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DOE OSTI2024

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Mathematics↗

DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Spatially varying colloidal phase behavior on multi-dimensional energy landscapes

A method is reported to determine equilibrium concentration profiles and local phase behavior for colloids on multi-dimensional energy landscapes. A general expression is derived based on local particle concentration and osmotic pressure differences that are balanced by forces on colloids due to energy landscape gradients. This analysis is applied to colloidal particles in high frequency AC electric fields within octupolar electrodes, where the energy landscape can be shaped in two dimensions. These results are also directly applicable to any particles having induced dipoles in spatially non-uniform electromagnetic fields. Predictions based on modelling colloids with an effective hard disk equation of state indicate inhomogeneous solid and fluid states coexisting on different shaped energy landscapes including multiple minima. Model predictions show excellent agreement with time-averaged Brownian Dynamic simulations at equilibrium. Findings demonstrate a general approach to understand colloidal phase behavior on energy landscapes due to external fields, which could enable control of colloidal microstructure on morphing energy landscapes and the inverse design of fields to assemble hierarchically structured colloidal materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Density matrix renormalization group description of the island of inversion isotopes F 28 – 33

Recent experiments have confirmed that the neutron-rich isotopes 28,29 F belong to the so-called island of inversion (IOI), a region of the nuclear chart around Z = 10 and N = 20 where nuclear structure deviates from the standard shell model predictions due to deformation and continuum effects. However, while the general principles leading to the IOI are relatively well understood, the details of the low-lying structure of the exotic fluorine isotopes 28–33 F are basically unknown. In this study, we perform large-scale shell model calculations including continuum states to investigate the properties of the neutron-rich isotopes 25–33 F, from a core of 24 O and using an effective two-body interaction with a small number of adjustable parameters in the central and tensor channels. We develop two models adjusted on experimentally confirmed states in 25,26 O and 25–27 F based on different assumptions concerning the positions of the neutron 0d 3/2 and 1p 3/2 shells, and solve the many-body problem using the density matrix renormalization group (DMRG) method for open quantum systems in an sd–fp model space. We obtain the first detailed spectroscopy of 25–33F in the continuum and show how the interplay between continuum effects and deformation explains the recent data on 28,29 F. Several deformed one- and two-neutron halo states are predicted in 29,31 F, and we provide some information about the possible structure of the heaviest fluorine isotopes. We also suggest several experimental studies of interest to constraint models and test the present predictions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Shape evolution in neutron-rich odd-even 105–109 Nb isotopes

Background: Neutron-rich nuclei around 𝑍 ≈ 40 are well known for exhibiting multiple shape transitions. Here, this region shows one of the sharpest shape transitions in the nuclear chart, evolving from a spherical vibrator at 𝑁 = 58 to a strongly deformed prolate shape at 𝑁 = 60. The largest deformations are observed for 38 Sr and 40 Zr . This abrupt shape transition disappears at 𝑍 = 36 and below, where a shape transition from spherical to oblate nuclei is predicted. On the other hand, for 𝑍 ≥ 42 and 𝑁 ≥ 60, the shape is known to evolve from axial to triaxial. While the even-𝑍 nuclei in this region have already been extensively studied, new insights can be gained from the studies of odd-𝑍 isotopes for a better understanding of the underlying mechanisms driving these phenomena. Purpose: The 41 Nb nuclei lie at the boundary between axially deformed Zr and triaxially deformed Mo nuclei. This work investigates the nuclear structure of very neutron-rich Nb nuclei up to 𝑁 = 68. The goal is to understand how the nuclear shape evolves as a function of isospin in this isotopic chain and provide new insights into the emergence of triaxial deformation. Methods: The structure of the neutron-rich Nb isotopes was investigated using state-of-the-art high-resolution 𝛾-ray spectroscopy of fission fragments produced via two different fission reactions. The use of 9 Be ⁢( 238 U, 𝑓) inverse kinematics, with a detection system comprising AGATA, EXOGAM, and VAMOS++, enabled the measurement of prompt and delayed 𝛾 rays from isotopically identified fission fragments, and 𝛾−𝛾−𝛾−𝛾 highfold data were obtained from a spontaneous fission source of 252 Cf using the Gammasphere array. Results: The level scheme of 105 Nb has been significantly extended, with the addition of two negative-parity bands observed for the first time. A new level scheme is proposed for 107 Nb, which is not in agreement with an earlier measurement, and new levels and transitions have been added to the very neutron-rich 109 Nb. The degree of triaxiality of the new bands is discussed on the basis of signature splitting analysis. The recently reported level scheme of 99 Nb has been revised. Conclusions: This systematic study on the Nb isotopic chain, compared to Zr and Mo, indicates that while the ground-state band exhibits a triaxial deformation, attributed to a proton hole coupled to a triaxially deformed Mo core, the negative-parity bands, based on isomeric bandheads, display an axially symmetric deformed structure, similar to that observed in the Zr isotopes, revealing the existence of a shape coexistence in the neutron-rich Nb nuclei.

