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DOE OSTI2025

Accuracy Guarantees and Quantum Advantage in Analog Open Quantum Simulation with and without Noise

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analog quantum simulation of geometrically local open quantum systems and provide evidence that this problem both is hard to simulate on classical computers and could be approximately solved on near-term quantum devices. First, given a noiseless quantum simulator, we show that the dynamics of local observables and the fixed-point expectation values of rapidly mixing local observables in geometrically local Lindbladians can be obtained to a precision of ϵ in time that is poly ( ϵ − 1 ) and uniform in system size. Furthermore, we establish that the quantum simulator would provide a superpolynomial advantage, in run-time scaling with respect to the target precision and either the evolution time (when simulating dynamics) or the Lindbladian’s decay rate (when simulating fixed points), over any classical algorithm for these problems, assuming BQP ≠ BPP . We then consider the presence of noise in the quantum simulator in the form of additional geometrically local Lindbladian terms. We show that the simulation tasks considered in this paper are stable to errors; i.e., they can be solved to a noise-limited, but system-size independent, precision. Finally, we establish that, assuming BQP ≠ BPP , there are stable geometrically local Lindbladian simulation problems such that, as the noise rate on the simulator is reduced, classical algorithms must take time superpolynomially longer in the inverse noise rate to attain the same precision as the analog quantum simulator. Published by the American Physical Society 2025

Kashyap, Vikram (ORCID:0000000208195207)↗

DOE OSTI2023

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations

Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.

Kannan, Ramakrishnan {ramki}↗

DOE OSTI2024

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or “calibrated,” based on ground-truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable tradeoffs between simulation accuracy and simulation speed.

Mc donald, Jesse↗

DOE OSTI2020

Progress on Understanding Rayleigh–Taylor Flow and Mixing Using Synergy Between Simulation, Modeling, and Experiment

Simultaneous advances in numerical methods and computing, theoretical techniques, and experimental diagnostics have all led independently to better understanding of Rayleigh–Taylor (RT) instability, turbulence, and mixing. In particular, experiments have provided significant motivation for many simulation and modeling studies, as well as validation data. Numerical simulations have also provided data that is not currently measurable or very difficult to measure accurately in RT unstable flows. Thus, simulations have also motivated new measurements in this class of buoyancy-driven flows. This overview discusses simulation and modeling studies synergistic with experiments and examples of how experiments have motivated simulations and models of RT instability, flow, and mixing. First, a brief summary of measured experimental and calculated simulation quantities, of experimental approaches, and of issues and challenges in the simulation and modeling of RT experiments is presented. Implicit large-eddy, direct numerical, and large-eddy simulations validated using RT experimental data are then discussed. This is followed by a discussion of modeling using analytical, modal, buoyancy–drag, and turbulent transport models of RT mixing experiments. The discussion will focus on three-dimensional RT mixing arising from multimode perturbations. Finally, this focused review concludes with a perspective on future simulation, modeling, and experimental directions for further research. Research in simulation and modeling of RT unstable flows, coupled with experiments, has made significant progress over the past several decades. This overview serves as an opportunity to both discuss progress and to stimulate future research on simulation and modeling of this unique class of hydrodynamically unstable turbulent flows.

42 ENGINEERING↗

DOE OSTI2014

TH-A-BRF-11: Image Intensity Non-Uniformities Between MRI Simulation and Diagnostic MRI

