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DOE OSTI2021

A method for convex black-box integer global optimization

Here we study the problem of minimizing a convex function on a nonempty, finite subset of the integer lattice when the function cannot be evaluated at noninteger points. We propose a new underestimator that does not require access to (sub)gradients of the objective; such information is unavailable when the objective is a blackbox function. Rather, our underestimator uses secant linear functions that interpolate the objective function at previously evaluated points. These linear mappings are shown to underestimate the objective in disconnected portions of the domain. Therefore, the union of these conditional cuts provides a nonconvex underestimator of the objective. We propose an algorithm that alternates between updating the underestimator and evaluating the objective function. We prove that the algorithm converges to a global minimum of the objective function on the feasible set. We present two approaches for representing the underestimator and compare their computational effectiveness. We also compare implementations of our algorithm with existing methods for minimizing functions on a subset of the integer lattice. We discuss the difficulty of this problem class and provide insights into why a computational proof of optimality is challenging even for moderate problem sizes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Application of thermal energy storage with electrified heating and cooling in a cold climate

Ground source heat pumps (GSHPs) have been shown to be an effective means of decarbonizing building heating and cooling, but the operation of these systems at scale presents challenges to sites and the grid due to increases in peak demand. Here, in this study, we investigate how the use of Thermal Energy Storage (TES), in the form of stratified water storage, could reduce the peak daily demand associated with GSHP systems and thus improve their cost effectiveness. This system was compared to a Combined Heat and Power (CHP) plant, to investigate the potential cost and emissions impacts of electrification on large energy users transitioning from high efficiency fossil fuel equipment. This study builds on previous research by using a significant campus area, and investigating the various impacts of an electrified system versus a state of the art fossil fuel system. An established method of TRNSYS modeling is used for the heat pumps and borehole heat exchangers (BHEs), along with the TES. The GSHP system with TES is modeled to follow industry standards and sized to provide an optimal balance of capital and operating costs. Costs were investigated for a unique large energy user rate structure, as well as for a more common generalized rate structure. The results show that the addition of TES to the GSHP system could reduce operating costs by 4.5%, but still incurs a 5.64% cost increase from the baseline CHP system. It is also shown, however, that a tactical integration of heat pumps into the CHP system is the most cost effective solution, only increasing the operating costs by 4.71%. Likewise, the impacts of different rate structures are highlighted, with the TES only resulting in 0.75% cost savings under the generalized utility rate. Furthermore, it is shown that thermal storage has great potential for demand reduction, with the addition of storage incurring a decrease of 7–22% in annual peak demand kW, dependent on the rate structure.

25 ENERGY STORAGE↗

DOE OSTI2020

FiXR : a framework to reconstruct fiber cross-sections from X-ray fiber diffraction experiments

Ab initio reconstruction methods have revolutionized the capabilities of small-angle X-ray scattering (SAXS), allowing the data-driven discovery of previously unknown molecular conformations, exploiting optimization heuristics and assumptions behind the composition of globular molecules. While these methods have been successful for the analysis of small particles, their impact on fibrillar assemblies has been more limited. The micrometre-range size of these assemblies and the complex interaction of their periodicities in their scattering profiles indicate that the discovery of fibril structures from SAXS measurements requires novel approaches beyond extending existing tools for molecular discovery. In this work, it is proposed to use SAXS measurements, together with diffraction theory, to infer the electron distribution of the average cross-section of a fiber. This cross-section is modeled as a discrete electron density with continuous support, allowing representations beyond binary distributions. Additional constraints, such as non-negativity or smoothness/connectedness, can also be added to the framework. The proposed approach is tested using simulated SAXS data from amyloid β fibril models and using measured data of Tobacco mosaic virus from SAXS experiments, recovering the geometry and density of the cross-sections in all cases. The approach is further tested by analyzing SAXS data from different amyloid β fibril assemblies, with results that are in agreement with previously proposed models from cryo-EM measurements. The limitations of the proposed method, together with an analysis of the robustness of the method and the combination with different experimental sources, are also discussed.