Abushawish, M. [Université Claude-Bernard Lyon 1 (↗

DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗

DOE OSTI2020

Inverse Compton Cooling in the Coronae of Simulated Black Hole Accretion Flows

Here we present a formulation for a local cooling function to be employed in the diffuse, hot corona region of 3D GRMHD simulations of accreting black holes. This new cooling function calculates the cooling rate due to inverse Compton scattering by considering the relevant microphysics in each cell in the corona and approximating the radiation energy density and Compton temperature thereby integrating over the thermal seed photon flux from the disk surface. The method either assumes the ion and electron temperatures are equal (1T) or calculates them separately (2T) using an instantaneous equilibrium approach predicated on the actual relevant rate equations (Coulomb and Compton). The method is shown to be consistent with a more detailed ray-tracing calculation where the bulk of the cooling occurs, but is substantially less costly to perform. As an example, we apply these methods to a harm3d simulation of a 10M⊙, non-spinning black hole, accreting at nominally 1% the Eddington value. This new approach leads to radiative efficiency values sime65% above Novikov–Thorne, with a larger fraction of total cooling in the corona as compared to simulations performed using the original target-temperature cooling function. Time-averaged post-processing reveals that the continuum spectral observations predicted from these simulations are qualitatively similar to actual X-ray binary data, especially so for the 1T approach, which yields a harder power-law component (Γ = 2.25) compared to the 2T version (Γ = 2.53).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Effective permeability of an immiscible fluid in porous media determined from its geometric state

Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process or path dependent with an underlying dependency on pore structure and wettability. For applications (fuel cells to underground CO 2 storage), it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modeling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an artificial neural network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. Finally, the developed ANN is based on a prescribed set of state variables based on physical insights that predict the effective permeability of 4500 unseen pore-scale geometrical states with R 2 = 0.98.

42 ENGINEERING↗

DOE OSTI2025

Excited electronic states of Na 2 and K 2 : The potential for long-lived “reservoir” states leading to collision induced population inversions

Potential energy curves (PECs) for the spin-free (ΛS) and spin–orbit (Ω) states associated with the four lowest-lying dissociation channels of Na 2 and K 2 were calculated at the SA-CASSCF/SO-CASPT2/aug-cc-pwCVQZ-DK level. The PECs of Na 2 were consistent with the experimental data and with the FS-CCSD (2,0) calculations, reproducing the double-well and the “shelf” character for some of the potentials of the excited states. For K 2 , the PECs behaved in a similar way and the spectroscopic parameters for the ground and the excited states are in good agreement with the available experimental values. The dissociation energy of K 2 was predicted to be D e = 4454 cm −1 , within an agreement of 5 cm −1 with the experiments. For Na 2 , D e = 5789 cm −1 compared to the experimental value of 6022 cm −1 . The inclusion of spin–orbit coupling effects resulted in avoided crossings, which affect the PECs. Spin–orbit changes the predicted curves for some excited Ω states arising from ΛS states that overlap each other, affecting their associated vibrational frequencies and bond distances. Here, the current studies of the low-lying states in K 2 reveal a similar structure to those of Na 2 , which suggests the accessibility of long-lived energy storing reservoir states and possible population inversions in K 2 following prior experimental work on the reaction of halogen atoms with Na 3 to produce excited states of Na 2 .