Purpose: MRI simulation for MRI-based radiotherapy demands that patients be setup in treatment position, which frequently involves use of alternative radiofrequency (RF) coil configurations to accommodate immobilized patients. However, alternative RF coil geometries may exacerbate image intensity non-uniformities (IINU) beyond those observed in diagnostic MRI, which may challenge image segmentation and registration accuracy as well as confound studies assessing radiotherapy response when MR simulation images are used as baselines for evaluation. The goal of this work was to determine whether differences in IINU exist between MR simulation and diagnostic MR images. Methods: ACR-MRI phantom images were acquired at 3T using a spin-echo sequence (TE/TR:20/500ms, rBW:62.5kHz, TH/skip:5/5mm). MR simulation images were obtained by wrapping two flexible phased-array RF coils around the phantom. Diagnostic MR images were obtained by placing the phantom into a commercial phased-array head coil. Pre-scan normalization was enabled in both cases. Images were transferred offline and corrected for IINU using the MNI N3 algorithm. Coefficients of variation (CV=σ/μ) were calculated for each slice. Wilcoxon matched-pairs and Mann-Whitney tests compared CV values between original and N3 images and between MR simulation and diagnostic MR images. Results: Significant differences in CV were detected between original and N3 images in both MRI simulation and diagnostic MRI groups (p=0.010, p=0.010). In addition, significant differences in CV were detected between original MR simulation and original and N3 diagnostic MR images (p=0.0256, p=0.0016). However, no significant differences in CV were detected between N3 MR simulation images and original or N3 diagnostic MR images, demonstrating the importance of correcting MR simulation images beyond pre-scan normalization prior to use in radiotherapy. Conclusions: Alternative RF coil configurations used in MRI simulation can Result in significant IINU differences compared to diagnostic MR images. The MNI N3 algorithm reduced MR simulation IINU to levels observed in diagnostic MR images. Funding provided by Advancing a Healthier Wisconsin.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Model simulations of Plum Island Ecosystems LTER low marsh site using ELM-PFLOTRAN

Model simulations using the E3SM Land Model (ELM) coupled to the PFLOTRAN reactive transport model via the Alquimia interface. The simulations were conducted for a tidal salt marsh at the Plum Island Ecosystems LTER near Rowley, Massachusetts, USA. Model simulations were forced using site-specific tidal cycles and salinity, and the simulations used a biogeochemical reaction network including aerobic decomposition, sulfate reduction, iron reduction, and methanogenesis. Model outputs include simulated carbon stocks, carbon dioxide and methane fluxes, and porewater concentrations of key solutes related to sulfur, iron, and carbon cycling. The model simulations included a saline simulation (with tidal sulfate inputs), a fresh simulation (with low salinity and low sulfate inputs), and a saline simulation with lower vegetation productivity to represent the effect of salinity on vegetation. These simulations were conducted to demonstrate that a new model framework incorporating subsurface redox and biogeochemical interactions into a land surface model could reproduce measured surface greenhouse gas fluxes and biogeochemical dynamics in tidal marsh ecosystems, and to test whether including redox interactions in a land surface model would allow the model to resolve contrasts in biogeochemical cycling and greenhouse gas production between saline and freshwater wetlands.The data package includes gzipped tar archives (which can be expanded using standard tar and gzip utilities) of model outputs from three model configurations: saline subsurface and reduced vegetation productivity related to salinity; saline subsurface with vegetation productivity not reduced; and freshwater. Also included are code for the modified E3SM model, Alquimia interface, and PFLOTRAN reactive transport simulator in gzipped tar format; plain text parameter and configuration files; python code files for visualizing model output and defining model configurations; and model output, tide and salinity forcing, and configuration files in netCDF format. See the README.md file in the data package for a detailed description of all files contained in the package. All files are in netCDF (.nc), gzipped tar archive (.tar.gz or .tgz), or text (all other files).Updated: May 13, 2024. Model output, E3SM code, PFLOTRAN input files, and python codes for visualizing results were updated to reflect changes made for the manuscript revision. The updated archive reflects the code and model output from the final accepted manuscript. Changes included updated reaction parameters reflecting improved parameterization and additional comparisons with field measurements. E3SM code changes included better support for multiple grid cells and improved flow and transport parameterization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Monthly averages of ED2 model simulations initialized with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

