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DOE OSTI2024

Ginkgo - A math library designed to accelerate Exascale Computing Project science applications

Large-scale simulations require efficient computation across the entire computing hierarchy. A challenge of the Exascale Computing Project (ECP) was to reconcile highly heterogeneous hardware with the myriad of applications that were required to run on these supercomputers. Mathematical software forms the backbone of almost all scientific applications, providing efficient abstractions and operations that are crucial to harness the performance of computing systems. Ginkgo is one such mathematical software library, nurtured by ECP, providing high-performance, user-friendly, and performance portable interfaces for applications in ECP and beyond. In this paper, we elaborate on Ginkgo’s philosophy of high-performance software that is sustainable, reproducible, and easy to use. We showcase the wide feature set of solvers and preconditioners available in Ginkgo and the central concepts involved in their design. We elaborate on four different ECP software integrations: MFEM, PeleLM + SUNDIALS, XGC, and ExaSGD that use Ginkgo to accelerate their science runs. Performance studies of different problems from these applications highlight the effectiveness of Ginkgo and the benefits incurred by these ECP applications.

Cojean, Terry↗

DOE OSTI2021

AtomSets as a hierarchical transfer learning framework for small and large materials datasets

Abstract Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (<400) and large structural data (>130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

Chen, Chi (ORCID:0000000180087043)↗

DOE OSTI2021

Lattice thermal transport in two-dimensional alloys and fractal heterostructures

Abstract Engineering thermal transport in two dimensional materials, alloys and heterostructures is critical for the design of next-generation flexible optoelectronic and energy harvesting devices. Direct experimental characterization of lattice thermal conductivity in these ultra-thin systems is challenging and the impact of dopant atoms and hetero-phase interfaces, introduced unintentionally during synthesis or as part of deliberate material design, on thermal transport properties is not understood. Here, we use non-equilibrium molecular dynamics simulations to calculate lattice thermal conductivity of $${\mathrm {(Mo|W)Se_2}}$$ ( Mo | W ) Se 2 monolayer crystals including $${\mathrm {Mo}}_{1-x}{\mathrm {W}}_x{\mathrm {Se_2}}$$ Mo 1 - x W x Se 2 alloys with substitutional point defects, periodic $${\mathrm {MoSe_2}|\mathrm {WSe_2}}$$ MoSe 2 | WSe 2 heterostructures with characteristic length scales and scale-free fractal $${\mathrm {MoSe_2}}|{\mathrm {WSe_2}}$$ MoSe 2 | WSe 2 heterostructures. Each of these features has a distinct effect on phonon propagation in the crystal, which can be used to design fractal and periodic alloy structures with highly tunable thermal conductivities. This control over lattice thermal conductivity will enable applications ranging from thermal barriers to thermoelectrics.

42 ENGINEERING↗

DOE OSTI2020

Hazard analysis for identifying common cause failures of digital safety systems using a redundancy-guided systems-theoretic approach

Replacing the existing aging analog instrumentation and control (I&C) systems with modern safety control and protection, digital technology offers one of the foremost means of performance improvements and cost reductions for the existing nuclear power plants (NPPs). However, the qualification of digital I&C systems remains a challenge, especially considering the issue of software common-cause failures (CCFs), which are difficult to address. With the application and upgrades of advanced digital I&C systems, software CCFs have become a potential threat to plant safety because most redundant designs use similar digital platforms or software in the operating and application systems. With complex designs of multilayer redundancy to meet the single-failure criterion, digital I&C safety systems (e.g., engineered safety-features actuation system [ESFAS]) are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. Here, this paper applies a modularized approach to conduct redundancy-guided systems-theoretic hazard analysis for an advanced digital ESFAS with multilevel redundancy designs. Systematic methods and risk-informed tools are incorporated to address both hardware and software CCFs, which provide guidance to eliminate the causal factors of potential single points of failure in the design of digital safety systems in advanced plant designs.

42 ENGINEERING↗

DOE OSTI2022

Digitally manufactured air plasma-on-water reactor for nitrate production

The sustainable production of food to support the increasing world population is one of humanity’s most pressing challenges. Plasma activated water, produced using renewable energy, can help fulfill plants’ needs in sustainable agriculture approaches. The design, implementation, and characterization of a digitally manufactured air plasma-on-water reactor (POWR) for the synthesis of nitrate as green nitrogen fertilizer is presented. The interaction of air plasma-generated reactive oxygen and nitrogen species with water produces nitrate (NO 3 - ) and related species, which are the main nitrogen-containing nutrients for plants. The mild conditions of the operation of the POWR opens the possibility to use plastics, particularly through digital manufacturing strategies such as 3D-printing, for its fabrication. A pin-to-plate reactor configuration powered by high-voltage alternating power is chosen due to its simplicity and efficacy. A computational thermal-fluid model is used to evaluate the design and attain expected operational characteristics. The experimental characterization of the POWR encompassed design and operation parameters, namely electrode-water spacing, air flow rate, and voltage level. A machine learning approach is implemented to extract and quantify characteristic features of the plasma–water interaction, such plasma volume and plasma–water interface area. Experimental results revealed that the nitrate production rate varies linearly with dimensionless plasma volume. The design, fabrication, and characterization methods presented can be adapted to other POWRs and help enable on-demand nitrogen fertilizer production at low environmental and economic cost.