Ab-initio methods↗

DOE OSTI2021

Feasibility of DEIM for retrieving the initial field via dimensionality reduction

When parameter estimation is solved in a high-dimensional space, the dimensionality reduction strategy becomes the primary consideration for alleviating the tremendous computational cost. Here, the discrete empirical interpolation method (DEIM) is explored to retrieve the initial condition (IC) by combining the polynomial chaos (PC) based ensemble Kalman filter (i.e. PC-EnKF), where a non-intrusive PC expansion is considered as a surrogate model in place of the forward model in the prediction step of the ensemble Kalman filter, resulting in fewer forward model integrations but with a comparable accuracy as Monte Carlo-based approaches. The DEIM acts as a hyper-reduction tool to provide the low-dimensional input for the high-dimensional initial field, which can be reconstructed using the information on the sparse interpolation grid points that is adaptively obtained through PC-EnKF data assimilation method. Thus an innovative framework to reconstruct the IC is developed. The detailed procedure at each assimilation iteration includes: the determination of the spatial interpolation points, the estimation of the initial values on the interpolation locations using the optimal observations, and the reconstruction of IC in the full space. The current study uses the reconstruction field of initial conditions of the Navier-Stokes equations as an example to illustrate the efficacy of our method. The experimental results demonstrate the proposed algorithm achieves a satisfactory reconstruction for the initial field. The proposed method helps to extend the applicable area of DEIM in solving inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites

Herein we present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The properties are inversely determined using only a few experimental tests. Developing composite manufacturing digital twins for SFRP composite processes, including injection molding and extrusion deposition additive manufacturing (EDAM) requires extensive experimental material characterization. In particular, characterizing the composite mechanical properties is time consuming and therefore, micromechanics models are used to fully identify the elasticity tensor. Hence, the objective of this paper is to infer the fiber orientation and the effective polymer modulus and therefore, identify the elasticity tensor of the composite with minimal experimental tests. To that end, we develop a hierarchical Bayesian model coupled with a micromechanics model to infer the fiber orientation and the polymer elastic modulus simultaneously which we then use to estimate the composite elasticity tensor. We motivate and demonstrate the methodology for the EDAM process but the development is such that it is applicable to other SFRP composites processed via other methods. Our results demonstrate that the approach provides a reliable framework for the inference, with as few as three tensile tests, while accounting for epistemic and aleatory uncertainty. Posterior predictive checks show that the model is able to recreate the experimental data well. The ability of the Bayesian approach to calibrate the material properties and its associated uncertainties, make it a promising tool for enabling a probabilistic predictive framework for composites manufacturing digital twins.

36 MATERIALS SCIENCE↗

DOE OSTI2024

How Does HF-DFT Achieve Chemical Accuracy for Water Clusters?

Bolstered by recent calculations of exact functional-driven errors (FEs) and density-driven errors (DEs) of semilocal density functionals in the water dimer binding energy , we investigate approximate FEs and DEs in neutral water clusters containing up to 20 monomers, charged water clusters, and alkali- and halide-water clusters. Our proxy for the exact density is r 2 SCAN 50, a 50% global hybrid of exact exchange with r 2 SCAN, which may be less correct than r 2 SCAN for the compact water monomer but importantly more correct for long-range electron transfers in the noncompact water clusters. We show that SCAN makes substantially larger FEs for neutral water clusters than r 2 SCAN, while both make essentially the same DEs. Unlike the case for barrier heights, these FEs are small in a relative sense and become large in an absolute sense only due to an increase in cluster size. SCAN@HF, short for SCAN evaluated on the Hartree–Fock (HF) density, produces a cancellation of errors that makes it chemically accurate for predicting the absolute binding energies of water clusters. Likewise, adding a long-range dispersion correction to r 2 SCAN@HF, as in the composite method HF-r 2 SCAN-DC4, makes its FE more negative than in r 2 SCAN@HF, permitting a near-perfect cancellation of FE and DE. r 2 SCAN by itself (and even more so, r 2 SCAN evaluated on the r 2 SCAN 50 density), is almost perfect for the energy differences between water hexamers, and thus probably also for liquid water away from the boiling point. Thus, the accuracy of composite methods like SCAN@HF and HF-r 2 SCAN-DC4 is not due to the HF density being closer to the exact density, but to a compensation of errors from its greater degree of localization. We also give an argument for the approximate reliability of this unconventional error cancellation for diverse molecular properties. Lastly, we confirm this unconventional error cancellation for the SCAN description of the water trimer via Kohn–Sham inversion of the CCSD(T) density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success

Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality (“greenness”) as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Prospects for Surrogate Neutron Capture Measurements with Radioactive Ion Beams and GODDESS

Neutron capture reactions are responsible for the synthesis of almost all of the elements heavier than iron through the slow s-process, that proceeds close to the line of stability, and the rapid r-process, with very neutron-rich waiting points. Uncertainties in (n,γ) rates in neutron rich nuclei, especially near closed neu- tron shells, can have significant impact [1] on the predictions of final abundances for different astrophysical scenarios for the r process. Understanding (n,γ) rates on neutron-rich fission fragments is also important for nuclear forensics and stockpile stewardship science. Ratkiewicz et al. [2 and references therein] has recently demonstrated that the (d,pγ) reaction is a valid surrogate for (n,γ), where the formation of the compound nucleus from the breakup of the deuteron has been calculated in a reaction model and the subsequent measured gamma-decay probabilities are reproduced with standard level density and strength functions in a Bayesian approach. In parallel to the surrogate validation efforts, we have demonstrated that the (d,pγ) reaction can be measured in inverse kinematics with Gammasphere ORRUBA: Dual Detectors for Experimental Structure Studies (GODDESS) [3] where the Gammasphere array of Compton-suppressed HPGe detectors is coupled to the Oak Ridge Rutgers University Barrel Array of position-sensitive silicon strip detectors. During the commissioning campaign we measured the (d,pγ) reaction with 134 Xe and 95 Mo beams, the latter to demonstrate the surrogate method in inverse kinematics. The present talk will present preliminary results from this campaign including γ-decay probabilities and prospects for surrogate (n,γ) measurements with 143 Ba fission-fragment beams.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Spatially and temporally resolved plasma parameter estimations of laser heated MagLIF relevant gas pipes at NIF

The ability to control laser pre-heat is an integral part of the inertial confinement fusion concept known as Magnetized Liner Inertial Fusion. This process is studied at the National Ignition Facility (NIF) where 4 of the 192 laser beams are propagated through a 1-cm long gas cell where they deposit >20 kJ of energy into the gaseous fuel via inverse bremsstrahlung absorption. This process ionizes the gas, producing a plasma that follows behind the laser front and expands over the radius of the cell. Emission from this plasma, as viewed by a gated x-ray detector, can be used to build spatially and temporally resolved estimations of the pre-heat plasma's density and temperature profiles. This can then be used to estimate the plasma pressure, internal energy, and radiation losses. Estimations show the evolution of the plasma in magnetized and unmagnetized gas cells filled with ambient temperature neopentane (C5H12) +1% Ar, as well as unmagnetized cryogenically cooled (32 K) deuterium +1% Ne filled targets. This analysis shows the effects of initial gas-fill density, composition, and axial magnetization on the time-dependent plasma parameters. Previously, these parameters at the NIF had not been experimentally characterized, and these estimations provided a potential new means of testing radiation magneto-hydrodynamic predictive capability models. Results in unmagnetized targets have strong agreement with simulations. However, in targets with a 19 T applied axial magnetic field, this method yields electron temperatures up to 100% hotter than those predicted by HYDRA codes.

