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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.

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DOE OSTI2024

Universal framework for simultaneous tomography of quantum states and SPAM noise

We present a general denoising algorithm for performing simultaneous tomography of quantum states and measurement noise. This algorithm allows us to fully characterize state preparation and measurement (SPAM) errors present in any quantum system. Our method is based on the analysis of the properties of the linear operator space induced by unitary operations. Given any quantum system with a noisy measurement apparatus, our method can output the quantum state and the noise matrix of the detector up to a single gauge degree of freedom. We show that this gauge freedom is unavoidable in the general case, but this degeneracy can be generally broken using prior knowledge on the state or noise properties, thus fixing the gauge for several types of state-noise combinations with no assumptions about noise strength. Such combinations include pure quantum states with arbitrarily correlated errors, and arbitrary states with block independent errors. This framework can further use available prior information about the setting to systematically reduce the number of observations and measurements required for state and noise detection. Our method effectively generalizes existing approaches to the problem, and includes as special cases common settings considered in the literature requiring an uncorrelated or invertible noise matrix, or specific probe states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

CEAZ: Accelerating Parallel I/O Via Hardware-Algorithm Co-Designed Adaptive Lossy Compression

As supercomputers continue to grow to exa-scale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also help us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ’s dual-quantization algorithm to our hardware use case. (4) We evaluate CEAC on five real-world datasets with both a single FPGA board and 256 nodes from Bridges2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2× of throughput and 9.6× of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 32.7× and 31.4×, respectively.

Zhang, Chengming↗
DOE OSTI2024

Simulator Data Analysis to Inform Digitalized Environment Impacts on Human Reliability

The U.S. Nuclear Regulatory Commission (NRC) has developed a human reliability analysis (HRA) method, termed the Integrated Human Event Analysis System for Event and Condition Assessment (IDHEAS-ECA), in order to estimate human error probabilities (HEPs) in risk-informed regulatory applications. To update the quantification part of IDHEAS-ECA, the NRC required human performance and error data from fully digitalized main control rooms (MCRs); therefore, it requested that Idaho National Laboratory (INL) revisit previous data collection studies and investigate how the following three factors impact human reliability: self-checking, peer-checking, and automation. The HRA data collection studies revisited were the Human Reliability Data Extraction (HuREX) project, developed by the Korea Atomic Energy Research Institute (KAERI), and the Simplified Human Error Experimental Program (SHEEP), developed by INL. HuREX is a representative HRA data collection study that collects human reliability data from full-scope simulators staffed by licensed operators. SHEEP, on the other hand, has been proposed to complement such full-scope studies by collecting data via simplified simulators staffed by non-licensed student operators. In the HuREX study, KAERI collected HRA data from fully digitalized MCRs for the Advanced Power Reactor (APR)–1400. The SHEEP data were obtained from simplified simulators that partially mimicked the features of digitalized MCRs. The present report mainly discusses how the impacts of the aforementioned three factors on human errors were derived from these two data collection studies.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Superconducting microwave cavities and qubits for quantum information systems

Superconducting microwave cavities featuring ultrahigh Q-factors, which measure the efficiency of energy storage in relation to energy loss in a system, are revolutionizing quantum computing by providing long coherence times exceeding 1 ms, crucial for the development of scalable multi-qubit quantum systems with low error rates. Here, in this work, we provide an in-depth analysis of recent advances in ultrahigh Q-factor cavities, integration of Josephson junction-based qubits, and bosonic-encoded qubits in 3D cavities. We examine the sources of quantum state dephasing caused by damping and noise mechanisms in cavities and qubits, highlighting the critical challenges that need to be addressed to achieve even higher coherence times. We critically survey the latest progress made in implementing single 3D qubits using superconducting materials, normal metals, and multi-qubit and multi-state quantum systems. Our work sheds light on the promising future of this research area, including novel materials for cavities and qubits, modes with nontrivial topological properties, error correction techniques for bosonic qubits, and new light–matter interaction effects.

3D printing↗
DOE OSTI2024

The right conditions for high-precision dynamic temperature and heat capacity measurement via pyrometry and conductivity