Biochemistry & Molecular Biology↗

DOE OSTI2024

Delta-Rice: A HDF5 Compression Plugin optimized for Digitized Detector Data

Delta-Rice is an HDF5 (The HDF Group et al., 2020) filter plugin that was developed to compress digitized detector signals recorded by the Nab experiment (Fry et al., 2019), a fundamental neutron physics experiment. This is a two-step process where incoming data is passed through a pre-processing filter and then compressed with Rice coding. A routine for determining the optimal pre-processing filter for a dataset is provided along with an example GPU deployment. When applied to data collected by the Nab data acquisition system, this method produced output files 29% their initial size, and was able to do so with an average read/write throughput in excess of 2 GB/s on a single CPU. Compared to the widely used Gzip compression routine, Delta-Rice reduces the file size by 33% more with over an order of magnitude increase in read/write throughput. Delta-Rice is available on CPU to users through the HDF5 library.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Optimizing Kernel Machines Using Deep Learning

Building highly non-linear and non-parametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models through the construction of similarity functions from data. These methods are particularly preferred in cases where the training data sizes are limited and when prior knowledge of the data similarities is available. Despite their usefulness, they are limited by the computational complexity and their inability to support end-to-end learning with a task specific objective. On the other hand, deep neural networks have become the de facto solution for end-to-end inference in several learning paradigms. In this article, we explore the idea of using deep architectures to perform kernel machine optimization, for both computational efficiency and end-to-end inferencing. To this end, we develop the DKMO (Deep Kernel Machine Optimization) framework, that creates an ensemble of dense embeddings using Nystrõm kernel approximations and utilizes deep learning to generate task-specific representations through the fusion of the embeddings. Intuitively, the filters of the network are trained to fuse information from an ensemble of linear subspaces in the RKHS. Furthermore, we introduce the kernel dropout regularization to enable improved training convergence. Finally, we extend this framework to the multiple kernel case, by coupling a global fusion layer with pre-trained deep kernel machines for each of the constituent kernels. Using case studies with limited training data, and lack of explicit feature sources, we demonstrate the effectiveness of our framework over conventional model inferencing techniques.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Scalable and compact magnetocaloric heat pump technology

Magnetocaloric heat pumping (MCHP) promises to be more efficient than traditional vapor compression while also eliminating the deleterious effects of gaseous refrigerants. While MCHP devices have shown the temperature spans and efficiencies needed for different heating and cooling applications, they struggle to become commercially viable due to their large size and mass, and resultant high cost. This paper evaluates a baseline MCHP device and explores methods to boost its system power density (SPD). The key components of the baseline system are the gadolinium packed-particle bed active magnetic regenerator (AMR) and a magnetic source composed of permanent magnets and high permeability magnetic steel. To enhance the SPD, the paper evaluates maximizing the AMR volume, opting for first-order magnetocaloric materials, optimizing the magnet and AMR geometry, and reducing the size of magnets and magnetic steel parts. At larger thermal powers, increasing the AMR diameter and the number of magnetic poles were evaluated. Using finite element models, solid models, and estimates of magnetocaloric material performance, thermal powers ranging from 37 W to 44 kW at a nominal 10 K temperature span were projected, and SPD was estimated to improve from 6 W/kg to 81 W/kg. Neglecting end effects, an upper limit of 114 W/g is estimated. Compared to SPD of off-the-shelf compressors with similar environment temperatures, MCHP power density using gadolinium is competitive up to roughly 200 W of cooling power. This is extended to 1 kW when using LaFeSi alloys and up to 3 kW in the limiting case. In conclusion, these results indicate that the performance and mass of MCHP can match that of compressors, which is a critical step toward cost-competitive magnetocaloric technology.

42 ENGINEERING↗

DOE OSTI2021

Beyond single crystals: Imaging rubrene polymorphism across crystalline batches through lattice phonon Raman microscopy

Polymorphism is an issue troubling numerous scientific fields. A phenomenon where molecules can arrange in different orientations in a crystal lattice, polymorphism in the field of organic photovoltaic materials can dramatically change electronic properties of these materials. Rubrene is a benchmark photovoltaic material showing high carrier mobility in only one of its three polymorphs. To use rubrene in devices, it is important to quantify the polymorph distribution arising from a particular crystal growth method. However, current methods for characterizing polymorphism are either destructive or inefficient for batch scale characterization. Lattice phonon Raman spectroscopy has the ability to distinguish between polymorphs based on low frequency intermolecular vibrations. Here, we present here the addition of microscopy to lattice phonon Raman spectroscopy, which allows us to not only characterize polymorphs efficiently and nondestructively through Raman spectroscopy but also concurrently gain information on the size and morphology of the polymorphs. We provide examples for how this technique can be used to perform large, batch scale polymorph characterization for crystals grown from solution and physical vapor transport.We end with a case study showing how Raman microscopy can be used to efficiently optimize a green crystal growth method, selecting for large orthorhombic crystals desired for rubrene electronic device applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A Scalable Mixed-Integer Decomposition Method for Security-Constrained Optimal Power Flow with Complementarity Constraints

