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DOE OSTI2022

The Simulation and Design of an On-Chip Superconducting Millimetre Filter-Bank Spectrometer

Abstract Superconducting on-chip filter banks provide a scalable, space saving solution to create imaging spectrometers at millimetre and submillimetre wavelengths. We present an easy to realise, lithographed superconducting filter design with a high tolerance to fabrication error. Using a capacitively coupled $$\lambda /2$$ λ / 2 microstrip resonator to define a narrow ( $$\lambda /\Delta \lambda = 300$$ λ / Δ λ = 300 ) spectral pass band, the filtered output of a given spectrometer channel directly connects to a lumped-element kinetic inductance detector. We show the tolerance analysis of our design, demonstrating $$<11\%$$ < 11 % change in filter quality factor to any one realistic fabrication error and a full filter-bank efficiency forecast to be 50% after accounting for fabrication errors and dielectric loss tangent.

Robson, G. (ORCID:0000000315279326)↗

DOE OSTI2026

PDE-constrained high-order mesh optimization

Here, we present a novel framework for PDE-constrained r-adaptivity of high-order meshes. The proposed method formulates mesh movement as an optimization problem, with an objective function defined as a convex combination of a mesh quality metric and a measure of the accuracy of the PDE solution obtained via finite element discretization. The proposed formulation achieves optimized, well-defined high-order meshes by integrating mesh quality control, PDE solution accuracy, and robust gradient regularization. We adopt the Target-Matrix Optimization Paradigm to control geometric properties across the mesh, independent of the PDE of interest. To incorporate the accuracy of the PDE solution, we introduce error measures that control the finite element discretization error. The implicit dependence of these error measures on the mesh nodal positions is accurately captured by adjoint sensitivity analysis. Additionally, a convolution-based gradient regularization strategy is used to ensure stable and effective adaptation of high-order meshes. We demonstrate that the proposed framework can improve mesh quality and reduce the error by up to 10 times for the solution of Poisson and linear elasto-static problems. The approach is general with respect to the dimensionality, the order of the mesh, the types of mesh elements, and can be applied to any PDE that admits well-defined adjoint operators.

Computer science↗

DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Back to Basics: First Principles of HRA

Human Reliability Analysis (HRA) continues to suffer from a myriad of technical gaps in its foundations. Core issues have been widely discussed but remain largely unaddressed, although the path taken to this point was paved with good intentions. Now, while this characterization may seem dramatic it is not intended to throw doubt onto HRA as a discipline or to diminish the value it has on increasing safety and resilience in nuclear power. Rather, it is intended to communicate that HRA has some serious challenges and is desperately in need of a critical evaluation. To quote Dougherty [1], “the misrepresentations that will undoubtedly arise may serve to provoke the necessary creativity.” What bears noting is that in the last thirty years since Alan Swain declared that “no adequate HRA had ever been done,” there has been little effort to correct the issues he highlighted. Human factors practice is still poorly implemented, many complex systems are fundamentally incompatible with reliable human performance, and there remains poor foundational support for many of the assumptions within HRA. We propose three categories of issues in HRA, philosophical, theoretical, and empirical and potential solutions to these issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Simulating Z 2 lattice gauge theory on a quantum computer

The utility of quantum computers for simulating lattice gauge theories is currently limited by the noisiness of the physical hardware. Various quantum error mitigation strategies exist to reduce the statistical and systematic uncertainties in quantum simulations via improved algorithms and analysis strategies. We perform quantum simulations of Z 2 gauge theory with matter to study the efficacy and interplay of different error mitigation methods: readout error mitigation, randomized compiling, rescaling, and dynamical decoupling. We compute Minkowski correlation functions in this confining gauge theory and extract the mass of the lightest spin-1 state from fits to their time dependence. Quantum error mitigation extends the range of times over which our correlation function calculations are accurate by a factor of 6 and is therefore essential for obtaining reliable masses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

BOSS DR12 full-shape cosmology: Λ CDM constraints from the large-scale galaxy power spectrum and bispectrum monopole