Deforestation and forest degradation (selective logging, fires, fragmentation) have impacted nearly 40% of the original extent of the Brazilian Amazon, and have markedly impacted forest structure across the region. To date, few studies analysed how shifts in forest structure from degradation influence the forest sensitivity to climate extremes, because of the complex interactions between forest structure and micro-environmental conditions. To address this knowledge gap, we carried out a series of simulations across the Brazilian Amazon using the Ecosystem Demography Model (ED2), using observed forest structure derived from 541 airborne lidar transects (375 ha each) and two scenarios representing forest recovery and expansion of degradation to investigate how shifts in forest structure impact ecosystem function under near-average and extreme climate conditions, as part of the manuscript Longo et al 2025 "Degradation and Deforestation Increase the Sensitivity of the Amazon Forest to Climate Extremes". This dataset provides the output results from the ED2 model simulations for the three simulations at monthly time scales, in NetCDF format. For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.We also provide file ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc, which classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Quantitative Prediction of Uranium Speciation and Amidoxime Binding in Seawater from Advanced Simulation Techniques

The goal of this project was to quantitatively predict stability of uranium species and thermochemistry of uranium binding with amidoxime ligands in realistic seawater conditions. The specific aims of the project were: (i) determine the stable species of uranium and its solvation structure in realistic seawater conditions; also speciation and solvation of uranium's major competing species in seawater, vanadium; (ii) accurately predict the free energies (and thereby stability constants) of binding between uranium and amidoxime; (iii) accurately predict the reaction enthalpies and entropies of binding between uranium and amidoxime as well as between vanadium and amidoxime. Main results and conclusions are as follows. First-principles molecular dynamic simulations found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable and that one Ca ion binds to the center UO 2 (CO 3 ) 3 4- anion stronger than the other Ca ion. This finding suggests that using time-resolved EXAFS spectra may confirm the asymmetry in binding of the two Ca ions in the aqueous Ca 2 UO 2 (CO 3 ) 3 complex. To consider the common ions, solvation of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater was simulated by classical molecular dynamics simulation. It was found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable in the model seawater. A Na+ ion was found to be closely associated with the Ca 2 UO 2 (CO 3 ) 3 complex by indirectly interacting with one axial oxygen atom of the UO 2 group bridged by a water molecule. In addition, the Na + ion interacts closely with one Ca 2+ ion than the other. The present simulations revealed the key role of common ions such as Na + in impacting the solvation, structure, and apparent charge of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater. The sequential processes of UO 2 2+ binding with the three CO 3 2- groups and the two Ca 2+ ions in pure water and in 0.1 M NaCl were simulated by classical molecular dynamics with both the non-constrained brute-force approach and umbrella sampling. The simulated free energies show excellent agreement with the experiment. The coordination structures of UO 2 (CO 3 ) 3 4- and CaUO 2 (CO 3 ) 3 2- were found to be significantly affected by the presence of Na + ions, leading to a monodentate binding of a carbonate group to U and a water molecule entering the first coordination shell of U. To shed light on the binding between uranyl and glutardiamidoxime (H 2 B), an important model and state-of-the-art ligand for seawater uranium extraction, a suite of computational methods was employed. From molecular dynamics (MD) simulations, it was found that the binding configurations of B 2- with uranyl favor the twofold distorted η 2 binding between the oximate ends (C=N-O-) and U, while HB - prefers the chelating mode for the oximate end with the neutral end being solvated by water. The free energies of sequential ligand binding to form UO 2 B, [UO 2 B 2 ] 2- , and [UO 2 (HB)B] - were simulated with umbrella sampling and very good agreement with the experimental values was achieved, which corroborates the structural insights into the binding mode. State-of-the-art polymeric sorbents employ both amidoximate and carboxylate groups on the side chains to achieve optimal U uptake and selectivity, so we simulated the binding of a model amidoximate–carboxylate bifunctional ligand with uranyl. Classical MD and free-energy simulations in 0.5 M NaCl showed that the carboxylate group binds first to uranyl, leading to a loose intermediate state, and then, the amidoximate group binds, resulting in a more stable and tight chelate state. Binding of the second bifunctional ligand follows a similar process, and the two ligands prefer a trans configuration around the uranyl group. The simulated free energies indicate that the two bifunctional ligands bind with uranyl 14 kcal/mol stronger than the two ligands with only amidoximate groups, confirming an important synergy between amidoximate and carboxylate groups in binding uranyl.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Investigation of thermolytic hydrogen generation rate of tank farm simulated and actual waste