The pursuit of accurate bulk temperature T under extreme conditions has been a long-standing goal of the high pressure science community, complicated by a lack of data to inform models. To reach these extremely high-pressure, high-temperature (high P − T) conditions, a combination of dynamic and heated static experiments (e.g., diamond or gem anvil cel experiments) are used. For example, in a diamond anvil cell (DAC) experiment, a sample placed in the DAC is first pressurized. Following pressurization, the sample T is increased either by heating the entire DAC (usually using resistive heating, and limited to ∼1000K) or by applying intense laser power to the sample surfaces. In a dynamic experiment, the process of pressurizing the sample also heats it. In the case of shock physics experiments, such heating is substantial, easily reaching thousands of Kelvin; in our work we have seen T ∼17000K. Most methods of measuring temperature at ambient are not compatible with experiments under these high-pressure, high-temperature conditions: thermocouples break, melt, or have conductivity properties that differ from ambient where they are calibrated; thermometers would melt; both are too slow. As a result most methods are based on non-contact techniques such as x-ray diffraction broadening, neutron scattering, or optical methods. Of these, optical methods using the visible and near-infrared region of the spectrum are the most commonly used as the sources and detectors are readily available. In the case of optical methods the optical depth, and therefore the measurement location, is limited to the surface. When a window or anvil material is used, heat flows from the sample into the window/anvil. Likewise, if the sample undergoes a change in thermodynamic state, such as expansion upon release, different T may be expected. As a result, the surface or apparent temperature T app measurement will differ from the bulk or interior temperature that is desired. This surface measurement must be related to the bulk measurement using thermal transport models and material models. While it is tempting to conclude that one should just use x-ray methods that directly probe the interior, even these methods have been shown to depend on thermal transport and material models. Regardless of the method used to create the high P − T condition, therefore, we must understand the role of thermal transport and material models upon our interpretation of the T measurement, as well as the errors and uncertainties associated with the choice of models used in the analysis. This is a substantial area of research and this paper is by no means a complete survey of the relevant sources of uncertainty. For example, we have yet to begin to address alternate transport models in a detailed manner (e.g., Tan-Ahrens), or the many models that use additional layers to approximate melting, turbulence, or epitaxial phenomena). Likewise, we have not explored the impact upon uncertainty of thermal models that use temperature-dependent thermal transport coefficients, or the wide range of material models that can be applied. Instead, this paper focuses on using one simple model, the Urtiew-Grover model, to understand the sources of error in T measurement so that we may identify how best to focus future research efforts to return the best improvements and avoid working on over-optimizing a single type of measurement. To this end, we work through some of the best and worst case scenarios for T measurement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Decay Curve Correction Analysis Report

The decay curve analysis that is done on the short-lived radionuclide gas samples is used to differentiate between gaseous radionuclides that have the same characteristic gamma decay energy, 511 kiloelectron-volts (keV). A sample of stack gas is isolated and the total counts in the 511 keV peak are counted repeatedly in 10-second intervals to evaluate the decay rate of the sample over time. Analysis of this decay data required a series of steps. First, a raw data report is generated by the gamma acquisition system, based on an analysis template within the acquisition software. The data report file was then loaded into Microsoft Word, and a macro was used to perform minor formatting (remove colons and insert tabs between data columns) to allow analysis within Excel. The file is then saved as a text file at this point. The text file is then uploaded into Excel and a series of macros are used to add labels, calculate radioactive decay constants, and analyze the gamma decay data using linear regression techniques. The analysis template has been used since 1998 for stack 53000303 (TA-53, building 0003, exhaust stack 03) and 2000 for stack 53000702. The overall process, including the gamma report format and the macros used in Word and Excel for processing the report, had remained unchanged until 2015. In October of 2015, staff made a change to the report template in the gamma acquisition software which resulted in an error in the calculations later performed by the Excel macro. This error was not caught until a more in-depth review of the analysis took place regarding 2020 data. This report covers a much more complete review of the issue that occurred regarding the decay curve analysis, a review of the calculations completed to correct the issue, a review of the updated decay curve analysis process, and recommendations for moving forward.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2022

Impact of Measured Spectrum Variation on Solar Photovoltaic Efficiencies Worldwide

In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada.

energy yield↗
DOE OSTI2024

Multilabel proportion prediction and out-of-distribution detection on gamma spectra of short-lived fission products

In the machine learning problem of multilabel classification, the objective is to determine for each test instance which classes the instance belongs to. In this work, we consider an extension of multilabel classification, called multilabel proportion prediction, in the context of radioisotope identification (RIID) using gamma spectra data. We aim to not only predict radioisotope proportions, but also identify out-of-distribution (OOD) spectra. We achieve this goal by viewing gamma spectra as discrete probability distributions, and based on this perspective, we develop a custom semi-supervised loss function that combines a traditional supervised loss with an unsupervised reconstruction error function. Our approach was motivated by its application to the analysis of short-lived fission products from spent nuclear fuel. In particular, we demonstrate that a neural network model trained with our loss function can successfully predict the relative proportions of 37 radioisotopes simultaneously. The model trained with synthetic data was then applied to measurements taken by Pacific Northwest National Laboratory (PNNL) to conduct analysis typically done by subject-matter experts. Here, we also extend our approach to successfully identify when measurements are OOD, and thus should not be trusted, whether due to the presence of a novel source or novel proportions.

