Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data fitting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Imaging Bragg Edge Analysis TooLs for Engineering Structures (iBeatles)

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) provides pulsed neutrons with energies varying from epithermal to cold. In preparation for VENUS, the neutron imaging beamline to be located at beam port 10, we have performed a series of experiments focused on wavelength-dependent radiography and computed tomography for a broad range of applications, from materials science to biological tissues.One of the time-of-flight (TOF) techniques that is of interest to the scientific community is the 2-dimensional mapping of phases and average crystalline plane orientation in samples both ex-situ and during applied stresses such as tensile loading and heating. This technique is known as Bragg edgeimaging and relies on the identification of changes of transmission values, fitting of the edge to measure its displacement, and thus identify the shift in lattice parameter due to stresses. One of the challenges of TOF imaging measurements is the amount of data and the inability to observe Bragg edge shifts in real time during an experiment. Thus, we have been focusing on creating a Python-based interface that allows fast data processing and instantaneous mapping and fitting of the Bragg edges, and their evolution through time. Python libraries and Jupyter notebooks have been implemented to facilitate decision making during an experiment. The advantage of the notebooks is the possibility to guide an experiment as they can quickly process and display Bragg edge data. These notebooks can be used independently, or can be combined in a Python Graphical User Interface (GUI) tool called iBeatles. This interface permits visualization and fitting of the Bragg edges, and ultimately back-projects the fitting results onto the radiographs to display a strain map. Assuming data collection has sufficient statistics, the strain mapping analysis can be performed on a pixel-by-pixel basis. This development is a step forward toward a better user experience at the future VENUS beamline in terms of live feedback and productivity. Analysis that used to take days of switching between different applications can now be done in minutes within the

Bilheux, JeanChristophe [Oak Ridge National Labora↗
DOE OSTI2023

Parameter-free prediction of phase transition in PbTiO 3 through combination of quantum mechanics and statistical mechanics

Thermodynamics of ferroelectric materials and their ferroelectric to paraelectric (FE-PE) transitions is commonly described by the phenomenological Landau theory and more recently by effective Hamiltonian and various potentials, all with model parameters fitted to experimental or theoretical data. Here, in this work, we show that the zentropy theory, which considers the total entropy of a system as a weighted sum of entropies of configurations that the system may experience and the statistical entropy among the configurations, can predict the FE-PE transition without fitting parameters. For PbTiO 3 , the configurations are identified as the FE configurations with 90- or 180° domain walls in addition to the ground state FE configuration without domain wall. With the domain wall energies predicted from first-principles based on density functional theory in the literature as the only inputs, the FE-PE transition for PbTiO 3 is predicted showing remarkable agreement with experiments, unveiling the microscopic fundamentals of the transition.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Performing Bayesian Analyses With AZURE2 Using BRICK: An Application to the 7 Be System

Phenomenological R-matrix has been a standard framework for the evaluation of resolved resonance cross section data in nuclear physics for many years. It is a powerful method for comparing different types of experimental nuclear data and combining the results of many different experimental measurements in order to gain a better estimation of the true underlying cross sections. Yet a practical challenge has always been the estimation of the uncertainty on both the cross sections at the energies of interest and the fit parameters, which can take the form of standard level parameters. Frequentist (χ 2 -based) estimation has been the norm. In this work, a Markov Chain Monte Carlo sampler, emcee, has been implemented for the R-matrix code AZURE2, creating the Bayesian R-matrix Inference Code Kit (BRICK). Bayesian uncertainty estimation has then been carried out for a simultaneous R-matrix fit of the 3 He (α,γ) 7 Be and 3 He (α,α) 3 He reactions in order to gain further insight into the fitting of capture and scattering data. Both data sets constrain the values of the bound state α-particle asymptotic normalization coefficients in 7 Be. The analysis highlights the need for low-energy scattering data with well-documented uncertainty information and shows how misleading results can be obtained in its absence.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Red Noise–based False Alarm Thresholds for Astrophysical Periodograms via Whittle’s Approximation to the Likelihood

