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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Estimation of Bare Surface Soil Moisture and Surface Roughness Parameter Using L-Band SAR Image Data

An algorithm based on a fit of the single-scattering Integral Equation Method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well- managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.

Shi, Jian-Cheng↗
NASA NTRSDate not supplied

Uncertainty Quantification of CFD Model Assumptions Against Sonic Boom Noise Prediction of a Commercial Supersonic Transport

This paper presents the results of uncertainty modeling of sonic boom noise generation from commercial supersonic transport considering the Spalart-Allmaras (SA) turbulence modeling parameters as well as Mach number, angle of attack and altitude. Sample generation and analysis for this uncertainty model was performed by UQPCE, which is a software package developed at the NASA Langley Research Center. To build the uncertainty model, 42 cases of sonic boom noise calculation were performed. Computation of the ground noise can be briefly summarized in two steps. First, the near field pressure waveforms are sampled from CFD calculation using the NASA Langley’s FUN3D solver. Second, this information is passed to an atmospheric propagation code, sBOOM, which solves an augmented Burger’s equation and simulates how the near field waveforms will change while passing through the atmosphere. The ground signature is further processed to obtain the perceived loudness, PLdB. Having a high spatial resolution near the shockwave in the CFD calculation is critical in sonic boom noise prediction. Because the variation in the input parameters for the current uncertainty quantification (UQ) study is likely to lead to change in shock location, angle and strength, the grid adaptation for shock capturing is independently applied for each condition. The final mesh used in the CFD calculation consists of approximately 420 million cells. The pressure signatures are sampled at three, four and five body lengths away from the aircraft to make sure the three dimensional effects around the aircraft are resolved. The results of the UQ analysis shows that within the three aleatory variables, the angle of attack had the most impact against ground noise, followed by the altitude and the Mach number. Between the two SA model parameters, the Kármán constant (𝜅) was significantly more important than the turbulent Prandtl number (𝜎), but these two parameters were only marginally significant in the overall prediction variance in ground noise. The UQ procedure explained in this paper can be widely applied to other model parameters.

Uncertainty Quantification↗
NASA NTRSDate not supplied

Uncertainty Quantification of CFD Model Assumptions Against Sonic Boom Noise Prediction of a Commercial Supersonic Transport

This paper presents the results of uncertainty modeling of sonic boom noise generation from commercial supersonic transport considering the Spalart-Allmaras (SA) turbulence modeling parameters as well as Mach number, angle of attack and altitude. Sample generation and analysis for this uncertainty model was performed by UQPCE, which is a software package developed at the NASA Langley Research Center. To build the uncertainty model, 42 cases of sonic boom noise calculation were performed. Computation of the ground noise can be briefly summarized in two steps. First, the near field pressure waveforms are sampled from CFD calculation using the NASA Langley’s FUN3D solver. Second, this information is passed to an atmospheric propagation code, sBOOM, which solves an augmented Burger’s equation and simulates how the near field waveforms will change while passing through the atmosphere. The ground signature is further processed to obtain the perceived loudness, PLdB. Having a high spatial resolution near the shock wave in the CFD calculation is critical in sonic boom noise prediction. Because the variation in the input parameters for the current uncertainty quantification (UQ) study is likely to lead to change in shock location, angle and strength, the grid adaptation for shock capturing is independently applied for each condition. The final mesh used in the CFD calculation consists of approximately 420 million cells. The pressure signatures are sampled at three, four and five body lengths away from the aircraft to make sure the three dimensional effects around the aircraft are resolved. The results of the UQ analysis shows that within the three aleatory variables, the angle of attack had the most impact against ground noise, followed by the altitude and the Mach number. Between the two SA model parameters, the Kármán constant (𝜅) was significantly more important than the turbulent Prandtl number (𝜎), but these two parameters were only marginally significant in the overall prediction variance in ground noise. The UQ procedure explained in this paper can be widely applied to other model parameters.