To support resolution of Potential Inadequacies in the Safety Analysis for the Savannah River Site (SRS) Tank Farm, Savannah River National Laboratory conducted research to determine the thermolytic hydrogen generation rate (HGR) with simulated and actual waste. Gas chromatography methods were developed and used with air-purged flow systems to quantify hydrogen generation from heated simulated and actual waste at rates applicable to the Tank Farm Documented Safety Analysis (DSA). Initial simulant tests with a simple salt solution plus sodium glycolate demonstrated the behavior of the test apparatus by replicating known HGR kinetics. Additional simulant tests with the simple salt solution excluding organics apart from contaminants provided measurement of the detection and quantification limits for the apparatus with respect to hydrogen generation. Testing included a measurement of HGR on actual SRS tank waste from Tank 38. A final series of measurements examined HGR for a simulant with the most common SRS Tank Farm organics at temperatures up to 140 °C. The following conclusions result from this testing. HGR was measured for an actual-waste Tank 38 sample. The HGR measurements at 80, 90, 100, and 110 °C were 1.7×10 -7 , 4.7×10 -7 , 5.9×10 -7 , and 2.6×10 -6 ft 3 hr -1 gal -1 , respectively. The Limit of Detection and Limit of Quantification for the HGR measurements were 2.6×10-8 ft3 hr-1 gal-1 and 5.1×10 -8 ft 3 hr -1 gal -1 , respectively. HGR was measured in a high boiling point (HBP) simulant with major known SRS Tank Farm organic compounds. Testing used fresh organics and did not simulate the long-term exposure of these organics already experienced in the waste. Hence, testing is expected to provide high bias to the hydrogen generation rates. The final HGR measurements at 75, 100, and 140 °C were 6.2×10 -7 , 3.0×10 -6 , and 8.1×10 -5 ft 3 hr -1 gal -1 , respectively. Decreases in the HGR were noted at isothermal conditions with steady gas tracer concentration, which clouds development of a temperature dependent correlation for generation rate. While the simulant contains high concentrations of major SRS organics tested at high temperatures found in the 3H Evaporator, this limited data set may not fully bound values under all possible operating conditions. The HGR measurement apparatus was successfully employed to replicate hydrogen production rate experiments. Simulant without deliberately added organic carbon was found to contain approximately 200 mg/L of organic carbon from tramp organics in the reagents used to prepare the solution. The tests using such simulant generated hydrogen at rates consistent with, but somewhat lower than, those predicted at equivalent carbon and aluminum loadings by methods used at the Hanford Site. Prior to identifying the organic contaminants, potential sources of hydrogen in simulants without deliberately added organic carbon were examined. Testing determined that this hydrogen is not due to caustic reaction of organic materials of construction within the reaction vessel, electrolysis due to current leak, anode/cathode reactions, corrosion, or the method of heating employed in the experiment. During the HBP and Tank 38 testing, a peak was identified at a retention time greater than the retention time of the inert tracer of krypton. This peak has been identified as methane, and the data have been reexamined to obtain methane concentrations. For the HBP testing, methane concentration was constant at the highest temperature studied (140 °C) and higher than hydrogen. At the lower temperatures (100 and 75 °C), methane and hydrogen tracked closely, with methane concentration generally lower than hydrogen. For Tank 38, methane concentration was approximately 1/3 of the hydrogen concentration during testing (100 and 110 °C). At lower temperatures for Tank 38 testing (90, 80, and 27 °C), methane fell below the quantification range.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

