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DOE OSTI2022

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

Abstract We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT–AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of $$7 \times 10^{6}$$ 7 × 10 6 times, in the best case, and $$7 \times 10^{3}$$ 7 × 10 3 times on average compared to a finite element solver. Furthermore, this BT–AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Machine learning reduces soft costs for residential solar photovoltaics

Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.

14 SOLAR ENERGY↗

DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Current inequality and future potential of US urban tree cover for reducing heat-related health impacts

Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO 2 e yr -1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO 2 e yr -1 . The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Nature-based solutions in mountain catchments reduce impact of anthropogenic climate change on drought streamflow

Quantifying how well Nature-based Solutions can offset anthropogenic climate change impacts is important for adaptation planning, but has rarely been done. Here we show that a widely-applied Nature-based Solution in South Africa – invasive alien tree clearing – reduces the impact of anthropogenic climate change on drought streamflow. Using a multi-model joint-attribution of climate and landscape-vegetation states during the 2015–2017 Cape Town “Day Zero” drought, we find that anthropogenic climate change reduced streamflow by 12–29% relative to a counterfactual world with anthropogenic emissions removed. This impact on streamflow was larger than corresponding reductions in rainfall (7–15%) and reference evapotranspiration (1.7–2%). Clearing invasive alien trees could have ameliorated streamflow reductions by 3–16% points for moderate invasions levels. Preventing further invasive alien tree spread avoided potential additional reductions of 10–27% points. Total clearing could not have offset the anthropogenic climate change impact completely. Invasive alien tree clearing is an important form of catchment restoration for managing changing hydroclimatic risk, but will need to be combined with other adaptation options as climate change accelerates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Establishing structure-sensitivity of ceria reducibility: real-time observations of surface-hydrogen interactions

The first layer of atoms on an oxide catalyst provides the first sites for adsorption of reactants and the last sites before products or oxygen are desorbed. We employ a unique combination of morphological, structural, and chemical analyses of a model ceria catalyst with different surface terminations under an H 2 environment to unequivocally establish the effect of the last layer of atoms on surface reduction. (111) and (100) terminated epitaxial islands of ceria are simultaneously studied in situ allowing for a direct investigation of the structure–reducibility relationship under identical conditions. Kinetic rate constants of Ce 4+ to Ce 3+ transformation and equilibrium concentrations are extracted for both surface terminations. Unlike the kinetic rate constants, which are practically the same for both types of islands, more pronounced oxygen release, and overall higher reducibility were observed for (100) islands compared to (111) ones. The findings are in agreement with coordination-limited oxygen vacancy formation energies calculated by density functional theory. The results point out the important aspect of surface terminations in redox processes, with particular impact on the catalytic reactions of a variety of catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Reducing sputter induced stress and damage for efficient perovskite/silicon tandem solar cells

Reducing damages caused by sputtering of transparent conductive oxide (TCO) electrodes is critical in achieving highly efficient and stable perovskite/silicon tandem solar cells. Here we study the sputter caused damage to bathocuproine (BCP), which is widely used in highly efficient p-i-n structure single junction perovskite solar cells. While BCP buffer layer protects the underneath layers from damage, itself can be damaged by sputtering of TCOs at a wide range of target-substrate distances, supported by molecular dynamic simulation. More intriguingly, it is observed that TCO easily peeled off after sputtering when the sputtering target is close to substrate. This is ascribed to formation of stress during cooling down process after sputtering due to different thermal expansion coefficients of the layers. Our studies explain why tin oxide (SnO2) made by atomic layer deposition can replace BCP for a much better tandem device performance. SnO2 has high affinity with sputtered TCO electrode to suppress peeling-off issue and has higher bond energy to resist sputter induced damage, thus it allows a wider window of target-substrate distances than BCP during TCO sputtering. Ultimately, we demonstrate an efficient perovskite/silicon monolithic tandem solar cell with efficiency of 26.0% to illustrate the beneficial effects of reduced stress and damage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Over 31% efficient indoor organic photovoltaics enabled by simultaneously reduced trap-assisted recombination and non-radiative recombination voltage loss

Indoor organic photovoltaics (OPVs) have shown great potential application in driving low-energy-consumption electronics for the Internet of Things. There is still great room for further improving the power conversion efficiency (PCE) of indoor OPVs, considering that the desired morphology of the active layer to reduce trap-assisted recombination and voltage losses and thus simultaneously enhance the fill factor (FF) and open-circuit voltage for efficient indoor OPVs remains obscure. Herein, by optimizing the bulk and interface morphology via a layer-by-layer (LBL) processing strategy, low leakage current and low non-radiative recombination loss can be synergistically achieved in PM6:Y6-O based devices. Detailed characterizations reveal the stronger crystallinity, purer domains and ideal interfacial contacts in the LBL devices compared to their bulk-heterojunction (BHJ) counterparts. The optimized morphology yields a reduced voltage loss and an impressive FF of 81.5%, and thus contributes to a high PCE of 31.2% under a 1000 lux light-emitting diode (LED) illumination in the LBL devices, which is the best reported efficiency for indoor OPVs. Additionally, this LBL strategy exhibits great universality in promoting the performance of indoor OPVs, as exemplified by three other non-fullerene acceptor systems. Finally, this work provides guidelines for morphology optimization and synergistically promotes the fast development of efficient indoor OPVs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Enhancing exciton diffusion by reducing energy disorder in organic solar cells