This project aimed to develop a scalable algorithm for security-constrained optimal power flow (SCOPF) under contingency scenarios. In particular, the SCOPF problem targeted in the GO Competition is challenging because of the nonconvexity, its nonsmoothness, and the problem size, which increases with the number of contingency events. Complementarity constraints imposed in post-contingency variables are particularly challenging because they lead to a violation of constraint qualifications at any feasible point.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Solutes that reduce yield strength anisotropies in magnesium from first principles

Using Labusch-type solid solution strengthening models parameterized with DFT-computed solute-dislocation interaction energies, we perform a computational search for 63 solutes across the periodic table to find those that lower anisotropy ratios (non-basal to basal CRSS) of magnesium potentially increasing its ductility per the von Mises criterion. For this purpose, we compute changes in strength for solutes as a function of composition and temperature, and compute anisotropy ratios for solutes that include both rare earth and non-rare earth elements. Here we specifically focus on solute-dislocation interaction energies in the following DFT-optimized dislocations as representative of three non-basal plastic deformation modes: $\langle c + a \rangle$ edge, (10$\bar1$2) tension twinning edge, and the (10$\bar{1}$1) compression twinning edge. We find that solute-induced changes in non-basal deformation modes can be approximated using a second-order polynomial in the size misfit of the solutes, which permits rapid screening of solutes. Our approach to identify solutes known to improve strengthening incorporates solute solubility, and suggests other solutes that not have been previously explored for strengthening. The 8 rare-earth solutes that our method suggests as the best, ordered by increasing anisotropy ratios at their optimal concentrations, are: Gd, Tb, Dy, Nd, Ho, Er, Tm, and Yb. The 12 non-rare-earth solutes that our method suggests as the best, ordered by increasing anisotropy ratios, are: Y, Mn, Sc, Pb, Ca, Ag, Bi, Tl, Zn, Li, Ga, and Al. Of these, Gd, Nd, Er, Yb, Y, Mn, Ca, Zn, Li, and Al are used in commercial Mg alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Optimizing bioenergy biofuel harvest: a comparative analysis of stepwise and integrated methods for economic and environmental sustainability

Switchgrass is a promising bioenergy feedstock due to its high biomass yield potential, adaptability to marginal lands, and low carbon intensity for feedstock production. However, accurate cost estimation and assessment of greenhouse gas (GHG) emissions for the energy-intensive harvesting process are essential for evaluating the sustainability of bioenergy. This study provides a comparative analysis of two harvesting methods: the Stepwise Method, which separates operations into multiple stages, and the Integrated Method, which combines mowing and raking into a single pass. The analysis was conducted under four scenarios based on field sizes and biomass yields. Using three years of field-scale switchgrass harvest data from 125 sites, GHG emissions, energy consumption, and harvesting costs were quantified using the GREET model and techno-economic analysis. Additionally, regression analysis identified key climate and operational factors affecting fuel consumption. The Stepwise method was the most cost-effective for large fields with high biomass yield, achieving the lowest harvesting costs ($37.70 per ton). In contrast, the Integrated Method performed better in small fields and low-yield conditions, reducing GHG emissions by 9 % and energy use by 5 %. Regression analysis confirmed that a larger field size reduced fuel consumption, while higher biomass yield and longer operational time increased fuel use. Maximum temperature also contributed to a slight increase in fuel consumption. Furthermore, these results provide actionable insights for optimizing harvesting strategies based on field-specific conditions and operational goals, contributing to the economic and environmental sustainability of bioenergy production.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