Astronomers who search for periodic signals using Lomb–Scargle periodograms rely on false alarm level (FAL) estimates to identify statistically significant peaks. Although FALs are often calculated from white noise models, many astronomical time series suffer from red noise. Prewhitening is a statistical technique in which a continuum model is subtracted from the log power spectrum estimate, after which the observer can proceed with a white-noise treatment. Here we present a prewhitening-based method of calculating frequency-dependent FALs. We fit power laws and autoregressive models of order 1 to each Lomb–Scargle periodogram by minimizing the Whittle approximation to the negative log-likelihood (NLL), then calculate FALs based on the best-fit model power spectrum. Our technique is a novel extension of the Whittle NLL to datasets with uneven time sampling. We demonstrate FAL calculations using observations of α Cen B, GJ 581, HD 192310, synthetic data from the radial velocity (RV) fitting challenge, and Kepler observations of a differential rotator. The Kepler data analysis shows that only true rotation signals are detected by red noise FALs, while white noise FALs suggest all spurious peaks in the low-frequency range are significant. A high-frequency sinusoid injected into α Cen B logR$'$ HK observations exceeds the 1% red noise FAL despite having only 8.9% of the power of the dominant rotation signal. In a periodogram of HD 192310 RVs, peaks associated with differential rotation and planets are detected against the 5% red noise FAL without iterative model fitting or subtraction. The software for calculating red noise–based FALs is available on GitHub.

Astrostatistics (1882)↗
DOE OSTI2022

The Radius of PSR J0740+6620 from NICER with NICER Background Estimates

We report a revised analysis for the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740+6620, studied previously with joint fits to NICER and XMM-Newton data by Riley et al. (2021) and Miller et al. (2021). We perform a similar Bayesian estimation for the pulse-profile model parameters, except that instead of fitting simultaneously the XMM-Newton data, we use the best available NICER background estimates to constrain the number of photons detected from the source. This approach eliminates any potential issues in the cross-calibration between these two instruments, providing thus an independent check of the robustness of the analysis. The obtained neutron star parameter constraints are compatible with the already published results, with a slight dependence on how conservative the imposed background limits are. A tighter lower limit causes the inferred radius to increase, and a tighter upper limit causes it to decrease. We also extend the study of the inferred emission geometry to examine the degree of deviation from antipodality of the hot regions. We show that there is a significant offset to an antipodal spot configuration, mainly due to the non-half-cycle azimuthal separation of the two emitting spots. The offset angle from the antipode is inferred to be above 25° with 84% probability. This seems to exclude a centered-dipolar magnetic field in PSR J0740+6620.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Discrete element modeling of switchgrass particles under compression and rotational shear

Switchgrass is a perennial herbaceous plant regarded as a biomass energy crop in the United States for its highadaptability and yield potential. Processing and handling of switchgrass particles are challenging due to the erratic mechanical and flow behavior originating from their intrinsic particulate properties. Here, we present a bonded-sphere discrete element model designed specifically for switchgrass particles. The model simultaneously captures three key particulate features, i.e., fibrous particle shapes, a wide range of particle sizes, and particle deformability. Realistic yet computationally efficient particle shape templates are created based on the image analysis data of switchgrass specimens. A fitting procedure is proposed to ensure both the particle width and length distributions are captured, a unique requirement for fibrous particles. Two full-scale numerical models, i.e., a uniaxial compression model and a Schulze ring shear model, are developed using information fromphysical experiments. The model is calibrated using experimental data of chopped-small switchgrass specimens, and then, is validated using data of chopped-large specimens in both compression and ring-shear tests. Numerical results show that the numerical models capture bulk densities accurately (with an error of 3%) while slightly underestimate the bulk friction angle. Furthermore, an extensive sensitivity analysis reveals that (1) switchgrass particles with rougher edges (due to different processing techniques) exhibit a higher shear strength and a lower flowability; (2) stiffer particles yield a lower bulk density (up to 21% lower) compared to more deformable particles, indicating particle deformability should be incorporated when modeling biomass flow in a preprocessing system.