Here, we present a full Λ CDM analysis of the BOSS DR12 dataset, including information from the power spectrum multipoles, the real-space power spectrum, the reconstructed power spectrum and the bispectrum monopole. This is the first analysis to feature a complete treatment of the galaxy bispectrum, including a consistent theoretical model and without large-scale cuts. Unlike previous works, the statistics are measured using window-free estimators: this greatly reduces computational costs by removing the need to window-convolve the theory model. Our pipeline is tested using a suite of high-resolution mocks and shown to be robust and precise, with systematic errors far below the statistical thresholds. Inclusion of the bispectrum yields consistent parameter constraints and shrinks the σ 8 posterior by 13% to reach < 5 % precision; less conservative analysis choices would reduce the error bars further. Our constraints are broadly consistent with Planck: in particular, we find H 0 = 69.6$^{+1.1}_{–1.3}$ km s –1 Mpc –1 , σ 8 = 0.692$^{+0.035}_{–0.041}$ and n s = 0.870$^{+0.067}_{–0.064}$, including a BBN prior on the baryon density. When n s is set by Planck, we find H 0 = 68.31$^{+0.83}_{–0.86}$ km s –1 Mpc –1 and σ 8 = 0.722$^{+0.032}_{–0.036}$. Our S 8 posterior, 0.751 ± 0.039, is consistent with weak lensing studies, but lower than Planck. Constraints on the higher-order bias parameters are significantly strengthened from the inclusion of the bispectrum, and we find no evidence for deviation from the dark matter halo bias relations. These results represent the most complete full-shape analysis of BOSS DR12 to-date, and the corresponding spectra will enable a variety of beyond-Λ CDM analyses, probing phenomena such as the neutrino mass and primordial non-Gaussianity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Error field identification through torque balance on a saturated island in DIII-D

Measurement of the electromagnetic torque on a magnetic island could be an attractive method for error field identification in the early phase of ITER operation. Previous DIII-D experiments (Strait 2014 Nucl. Fusion 54 073004; Shiraki et al 2015 Plasma Phys. Control. Fusion 57 025016) have demonstrated the principle of this approach using a stationary or slowly rotating island, while recent developments in magnetic data analysis (Sweeney and Strait 2019 Phys. Plasmas 26 012509) allow the field of a rapidly rotating island to be readily distinguished from that of the wall currents induced by its rotation. In a recent experiment, a rotating n = 1 magnetic perturbation forced a saturated magnetic island to rotate, thus sampling all toroidal phases periodically in a single discharge. The phase and amplitude of the error field are inferred from analysis of the time-dependent torque balance on the island, including torques from the error field, the applied magnetic perturbation, and the wall currents induced by rotation of the applied perturbation and the island. Furthermore, results agree well with those from more conventional methods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Deployment of neural-network-based neutron microscopic cross sections in the Griffin reactor physics application

The capability to utilize neural networks to predict macroscopic and microscopic cross section parametric spaces has been developed for the Griffin reactor physics application. The LibTorch interface enables Griffin's MOOSE-based materials to interact with LibTorch-trained models, allowing for the evaluation of complex macroscopic or microscopic cross section spaces, which are then used to evaluate the neutronic properties of the Griffin finite element model. This study benchmarks traditional ISOXML-formatted tabulation libraries against neural network-based models for 279 nuclides on 20,160 grid points for zero-dimensional and two-dimensional reactor models. Benchmark metrics include the fundamental mode eigenvalue, fission and absorption rates, and various temperature coefficients of reactivity (isothermal, fuel, and moderator). From the perspective of storage space, the complete set of LibTorch models uses 11 MB on disk, compared to the 10 GB for the ISOXML multigroup library that covers the same grid space. For the two-dimensional performance case considered in Griffin, the Torch model uses 97% less RAM than the reference ISOXML dataset while runtime increases by a factor of 3 when using the LibTorch model compared to the ISOXML dataset with multi-linear interpolation. The LibTorch model consistently yields errors within 0.01% for most analyzed quantities except for the temperature coefficients of reactivity where the maximum discrepancies are up to 0.3 $\frac{pcm}{K}$. Due to the neural network attempting to best predict quantities with no regard for a positive or negative bias for any given quantity, predictions may experience random fluctuations, resulting in both positive and negative errors. Future work will entail both depletion and coupled transient analysis to determine the predictive capabilities of Griffin with neural network-based cross sections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

High-Accuracy Mesh-Free Quadrature for Trimmed Parametric Surfaces and Volumes

This work presents a high-accuracy, mesh-free, generalized Stokes theorem-based numerical quadrature scheme for integrating functions over trimmed parametric surfaces and volumes. The algorithm relies on two fundamental steps: (1) We iteratively reduce the dimensionality of integration using the generalized Stokes theorem to line integrals over trimming curves, and (2) we employ numerical antidifferentiation in the generalized Stokes theorem using high-order quadrature rules. Additionally, the scheme achieves exponential convergence up to trimming curve approximation error and has applications to computation of geometric moments, immersogeometric analysis, conservative field transfer between high-order curvilinear meshes, and initialization of multi-material simulations. We compare the quadrature scheme to commonly-used quadrature schemes in the literature and show that our scheme is much more efficient in terms of number of quadrature points used. We provide an open-source implementation of the scheme in MATLAB as part of QuaHOG, a software package for Quadrature of High-Order Geometries.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Water Mass Transformation Budgets in Finite‐Volume Generalized Vertical Coordinate Ocean Models