Uncertainty Quantifications↗
NASA NTRS1978

The effects of quantization on signal processing

Typically an analog signal from a space system is sampled, quantized by Analog-to-Digital (A/D) conversion, merged into a bit stream, communicated to a ground station, received by the ground station, and processed by the ground station to extract useful information for dissemination to the users. The cost of each of these steps is reduced as the number of quantization steps is reduced in the A/D converter. The number of quantization steps should be as small as possible without losing the required information content. This report deals specifically with the accuracy of averages as a function of the number of quantized samples used to compute the averages with the noise on the analog signal as a parameter. For example, the success of the Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) Demonstration depends upon temporally averaging multiple samples in an effort to reduce noise to a sufficiently low level such that temperature profile sounding is made possible. A tutorial description of this process is presented.

Montgomery, H. E.↗
DOE OSTI2023

VST ATLAS galaxy cluster catalogue I: cluster detection and mass calibration

Taking advantage of ∼4700 deg 2 optical coverage of the Southern sky offered by the VST ATLAS survey, we construct a new catalogue of photometrically selected galaxy groups and clusters using the orca cluster detection algorithm. The catalogue contains ∼22 000 detections with N 200 > 10 and ∼9000 with N 200 > 20. We estimate the photometric redshifts of the clusters using machine learning and find the redshift distribution of the sample to extend to z ∼ 0.7, peaking at z ∼ 0.25. We calibrate the ATLAS cluster mass-richness scaling relation using masses from the MCXC, Planck, ACT DR5, and SDSS redMaPPer cluster samples. We estimate the ATLAS sample to be > 95 per cent complete and > 85 per cent pure at z < 0.35 and in the M 200m >1 x 10 14 h -1 M ⊙ mass range. At z < 0.35, we also find the ATLAS sample to be more complete than redMaPPer, recovering a ~ 40 per cent higher fraction of Abell clusters. This higher sample completeness places the amplitude of the z < 0.35 ATLAS cluster mass function closer to the predictions of a ΛCDM model with parameters based on the Planck CMB analyses, compared to the mass functions of the other cluster samples. However, strong tensions between the observed ATLAS mass functions and models remain. We shall present a detailed cosmological analysis of the ATLAS cluster mass functions in paper II. In the future, optical counterparts to X-ray-detected eROSITA clusters can be identified using the ATLAS sample. The catalogue is also well suited for auxiliary spectroscopic target selection in 4MOST. The ATLAS cluster catalogue is publicly available at http://astro.dur.ac.uk/cosmology/vstatlas/cluster_catalogue/.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2023

Homogeneity Study of ZrC, NbC, and TaC Binary Carbide Fuels for the Application of Nuclear Thermal Propulsion

In alignment with NASA and DARPA goals, efforts towards Nuclear Thermal Propulsion (NTP) have increased in recent years. As progress is made, a main challenge still exists; the fuel in an NTP system must survive the extreme environment experienced during operating conditions. Three fuel forms have been studied including a ceramic-metallic matrix, a ceramic-ceramic matrix, and solid solution carbides. Solid solution carbides were studied previously as a fuel for NTP and was shown to be a promising technology; however, the programs were canceled before demonstration in an NTP engine test. Of the refractory metal carbides of interest, zirconium carbide (ZrC), niobium carbide (NbC) and tantalum carbide (TaC) were studied. ZrC, NbC, and TaC powders were consolidated in monocarbide (ZrC, NbC, TaC) and bi-carbide (ZrC-NbC, ZrC-TaC, NbC-TaC) forms using spark plasma sintering (SPS), with 50-50 mol% compositions of the bi-carbides of interest. The time and temperature conditions to achieve a completely homogeneous solid solution for the mixed carbides is of interest for NTP fuel applications, since this homogeneity is considered to be essential for this fuel form. Homogeneity is typically not achieved during sintering, as surface diffusion (predominant for sintering to near ideal density) tends to occur at lower temperatures than bulk volumetric diffusion (required for solute interdiffusion to achieve a homogeneous solid solution). High temperature isochronal annealing was performed to identify the conditions to achieve a chemically homogeneous sample. The solute interpenetration vs annealing temperature was quantified using energy dispersion spectroscopy (EDS) in a scanning electron microscope (SEM). X-ray diffraction (XRD) was used to accurately measure the phase stability and lattice parameter as a function of annealing conditions. The diffusion data from each sample was then used to determine self-diffusion coefficients for interdiffusion of the Nb, Zr, and Ta solutes and compared to existing literature values. The minimum annealing conditions to achieve complete chemical homogeneity for each carbide was determined.