09 BIOMASS FUELS↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg 2 . We use DES Year 3 weak-lensing data for 688 clusters with redshifts 𝑧 < 0.95 and HST weak-lensing data for 39 clusters with 0.6 < 𝑧 < 1.7. The weak-lensing measurements enable robust mass measurements of sample clusters and allow us to empirically constrain the SZ observable-mass relation without having to make strong assumptions about, e.g., the hydrodynamical state of the clusters. For a flat Λ⁢ CDM cosmology, and marginalizing over the sum of massive neutrinos, we measure Ω m = 0.286 ± 0.032, 𝜎 8 = 0.817 ± 0.026, and the parameter combination 𝜎 8 ⁢(Ω m /0.3) 0.25 = 0.805 ± 0.016. Our measurement of 𝑆 8 ≡ 𝜎 8 ⁢$\sqrt{Ω_{m}/0.3}$ = 0.795 ± 0.029 and the constraint from Planck CMB anisotropies (2018 TT, TE, EE+lowE) differ by 1.1⁢𝜎. In combination with that Planck dataset, we place a 95% upper limit on the sum of neutrino masses ∑𝑚 𝜈 < 0.18 eV. When additionally allowing the dark energy equation of state parameter 𝑤 to vary, we obtain 𝑤 = −1.45 ± 0.31 from our cluster-based analysis. In combination with Planck data, we measure 𝑤 =−1.3⁢4$^{+0.22}_{−0.15}$, or a 2.2⁢𝜎 difference with a cosmological constant. We use the cluster abundance to measure 𝜎8 in five redshift bins between 0.25 and 1.8, and we find the results to be consistent with structure growth as predicted by the Λ⁢ CDM model fit to Planck primary CMB data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗
DOE OSTI2022

Machine learning of consistent thermodynamic models using automatic differentiation

In this study, we propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Testing H 0 in Acoustic Dark Energy with Planck and ACT Polarization

The canonical acoustic dark energy model (cADE), which is based on a scalar field with a canonical kinetic term that rapidly converts potential to kinetic energy around matter radiation equality, alleviates the Hubble tension found in ΛCDM. Here, we show that it successfully passes new consistency tests in the CMB damping tail provided by the ACT data, while being increasingly constrained and distinguished from alternate mechanisms by the improved CMB acoustic polarization data from Planck. The best fit cADE model to a suite of cosmological observations, including the SH0ES H 0 measurement, has H 0 = 70.25 compared with 68.23 (km s –1 Mpc –1 ) in ΛCDM and a finite cADE component is preferred at the 2.8σ level. The ability to raise H0 is now mainly constrained by the improved Planck acoustic polarization data, which also plays a crucial role in distinguishing cADE from the wider class of early dark energy models. ACT and Planck TE polarization data are currently mildly discrepant in normalization and drive correspondingly different preferences in parameters. Improved constraints on intermediate scale polarization approaching the cosmic variance limit will be an incisive test of the acoustic dynamics of these models and their alternatives.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Human–mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model

Abstract Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human–mouse difference in radiation‐related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age‐related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per‐lifespan basis (0.72‐ and 0.71‐fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species‐independent radiation‐induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation‐associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

Oncology↗
DOE OSTI2020

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered “Planet 9”, may be present in the outer solar system. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 ± 0.001. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Community Based Data of Potentiometric Titration of Iron Oxides: Ferrihydrite (HFO), Goethite, Hematite, Magnetite

This data release includes experimental data of potentiometric titration for iron oxides. The data in the provided .csv files is not our own experimental data but have been compiled from the multiple literature sources. The master database is L-SCIE (LLNL Surface Complexation/Ion Exchange) database, and the provided .csv files are extracted data from L-SCIE. The .csv files were obtained by using the Lawrence Livermore National Laboratory Surface Complexation Database Converter (SCDC) code written in the R programming language (free licensing available at https://ipo.llnl.gov/technologies/software/llnl-surface-complexation-database-converter-scdc).The released data was used for developing a comprehensive community data-driven surface complexation modeling (SCM) framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Neutron Capture and Transmission Measurements of 54 Fe at the RPI LINAC [Slides]

Transmission and capture measurements provide a wholistic set of experimental data to start resonance evaluation on 54 Fe. Analysis thus far supports that changes are needed in 54 Fe evaluation. Major accomplishments include the completion of radiative capture measurements of 54 Fe and the completion of total cross section measurements of 54 Fe. Future work includes the completion of data analysis relevant to capture and transmission measurements (May 2022), complete fitting of new resonance parameters to 54 Fe data (early 2023), and completion of all required deliverables for graduation (May 2023).

07 ISOTOPE AND RADIATION SOURCES↗