Bremsstrahlung↗

DOE OSTI2021

Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological implications from two decades of spectroscopic surveys at the Apache Point Observatory

We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Lyα forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, r d , from eight different samples and six measurements of the growth rate parameter, fσ 8 , from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, ΛCDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization, under the same model, the BAO data provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Independent of distance measurements, the SDSS RSD data complement weak lensing measurements from the Dark Energy Survey (DES) in demonstrating a preference for a flat ΛCDM cosmological model when combined with Planck measurements. The combined BAO and RSD measurements indicate σ 8 =0.85±0.03, implying a growth rate that is consistent with predictions from Planck temperature and polarization data and with General Relativity. When combining the results of SDSS BAO and RSD, Planck, Pantheon Type Ia supernovae (SNe Ia), and DES weak lensing and clustering measurements, all multiple-parameter extensions remain consistent with a ΛCDM model. Regardless of cosmological model, the precision on each of the three parameters, Ω Λ , H 0 , and σ 8 , remains at roughly 1%, showing changes of less than 0.6% in the central values between models. In a model that allows for free curvature and a time-evolving equation of state for dark energy, the combined samples produce a constraint Ω K =-0.0022±0.0022. The dark energy constraints lead to w 0 =-0.909±0.081 and w a =$-0.49_{-0.30}^{+0.35}$, corresponding to an equation of state of w p =-1.018±0.032 at a pivot redshift z p =0.29 and a Dark Energy Task Force Figure of Merit of 94. The inverse distance ladder measurement under this model yields H 0 =68.18±0.79 km s -1 Mpc -1 , remaining in tension with several direct determination methods; the BAO data allow Hubble constant estimates that are robust against the assumption of the cosmological model. In addition, the BAO data allow estimates of H 0 that are independent of the CMB data, with similar central values and precision under a ΛCDM model. Our most constraining combination of data gives the upper limit on the sum of neutrino masses at Σm ν <0.115 eV (95% confidence). Finally, we consider the improvements in cosmology constraints over the last decade by comparing our results to a sample representative of the period 2000-2010. We compute the relative gain across the five dimensions spanned by w, Ω k , Σm ν , H 0 , and σ 8 and find that the SDSS BAO and RSD data reduce the total posterior volume by a factor of 40 relative to the previous generation. Finally, adding again the Planck, DES, and Pantheon SN Ia samples leads to an overall contraction in the five-dimensional posterior volume of 3 orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. Furthermore, the combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Validation of the HyRAM+ physics models for use with propane

The Hydrogen Risk Assessment Model Plus (HyRAM+) toolkit combines quantitative risk assessment with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. HyRAM+ is differentiated from HyRAM in that it includes models and leak data for other alternate fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for propane which is being used as a surrogate for autogas, which is a mixture of propane and butane and used in internal combustion engines in vehicles. For flame length comparisons, five previously published experiments from peer reviewed journals were used to validate our models. The validation looked at flame lengths and flame widths with respect to different leak diameters, mass flow rates, and source pressures. Most of the sources included more than one set of experimental data, which were collected using different methods (CCD cameras, IR visualization etc.). In general, HyRAM+ overpredicts the flame lengths by around 65%. For heat and radiation models, we compared the heat flux and radiation data reported from two different sources to the values calculated by HyRAM+. For higher mass flow rates, the HyRAM+ calculated flame length results gave a better estimate of what is found in the experiments (65% error), but a higher error (85%) is observed between the HyRAM+ calculated lengths and the experimental flame lengthsfor lower mass flows. Some differences can be attributed to outdoor environmental effects (i.e. wind speed) and uncertainties in jet flame shapes. The propane flame trajectory is predicted for a high Reynolds number case with Re = 12,500 and a low Reynolds number case where Re = 2,000. The Re=12,500 case which is momentum dominated matches well with the experimental flame trajectory, but the agreement for the bouancy driven low Reynolds number case is not as good. Dispersion modeling for unignited propane was also analyzed. We compared the mole fraction, mixture fraction, mean velocity, concentration half width, and inverse mass concentration over an axial distance from different credible journals to the values calculated by HyRAM+. The results display good agreement but generally, HyRAM+ predicts a wider profile for mole fraction and mixture fraction experiments. Overall, HyRAM+’s results are reasonable for predicting the flame length, heat flux, flame trajectory, and dispersion for propane and can be used in risk analyses

08 HYDROGEN↗