nuclear thermal propulsion↗
DOE OSTI2026

FolpsD: combining EFT and phenomenological approaches for joint power spectrum and bispectrum analyses

We present a theoretical model for the power spectrum and bispectrum of galaxy clustering that exploits the complementarity between small-scale power spectrum information and large-scale bispectrum measurements. We extend the FOLPS code by combining its one-loop EFT galaxy power spectrum with a tree-level galaxy bispectrum projected onto the tripolar spherical harmonics (Sugiyama) basis. To access additional small-scale information, we also consider a line-of-sight damping factor in both statistics, mirroring approaches commonly used in studies of redshift-space distortions. We test the model using DESI DR2 galaxy mocks. Even without damping, the joint analysis of the EFT power spectrum and bispectrum significantly improves constraints and reduces parameter degeneracies relative to power spectrum analyses alone. For LRG-like samples, including the damping further extends the range beyond $k\sim 0.3 \,h \text{Mpc}^{-1}$ in the power spectrum and $k \sim 0.24 \,h \text{Mpc}^{-1}$ in the bispectrum without introducing statistically significant parameter biases. This leads to up to $\sim 30\%$ tighter constraints on $A_s$ and $ω_{cdm}$. For low signal-to-noise tracers such as QSOs, however, the damping parameters are weakly constrained and can absorb noise fluctuations, leading to shifts in inferred parameters. Similar limitations may arise in models where cosmological information is encoded in power-spectrum shape features degenerate with the damping, such as scenarios with massive neutrinos. In contrast, for $w_0w_a$CDM we obtain $15\%$ and $21\%$ tighter constraints on $w_0$ and $w_a$, respectively, yielding a deviation from constant dark energy at slightly more than the $1σ$ level using full-shape information alone. The code is publicly available at https://github.com/cosmodesi/FolpsD

Bansal, P. [Michigan U., MCTP; Michigan U.] (ORCID↗
DOE OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Superconducting Quantum Metamaterials from Convergence of Soft and Hard Condensed Matter Science

Superconducting quantum metamaterials are expected to exhibit a variety of novel properties, but have been a major challenge to prepare as a result of the lack of appropriate synthetic routes to high-quality materials. Here, the discovery of synthesis routes to block copolymer (BCP) self-assembly-directed niobium nitrides and carbonitrides is described. The resulting materials exhibit unusual structure retention even at temperatures as high as 1000 °C and resulting critical temperature, T c , values comparable to their bulk analogues. Applying the concepts of soft matter self-assembly, it is demonstrated that a series of four different BCP-directed mesostructured superconductors are accessible from a single triblock terpolymer. Resulting materials display a mesostructure-dependent T c without substantial variation of the XRDmeasured lattice parameters. Lastly, field-dependent magnetization measurements of a sample with double-gyroid morphology show abrupt jumps comparable in overall behavior to flux avalanches. Results suggest a fruitful convergence of soft and hard condensed matter science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Coupling Thermomechanical Processing and Alloy Design to Improve Textures in Mg-Zn-Ca Sheet Alloys

Abstract The effect of Ca and Zn additions on the microstructure and texture evolution during thermomechanical processing of Mg-Zn-Ca sheet alloys was systematically investigated and quantified. Plane strain compression testing in a Gleeble thermomechanical simulator was used to physically simulate a 10-pass rolling schedule, while allowing for careful control and monitoring of the processing parameters. Textures in the as-deformed ternary alloy samples demonstrate a weak maximum basal intensity and spreading in the transverse direction. Increasing the Zn content to 3.2 wt.% in the ternary alloys resulted in samples that exhibited weak textures in the as-deformed state. Importantly, static recrystallization (SRX) during post-deformation annealing of these alloys promoted a desirable annular texture, with the c -axis tipped from the normal direction and a lower basal texture intensity. The evolution in texture during SRX is associated with as-deformed microstructures with broad grain orientation spreads and a low degree of recrystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterization of high thermal conductivity fuel surrogates before and after ion irradiation