All-Atom Simulation of 3D Hot Spot Formation in Shocked TATB Explosive

TATB is an insensitive high explosive (IHE) critical to the stockpile that is challenging to model at the continuum scale. Advanced detonation models in the Cheetah high explosive chemistry code require validation though subscale simulations. High explosive initiation is determined by micron-scale physics of hot spots formed a shock-collapsed pores. Pore sizes between 100 nm and 1 μm are believed to be the most important for determining the shock sensitivity of TATB. This range of pore sizes is difficult to access at the atomic scale through allatom molecular dynamics (MD) simulations, even with Sierra-class computers. Quasi-2D simulations are widely used and allow much larger pore sizes (up to 400 nm) to be studied, but the applicability of 2D simulations to the actual 3D pore response is not understood. Resolving these uncertainties through “full physics” MD modeling is key for generalizing, parameterizing, and validating the kinds of continuum models used to inform design, safety, and performance. This work was a continuation of FY20 efforts pushing simulations to full 3D with the largest-ever all-atom simulations of an explosive. These were the first all-atom full-3D simulations of large hot spots thought to govern explosive detonation and required over a billion atoms. Simulations were performed using LAMMPS, an open SNL science code. MD explosive models present unique challenges, even for established codes such as LAMMPS. Their model forms are more complex than typical models for metals, while simulating high temperature-pressure conditions is demanding and increases computational cost. Scaling problems in GPU-enabled MD algorithms initially limited simulations to <100 million atoms but were resolved through collaboration with SNL. An overall 24x speedup was obtained relative to CPU machines. Specialized analysis of these simulations required a bottom-up refactoring and algorithm parallelization of in-house codes and application of computer vision algorithms to extract meaningful information.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Frequency Domain Reflectometry (FDR) Simulation Techniques for Digital Twin Representation of an Electrical Cable

Simulation of cable system response to frequency domain reflectometry (FDR) tests can be instrumental to understanding these tests and the nature and influence of various cable anomalies on test signatures. Reflectometry simulations are based upon a finite element representation of cable conductors and insulation to produce an S-parameter at each evaluated frequency. The aggregate collection of cable model S-parameters can simulate the influence on a test signal injected into a physical cable. Such an approach was undertaken in this work to produce a digital twin simulation of a low-voltage electrical cable. The electrical cable digital twin examined the influence of test simulation parameters and the relative influence of cable anomalies, including thermal aging, water or moisture exposure, water or moisture ingress, and other anomalies. The digital twin in this work included modeling of the conductors, insulation, jacket, and surrounding environment (air, water, etc.). The digital twin could be expanded to include cable bends, junctions and splices, branch or T systems, and termination impedances of motors or instruments. Observations and conclusions of this work include: 1. Fully 3D digital twin simulation of an electrical cable using an FDR approach is possible. However, there are tradeoffs between simulation fidelity and solution time, which must be balanced to ensure the simulation solves in an adequate amount of time (e.g., less than 20 minutes). Simulation parameters to balance include frequency bandwidth, number of frequencies, mesh density, connection impedance, and permittivity tolerance. 2. The digital twin simulation can explain FDR sensitivity to various cable anomalies, including entry and exit from an oven or water bath. 3. The digital twin simulation FDR response attenuates with distance along the cable and is further affected by the frequency bandwidth, which is similar to that observed with physical measurements. 4. The resolution of the digital twin FDR peaks increased with increasing bandwidth and with increasing number of frequencies, again similar to physical measurements. 5. The presence of multiple anomalies in the digital twin does not substantially attenuate the FDR response to anomalies located beyond the first encountered anomaly and impedance mismatch. 6. Spectral variation of the permittivity did not have a significant effect on the FDR response compared to a fixed nominal value. 7. Extension of the digital twin to 1000 ft still allowed for detection of distal anomalies near the far end of the electrical cable from the instrument connection point. 8. The ARENA test bed facilitates efficient NDE evaluations of well understood cable anomalies with various NDE methods without risking actual plant damage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Simulations of ocean deoxygenation in the historical era: insights from forced and coupled models

