Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ERRORS”

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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Self-interaction correction in water–ion clusters

We study the importance of self-interaction errors in density functional approximations for various water–ion clusters. We have employed the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method in conjunction with LSDA, PBE, and SCAN to describe binding energies of hydrogen-bonded water–ion clusters, i.e., water–hydronium, water– hydroxide, water–halide, as well as non-hydrogen-bonded water–alkali clusters. In the hydrogen-bonded water–ion clusters, the building blocks are linked by hydrogen atoms, although the links are much stronger and longer-ranged than the normal hydrogen bonds between water molecules, because the monopole on the ion interacts with both permanent and induced dipoles on the water molecules. We find that self-interaction errors overbind the hydrogen-bonded water– ion clusters and that FLOSIC reduces the error and brings the binding energies into closer agreement with higher–level calculations. Here, the non-hydrogen-bonded water–alkali clusters are not significantly affected by self-interaction errors. Self-interaction corrected PBE predicts the lowest mean unsigned error in binding energies (≤ 50 meV/H 2 O) for hydrogen-bonded water–ion clusters. Self-interaction errors are also largely dependent on the cluster size, and FLOSIC does not accurately capture the subtle variation in all clusters, indicating the need for further refinement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deciphering baryonic feedback with galaxy clusters

Abstract Upcoming cosmic shear analyses will precisely measure the cosmic matter distribution at low redshifts. At these redshifts, the matter distribution is affected by galaxy formation physics, primarily baryonic feedback from star formation and active galactic nuclei. Employing measurements from theMagneticumandIllustrisTNGsimulations and a dark matter + baryon (DMB) halo model, this paper demonstrates that Sunyaev-Zel'dovich (SZ) effect observations of galaxy clusters, whose masses have been calibrated using weak gravitational lensing, can constrain the baryonic impact on cosmic shear with statistical and systematic errors subdominant to the measurement errors of DES-Y3 and LSST-Y1, with systematic errors on S 8 and Ω m reaching 10% and 50% of the statistical errors, respectively. For LSST-Y6 and Roman surveys, these systematic errors increase to 150% and 100% of the statistical errors, indicating the necessity for further model developments for future surveys. We further dissect the contributions from different scales and halos with different masses to cosmic shear, highlighting the dominant role of SZ clusters at scales critical for cosmic shear analyses. These findings suggest a promising avenue for future joint analyses of Cosmic Microwave Background (CMB) and lensing surveys.

Astronomy & Astrophysics↗
DOE OSTI2022

Characterizing Midcircuit Measurements on a Superconducting Qubit Using Gate Set Tomography

Measurements that occur within the internal layers of a quantum circuit—midcircuit measurements—are a useful quantum-computing primitive, most notably for quantum error correction. Midcircuit measurements have both classical and quantum outputs, so they can be subject to error modes that do not exist for measurements that terminate quantum circuits. In this work, we show how to characterize midcircuit measurements, modeled by quantum instruments, using a technique that we call quantum instrument linear gate set tomography (QILGST). We then apply this technique to characterize a dispersive measurement on a superconducting transmon qubit within a multiqubit system. By varying the delay time between the measurement pulse and subsequent gates, we explore the impact of residual cavity photon population on measurement error. QILGST can resolve different error modes and quantify the total error from a measurement; in our experiment, for delay times above 1000 ns we measure a total error rate (i.e., half diamond distance) of ϵ$\diamond$ = 8.1 ± 1.4%, a readout fidelity of 97.0 ± 0.3%, and output quantum-state fidelities of 96.7 ± 0.6% and 93.7 ± 0.7 % when measuring 0 and 1, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Circuit-Based Leakage-to-Erasure Conversion in a Neutral-Atom Quantum Processor