Anomaly detection↗
DOE OSTI2023

Dissolved gas recovery from water using a sidestream hollow-fiber membrane module: First principles model synthesis and steady-state validation

This paper presents a first-principles model for the recovery of dissolved gases from liquids using a sidestream hollow-fiber membrane module. The model avoids the use of new empirical coefficients, thus providing a parametric understanding of the process behavior for future design and optimization of membrane modules. This type of first-principles model could be particularly useful when gas recovery is beneficial to biological or chemical reactions of interest, such as the acetogenesis reactions in two-stage anaerobic digesters. The steady-state behavior of the model was validated against both new experimental data for the recovery of H 2 , CH 4 and H 2 –CH 4 mixtures from pure water, as well as existing published data. The modeled gas recovery predictions agreed with experimental data to an absolute average error of 13%, and an average R value of 0.98. Parametric analysis of mixed-gas recovery suggests possible key transition points in the composition of the recovered gases. For example, at 40 °C, increasing trans-membrane pressure while keeping hydraulic residence time (HRT) under 0.5 s will result in an increase in the ratio of H 2 to CH 4 recovered. Otherwise, increasing trans-membrane pressure will instead decrease the ratio of H 2 to CH 4 recovered. The model has potential to be extended to transient analysis, but has yet to be validated with transient experimental data. Furthermore, this model was successfully implemented in both Python and MATLAB, and provides valuable insights for future net-energy optimization for anaerobic digestion systems with in-situ gas recovery.

Anaerobic Digestion↗
DOE OSTI2020

Correction to Unveiling the Role of Al 2 O 3 in Preventing Surface Reconstruction During High-Voltage Cycling of Lithium-Ion Batteries

A correction to this article was necessary to replace the original Figure 2 with the actual data in the revised Figure 2 shown here. An error was made while preparing the graphs from the neutron diffraction analysis software, and panel a was accidentally inserted into the panel b–d spots, such that all four panels were replicates. Since the original analysis of the data collected was made correctly, the conclusions and key points related to this section and the whole article remain unchanged. The revised goodness factors are slightly different because the refinements were repeated with small changes to the refinement conditions. The corresponding author has obtained approval from all coauthors in the article prior the submission of the Correction. The authors apologize for any inconveniences caused.

25 ENERGY STORAGE↗
DOE OSTI2020

Detecting and tracking drift in quantum information processors

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Neptunium transition probabilities estimated through laser induced breakdown spectroscopy (LIBS) measurements

in tis report calibration free–laser induced breakdown spectroscopy (CF-LIBS) approaches are promising for the analysis of radioactive materials; however, a lack of reported fundamental properties for these materials is an obstacle to implementation. Spectra of laser induced plasmas containing Np and Sr, along with Saha–Boltzmann methods, were used to estimate the first reported transition probabilities (TPs) of Np. These TPs enabled the first attempt at CF-LIBS analysis of radioactive samples to predict Np/Sr ratios with a mean error of 3.86%. The presented methodology of TP estimation will be applied to the study of other actinides and allow broader applications of CF-15 LIBS in the nuclear field.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Influence of anomalous perpendicular transport on linear tearing mode dynamics in tokamak plasmas

The analysis of a previous paper [A. Cole and R. Fitzpatrick, Phys. Plasmas 13, 032503 (2006)] that maps out all of the two-fluid response regimes of a linear tearing layer interacting with an externally generated resonant magnetic perturbation, in a large aspect-ratio tokamak plasma, is generalized to take into account realistic levels of perpendicular particle transport. A new response-regime map is obtained that differs substantially, in many respects, from the old one. The improved analysis is first employed to find all of the two-fluid growth-rate regimes of a non-interacting low-mode-number tearing mode in a large aspect-ratio tokamak plasma. The analysis is then used to determine the scaling of the error-field penetration threshold with machine parameters in large aspect-ratio tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A new upper bound for the growth factor in Gaussian elimination with complete pivoting

Abstract The growth factor in Gaussian elimination measures how large the entries of an LU factorization can be relative to the entries of the original matrix. It is a key parameter in error estimates, and one of the most fundamental topics in numerical analysis. We produce an upper bound of for the growth factor in Gaussian elimination with complete pivoting — the first improvement upon Wilkinson's original 1961 bound of .

Bisain, Ankit [Department of Mathematics Massachus↗
DOE OSTI2023

Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature

Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO3 concentration with varying temperature levels. A training set, which covered HNO3 concentrations (0.1–8 M) and temperature (10–40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO 3 and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO 3 concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO 3 concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Impact of point spread function higher moments error on weak gravitational lensing

ABSTRACT Weak gravitational lensing is one of the most powerful tools for cosmology, while subject to challenges in quantifying subtle systematic biases. The point spread function (PSF) can cause biases in weak lensing shear inference when the PSF model does not match the true PSF that is convolved with the galaxy light profile. Although the effect of PSF size and shape errors – i.e. errors in second moments – is well studied, weak lensing systematics associated with errors in higher moments of the PSF model require further investigation. The goal of our study is to estimate their potential impact for LSST weak lensing analysis. We go beyond second moments of the PSF by using image simulations to relate multiplicative bias in shear to errors in the higher moments of the PSF model. We find that the current level of errors in higher moments of the PSF model in data from the Hyper Suprime-Cam survey can induce a ∼0.05 per cent shear bias, making this effect unimportant for ongoing surveys but relevant at the precision of upcoming surveys such as LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.

Mohseni, Masoud↗