy density (SED) of the quasar and there is no evolution in the SED over the redshift range 1.5 < z < 4.0. C iv blueshifts are highest when L/L Edd ≥ 0.2 and M BH ≥ 10 9 M ⊙ for the entire 1.5 < z < 4.0 sample. We find that luminosity matching samples as a means to explore the evolution of their rest-frame UV emission line properties is only viable if the samples are also matched in the M BH –L/L Edd plane. Quasars at z ≥ 6 are on average less massive and have higher Eddington-scaled accretion rates than their luminosity-matched counterparts at 1.5 < z < 4.0, which could explain the observed evolution in their UV line properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Linking transients to their host galaxies – II. A comparison of host galaxy properties and rate dependencies across supernova types

ABSTRACT We use the latest data set of supernova (SN) host galaxies to investigate how the host properties – stellar mass, star formation rate (SFR), metallicity, absolute magnitude, and colour–differ across SN types, with redshift-driven selection effects controlled. SN Ib and Ic host galaxies, on average, are more massive, metal-rich, and redder than SN II hosts. For subtypes, SN Ibn and Ic-BL have bluer hosts than their normal SN Ib and Ic siblings; SN IIb has consistent host properties with SN Ib, while hosts of SN IIn are more metal-rich than those of SN II. Hydrogen-deficient superluminous SNe feature bluer and lower luminosity hosts than most subtypes of core-collapse supernova (CC SN). Assuming simple proportionality of CC SN rates and host SFRs does not recover the observed mean host properties; either a population of long-lived progenitors or a metallicity-dependent SN production efficiency better reproduces the observed host properties. Assuming the latter case, the rates of SN II are insensitive to host metallicity, but the rates of SN Ib and Ic are substantially enhanced in metal-rich hosts by a factor of $\sim 10$ per dex increase in metallicity. Hosts of SN Ia are diverse in their observed properties; subtypes including SN Ia-91T, Ia-02cx, and Ia-CSM prefer star-forming hosts, while subtypes like SN Ia-91bg and Ca-rich prefer quiescent hosts. The rates of SN Ia-91T, Ia-02cx, and Ia-CSM are closely dependent on, or even proportional to, their host SFRs, indicating relatively short-lived progenitors. Conversely, the rates of SN Ia-91bg and Ca-rich transients are proportional to the total stellar mass, favouring long-lived progenitors.

Qin, Yu-Jing (ORCID:0000000336586026)↗
DOE OSTI2021

Thermodynamic properties of ε -Fe with thermal electronic excitation effects on vibrational spectra

The thermodynamic properties of hexagonal-close-packed iron (ε–Fe) are essential for investigating the internal structure and dynamic properties of planetary cores. Despite their importance to planetary sciences, experimental investigations of ε–Fe at relevant conditions are still challenging. Therefore, ab initio calculations are crucial to elucidating the thermodynamic properties of this system. Here, we use a free energy calculation scheme based on the phonon gas model compatible with temperature-dependent phonon frequencies. We investigate the effects of electronic thermal excitations, which introduces a temperature dependence on phonon frequencies, and the implication for the thermodynamic properties of ε–Fe at extreme pressure (P) and temperature (T) conditions. We disregard phonon-phonon interactions, i.e., anharmonicity and their effect on phonon frequencies. Nevertheless, the current scheme is also applicable to T -dependent anharmonic frequencies. We conclude that the impact of thermal electronic excitations on vibrational properties is not significant up to ~4000 K at 200 GPa but should not be ignored at higher temperatures or pressures. However, the static free energy F st must always include the effect of thermal excitation fully in a continuum of T. Furthermore, our results for isentropic equations of state show good agreement with data from recent ramp compression experiments up to 1400 GPa conducted at the National Ignition Facility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Correlating the properties of amorphous silicon with its flexibility volume