Ocean deoxygenation due to anthropogenic warming represents a major threat to marine ecosystems and fisheries. Challenges remain in simulating the modern observed changes in the dissolved oxygen (O 2 ). Here, we present an analysis of upper ocean (0-700m) deoxygenation in recent decades from a suite of the Coupled Model Intercomparison Project phase 6 (CMIP6) ocean biogeochemical simulations. The physics and biogeochemical simulations include both ocean-only (the Ocean Model Intercomparison Project Phase 1 and 2, OMIP1 and OMIP2) and coupled Earth system (CMIP6 Historical) configurations. We examine simulated changes in the O 2 inventory and ocean heat content (OHC) over the past 5 decades across models. The models simulate spatially divergent evolution of O 2 trends over the past 5 decades. The trend (multi-model mean and spread) for upper ocean global O 2 inventory for each of the MIP simulations over the past 5 decades is 0.03 ± 0.39×10 14 [mol/decade] for OMIP1, -0.37 ± 0.15×10 14 [mol/decade] for OMIP2, and -1.06 ± 0.68×10 14 [mol/decade] for CMIP6 Historical, respectively. The trend in the upper ocean global O2 inventory for the latest observations based on the World Ocean Database 2018 is -0.98×10 14 [mol/decade], in line with the CMIP6 Historical multi-model mean, though this recent observations-based trend estimate is weaker than previously reported trends. A comparison across ocean-only simulations from OMIP1 and OMIP2 suggests that differences in atmospheric forcing such as surface wind explain the simulated divergence across configurations in O 2 inventory changes. Additionally, a comparison of coupled model simulations from the CMIP6 Historical configuration indicates that differences in background mean states due to differences in spin-up duration and equilibrium states result in substantial differences in the climate change response of O 2 . Finally, we discuss gaps and uncertainties in both ocean biogeochemical simulations and observations and explore possible future coordinated ocean biogeochemistry simulations to fill in gaps and unravel the mechanisms controlling the O 2 changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates

Abstract Cosmological simulations of galaxy formation are an invaluable tool for understanding galaxy formation and its impact on cosmological parameter inference from large-scale structures. However, their high computational cost is a significant obstacle for running simulations that probe cosmological volumes comparable to those analyzed by contemporary large-scale structure experiments. In this work, we explore the possibility of obtaining high-precision galaxy clustering predictions from small-volume hydrodynamical simulations such as MillenniumTNG and FLAMINGO via control variates. In this approach, the hydrodynamical full-physics simulation is paired with a matched low-resolution gravity-only simulation. By learning the galaxy–halo connection from the hydrodynamical simulation and applying it to the gravity-only counterpart, one obtains a galaxy population that closely mimics the one in the more expensive simulation. One can then construct an estimator of galaxy clustering that combines the clustering amplitudes in the small-volume hydrodynamical and gravity-only simulations with clustering amplitudes in a large-volume gravity-only simulation. Depending on the galaxy sample, clustering statistic, and scale, this galaxy clustering estimator can have an effective volume of up to around 100 times the volume of the original hydrodynamical simulation in the nonlinear regime. With this approach, we can construct galaxy clustering predictions from existing simulations that are precise enough for mock analyses of next-generation large-scale structure surveys such as the Dark Energy Spectroscopic Instrument and the Legacy Survey of Space and Time.

Doytcheva, Alexandra (ORCID:0009000111254888)↗

DOE OSTI2014

Simulating weather regimes: impact of model resolution and stochastic parameterization