A highly crystalline, highly emissive, and wide-bandgap polymer AC174 with an extremely small Stokes shift is designed and synthesized in water, and is used to reduce system energetic disorder and increase the exciton diffusion length of the classical PM6:Y6 blend system. AC174 is incompatible with PM6 and Y6, improves molecular packing, and reduces system energetic disorder. The long-range Förster resonance energy transfer between the donor and acceptor is enhanced, and the exciton diffusion constant and exciton lifetime are increased, leading to a longer exciton diffusion length and more efficient exciton dissociation and charge generation. The addition of AC174 simultaneously improves the open-circuit voltage, short-circuit current density (J SC ) and fill factor of PM6:Y6 devices; especially the highest internal quantum efficiency approaches 100%, and the highest J SC is 28.4 mA cm –2 . Ternary devices with 5% AC174 in the donors achieve a power conversion efficiency of 17.2%, higher than those of the parent binary devices based on PM6:Y6 (15.9%) and AC174:Y6 (3.24%).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Origin of hydroxyl pair formation on reduced anatase TiO 2 (101)

The interaction of water with metal oxide surfaces is of key importance to several research fields and applications. Because of its ability to photo-catalyze water splitting, reducible anatase TiO 2 (a-TiO 2 ) is of particular interest. Here, we combine experiments and theory to study the dissociation of water on bulk-reduced a-TiO 2 (101). Following large water exposures at room temperature, point-like protrusions appear on the a-TiO 2 (101) surface, as shown by scanning tunneling microscopy (STM). These protrusions originate from hydroxyl pairs, consisting of terminal and bridging OH groups, OH t /OH b , as revealed by infrared reflection absorption spectroscopy (IRRAS) and valence band experiments. Utilizing density functional theory (DFT) calculations, we offer a comprehensive model of the water/a-TiO 2 (101) interaction. In conclusion, this model also explains why the hydroxyl pairs are thermally stable up to ~480 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A Novel Partitioned Approach for Reduced Order Model—Finite Element Model (ROM-FEM) and ROM-ROM Coupling

Partitioned methods allow one to build a simulation capability for coupled problems by reusing existing single-component codes. In so doing, partitioned methods can shorten code development and validation times for multiphysics and multiscale applications. In this work, we consider a scenario in which one or more of the “codes” being coupled are projection-based reduced order models (ROMs), introduced to lower the computational cost associated with a particular component. We simulate this scenario by considering a model interface problem that is discretized independently on two non-overlapping subdomains. Here we then formulate a partitioned scheme for this problem that allows the coupling between a ROM “code” for one of the subdomains with a finite element model (FEM) or ROM “code” for the other subdomain. The ROM “codes” are constructed by performing proper orthogonal decomposition (POD) on a snapshot ensemble to obtain a low-dimensional reduced order basis, followed by a Galerkin projection onto this basis. The ROM and/or FEM “codes” on each subdomain are then coupled using a Lagrange multiplier representing the interface flux. To partition the resulting monolithic problem, we first eliminate the flux through a dual Schur complement. Application of an explicit time integration scheme to the transformed monolithic problem decouples the subdomain equations, allowing their independent solution for the next time step. We show numerical results that demonstrate the proposed method’s efficacy in achieving both ROM-FEM and ROM-ROM coupling.

de Castro, Amy↗

DOE OSTI2019

Memory embedded non-intrusive reduced order modeling of non-ergodic flows

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach we present in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.

42 ENGINEERING↗

DOE OSTI2020

Reduced scaling extended multi-state CASPT2 (XMS-CASPT2) using supporting subspaces and tensor hyper-contraction

We present a reduced scaling formulation of the extended multi-state CASPT2 (XMS-CASPT2) method, which is based on our recently developed state-specific CASPT2 (SS-CASPT2) formulation using supporting subspaces and tensor hyper-contraction. By using these two techniques, the off-diagonal elements of the effective Hamiltonian can be computed with only O(N 3 ) operations and O(N 2 ) memory, where N is the number of basis functions. Furthermore, this limits the overall computational scaling to O(N 4 ) operations and O(N 2 ) memory. Thus, excited states can now be obtained at the same reduced (relative to previous algorithms) scaling we achieved for SS-CASPT2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Experimental demonstration of the reduced expansion of a laser-heated surface using a low density foam layer, pertaining to advanced hohlraum designs with less wall-motion

