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DOE OSTI2024

Random projection using random quantum circuits

The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [ ∼ O ( n ) ] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024

Kumaran, Keerthi (ORCID:0009000949125721)↗

DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data

Three-dimensional, time-resolved blood flow measurement (4D-flow) is a powerful research and clinical tool, but improved resolution and scan times are needed. Therefore, this study aims to (1) present a postprocessing framework for optimization-driven simulation-based flow imaging, called 4D-flow High-resolution Imaging with a priori Knowledge Incorporating the Navier-Stokes equations and the discontinuous Galerkin method (4D-flow HIKING), (2) investigate the framework in synthetic tests, (3) perform phantom validation using laser particle imaging velocimetry, and (4) demonstrate the use of the framework in vivo. An optimizing computational fluid dynamics solver including adjoint-based optimization was developed to fit computational fluid dynamics solutions to 4D-flow data. Synthetic tests were performed in 2D, and phantom validation was performed with pulsatile flow. Reference velocity data were acquired using particle imaging velocimetry, and 4D-flow data were acquired at 1.5 T. In vivo testing was performed on intracranial arteries in a healthy volunteer at 7 T, with 2D flow as the reference. Results Synthetic tests showed low error (0.4%-0.7%). Phantom validation showed improved agreement with laser particle imaging velocimetry compared with input 4D-flow in the horizontal (mean -0.05 vs -1.11 cm/s, P < .001; SD 1.86 vs 4.26 cm/s, P < .001) and vertical directions (mean 0.05 vs -0.04 cm/s, P = .29; SD 1.36 vs 3.95 cm/s, P < .001). In vivo data show a reduction in flow rate error from 14% to 3.5%. Phantom and in vivo results from 4D-flow HIKING show promise for future applications with higher resolution, shorter scan times, and accurate quantification of physiological parameters.

4D-flow MRI↗

DOE OSTI2024

Shape restricted additive hazards models: Monotone, unimodal, and U‐shape hazard functions

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time‐dependent covariate with monotone or U‐shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.

Mathematical & Computational Biology↗

DOE OSTI2023

Developing Ultrahigh-Resolution E3SM Land Model for GPU Systems

Designing and refactoring complex scientific code, such as the E3SM land model (ELM), for new computing architectures is challenging. This paper presents design strategies and technical approaches to develop a data-oriented, GPU-ready ELM model using compiler directives (OpenACC/OpenMP). We first analyze the datatypes and processes in the original ELM code. Then we present design considerations for ultrahigh-resolution ELM (uELM) development for massive GPU systems. These techniques include the global data-oriented simulation workflow, domain partition, code porting and data copy, memory reduction, parallel loop restructure and flattening, and race condition detection. We implemented the first version of uELM using OpenACC targeting the NVidia GPUs in the Summit supercomputer at Oak Ridge National Laboratory. During the implementation, we developed a software tool (named SPEL) to facilitate code generation, verification, and performance tuning using these techniques. The first uELM implementation for Nvidia GPUs on Summit delivered promising results: 1) over 98% of the ELM code was automatically generated and tuned by scripts. Most ELM modules had better computational performances than the original ELM code for CPUs. The GPU-ready uELM is more scalable than the CPU code on fully-loaded Summit nodes. Example profiling results from several modules are also presented to illustrate the performance improvements and race condition detection. The lessons learned and toolkit developed in the study are also suitable for further uELM deployment using OpenMP on the first US exascale computer, Frontier, equipped with AMD CPUs and GPUs.

Schwartz, Peter↗

DOE OSTI2023

A simple way to integrate distributed storage into a wholesale electricity market

Abstract Current plans to decarbonize the electric supply system imply that the generation from wind and solar sources will grow substantially. This growth will increase the uncertainty of system operations due to the inherent variability of these renewable sources, and as a result, more reserve capacity will be required to provide the ramping (flexibility) needed for reliable operations. This paper assumes that all of the increased uncertainty comes from wind farms on the grid, and it shows how distributed storage managed locally by aggregators can provide the ramping needed without introducing a separate market for flexibility. This can be accomplished when the aggregators minimize the expected daily cost of the energy purchased from the grid for their customers by submitting optimal bids into the wholesale market with high and low price thresholds for discharging and charging the storage. This model is illustrated using a stochastic multi-period security constrained optimal power flow together with realistic data for a reduction of the network in the Northeast Power Coordinating Council region of the United States. The results show that the bidding strategy for distributed storage provides ramping to the grid just as effectively as storage managed by a system operator.

Lamadrid, Alberto J.↗

DOE OSTI2025

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.