Water Mass Transformation (WMT) theory provides conceptual tools that in principle enable innovative analyses of numerical ocean models; in practice, however, these methods can be challenging to implement and interpret, and therefore remain under-utilized. Our aim is to demonstrate the feasibility of diagnosing all terms in the water mass budget and to exemplify their usefulness for scientific inquiry and model development by quantitatively relating water mass changes, overturning circulations, boundary fluxes, and interior mixing. We begin with a pedagogical derivation of key results of classical WMT theory. We then describe best practices for diagnosing each of the water mass budget terms from the output of Finite-Volume Generalized Vertical Coordinate (FV-GVC) ocean models, including the identification of a non-negligible remainder term as the spurious numerical mixing due to advection scheme discretization errors. We illustrate key aspects of the methodology through the analysis of a polygonal region of the Greater Baltic Sea in a regional demonstration simulation using the Modular Ocean Model v6 (MOM6). We verify the convergence of our WMT diagnostics by brute-force, comparing time-averaged (“offline”) diagnostics on various vertical grids to timestep-averaged (“online”) diagnostics on the native model grid. Finally, we briefly describe a stack of xarray-enabled Python packages for evaluating WMT budgets in FV-GVC models (culminating in the new xwmb package), which is intended to be model-agnostic and available for community use and development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Planck 2018 results - VIII. Gravitational lensing

We present measurements of the cosmic microwave background (CMB) lensing potential using the final Planck 2018 temperature and polarization data. Using polarization maps filtered to account for the noise anisotropy, we increase the significance of the detection of lensing in the polarization maps from 5σ to 9σ. Combined with temperature, lensing is detected at 40σ. We present an extensive set of tests of the robustness of the lensing-potential power spectrum, and construct a minimum-variance estimator likelihood over lensing multipoles 8 ≤ L ≤ 400 (extending the range to lower L compared to 2015), which we use to constrain cosmological parameters. We find good consistency between lensing constraints and the results from the Planck CMB power spectra within the ΛCDM model. Combined with baryon density and other weak priors, the lensing analysis alone constrains σ 8 Ωm 0.25 = 0.589 ± 0.020 (1σ errors). Also combining with baryon acoustic oscillation data, we find tight individual parameter constraints, σ8 = 0.811 ± 0.019, H0 = 67.9-1.3+1.2 km s -1 Mpc -1 , and Ω m = 0.303 -0.018 +0.016 . Combining with Planck CMB power spectrum data, we measure σ 8 to better than 1% precision, finding σ 8 = 0.811 ± 0.006. CMB lensing reconstruction data are complementary to galaxy lensing data at lower redshift, having a different degeneracy direction in σ 8 - Ω m space; we find consistency with the lensing results from the Dark Energy Survey, and give combined lensing-only parameter constraints that are tighter than joint results using galaxy clustering. Using the Planck cosmic infrared background (CIB) maps as an additional tracer of high-redshift matter, we make a combined Planck-only estimate of the lensing potential over 60% of the sky with considerably more small-scale signal. We additionally demonstrate delensing of the Planck power spectra using the joint and individual lensing potential estimates, detecting a maximum removal of 40% of the lensing-induced power in all spectra. The improvement in the sharpening of the acoustic peaks by including both CIB and the quadratic lensing reconstruction is detected at high significance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

The imprint of cosmic voids from the DESI Legacy Survey DR9 Luminous Red Galaxies in the Planck 2018 lensing map through spectroscopically calibrated mocks

