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DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

DOE OSTI2023

Carbon-Assisted, Continuous Syngas Production in a Chemical Looping Scheme

In the current energy and environment scenario, it is imperative to develop energy efficient routes for chemical manufacturing that also pave the way for mitigation of greenhouse gas emissions. This work presents an efficient pathway for continuous syngas production via a chemical looping conversion of the two most potent greenhouse gases—CH 4 , and CO 2 . The well-known dry-reforming process of converting CH 4 , and CO 2 to syngas is energy-intensive and suffers from catalyst deactivation. The chemical looping approach, on the other hand, provides avenues for mitigating catalyst deactivation and enabling improved energy efficiency. The key to such process enhancements lies in the intricate structure–function relationships of the catalyst and its correlation to the process variables. We present the reduction and oxidation characteristics of 5 wt.% Ni/Ce 1-x Zr x O 2 -based catalysts (x = 0, 0.4, and 0.625). We demonstrate low temperature CH4 activation over Ni-promoted samples as opposed to pure Ce 1-x Zr x O 2 . Moreover, our results depict an optimum regeneration of these catalysts when oxidized by CO 2 , and H 2 O, which allows for chemical looping operation of steam reforming of methane as well. Process variables were tuned to optimize the CH 4 conversion (over 80%), and H 2 /CO ratio at 650 °C. The critical surface reactions—carbon accumulation and gasification, and thermocatalytic CO 2 splitting were investigated to elucidate the dynamic nature of the catalyst surface. In conclusion, the impact of this work lies in showcasing the opportunities to design chemical looping reactors for energy efficient syngas production from waste greenhouse gases.

25 ENERGY STORAGE↗

DOE OSTI2023

Synthesis and Observation of Emergent Phenomena in Epitaxial Heusler Compound Heterostructures

The proposal was on the synthesis and observation of emergent phenomena in epitaxial Heusler compound heterostructures. The large range of properties and number of Heusler compounds opens up a wide number of potential compounds that will exhibit emergent phenomena. The similarity, large range of relatively inexpensive, large area, high crystal quality, III-V bulk substrates, lattice parameters and the ability to tune the lattice parameters through ternary or quaternary III-V compound semiconductor epitaxial growth, makes III-V semiconductors an ideal choice for substrates for epitaxial growth of Heusler compounds. A number of half Heusler compounds have been predicted to exhibit band inversion, making them topological and are therefore expected to exhibit spin-momentum locked topological surface states with linear dispersion. Others are predicted to be semimetals with Weyl points and others semiconducting and magnetic. During the course of this grant, emphasis has been on investigating Heusler compounds with emergent phenomena and demonstrating the ability to tune their properties through alloying and strain. We have grown toplogical semimetal (PtLuSb, PtMnBi), Weyl (Co 2 MnAl, Co 2 TiGe), half metal (PtMnSb, Co 2 MnSi, Co 2 MnAl x Si 1-x , Co2FeAl), and semiconducting (CoTiSb, NiTiSn) and tuned their properties through alloying and epitaxial strain. We also investigated the closely related materials of rare-earth monopnictide, some of which have also been predicted to be topological. During the attempts to grow the PtMnBi, it was discovered that Bi, another predicted topological material when ultrathin, could be grown epitaxially on InSb, results for which are also reported here. The main focus for this effort has been on using variable photon energy and spin-dependent angle resolved photoemission (ARPES) to determine bulk band structure and surface states of pristine epitaxial films grown on III-V semiconductor and MgO substrates and correlate results with theory and transport measurements. Theory has been critical to interpretation of experimental results and has been essential in guiding experiments. The research benefited from several strong collaborations between the PIs and the beamline scientists at the Advanced Light Source at Lawrence Berkeley Laboratory, the Stanford Linear Accelerator Center (SLAC) at Stanford and at the Max Lab at Lund University in Sweden. The strong experiment - theory collaboration between the PI’s groups, the Palmstrøm group at UCSB and the Janotti group at the University of Delaware, has been critical for interpreting the experimental ARPES and magnetotransport measurements results and making predictions to guide experiments. Weekly interactive Zoom meetings made this work well. A collaboration between the Palmstrøm group and Dr. Alexei Fedorov at the Advanced Light Source (ALS) resulted in significant modifications to his end chamber to accommodate the vacuum suitcase that was designed and constructed in the Palmstrøm group at UCSB. In collaboration with beamline scientists, Drs. Makoto Hashimoto and Donghui Lu at SLAC, Palmstrøm made modifications to the vacuum suitcase and developed special sample holders that allowed samples to be grown in the Palmstrøm MBE systems at UCSB and transported in the UHV vacuum suitcase to SLAC for ARPES measurements. The development of the vacuum suitcase was essential for this grant as it has allowed variable photon energies to be used to identify surface versus bulk states on samples that could not be capped and decapped using As- or Sb-capping layers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗

DOE OSTI2016

Insights into the variability of nucleated amyloid polymerization by a minimalistic model of stochastic protein assembly

Self-assembly of proteins into amyloid aggregates is an important biological phenomenon associated with human diseases such as Alzheimer’s disease. Amyloid fibrils also have potential applications in nano-engineering of biomaterials. The kinetics of amyloid assembly show an exponential growth phase preceded by a lag phase, variable in duration as seen in bulk experiments and experiments that mimic the small volumes of cells. Here, to investigate the origins and the properties of the observed variability in the lag phase of amyloid assembly currently not accounted for by deterministic nucleation dependent mechanisms, we formulate a new stochastic minimal model that is capable of describing the characteristics of amyloid growth curves despite its simplicity. We then solve the stochastic differential equations of our model and give mathematical proof of a central limit theorem for the sample growth trajectories of the nucleated aggregation process. These results give an asymptotic description for our simple model, from which closed form analytical results capable of describing and predicting the variability of nucleated amyloid assembly were derived. We also demonstrate the application of our results to inform experiments in a conceptually friendly and clear fashion. Our model offers a new perspective and paves the way for a new and efficient approach on extracting vital information regarding the key initial events of amyloid formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Predicting critical heat flux with uncertainty quantification and domain generalization using conditional variational autoencoders and deep neural networks

Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux (CHF) data used for the 2006 Groeneveld lookup table. To compare with traditional methods, a fine-tuned deep neural network (DNN) regression model was evaluated on the same dataset. Both models achieved small mean absolute relative errors, with the CVAE showing more favorable results. Uncertainty quantification (UQ) was performed using repeated CVAE sampling and DNN ensembling. The DNN ensemble improved performance over the baseline, while the CVAE maintained consistent results with less variability and higher confidence. Both models achieved small errors inside and outside the training domain, with slightly larger errors outside. Altogether, the CVAE performed better than the DNN in predicting CHF and exhibited better uncertainty behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Sources of Li isotope bias during SIMS analysis of standard glasses

The 7 Li/ 6 Li ratios in fourteen USGS, MPI-DING, and NIST glass reference materials (RMs) were analyzed by LG-SIMS to assess compositional matrix effects, evaluate RM δ 7 Li homogeneity, and calculate the useful yield of lithium in silicate glasses. The analyzed RMs cover a range of SiO 2 contents from 45.5 to 75.6% (komatiite to rhyolite), the largest compositional range yet evaluated for δ 7 Li matrix effects. We observe a matrix-induced bias (matrix effect) of up to 18‰ over the studied range that linearly correlates with SiO 2 content, demonstrating that SiO 2 content is a critical factor that must be considered when using silicate glasses to standardize for silicate samples of unknown δ 7 Li composition. All RMs were found to be sufficiently reproducible to aid in standardization of variable SiO 2 contents and appear isotopically homogeneous at the precision of our measurements. Lithium ionizes very efficiently in these RMs, with measurable yields >5% in all fused-rock RMs and >15% in low SiO 2 fused-rock RMs measured at high beam intensities. Finally, fused-rock RMs ionize lithium 2-3x more efficiently than synthetic glass RMs, which may be related to differences in major oxide chemistry and the reduced nature of the synthetic glasses relative to their fused counterparts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A theoretical kinetic study of ĊH 3 + ṄH 2 : From electronic structure to NH 3 /CH 4 combustion modelling implications