For metallic glasses, “flexibility volume” has recently been introduced as a property-revealing indicator of the structural state the glass is in. This parameter incorporates the atomic volume and the vibrational mean-square displacement, to combine both static structure and dynamics information. Flexibility volume was shown to quantitatively correlate with the properties of metallic glasses. However, it remains to be examined if this parameter is useful for other types of glasses with bonding characteristics, atomic packing structures, as well as properties that are distinctly different from metallic glasses. In this paper, we tackle this issue through systematic molecular-dynamics simulations of amorphous silicon (a-Si) models produced with different cooling rates, as a-Si is a prototypical covalently bonded network glass whose structure and properties cannot be characterized using structural parameters such as free volume used for metallic and polymeric glasses. Specifically, we demonstrate a quantitative prediction of the shear modulus of a-Si from the flexibility for atomic motion. This flexibility volume descriptor, when evaluated on the atomic scale, is shown to also correlate well with local packing, as well as with the propensity for thermal relaxations and shear transformations, providing a metric to map out and explain the structural and mechanical heterogeneity in the amorphous material. Overall, this case study of a model of covalently bonded network a-Si, together with our earlier demonstration for metallic glasses, points to the universality of flexibility volume as an indicator of the structure state to link with properties, applicable across amorphous materials with different chemical bonding and atomic packing structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Anomalous random correlations of force constants on the lattice dynamical properties of disordered Au-Fe alloys

Here, gold iron (Au-Fe) alloys are of immense interest due to their biocompatibility, anomalous Hall conductivity, and applications in various medical treatments. However, irrespective of the method of preparation, they often exhibit a high level of disorder with properties sensitive to the thermal or magnetic annealing temperatures. We calculate the lattice dynamical properties of Au 1–x Fe x alloys using density functional theory methods where, being multisite properties, reliable interatomic force constant (IFC) calculations in disordered alloys remain a challenge. We follow a twofold approach: an accurate IFC calculation in an environment with nominally zero chemical pair correlations to mimic the homogeneously disordered alloy and a configurational averaging for the desired phonon properties (e.g., dispersion, density of states, and entropy). We find an anomalous change in the IFC's and phonon dispersion (split bands) near x=0.19, which is attributed to the local stiffening of the Au-Au bonds when Au is in the vicinity of Fe. Other results based on mechanical and thermophysical properties reflect a similar anomaly: Phonon entropy, e.g., becomes negative below x=0.19, suggesting a tendency for chemical unmixing, reflecting the onset of a miscibility gap in the phase diagram. Our results match fairly well with reported data wherever available.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Ground-State Properties of Unitary Bosons: From Clusters to Matter

The properties of cold Bose gases at unitarity have been extensively investigated in the last few years both theoretically and experimentally. In this paper we use a family of interactions tuned to two-body unitarity and very weak three-body binding to demonstrate the universal properties of both clusters and matter. We determine the universal properties of finite clusters up to 60 particles and, for the first time, explicitly demonstrate the saturation of energy and density with particle number and compare with bulk properties. At saturation in the bulk we determine the energy, density, two- and three-body contacts, and the condensate fraction. We find that uniform matter is more bound than three-body clusters by nearly 2 orders of magnitude, the two-body contact is very large in absolute terms, and yet the condensate fraction is also very large, greater than 90%. Finally, equilibrium properties of these systems may be experimentally accessible through rapid quenching of weakly interacting boson superfluids.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Preparation, mechanical properties, and toughening mechanisms of SiCw/SiCp‐reinforced zirconia‐toughened alumina ceramics

ABSTRACT Zirconia‐toughened alumina (ZTA) ceramics with high mechanical properties were sintered by hot‐pressing method using SiC particles (SiCp) and SiC whiskers (SiCw) as the reinforcing agents simultaneously. The influences of sintering temperature, SiCp, and SiCw contents on the microstructure and mechanical properties of ZTA ceramics were investigated. It was found that both SiCp and SiCw could contribute to grain refinement significantly and promote the mechanical properties of the ceramics. However, the excess addition of SiCp or SiCw led to the formation of pores with large sizes and degraded the mechanical properties instead. When 13 wt% SiCp was introduced, the maximum flexural strength of 1180.0 MPa and fracture toughness of 15.9 MPa·m 1/2 were obtained, whereas the maximum flexural strength of 1314.0 MPa and fracture toughness of 14.7 MPa·m 1/2 were achieved at 20 wt% SiCw. Interestingly, the simultaneous addition of SiCp and SiCw could further improve the mechanical properties, and the highest flexural strength of 1334.0 MPa and fracture toughness of 16.0 MPa·m 1/2 were achieved at a SiCw/SiCp ratio of 16/4. The reinforcement mechanisms in the ceramics mainly included the phase transformation toughening of ZrO 2 , the crack deflection and bridging of SiCp and SiCw, and the pull‐out of SiCw.