Atom-loss errors are a major limitation of current state-of-the-art neutral-atom quantum computers and pose a significant challenge for scalable systems. In a quantum processor with cesium atoms, we demonstrate proof-of-principle circuit-based conversion of this form of leakage error to erasure errors via leakage-detection units (LDUs), which nondestructively map information about the presence or absence of the qubit onto the state of an ancilla. We benchmark the performance of the LDU using a three-outcome low-loss state-detection method and find that the LDU detects atom-loss errors with approximately 93.4% accuracy, limited by technical imperfections of our apparatus. We further compile and execute a SWAP LDU, wherein the roles of the original data atom and ancilla atom are exchanged under the action of the LDU, providing “free refilling” of atoms in the case of atom loss. This circuit-based leakage-to-erasure error conversion is a critical component of a neutral-atom quantum processor where the quantum information may significantly outlive the lifetime of any individual atom in the quantum register. Finally, we demonstrate that LDUs may also be used to handle other forms of leakage errors where population moves to states outside of the computational subspace.

Chow, Matthew N. H. [Sandia National Laboratories ↗
DOE OSTI2021

Characterizing Impacts of Storage Faults on HPC Applications: A methodology and insights

In recent years, the increasing complexity in scientific simulations and emerging demands for training heavy artificial intelligence models require massive and fast data accesses, which urges high-performance computing (HPC) platforms to equip with more advanced storage infrastructures such as solid-state disks (SSDs). While SSDs offer high-performance I/O, it remains unclear about the reliability challenges faced by the HPC applications under the SSD-related failures, in particular, failures resulting in data corruptions. The goal of this paper is to understand the impact of SSD-related data corruptions on the behaviors of complex HPC applications. To this end, we propose FFIS, a FUSE-based fault injection framework that systematically introduces storage faults into the application layer to model the errors originated from SSDs. FFIS is able to plant different I/O related faults into the data returned from underlying file systems, which also enables the investigation on the error resilience characteristics of the scientific file format for the first time. We demonstrate the use of FFIS with three representative real HPC applications, show how each application reacts to the data corruptions, and provide insights on the error resilience of the widely-adopted HDF5 file format for the HPC applications.

Fang, Bo↗
DOE OSTI2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

Recent proliferation of distributed energy sources in distribution or sub-transmission systems necessitates close monitoring of these three-phase power grids which typically operate under unbalanced loading conditions. Unlike the transmission systems where the network equations are commonly based on the positive sequence component models, a detailed three phase model will have to be used in implementing network applications for these systems. In the specific case of the state estimator, where measurement and parameter errors may bias the solution, bad data and parameter error detection algorithms should also be incorporated. Implementing the state estimator and error detection algorithms for three-phase systems impose additional computational burden and modifications to the state estimation code. This paper proposes a practical solution to avoid these issues by using synchronized phasor measurements and modal decoupling. The previously developed parameter error detection algorithm based on the normalized Lagrange multipliers (NLM) test is applied to the measurements independently in each mode in parallel, not only saving CPU time but also avoiding new code development for a three-phase estimator. Different parameter error scenarios are created and tested to verify the effectiveness of the proposed error detection approach.

Khalili, Ramtin↗
DOE OSTI2021

Scalable Programming Workflows for Validation of Quantum Computers

Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.

Nguyen, Thien↗
DOE OSTI2022

Superconvergence of Online Optimization for Model Predictive Control

We develop a one-Newton-step-per-horizon, online, lag-L, model predictive control (MPC) algorithm for solving discrete-time, equality-constrained, nonlinear dynamic programs. Based on recent sensitivity analysis results for the target problems class, we prove that the approach exhibits a behavior that we call superconvergence; that is, the tracking error with respect to the full horizon solution is not only stable for successive horizon shifts, but also decreases with increasing shift order to a minimum value that decays exponentially in the length of the receding horizon. The key analytical step is the decomposition of the one-step error recursion of our algorithm into algorithmic error and perturbation error. We show that the perturbation error decays exponentially with the lag between two consecutive receding horizons, while the algorithmic error, determined by Newton’s method, achieves quadratic convergence instead. Overall this approach induces our local exponential convergence result in terms of the receding horizon length for suitable values of L. In conclusion, numerical experiments validate our theoretical findings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Doppler-Lidar Evaluation of HRRR-Model Skill at Simulating Summertime Wind Regimes in the Columbia River Basin during WFIP2

Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s -1 and RMSEs of 2-3 m s -1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Finally, measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

17 WIND ENERGY↗
DOE OSTI2024

Mitigative Strategies for Recovering From Large Language Model Trust Violations

In this study, we investigated strategies to address trust issues arising from errors in large language models (LLMs). The study examined the impact of confidence scores, system capability explanations, and user feedback on trust restoration post-error. 68 participants viewed the responses of an LLM to 20 general trivia questions, with an error introduced on the third trial. Each participant was presented with one mitigation strategy. Participants rated their overall trust in the model and the reliability of the answer. Results showed an immediate drop in trust after the error; however, there were no differences across the three strategies in trust recovery. All conditions had a logarithmic trend in trust recovery following error. Differences in overall trust were predicted by perceived reliability of the answer, suggesting that participants were evaluating results critically and using that to inform their trust in the model. Qualitative data supported this finding; participants expressed lasting distrust despite the LLM’s later accuracy. Results showcase the need to prioritize accuracy in LLM deployment, because early errors may irrevocably damage user trust calibration and later adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Analysis of picosecond coherent anti-Stokes Raman spectra for gas-phase diagnostics

We present a hybrid frequency- and time-domain solution, applicable to the case of picosecond coherent anti-Stokes Raman scattering (CARS), for gas-phase diagnostics. A solution has been derived based on both physical arguments and four-wave mixing equations for picosecond CARS, with pulse durations that are comparable to the dephasing time scale for gas-phase Raman coherence—a regime where commonly employed solutions for impulsive (femtosecond) or cw (nanosecond) pump/Stokes forcing are not strictly valid. We present the ps-CARS spectrum in the form of incoherent sums of CARS intensity spectra, calculated from the fundamental solution for impulsive pump/Stokes Raman preparation. The solution was examined for temperatures from 1000–3000 K, for four plausible experimental configurations, with laser pulse durations of 50–150 ps, and probe pulse delays from −20 to 240 ps. Approximations based on cw and impulsive pump/Stokes preparation to fit picosecond CARS spectra at atmospheric pressure were examined and the relative thermometric accuracy and computational cost of these approximations were quantified for the case of a zero nonresonant CARS contribution, and a nonresonant susceptibility equal to 10% of the Raman-resonant value at the N 2 bandhead. The nanosecond CARS approximation can result in large fitting errors when the probe pulse time delay is less than the probe pulse duration. Errors as large as 10–20% are observed in the fit temperatures for a zero picosecond probe pulse delay, when the nonresonant background is neglected, largely due to an inability of the time-independent cw model to capture transient frequency spread dephasing effects at the Q -branch bandhead. The inclusion of a nonresonant background results in 40–60% thermometry errors with a nanosecond model at a zero-probe delay. Time-dependent impulsive calculations used for femtosecond CARS better approximate the structure of the N 2 bandhead, reducing temperature fitting errors to 5–10% at a short probe pulse delay. The impulsive approximation results in errors up to 10% at intermediate probe pulse delays, where the coherence of the pump and probe pulses leads to multiple terms in the picosecond CARS solution. Both approximations improve as the probe pulse delay exceeds the probe duration. The nanosecond approximation results in a 2–3% error, while the impulsive model results in differences of less than 1% in some cases. Fits to experimental data obtained using short, ∼60ps pulses at a zero probe time delay and longer 100 ps pulses at a substantial 200 ps delay are presented with accuracies of 1–3% in the fit temperature.