The simulation of quasi-persistent regime structures in an atmospheric model with horizontal resolution typical of the Intergovernmental Panel on Climate Change fifth assessment report simulations, is shown to be unrealistic. A higher resolution configuration of the same model, with horizontal resolution typical of that used in operational numerical weather prediction, is able to simulate these regime structures realistically. The spatial patterns of the simulated regimes are remarkably accurate at high resolution. A model configuration at intermediate resolution shows a marked improvement over the low-resolution configuration, particularly in terms of the temporal characteristics of the regimes, but does not produce a simulation as accurate as the very-high-resolution configuration. It is demonstrated that the simulation of regimes can be significantly improved, even at low resolution, by the introduction of a stochastic physics scheme. At low resolution the stochastic physics scheme drastically improves both the spatial and temporal aspects of the regimes simulation. These results highlight the importance of small-scale processes on large-scale climate variability, and indicate that although simulating variability at small scales is a necessity, it may not be necessary to represent the small-scales accurately, or even explicitly, in order to improve the simulation of large-scale climate. It is argued that these results could have important implications for improving both global climate simulations, and the ability of high-resolution limited-area models, forced by low-resolution global models, to reliably simulate regional climate change signals.

Meteorology & Atmospheric Sciences↗

DOE OSTI2016

Emulation of dynamic simulators with application to hydrology

Many simulation-intensive tasks in the applied sciences, such as sensitivity analysis, parameter inference or real time control, are hampered by slow simulators. Emulators provide the opportunity of speeding up simulations at the cost of introducing some inaccuracy. An emulator is a fast approximation to a simulator that interpolates between design input–output pairs of the simulator. Increasing the number of design data sets is a computationally demanding way of improving the accuracy of emulation. We investigate the complementary approach of increasing emulation accuracy by including knowledge about the mechanisms of the simulator into the formulation of the emulator. To approximately reproduce the output of dynamic simulators, we consider emulators that are based on a system of linear, ordinary or partial stochastic differential equations with a noise term formulated as a Gaussian process of the parameters to be emulated. This stochastic model is then conditioned to the design data so that it mimics the behavior of the nonlinear simulator as a function of the parameters. The drift terms of the linear model are designed to provide a simplified description of the simulator as a function of its key parameters so that the required corrections by the conditioned Gaussian process noise are as small as possible. The goal of this paper is to compare the gain in accuracy of these emulators by enlarging the design data set and by varying the degree of simplification of the linear model. We apply this framework to a simulator for the shallow water equations in a channel and compare emulation accuracy for emulators based on different spatial discretization levels of the channel and for a standard non-mechanistic emulator. Our results indicate that we have a large gain in accuracy already when using the simplest mechanistic description by a single linear reservoir to formulate the drift term of the linear model. Adding some more reservoirs does not lead to a significant improvement in accuracy. However, the transition to a spatially continuous linear model leads again to a similarly large gain in accuracy as the transition from the non-mechanistic emulator to that based on one reservoir.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Evaluation of the Impact of Horizontal Grid Spacing in Terra Incognita on Coupled Mesoscale–Microscale Simulations Using the WRF Framework

Coupled mesoscale-microscale simulations are required to provide time-varying weather-dependent inflow and forcing for large-eddy simulations under general flow conditions. Such coupling necessarily spans a wide range of spatial scales (i.e., ~10 m to ~10 km). In this paper, we use simulations that involve multiple nested domains with horizontal grid spacings in the terra incognita (i.e., ≲km) that may affect simulated conditions in both the outer and inner domains. We examine the impact on simulated wind speed and turbulence associated with forcing provided by a terrain with grid spacing in the terra incognita. We perform a suite of simulations that use combinations of varying horizontal grid spacings and turbulence parameterization/modeling using the Weather Research and Forecasting (WRF) Model using a combination of planetary boundary layer (PBL) and large-eddy simulation subgrid-scale (LES-SGS) models. The results are analyzed in terms of spectral energy, turbulence kinetic energy, and proper orthogonal decomposition (POD) energy. The results show that the output from the microscale domain depends on the type of turbulence model (e.g., PBL or LES-SGS model) used for a given horizontal grid spacing but is independent of the horizontal grid spacing and turbulence modeling of the parent domain. Simulation using a single domain produced less POD energy in the first few modes compared to a coupled simulation (one-way nesting) for similar horizontal grid spacing, which highlights that coupled simulations are required to accurately pass the mesoscale features into the microscale domain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Whistler turbulence heating of electrons and ions: Three-dimensional particle-in-cell simuations