Liu, Longyang↗
DOE OSTI2023

Prediction and Validation of Flow Properties in Porous Lattice Structures

High-porosity metal foams have been extensively studied as an attractive candidate for efficient and compact heat exchanger design. With the advancements in additive manufacturing, such foams can be manufactured with controlled topology to yield highly tailorable mechanical and transport properties. In this study, a lattice Boltzmann method (LBM)-based pore-scale model is implemented to simulate the fluid flow in additively manufactured (AM) metal foams with unit cell topologies of Cube, Face Diagonal (FD)-Cube, Tetrakaidecahedron (TKD), and Octet lattices. The pressure gradient versus average velocity profiles predicted by the LBM model were validated against in-house measurements on the AM lattice samples with the same unit cell topologies. Based on the simulation results, a novel hybrid model is proposed to accurately predict the volume averaged flow properties (permeability and inertial coefficients) of the four structures. Specifically, the linear LBM (neglecting inertial forces) is first implemented to obtain the intrinsic permeability, and then the standard LBM is applied to obtain the inertial coefficient. Convenient correlations for those flow properties as a function of porosity and fiber diameter are constructed. The effects of the AM print qualities on the flow properties are also discussed. The advantages of the hybrid model compared to the polynomial fitting approach for determining flow properties are discussed and compared quantitatively. The hybrid model and presented results are valuable for flow and thermal transport evaluation when designing new metal foams for specific applications and with different materials and topologies. Finally, the presented correlations based on pore-scale simulations can also be conveniently used in volume-averaged models to predict the macroscale flow behavior in such complex structures.

42 ENGINEERING↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗
DOE OSTI2020

ASSESSING FUEL PROPERTY EFFECTS ON CAVITATION AND EROSION PROPENSITY USING A COMPUTATIONAL FUEL SCREENING TOOL

To advance compression ignition combustion strategies, researchers have evaluated fuel property effects and their impact on achieving higher efficiencies and lower emissions levels relative to current capabilities. Within the Department of Energy's Co-Optima initiative, there has been a recent focus on understanding the influence of fuel properties on fuel injection performance. To help identify candidate fuels that can meet desired injector performance metrics, a computational fuel screening tool is under development that can link fuel properties with the tendency of a given fuel to cavitate and lead to cavitation-induced erosion. In the initial development of this tool, five liquid fuel properties were selected to represent candidate fuels, namely density, viscosity, vapor pressure, surface tension, and heat of vaporization. A design of experiments methodology was employed to generate a set of pseudo fuel cases, which can represent the main effects and interactions among the fuel properties and be related to cavitation erosion predictions. Large eddy simulations were performed using a mixture modeling approach to predict the cavitation and erosion propensity of these pseudo-fuels in terms of the mean fuel vapor mole fraction and stored impact energy from repeated cloud collapse events. The low order regression models generated from this study revealed the importance of liquid fuel density on cavitation formation, whereas liquid viscosity was found to have a strong negative correlation with erosion severity. The surrogate models were then used in the fuel screening tool to rank the cavitation and erosion tendency of four candidate single-component fuels: methyl decanoate, isooctane, ethanol and n-dodecane. While the fuel screening tool was not able to quantitatively predict the cavitation and erosion response metrics, the tool was able to accurately rank the relative cavitation and erosion propensity of the four fuels. Overall, ethanol and iso-octane were observed to produce the highest levels of cavitation and erosion, respectively.

cavitation-induced erosion↗