Kearney, Sean P.↗
DOE OSTI2024

Predicting ptychography probe positions using single-shot phase retrieval neural network

Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of 10 2 pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Blind Modeling Validation Exercises Using the Horizontal Dry Cask Simulator

The U.S. Department of Energy (DOE) established a need to understand the thermal-hydraulic properties of dry storage systems for commercial spent nuclear fuel (SNF) in response to a shift towards the storage of high-burnup (HBU) fuel (> 45 gigawatt days per metric ton of uranium, or GWd/MTU). This shift raises concerns regarding cladding integrity, which faces increased risk at the higher temperatures within spent fuel assemblies present within HBU fuel compared to low-burnup fuel (≤ 45 GWd/MTU). A dry cask simulator (DCS) was built at Sandia National Laboratories (SNL) in Albuquerque, New Mexico to produce validation-quality data that can be used to test the accuracy of the modeling used to predict cladding temperatures. These temperatures are critical to evaluating cladding integrity throughout the storage cycle of commercial spent nuclear fuel. A model validation exercise was previously carried out for the DCS in a vertical configuration. Lessons learned during the previous validation exercise have been applied to a new, blind study using a horizontal dry cask simulator (HDCS). Three modeling institutions – the Nuclear Regulatory Commission (NRC), Pacific Northwest National Laboratory (PNNL), and Empresa Nacional del Uranio, S.A., S.M.E. (ENUSA) – were granted access to the input parameters from the DCS Handbook, SAND2017-13058R, and results from a limited data set from the horizontal BWR dry cask simulator tests reported in the HDCS update report, SAND2019-11688R. With this information, each institution was tasked to calculate peak cladding temperatures and air mass flow rates for ten HDCS test cases. Axial as well as vertical and horizontal transverse temperature profiles were also calculated. These calculations were done using modeling codes (ANSYS/Fluent, STAR-CCM+, or COBRA-SFS), each with their own unique combination of modeling assumptions and boundary conditions. For this validation study, the ten test cases of the horizontal dry cask simulator were defined by three independent variables – fuel assembly decay heat (0.5 kW, 1 kW, 2.5 W, and 5 kW), internal backfill pressure (100 kPa and 800 kPa), and backfill gas (helium and air). The plots provided in Chapter 3 of this report show the axial, vertical, and horizontal temperature profiles obtained from the dry cask simulator experiments in the horizontal configuration and the corresponding models used to describe the thermal-hydraulic behavior of this system. The tables provided in Chapter 3 illustrate the closeness of fit of the model data to the experiment data through root mean square (RMS) calculations of the error in peak cladding temperatures (PCTs), PCT axial locations, axial temperature profiles, vertical and horizontal temperature profiles at two different axial locations, and air mass flow rates for the ten test cases, normalized by the experimental results. The model results are assigned arbitrary model numbers to retain anonymity. Due to the relatively flat axial temperature profiles, small temperature gradients resulted in large deviations of all models’ PCT axial location from the experimental PCT axial location. When the PCT axial location error is excluded in the calculation of the combined RMS of the normalized errors that considers PCT, the temperature profiles, and the air mass flow rates, the model data fits the experimental data to within 5%. When the vault information is excluded, the model data fits the experimental data to within 2.5%. An error analysis was developed further for one model, using the model and experimental uncertainties in each validation parameter to calculate validation uncertainties. The uncertainties for each parameter were used to define quantifiable validation criteria. For this analysis, the model was considered validated for a given comparison metric if the normalized error in that metric divided by the validation uncertainty was less than or equal to 1. When considering the combined RMS of the normalized errors of all metrics divided by their validation uncertainties, the model was found to have satisfied the criterion for model validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Preliminary Sensitivity Analysis for Sensors Impacts on Building Control Performance