In this study, the decay of whistler turbulence in a collisionless, homogeneous, magnetized plasma is studied using three-dimensional particle-in-cell simulations. The simulations are initialized with a narrowband, relatively isotropic distribution of long wavelength whistler modes. A first ensemble of simulations at electron beta ${\beta }_{{\rm{e}}}$ = 0.25 and ion-to-electron mass ratio ${m}_{{\rm{i}}}$/${m}_{{\rm{e}}}$ = 400 is carried out on a domain cube of dimension $L{\omega }_{\mathrm{pi}}$/c = 5.12 where ${\omega }_{\mathrm{pi}}$ is the ion plasma frequency. The simulations begin with a range of dimensionless fluctuating field energy densities, ${\epsilon }_{{\rm{o}}}$, and follow the fluctuations as they cascade to broadband, anisotropic turbulence which dissipates at shorter wavelengths, heating both electrons and ions. The electron heating is stronger and preferentially parallel/antiparallel to the background magnetic field ${{\boldsymbol{B}}}_{{\rm{o}}};$ the ion energy gain is weaker and is preferentially in directions perpendicular to ${{\boldsymbol{B}}}_{{\rm{o}}}$. The important new results here are that, over 0.01 < ${\epsilon }_{{\rm{o}}}$ < 0.25, the maximum rate of electron heating scales approximately as ${\epsilon }_{{\rm{o}}}$, and the maximum rate of ion heating scales approximately as ${\epsilon }_{{\rm{o}}}^{1.5}$. A second ensemble of simulations at ${\epsilon }_{{\rm{o}}}$ = 0.10 and ${\beta }_{{\rm{e}}}$ = 0.25 shows that, over 25 < ${m}_{{\rm{i}}}$/${m}_{{\rm{e}}}\;$< 1836, the ratio of the maximum ion heating rate to the maximum electron heating rate scales approximately as ${m}_{{\rm{e}}}$/${m}_{{\rm{i}}}$.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Suitability of Synthetic Driving Profiles from Traffic Micro-Simulation for Real-World Energy Analysis: Preprint

A shift towards increased levels of driving automation is generally expected to result in improved safety and traffic congestion outcomes. However, little empirical data exists to estimate the impact that automated driving could have on energy consumption and greenhouse gas emissions. In the absence of empirical data on differences between drive cycles from present day vehicles (primarily operated by humans) and future vehicles (partially or fully operated by computers) one approach is to model both situations over identical traffic conditions. Such an exercise requires traffic micro-simulation to not only accurately model vehicle operation under high levels of automation, but also (and potentially more challenging) vehicle operation under present day human drivers. This work seeks to quantify the ability of a commercial traffic micro-simulation program to accurately model real-world drive cycles in vehicles operated primarily by humans in terms of driving speed, acceleration, and simulated fuel economy. Synthetic profiles from models of freeway and arterial facilities near Atlanta, Georgia, are compared to empirical data collected from real-world drivers on the same facilities. Empirical and synthetic drive cycles are then simulated in a powertrain efficiency model to enable comparison on the basis of fuel economy. Synthetic profiles from traffic micro-simulation were found to exhibit low levels of transient behavior relative to the empirical data. Even with these differences, the synthetic and empirical data in this study agree well in terms of driving speed and simulated fuel economy. The differences in transient behavior between simulated and empirical data suggest that larger stochastic contributions in traffic micro-simulation (relative to those present in the traffic micro-simulation tool used in this study) are required to fully capture the arbitrary elements of human driving. Interestingly, the lack of stochastic contributions from models of human drivers in this study did not result in a significant discrepancy between fuel economy simulations based on synthetic and empirical data; a finding with implications on the potential energy efficiency gains of automated vehicle technology.

33 ADVANCED PROPULSION SYSTEMS↗