The ablative expansion of laser-heated materials is important for determining how hohlraum cavities can be utilized for inertial confinement fusion. The utility of a low-density foam layer to reduce the density of the expanding heated hohlraum wall is demonstrated here in a series of experiments on the National Ignition Facility. X-ray radiography measurements of the expanding foam-lined Au wall in low aspect-ratio cylindrical geometry are used to compare the impact of Au-doped CH and Ta 2 O 5 foams between 10 and 40 mg/cc on the wall expansion. HYDRA Simulations are used to estimate the x-ray transmission at the 1/4 nc surface, which is important in understanding the absorption of laser light by the plasma. These demonstrate for the first time that a foam layer reduces the expansion of a hohlraum-like target and illustrate that the interplay between the expanding foam plasma and the shock reflected by the hohlraum wall is critical in optimizing foam-liner parameters to achieve the maximum time for a symmetric drive on a capsule.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Reduced scaling formulation of CASPT2 analytical gradients using the supporting subspace method

We present a reduced scaling and exact reformulation of state specific complete active space second-order perturbation (CASPT2) analytical gradients in terms of the MP2 and Fock derivatives using the supporting subspace method. This work follows naturally from the supporting subspace formulation of the CASPT2 energy in terms of the MP2 energy using dressed orbitals and Fock builds. For a given active space configuration, the terms corresponding to the MP2-gradient can be evaluated with O(N5) operations, while the rest of the calculations can be computed with O(N3) operations using Fock builds, Fock gradients, and linear algebra. When tensor-hyper-contraction is applied simultaneously, the computational cost can be further reduced to O(N4) for a fixed active space size. The new formulation enables efficient implementation of CASPT2 analytical gradients by leveraging the existing graphical processing unit (GPU)-based MP2 and Fock routines. We present benchmark results that demonstrate the accuracy and performance of the new method. Example applications of the new method in ab initio molecular dynamics simulation and constrained geometry optimization are given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

On closures for reduced order models - A spectrum of first-principle to machine-learned avenues

For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr–Sommerfeld stability equation and numerous vortex models, of which the von Karman vortex street is one of the most prominent. Subsequent ROMs typically relied on first principles, like mathematical Galerkin models, weakly nonlinear stability theory, and two- and three-dimensional vortex models pioneered the data-driven proper orthogonal decomposition (POD) modeling. In early POD modeling, available data were used to build an optimal basis, which was then utilized in a classical Galerkin procedure to construct the ROM, but data have made a profound impact on ROMs beyond the Galerkin expansion. In this paper, we take a modest step and illustrate the impact of data-driven modeling on one significant ROM area. Specifically, we focus on ROM closures, which are correction terms that are added to the classical ROMs in order to model the effect of the discarded ROM modes in under-resolved simulations. Through simple examples, we illustrate the main modeling principles used to construct the classical ROMs, motivate and introduce modern ROM closures, and show how data-driven modeling, artificial intelligence, and machine learning have changed the standard ROM methodology over the last two decades. Finally, we outline our vision on how the state-of-the-art data-driven modeling can continue to reshape the field of reduced order modeling.

42 ENGINEERING↗

DOE OSTI2021

Reduced bandwidth Compton photons from a laser-plasma accelerator using tailored plasma channels

It has been demonstrated experimentally that laser plasma accelerators can produce multi-100 MeV electron bunches with a few percent energy spread, and from these electrons, multi-MeV quasi-monoenergetic photons have been demonstrated based on Compton up-scattering from a counter-propagating laser. This offers the potential of a high-quality, narrow-bandwidth, compact, photon source with broad application. The bandwidth of the resulting photons depends directly on the distribution of the electron bunch and is limited, in particular, by the bunch divergence (i.e., the spread in transverse velocity angle). At the same time, the ability to decelerate electrons after scattering is important to source deployment. We describe a series of plasma structures that expand and then collimate the electron bunch, reducing its divergence and thus reducing the bandwidth of the scattered photons while enabling both high performance scattering and deceleration. Furthermore, these plasma structures are demonstrated in simulations of the accelerator system, showing the potential to reach few-percent photon spread which is important for applications using nuclear resonance fluorescence.

42 ENGINEERING↗

DOE OSTI2022

Reduced photothermal heating in diamonds enriched with H3 point defects

Solid-state laser refrigeration of semiconductors remains an outstanding experimental challenge. In this work, we show that, following excitation with a laser wavelength of 532 nm, bulk diamond crystals doped with H3 centers both emit efficient up-conversion (anti-Stokes) photoluminescence and also show significantly reduced photothermal heating relative to crystals doped with nitrogen–vacancy (NV) centers. The H3 center in diamond is a highly photostable defect that avoids bleaching at high laser irradiances of 10–70 MW/cm 2 and has been shown to exhibit laser action, tunable over the visible band of 500–600 nm. Here, the observed reduction of photothermal heating arises due to a decrease in the concentration of absorbing point defects, including NV-centers. These results encourage future exploration of techniques for H3 enrichment in diamonds under high-pressure, high-temperature conditions for the simultaneous anti-Stokes fluorescence cooling and radiation balanced lasing in semiconductor materials. Reducing photothermal heating in diamond through the formation of H3 centers also opens up new possibilities in quantum sensing via optically detected magnetic resonance spectroscopy at ambient conditions.

42 ENGINEERING↗