Bayesian parameter estimation↗

DOE OSTI2022

Experimental results of a magnetically coupled piezoelectric actuator to relieve microchannel heat exchanger maldistribution

Refrigerant maldistribution is a common issue in microchannel heat exchangers. Refrigerant maldistribution can have significant negative effects on the heat transfer performance and increase the pressure drop, leading to increased superheat temperature at the outlet and increased compressor work, lowering system efficiency. In this work, a novel piezoelectric-driven magnetic actuator (PEDMA) was fabricated and inserted into the inlet header of the microchannel evaporator, with the goal of improving the non-uniformity of the flow distribution by regulating the flow in the header to the microchannels. Two prototype PEDMA designs were tested and showed significant impact on the refrigerant flow in the region of actuation. In the first design, the experimental data showed superheat reductions up to 9 °C when the individual channel temperatures were measured. The second design redistributed the refrigerant such that a 1% increase in capacity was calculated for the whole heat exchanger without significantly impacting the heating efficiency. The experimental results demonstrate the successful operation of the PEDMA devices and represent a new method of active flow distribution control which is based on a simple design with minimal additional energy consumption, and which can be inserted into microchannel heat exchanger headers to potentially relieve the refrigerant maldistribution.

42 ENGINEERING↗

DOE OSTI2020

Dynamic Ligand Surface Chemistry of Excited PbS Quantum Dots

The ligand shell around colloidal quantum dots mediates the electron and energy transfer processes that underpin their use in optoelectronic and photocatalytic applications. In this work, we show that the surface chemistry of carboxylate anchoring groups of oleate ligands passivating PbS quantum dots undergoes significant changes when the quantum dots are excited to their excitonic states. Additionally, we directly probe the changes of surface chemistry using time-resolved mid-infrared spectroscopy that records the evolution of the vibrational frequencies of carboxylate groups following excitation of the electronic states. The data reveal a reduction of the Pb-O coordination of carboxylate anchoring groups to lead atoms at the quantum dot surfaces. The dynamic surface chemistry of the ligands may increase their surface mobility in the excited state and enhance the ability of molecular species to penetrate the ligand shell to undergo energy and charge transfer processes that depend sensitively on distance.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Computers in neutron science

Abstract Neutron scattering provides a sensitive probe for materials structure and dynamics and has had a profound impact on research ranging from quantum topological materials to archaeology to aerospace alloys. These experiments are incredibly data-rich and often challenging to interpret. Computational studies have been integral in the growing success of neutron science. From data collection and reduction to analysis and interpretation, computers are used at every stage of modern neutron experiments. State-of-the-art software allows for a deeper understanding of these powerful unique experiments. This themed issue, guest-edited by Dr. Jeff Armstrong, Dr. Keith T Butler, and Dr. Matthew R Ryder, brings together a collection of research highlighting the state-of-the-art in the application of computers in neutron science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

The Influence of Pressure on Flame-Flow Characteristics of a Reacting Jet in Crossflow

This work experimentally investigates the effects of elevated combustor pressures on the characteristics of a lean premixed reacting methane/air jet injected into a lean vitiated crossflow using a 12.7 mm axial jet. Experiments were conducted in an axially staged combustor, which implements a reacting jet in crossflow (RJIC) configuration and operates over a pressure range of 1–5 atmospheres. Simultaneous CH* chemiluminescence and particle image velocimetry (PIV) are used to study the flow field and flame behavior. The results show that the reacting jet trajectory exhibits greater penetration with elevated pressure, which is a novel finding compared to available data in the literature. However, the flame liftoff point and ignition delay time both decreased with elevated pressure, which was attributed to decreased vorticity along the flame boundary which corresponds to increased Damköhler numbers (Da). Emissions measurements confirm the NOx increase with pressure as reported in the literature for single-stage gas turbine combustors. Concurrently, emission measurements for the staged configuration show the strong NOx benefit of the RJIC system: the data prove a reduction of global outlet emission levels at elevated pressure with the axially staged configuration. Here, the axial emission reduction was attributed to the decreasing liftoff at elevated pressure levels. Hence, the research emphasizes that the flame and emission characteristics are coupled; they are not only dependent on the geometric parameters and momentum flux ratios but are also a function of pressure.

03 NATURAL GAS↗

DOE OSTI2022

Strong suppression of heat conduction in a laboratory replica of galaxy-cluster turbulent plasmas

In conventional gases and plasmas, it is known that heat fluxes are proportional to temperature gradients, with collisions between particles mediating energy flow from hotter to colder regions and the coefficient of thermal conduction given by Spitzer’s theory. However, this theory breaks down in magnetized, turbulent, weakly collisional plasmas, although modifications are difficult to predict from first principles due to the complex, multiscale nature of the problem. Understanding heat transport is important in astrophysical plasmas such as those in galaxy clusters, where observed temperature profiles are explicable only in the presence of a strong suppression of heat conduction compared to Spitzer’s theory. To address this problem, we have created a replica of such a system in a laser laboratory experiment. Our data show a reduction of heat transport by two orders of magnitude or more, leading to large temperature variations on small spatial scales (as is seen in cluster plasmas).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Scalable Tensor Methods for Nonuniform Hypergraphs