High thermal conductivity nuclear fuels offer important potential advantages over traditional oxide-based fuels such as higher burnup, reduction in fission gas release, and better overall safety of the system. One proposed approach to high thermal conductivity fuels utilizes high thermal conductivity nonfissile additives with UO 2 fuel to lower the fuel operating temperature and thereby take advantage of the highly favorable radiation resistance of UO 2 at lower operating temperatures. However, differential swelling in the matrix and high conductivity additive phases during high dose irradiation could lead to internal cracking and poor performance. In the current study, ceria (CeO 2 ) and zirconia (ZrO 2 ) surrogate matrices were used to model UO2 behavior. Additives of 10 vol. % Al 2 O 3 or SiC in the form of short fibers or platelets were used for the high conductivity second phase. The nuclear fuel surrogates were sintered to achieve densities greater than 93% of the ideal values. Scanning electron microscopy (SEM) imaging and X-ray diffraction confirmed the uniform distribution of the second phase and that no intermetallic second phase was formed during sintering. The thermal conductivity of the sintered samples was measured from 50 °C to 900 °C and confirmed the desirable increase compared to pure CeO 2 /ZrO 2 pellets. Samples were irradiated with 20 MeV Ni 6+ ions at midrange doses ranging from 1 to 15 displacements per atom (dpa) and temperatures from 300 °C to 700 °C. Post irradiation characterization revealed a good stability of the samples at low to medium doses with matrix lattice parameter swelling of < 0.14 % but showed a significant microstructural deterioration and decrease of the mechanical properties at 15 dpa.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Failure investigation of nuclear grade POCO graphite target in high energy neutrino physics through numerical simulation

In many high energy physics neutrino beamlines, targets are made up of isotropic POCO graphite grade for production of neutrinos for high energy particle physics research and are bombarded with highly energetic pulsed proton beam. The pulsed proton beam with a small beam size creates thermal stress waves as well as radiation damage such as displacement damage, void formation, swelling, and gas formation amongst others. As a result, after a few years of operation one of the longest operated targets appears to have undergone bulk swelling and fractured along the beam centerline. A complex interaction of dynamic loading due to beam, material degradation due to irradiation are thought to have caused such a failure. Here, we present a numerical simulation to capture the combined effects of swelling and the dynamic effect of thermal stress waves due to beam heating, to explain the failure of target. An empirical formula has been developed to take into account swelling as a function of temperature and proton fluence and has been implemented in a commercial finite element code. Extensive X-ray diffraction (XRD) were performed on graphite samples from the failed target to understand the lattice parameter changes due to irradiation and build a valid empirical model. Simulation results show that swelling plays a major role in elevating the stress state in the material exceeding the failure strength while the pulsed beam heating also introduces fatigue loading that would explain brittle fracture emanating from inside of the material and propagating outward. Such empirical formulations will help in identifying the threshold values of critical parameters to extend the service life of future multi-megawatt neutrino targets and targets in other accelerator environments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Nanoscale Compositional Mapping of Commercial LiNi 0.8 Co 0.15 Al 0.05 O 2 Cathodes Using Atom Probe Tomography