The cross-correlation of cosmic voids with the lensing convergence (κ) map of the Cosmic Microwave Background (CMB) fluctuations provides a powerful tool to refine our understanding of the current cosmological model. However, several studies have reported a moderate tension (up to ∼2σ) between the lensing imprint of cosmic voids on the observed CMB and the ΛCDM signal predicted by simulations. To address this “lensing-is-low” tension and to obtain new, precise measurements of the signal, we exploit the large DESI Legacy Survey Luminous Red Galaxy (LRG) data set, covering approximately 19 500 deg 2 of the sky and including about 10 million LRGs at z < 1.05. Our ΛCDM template was created using the Buzzard mocks, which we specifically calibrated to match the clustering properties of the observed galaxy sample by exploiting more than one million DESI spectra. We identified our catalogs of 3D voids in the range 0.35 < z < 0.95 and cross-correlated them through a stacking methodology, dividing the sample into bins according to the redshift and λv values of the voids. For the full void sample, we report a 14σ detection of the lensing signal, with A κ = 1.016 ± 0.054, which increases to 17σ when considering the void-in-void (A κ = 0.944 ± 0.064) and the void-in-cloud (A κ = 0.975 ± 0.060) populations individually, the highest detection significance for studies of this kind. We observe a full agreement between observations and ΛCDM mocks across all redshift bins, sky regions, and void populations considered. In addition to these findings, our analysis highlights the importance of accurately matching sparseness and redshift error distributions between mocks and observations, as well as the role of λ v in enhancing the signal-to-noise ratio through void population discrimination.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Parton physics from a heavy-quark operator product expansion: Lattice QCD calculation of the fourth moment of the pion distribution amplitude

The pion light-cone distribution amplitude (LCDA) is an essential nonperturbative input for a range of high-energy exclusive processes in quantum chromodynamics. Building on our previous work, the continuum limit of the fourth Mellin moment of the pion LCDA is determined in quenched QCD using quark masses which correspond to a pion mass of 𝑚 𝜋 = 550 MeV. This calculation finds ⟨𝜉 2 ⟩ = 0.202⁢(8)⁢(9) and ⟨𝜉 4 ⟩ = 0.039⁢(28)⁢(11) where the first error indicates the combined statistical and systematic uncertainty from the analysis and the second indicates the uncertainty from working with Wilson coefficients computed to next-to-leading order. These results are presented in the $\overline{\textrm{MS}}$ scheme at a renormalization scale of 𝜇 = 2 GeV.

Detmold, William [Massachusetts Inst. of Technolog↗

DOE OSTI2021

Field-Array Benchmark of Commercial Bifacial PV Technologies with Publicly Available Data

We present results for a 75-kW field array deployed with rows of 5 different commercially available bifacial technologies. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed in the 10-row field. High accuracy string-level DC monitoring and module-level measurements have been recorded for six months. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. A custom-module to measure shading loss from the torque-tube was installed. One month data shows up to 6% irradiance non-uniformity. A fixed-tilt test-site with 3 years of bifacial data is also presented, with a bifacial gain of 3%.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2024

A tutorial review of machine learning-based model predictive control methods

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

Wu, Zhe [Department of Chemical and Biomolecular E↗

DOE OSTI2023

IDAES-PSE 2.3.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications.. New Features and Models • New diagnostics toolboxes and examples o Tutorial for using the DiagnosticsToolbox o Methods to identify potential evaluation errors in models o SVDToolbox for performing singular value decomposition analysis on models to identify possible degeneracies and poor scaling o DegeneracyHunter for identifying irreducible degenerate sets in models • Model for solid-liquid separation which takes two inputs (solid and liquid streams) and produces three outlets (solids, liquid entrained with solids and pure liquid) • Example of temperature swing adsorption in models_extra Deprecation Warnings • With the update to Pyomo 6.7.0, the plate heat exchanger model has begun failing tests on some CI platforms. If this is not addressed by the February release, this model may be removed due to failing tests. See #1294. Offers to help identify the cause and fix this issue are welcome. Improvements and bug fixes • Fixed oversight which precluded using Mixer and Separator models when inherent reactions were present in property package • Added reporting methods to the MSContactor model • Minor corrections in some examples where values were being fixed outside of declared variable bounds

DiagnosticsToolbox,IDAES,PSE,Process Systems Engin↗

DOE OSTI2020

Measurement Uncertainty in One-of-a-kind Experiments

A golden standard in science is to repeat an experimental measurement multiple times and calculate the measured value with experimental uncertainty by following well developed statistical procedures. For various reasons – cost, technical difficulty, international treaties, ethics of dealing with human or animal subjects, ecology - many important experiments and observations can not be repeated. Astronomy, earthquakes, hurricanes produce data from one-of-a-kind events. When information is not available in any other way, it should not be dismissed as qualitative, anecdotal evidence only. Analyzed in a mathematically rigorous way, it produces quantitative experimental data. Analysis of data from one-of-a-kind event differs from analysis of repeated experiments’ data. For repeated experiments, the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single value. Measurement uncertainty depends on the measurement system only. Digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one–of–a kind experiment are analyzed, only the measurement uncertainty can be reported.

42 ENGINEERING↗