This report describes the preliminary sensitivity analysis for sensor impacts on building control performance through the US Department of Energy’s Oak Ridge National Laboratory’s Flexible Research Platform (FRP-2) building. The rooftop unit system provides cooling and heating to the building. The main heating coil is a gas heating coil. Each zone is served by a variable air volume box with an electricity reheat coil. The rooftop unit and variable air volume box controls adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. For sensors, the incipient (time-changing) sensor errors, including bias sensor error and precision sensor error, are the inputs of interest. The outputs are energy consumption and thermal comfort (e.g., the predicted percentage of dissatisfied occupants). The large-scale simulation (3,600 cases) was conducted on a cloud platform by integrating sensor errors and ASHRAE Guideline 36 control sequences into an emulator based on the EnergyPlus simulation program with Python energy management system feature. The surrogate models were developed based on cloud simulation results. The uncertainty analysis showed that the sensor errors substantially affect building energy consumption and thermal comfort. The sensitivity analysis shows a ranking of sensor error impacts for each interested output item (e.g., cooling energy, reheat coil heating energy, predicted percentage of dissatisfied occupants). In FY 2022, sensor locations, types, and costs will be evaluated. The field test in Oak Ridge National Laboratory’s Flexible Research Platform building regarding sensor impacts will also be performed. Finally, a comparative analysis will be conducted based on the field test results and emulator results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Assessment of the Impact of Realistic Sensor Physics and the Integration of Ex-Core Sensors on Reactor Power Synthesis

In the work documented in this report, a weighting function–based core power synthesis method was applied to multiple Monte Carlo N-Particle (MCNP) reactor models, which are informed based on simulated self-powered neutron detector (SPND) responses. The weighting function method used has been coined the point-based iterative (PBI) method. The goal of this application is to assess the impact of considering realistic sensor physics in the generation of the simulated SPND outputs as well as to consider how the synthesis is impacted based on the inclusion of ex-core detectors in the model. The NuScale small modular reactor (SMR) and Westinghouse AP1000 pressurized water reactor (PWR) are the models that served as the testbeds for the assessment of realistic sensor physics; this was achieved by using Geant4 SPND models in comparison with analytical models, such that the effect of electron transport in realistic SPND geometries in the Geant4 model can be understood in terms of synthesis error and convergence time. The comparison was considered for fuel burnup–induced perturbations, for a range of sensor string densities and synthesized power distribution axial fidelities. The Texas A&M Testing, Research, Isotopes, General Atomics Reactor (TAMU TRIGA) reactor MCNP model was used to assess the impact of ex-core sensors; this was done by performing synthesis with and without the ex-core detectors and by quantifying the synthesis error and number of iterations associated with Gaussian-type perturbations in many locations in the core. The TAMU TRIGA model was particularly pertinent for this study because of the interest in future experimental tests with SPNDs in this reactor, as well as the ease of modifying the MCNP model to include ex-core detectors with heterogeneously described response functions. Results from the comparison between the Geant4 and analytical SPND models indicate that similar average and maximum synthesis errors were obtained for burnup-induced perturbations in both the NuScale SMR and the AP1000. This was true for a range of sensor string densities and axial fidelities. However, there were marked differences between both the Geant4 and analytically informed models in terms of the iterations required to converge on the synthesized power distribution. Namely, the Geant4-informed models tended to lead to fewer iterations, except for a few sensor–core configurations that had particularly numerous iterations. Results from the ex-core sensor assessment with the TAMU TRIGA model indicate that the inclusion of ex-core sensors drastically reduces the synthesis error of Gaussian-type perturbations close to the edge of the core, and it slightly reduces synthesis errors for perturbations closer to the center of the core. This was achieved with a minimal increase in computational cost—that is, the number of iterations required for convergence. The errors were identified to be in the same location as the perturbation in the core, indicating that the methodology remains robust for unperturbed regions of the core. A secondary result from this study with the TAMU TRIGA was yielded by analysis of the neutron flux levels in the in-core and ex-core sensor locations of the core; these flux levels indicate that SPNDs could be used as both in-core and ex-core sensors, so long as the emitter material is sensitive to thermal neutrons. The results from these studies provide a quantitative understanding of the importance of considering realistic sensor physics and including ex-core sensors to perform accurate and timely power distribution synthesis of a reactor core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Data for: Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources

Data package for manuscript "Climatic Imprint on Interfacially-Controlled Platinum-Palladium Resources" by Emily G. Wright, Ivey Wang, Yihang Fang, Elaine D. Flynn, and Jeffrey G. Catalano. This dataset contains adsorption results from experiments designed to investigate the effect of chloride on Pd(II) adsorption to goethite and Pt(II) adsorption to hematite and goethite, including lab experiments, X-ray absorption fine structure spectroscopy, and models of retention within a laterite. See the associated manuscript for full methods information. The file "Wright2025_PtAds_data.csv" contains the target starting Pt concentration (uM), final aqueous Pt and associated error (in uM), calculated adsorbed Pt and associated error (in umol/m2), target and measured aqueous chloride (mM), target aqueous nitrate (mM), final pH, and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, S20, and S22 in the associated manuscript. The file "Wright2025_PdAds_data.csv" contains the target starting Pd concentration (uM), final aqueous Pd and associated error (in uM), calculated adsorbed Pd and associated error (in umol/m2), target and measured aqueous chloride (mM), and mineral concentration/loading (g/L). Associated mineral-free controls (mineral loading = 0 g/L) are included; the aqueous Pd error was not calculated and chloride was not measured in every sample. These data appear in Figures 1, S3, S4, S5, and S20 in the associated manuscript. The file "Wright2025_MineralBatches_data.csv" contains the mineral identity and BET specific surface area (m2/g) for every mineral batch synthesized and used in experiments. The annealing time used is listed for hydrothermally annealed goethite. These data appear in Table S2 in the associated manuscript. The file "Wright2025_XRD_data.csv" contains the XRD patterns for every mineral batch synthesized as the counts as a function of two theta (in degrees). See "Wright2025_MineralBatches_data.csv" for more details on specific mineral batches. These data appear in Figure S2 in the associated manuscript. The file "Wright 2025_ZetaPotential_data.csv" contains the measured zeta potentials for samples of goethite (batch G2) at pH 4 the presence of varying amounts of sodium chloride. These data appear in Table S3 in the associated manuscript. The file "Wright2025_XAFSSamples_data.csv" contains the specific mineral batch, measured final aqueous Pd or Pt (uM), measured final aqueous chloride (mM), and estimated adsorbed Pd or Pt (umol/m2) of all XAFS samples. These data appear in Tables S4, S7, S8, and S10 in the associated manuscript. The files "Wright2025_PdXAFS_data.csv" and "Wright2025_PtXAFS_data.csv" contain the normalized spectra of Pd and Pt, respectively, adsorbed to minerals at varying chloride concentrations. See "Wright2025_XAFSSamples_data.csv" for a guide to sample names. Note that "05" in a sample name is equivalent to "0.5". These data appear in Figures 2, S6, S7, S8, S12, S13, and S14 in the associated manuscript. The file "Wright2025_LateriteProfileProfileModelParameters_data.csv" include the ratio of hematite to hematite and goethite in two synthetic, modeled profiles, as well as the modeled surface areas of goethite and hematite as a function of relative depth within the modeled weathering zone. These data were used, in conjunction with equations presented in the paper, to calculate the theoretical concentrations of Pd and Pt (and the resulting Pt/Pd ratio) within the profiles. These data appear in Figure 3 in the associated manuscript. The file "Wright2025_Imagery_data.zip" is a zipped folder containing the TEM and STEM images appear in Figures S18 and S19. Individual files are labeled as either STEM (Fig. S18) or TEM (Fig. S19) with a letter representing the part of the multipart figure.

58 GEOSCIENCES↗
DOE OSTI2021

Leveraging Randomized Compiling for the QITE Algorithm

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1\%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

Ville, Jean-Loup↗
DOE OSTI2021

Determining diagnostic coverage for memory using redundant execution

Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.

Bramley, Richard Gavin↗