Carbon–nitrogen interaction reactions play an important role in governing the reactivity of ammonia blended fuels. However, there remains uncertainties regarding their detailed reaction pathways and rate constants, hampering the development of high-fidelity chemical kinetic models. In this study, the kinetics of ĊH 3 + ṄH 2 , a key C–N interaction reaction in ammonia/methane blend combustion have been investigated. The potential energy surface has been explored using the high-level ANL0F method, yielding highly accurate stationary point energies that agree with ATcT values within 0.1 kcal mol –1 . Variable reaction coordinate transition state theory is used to treat the barrierless association and decomposition reaction channels, based on directly sampled radical-radical interaction energies at the CASPT2-F12(2e,2o)/cc-pVTZ-F12 level of theory. The minimum transitional mode numbers of states obtained are then coupled with the RRKM/master equation to calculate temperature- and pressure-dependent rate constants. Our a priori calculations capture available experimental measurements from the literature very well. The calculated rate constants have been incorporated into an NH 3 /CH 4 chemical kinetic model currently under development at the University of Galway. The effect of the updated kinetic data for ĊH 3 + ṄH 2 on model predicted NH 3 /CH 4 fuel reactivity is elucidated.

ab initio↗

DOE OSTI2018

Effect of compositional changes on microstructure in additively manufactured aluminum alloy 2139

Highlights: • Volatile elements preferentially vaporize during additive manufacturing. • Vapor losses affect precipitate habit plane in aluminum alloy 2139. • Above 0.2 wt% Mg the precipitate forms on the preferred {111} habit plane. • Higher strength is associated with precipitates on the {111} habit planes. - Abstract: A matrix of nine experiments was conducted on aluminum alloy 2139 samples made by an electron beam directed energy deposition additive manufacturing process to characterize the effect of magnesium vaporization loss on microstructure and properties. The nine-sample matrix included three different magnesium levels deposited onto material at three different temperatures. The goal was to quantify the variability in the deposited material as a function of allowable magnesium content and the local temperature of the substrate or previous layer. The results show that temperature variations in the substrate up to 100 K have no significant effect on the amount of magnesium loss; however, relatively large variations in Mg content, up to a ±0.25 wt%, can significantly affect the microstructure and mechanical properties. This is due to a change in how the precipitate phase forms, which influences the mechanism by which the alloy is strengthened.

36 MATERIALS SCIENCE↗

DOE OSTI2024

TDCOSMO - XVI. Measurement of the Hubble constant from the lensed quasar WGD 2038–4008

Time-delay cosmography is a powerful technique to constrain cosmological parameters, particularly the Hubble constant (H0). The TDCOSMO Collaboration is performing an ongoing analysis of lensed quasars to constrain cosmology using this method. In this work, we obtain constraints from the lensed quasar WGD 2038−4008 using new time-delay measurements and previous mass models by TDCOSMO. This is the first TDCOSMO lens to incorporate multiple lens modeling codes and the full time-delay covariance matrix into the cosmological inference. The models are fixed before the time delay is measured, and the analysis is performed blinded with respect to the cosmological parameters to prevent unconscious experimenter bias. We obtain DΔ t = 1.68−0.38+0.40 Gpc using two families of mass models, a power-law describing the total mass distribution, and a composite model of baryons and dark matter, although the composite model is disfavored due to kinematics constraints. In a flat ΛCDM cosmology, we constrain the Hubble constant to be H0 = 65−14+23 km s−1 Mpc−1. The dominant source of uncertainty comes from the time delays, due to the low variability of the quasar. Future long-term monitoring, especially in the era of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, could catch stronger quasar variability and further reduce the uncertainties. This system will be incorporated into an upcoming hierarchical analysis of the entire TDCOSMO sample, and improved time delays and spatially-resolved stellar kinematics could strengthen the constraints from this system in the future.Key words: gravitational lensing: strong / cosmological parameters / distance scale⋆ Corresponding author; kcwong19@gmail.com.⋆⋆ NHFP Einstein fellow.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection

Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Photoelectron spectroscopy with entangled photons; enhanced spectrotemporal resolution

In this theoretical study, we show how photoelectron signals generated by time-energy entangled photon pairs can monitor ultrafast excited state dynamics of molecules with high joint spectral and temporal resolutions, not limited by the Fourier uncertainty of classical light. This technique scales linearly, rather than quadratically, with the pump intensity, allowing the study of fragile biological samples with low photon fluxes. Since the spectral resolution is achieved by electron detection and the temporal resolution by a variable phase delay, this technique does not require scanning the pump frequency and the entanglement times, which significantly simplifies the experimental setup, making it feasible with current instrumentation. Application is made to the photodissociation dynamics of pyrrole calculated by exact nonadiabatic wave packet simulations in a reduced two nuclear coordinate space. This study demonstrates the unique advantages of ultrafast quantum light spectroscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Generative machine learning for detector response modeling with a conditional normalizing flow

In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. Here, we present a conditional normalizing flow model ($\mathcal{CNF}$) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the $\mathcal{CNF}$ model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses. We make our code available at https://github.com/allixu/normalizing_flow_for_detector_response

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Angle calculations using two detector circles and wavelength

Here, a `2D + λ' mode using two detector angles and wavelength is added to the modes of operation of a six-circle diffractometer, i.e. `4S + 2D' diffractometer [You (1999). J. Appl. Cryst. 32, 614–623]. In synchrotron sources, the X-ray energy, and hence the wavelength, can be a degree of freedom for angle calculations. For diffractometers with two detector circles, therefore, the variable X-ray energy can be the third degree of freedom, sufficient for accessing reciprocal space without rotating the sample. This mode is useful for limited sample rotations or X-ray beams focused smaller than the diffractometer circle of confusion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a through going network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density. This submission includes the published journal article detailing this work, the published 3-D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3-D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using existing R programs.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Specific conductivity, pH, dissolved oxygen, water temperature and alkalinity in-situ data; Slate River floodplain, Crested Butte, CO; March 2021-October 2021

This data package includes a time-series of field measurements from March to October 2021 in groundwater and surface water from the Slate River floodplain in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used.All files are in csv format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

In-situ electrochemical and water quality data; Slate River and East River floodplains, Crested Butte, CO; May 2022-September 2022

This data package includes a time-series of field measurements from May to September 2022 in groundwater and surface water from the Slate River and East River floodplains in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used.All files are in csv format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Is the BPD Nationally Representative? A Comparison of the Building Performance Database to the Commercial Buildings Energy Consumption Survey

The Building Performance Database (BPD) contains building characteristics and energy use data that can be used to benchmark buildings against their peers. However, the BPD contains data from any source that was willing to contribute data, regardless of whether that dataset is representative of the national building stock. This report explores the representativeness of the BPD. Namely, we compared the BPD to the Commercial Buildings Energy Consumption Survey (CBECS), which was designed to be a nationally representative sample of buildings. We divided the datasets into subgroups based on building type and location, we focused on three key variables (floor area, site energy use intensity, and source energy use intensity), and we compared their statistical properties in the BPD to the CBECS. We found that while the BPD contains significantly more buildings with larger floor areas, the BPD is reasonably representative in terms of site and source energy use intensity and does not appear to have a systemic bias towards energy efficient buildings.

42 ENGINEERING↗