The persistence of large scale structures. Part I. Primordial non-Gaussianity

Abstract We develop an analysis pipeline for characterizing the topology of large scale structure and extracting cosmological constraints based on persistent homology . Persistent homology is a technique from topological data analysis that quantifies the multiscale topology of a data set, in our context unifying the contributions of clusters, filament loops, and cosmic voids to cosmological constraints. We describe how this method captures the imprint of primordial local non-Gaussianity on the late-time distribution of dark matter halos, using a set of N-body simulations as a proxy for real data analysis. For our best single statistic, running the pipeline on several cubic volumes of size 40 (Gpc/h) 3 , we detect f NL loc =10 at 97.5% confidence on ~ 85% of the volumes. Additionally wetest our ability to resolve degeneracies betweenthe topological signature of f NL loc and variation of σ 8 and argue that correctly identifying nonzero f NL loc in this case is possible via an optimal template method. Our method relies on information living at $\mathcal{O}$(10) Mpc/h, a complementary scale with respect to commonly used methods such as the scale-dependent bias in the halo/galaxy power spectrum. Therefore, while still requiring a large volume, our method does not require sampling long-wavelength modes to constrain primordial non-Gaussianity. Moreover, our statistics are interpretable: we are able to reproduce previous results in certain limits and we make new predictions for unexplored observables, such as filament loops formed by dark matter halos in a simulation box.

Astronomy & Astrophysics↗

DOE OSTI2018

Phase stability and interlayer interaction of blue phosphorene

In this work, we study the interlayer interactions between sheets of blue phosphorus with quantum Monte Carlo (QMC) methods. Here, we find that as previously observed in black phosphorus, interlayer binding of blue phosphorus cannot be described by van der Waals (vdW) interactions alone within the density functional theory framework. Specifically, while some vdW density functionals produced reasonable binding curves, none of them could provide a correct, even qualitatively, description of charge redistribution due to interlayer binding. We also show that small systematic errors in common practice QMC calculations, such as the choice of optimized geometry and finite-size corrections, are non-negligible given the energy and length scales of this problem. We mitigate some of the major sources of error and report QMC-optimized lattice constant, stacking, and interlayer binding energy for blue phosphorus. Finally, it is strongly suggested that these considerations are important and quite general in the modeling of two-dimensional phosphorus allotropes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗

DOE OSTI2021

hr-Adaptivity for nonconforming high-order meshes with the target matrix optimization paradigm

In this study, we present an $hr$ -adaptivity framework for optimization of high-order meshes. This work extends the r -adaptivity method by Dobrev et al. (Comput Fluids, 2020), where we utilized the Target-Matrix Optimization Paradigm (TMOP) to minimize a functional that depends on each element’s current and target geometric parameters: element aspect-ratio , size , skew , and rotation . Since fixed mesh topology limits the ability to achieve the target size and aspect-ratio at each position, in this paper, we augment the r -adaptivity framework with nonconforming adaptive mesh refinement to further reduce the error with respect to the target geometric parameters. The proposed formulation, referred to as $hr$ -adaptivity, introduces TMOP-based quality estimators to satisfy the aspect-ratio target via anisotropic refinements and size target via isotropic refinements in each element of the mesh. The methodology presented is purely algebraic, extends to both simplices and hexahedra/quadrilaterals of any order, and supports nonconforming isotropic and anisotropic refinements in 2D and 3D. Using a problem with a known exact solution, we demonstrate the effectiveness of $hr$ -adaptivity over both r - and $h$ -adaptivity in obtaining similar accuracy in the solution with significantly fewer mesh nodes. We also present several examples that show that $hr$ -adaptivity can help satisfy geometric targets even when $r$ -adaptivity fails to do so, due to the topology of the initial mesh.

42 ENGINEERING↗

DOE OSTI2019

Implementation of a Particle Accelerator Beam Dynamics Code on Multi-Node GPUs

Particle accelerators play an important role in a wide range of scientific discoveries and industrial applications. The self-consistent multi-particle simulation based on the particle-in-cell (PIC) method has been used to study charged particle beam dynamics inside those accelerators. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high-resolution applications. In this paper, we implemented a parallel beam dynamics PIC code on multi-node hybrid architecture computers with multiple Graphics Processing Units (GPUs). We used two methods to parallelize the PIC code on multiple GPUs and observed that the replication method is a better choice for moderate problem size and current computer hardware while the domain decomposition method might be a better choice for large problem size and more advanced computer hardware that allows direct communications among multiple GPUs. Using the multi-node hybrid architectures at Oak Ridge Leadership Computing Facility (OLCF), the optimized GPU PIC code achieves a reasonable parallel performance and scales up to 64 GPUs with 16 million particles.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.

Fadnavis, Shreyas↗

DOE OSTI2025

Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems

Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

25 ENERGY STORAGE↗