While multilinear algebra appears natural for studying the multiway interactions modeled by hypergraphs, tensor methods for general hypergraphs have been stymied by theoretical and practical barriers. A recently proposed adjacency tensor is applicable to nonuniform hypergraphs, but is prohibitively costly to form and analyze in practice. We develop tensor times same vector (TTSV) algorithms for this tensor which improve complexity from $O(n^r)$ to a low-degree polynomial in $r$, where $n$ is the number of vertices and $r$ is the maximum hyperedge size. Our algorithms are implicit, avoiding formation of the order $r$ adjacency tensor. Here, we demonstrate the flexibility and utility of our approach in practice by developing tensor-based hypergraph centrality and clustering algorithms. We also show these tensor measures offer complementary information to analogous graph-reduction approaches on data, and are also able to detect higher-order structure that many existing matrix-based approaches provably cannot.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Algorithms for Efficient Reproducible Floating Point Summation

We define “reproducibility” as getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should not affect the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociative floating point addition, makes reproducibility challenging even for summation, or operations like the BLAS. We describe a “reproducible accumulator” data structure (the “binned number”) and associated algorithms to reproducibly sum binary floating point numbers, independent of summation order. We use a subset of the IEEE Floating Point Standard 754-2008 and bitwise operations on the standard representations in memory. Our approach requires only one read-only pass over the data, and one reduction in parallel, using a 6-word reproducible accumulator (more words can be used for higher accuracy), enabling standard tiling optimization techniques. Summing n words with a 6-word reproducible accumulator requires approximately 9 n floating point operations (arithmetic, comparison, and absolute value) and approximately 3 n bitwise operations. The final error bound with a 6-word reproducible accumulator and our default settings can be up to 2 29 times smaller than the error bound for conventional (recursive) summation on ill-conditioned double-precision inputs.

Computer Science↗

DOE OSTI2021

Electrochemistry-Induced Restructuring of Tin-Doped Indium Oxide Nanocrystal Films of Relevance to CO 2 Reduction

The electrochemical reduction of CO 2 into fuels using renewable electricity presents an opportunity to utilize captured CO 2 . Electrocatalyst development has been a primary focus of research in this area. This is especially true at the nanoscale, where researchers have focused on understanding nanostructure-property relationships. However, electrocatalyst structure may evolve during operation. Indium- and tin-based oxides have been widely studied as electrocatalysts for CO 2 reduction to formate, but evolution of these catalysts during operation is not well-characterized. Here, we report the evolution of nanoscale structure of precise tin-doped indium oxide nanocrystals under CO 2 reduction conditions. We show that sparse monolayer nanocrystal films desorb from the electrode upon charging, but thicker nanocrystal films remain, likely due to an increased number of physical contacts. Upon applying a cathodic voltage of –1.0 V vs RHE or greater, the original 10-nm diameter nanocrystals are no longer visible, and instead form a larger microstructural network. Elemental analysis suggests the network is an oxygen-deficient indium-tin metal alloy. We hypothesize that this morphological evolution is the result of nanocrystal sintering due to oxide reduction. Furthermore, these data provide insights into the morphological evolution of tin-doped indium oxide nanocrystal electrocatalysts under reducing conditions and highlight the importance of post-electrochemical structural characterization of electrocatalysts.

25 ENERGY STORAGE↗

DOE OSTI2022

Performance improvements of the windowed multipole formalism using a rational fraction approximation of the Faddeeva function

The windowed multipole (WMP) formalism was introduced as a way to calculate Doppler broadened cross sections on the fly during Monte Carlo simulations. While more arithmetic is needed compared to point-wise cross section look-ups, performance remained competitive from the large memory reductions and sequential data access. The single most expensive function call in a depleted fuel assembly problem using WMP comes from the evaluation of the Faddeeva function, which previously relied on a highly accurate, highly-branching algorithm. This paper explores the use of rational fraction approximations tailored to the domain interest of reactor physics applications and the development of lower accuracy approximations sufficient for our application. The rational approximations were implemented and tested in OpenMC on an infinite medium problem to stress the cross section calculation routine and a PWR assembly problem. In both cases, the rational approximation nearly eliminated the ∼ 20% penalty previously observed when comparing to point-wise libraries. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Probabilistic modeling approach for tracking each collision and breakup of computational parcels

A modeling approach for binary collision and particle breakup is presented for a stochastic particle model in which a computational parcel represents a number of similar physical particles. Presented in this report includes sampling from a distribution of discrete data, parcel number reduction, probabilistic calculation of collision rates, and various particle-particle interactions. Modeling of elastic scattering, coalescence and agglomeration, and breakup are discussed for solid-solid, solid-liquid, and liquid-liquid particle collisions. Numerical procedures are also summarized.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

FAIR data infrastructure and tools for AI-assisted streamflow prediction

Focal Area(s) Areas: We discuss how the integration of AI into Earth Science models can impact streamflow predictions at both the science and data levels. Doing so, we address cross-cutting needs related to the goal of making data FAIR (Findable, Accessible, Interoperable, and Re-usable [1]) for seamless use with Artificial Intelligence/Machine Learning (AI/ML) in Earth System Science at DOE. A novel idea is that AI/ML itself can help with the FAIR data goal and address issues in targeted areas e.g. missing data, data quality and reduction. In addition, the interpretability of results obtained with new AI methods is poised to impact broader scientific challenges in hydrology..

54 ENVIRONMENTAL SCIENCES↗