Nickel-rich cathodes provide improved specific capacity that lead to higher gravimetric energy density, critical for electric vehicles. However, poor long-term capacity retention at elevated temperatures/high C-rates (the rate of charge and discharge on a battery) stem from material issues: surface phase changes, corrosive side reactions with the electrolyte, ion dissolution and propagation of cracks. Introducing dopants, developing nanoscale surface coatings and graded core-shell structures have all improved the electrochemical performance of nickel-rich cathodes. However, a material level understanding on the effect of Li composition and distribution in Ni-rich cathodes is limited, due to a lack of characterization methods available that can directly image Li at the nanoscale. Hence, it is critical to establish methods such as atom probe tomography (APT), having both nanometer scale spatial resolution and high compositional sensitivity to quantitatively analyze battery cathodes. To fully realize its potential as a method for quantitative compositional analysis of commercial Li-ion batteries, we provide a comprehensive description of the challenges in sample preparation and analyze the dependency of the analysis parameters, specifically laser pulse energy on the measured stoichiometry of elements in a high Ni content cathode material; LiNi 0.8 Co 0.15 Al 0.05 O 2 (NCA). In this work, our findings show the stoichiometry variations cannot be explained by charge state ratios or Ga implantation damage alone during FIB preparation, indicating that additional factors such as crystallographic orientation may need to be considered to achieve quantitative nanoscale compositional analysis of such battery cathodes using APT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Robust Atomic-Resolution Imaging of Lithium in Battery Materials by Center-of-Mass Scanning Transmission Electron Microscopy

The performance of energy storage materials is often governed by their structure at the atomic scale. Conventional electron microscopy can provide detailed information about materials at these length scales, but direct imaging of light elements such as lithium presents a challenge. While several recent techniques allow lithium columns to be distinguished, these typically either involve complex contrast mechanisms that make image interpretation difficult or require significant expertise to perform. In this work, we demonstrate how center-of-mass scanning transmission electron microscopy (CoM-STEM) provides an enhanced ability for simultaneous imaging of lithium and heavier element columns in lithium ion conductors. Through a combination of experiments and multislice electron scattering calculations, we show that CoM-STEM is straightforward to perform and produces directly interpretable contrast for thin samples, while being more robust to variations in experimental parameters than previously demonstrated techniques. As a result, CoM-STEM is positioned to become a reliable and facile method for directly probing all elements within energy storage materials at the atomic scale.

25 ENERGY STORAGE↗
DOE OSTI2025

Drying of strawberries with airborne ultrasound and other integrated dehydration mechanisms

This article presents a comprehensive investigation into the drying of strawberries using multiple dehydration methods with an emphasis on energy efficiency and sustainability. Initially, an airborne ultrasonic transducer with a frequency of 21 kHz was employed in batch operations to examine the effects of controlling parameters, including applied power, distance from the transducer plate, sample thickness, and sample holder type. The Energy Ratio, defined as the ratio of thermal energy required to evaporate moisture to ultrasonic energy, was observed to reach up to 2.2, particularly during the initial stages of drying. Subsequently, the Smart Dryer integrated airborne ultrasound (US) dehydration, slot jet reattachment (SJR) nozzle convective drying, infrared (IR) drying, and their combinations, enabling a systematic exploration of various drying conditions on the drying time and quality of strawberries. The integration of airborne US with SJR nozzles and IR drying demonstrates a promising approach for optimizing drying processes. This method not only preserves the quality of dried strawberries but also improves the energy factor to 0.88, reducing drying time by 89% compared with other conditions. Key quality attributes of the dried strawberries, such as color and water activity, were evaluated to understand the influence of each drying method. The findings highlight the potential of these integrated drying techniques as sustainable solutions for efficient and high-quality strawberry dehydration.

42 ENGINEERING↗
DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Magnetic structure and exchange interactions in the layered semiconductor CrPS 4

Compounds with two-dimensional (2D) layers of magnetic ions weakly connected by van der Waals bonding offer routes to enhance quantum behavior, stimulating both fundamental and applied interest. CrPS 4 is one such magnetic van der Waals material, however, it has undergone only limited investigation. Here we present a comprehensive series of neutron scattering measurements to determine the magnetic structure and exchange interactions. The observed magnetic excitations allow a high degree of constraint on the model parameters not normally associated with measurements on a powder sample. The results demonstrate the 2D nature of the magnetic interactions, while also revealing the importance of interactions along 1D chains within the layers. The subtle role of competing interactions is observed, which manifest in a nontrivial magnetic transition and a tunable magnetic structure in a small applied magnetic field through a spin-flop transition. Our results on the bulk compound provide insights that can be applied to an understanding of the behavior of reduced layer CrPS 4 .

36 MATERIALS SCIENCE↗