Physics↗

DOE OSTI2024

Tuning Surface Adhesion Using Grayscale Electron-beam Lithography

Surface texturing of manufactured products tailors their properties, such as friction, adhesion, biocompatibility, or fluid interactions. However, advancements in this area are largely the result of trial-and-effort testing and generally lack a science-guided framework for determining the surface topography that will optimize performance. The present investigation explores grayscale electron-beam lithography as a means to create multiscale surface patterns to control surface performance. Here, we created and characterized a set of surface textures on a silicon wafer; the textures were superpositions of sine waves of varying wavelengths and amplitudes. First, the multiscale topography of the patterned surface was characterized, using profilometry and atomic force microscopy, to understand its fidelity to the designed-in pattern. The results of this analysis demonstrated how grayscale lithography accurately controlled the lateral size of features but was less precise on the vertical height of the surface, and also introduced inherent roughness below the scale of patterning. Second, a micromechanical tester was used to characterize the adhesion of the surfaces with large-scale polished silicon spheres. The results showed that adhesion could be tailored, with significant contribution from all of the designed-in length scales of topography. The strength of adhesion did not correlate with conventional roughness parameters but could be accurately modeled using simple numerical integration. Taken together, this investigation demonstrates the promise and challenges of grayscale e-beam lithography with multiscale patterns as a method for the tailoring of surface performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Compact, All-PM Fiber Integrated and Alignment-Free Ultrafast Yb:Fiber NALM Laser With Sub-Femtosecond Timing Jitter

We report a simple and compact design of a dispersion compensated mode-locked Yb:fiber oscillator based on a nonlinear amplifying loop mirror (NALM). The fully polarization maintaining (PM) fiber integrated laser features a chirped fiber Bragg grating (CFBG) for dispersion compensation and a fiber integrated compact non-reciprocal phase bias device, which is alignment-free. The main design parameters were determined by numerically simulating the pulse evolution in the oscillator and by analyzing their impact on the laser performance. Experimentally, we achieved an 88 fs compressed pulse duration with sub-fs timing jitter at 54 MHz repetition rate and 51 mW of output power with 5.5×10 -5 [20 Hz, 1 MHz] integrated relative intensity noise (RIN). Furthermore, we demonstrate tight phase-locking of the laser's carrier-envelope offset frequency ( fceo ) to a stable radio frequency (RF) reference and of one frequency comb tooth to a stable optical reference at 291 THz.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Tunable rainbow light trapping in ultrathin resonator arrays

Abstract Rainbow light trapping in plasmonic devices allows for field enhancement of multiple wavelengths within a single device. However, many of these devices lack precise control over spatial and spectral enhancement profiles and cannot provide extremely high localised field strengths. Here we present a versatile, analytical design paradigm for rainbow trapping in nanogroove arrays by utilising both the groove-width and groove-length as tuning parameters. We couple this design technique with fabrication through multilayer thin-film deposition and focused ion beam milling, which enables the realisation of unprecedented feature sizes down to 5 nm and corresponding extreme normalised local field enhancements up to 10 3 . We demonstrate rainbow trapping within the devices through hyperspectral microscopy and show agreement between the experimental results and simulation. The combination of expeditious design and precise fabrication underpins the implementation of these nanogroove arrays for manifold applications in sensing and nanoscale optics.

42 ENGINEERING↗

DOE OSTI2023

A standard capsule design for structural material testing in the Advanced Test Reactor

Nuclear materials testing is commonly completed in various material test and research reactors throughout the world, including the Advanced Test Reactor (ATR), but the current capsule design, analysis, and fabrication process can take years to complete. To decrease the costs and time associated with materials testing, a standard capsule has been designed which houses various specimen geometries and allows irradiation in virtually any ATR test position. The standard capsule features locking end caps which connect and lock together to form the capsule stack, eliminating the need for a basket and maximizing the quantity of specimens which are contained within the capsule. A customizable internal gas gap provides thermal resistance between the specimens and reactor coolant, making specimen temperatures from approximately 370 to 1000 K achievable. The flexibility of the capsule design allows experimenters to choose irradiation positions based off desired neutron flux, with typical fluences per cycle ranging from 8.8x10 19 to 2.3x10 21 n/cm 2 , depending on experiment position. Here this paper presents and discusses the standard capsule design and analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Localizing resonant magnetic perturbations for edge localized mode control in KSTAR

An external 3D magnetic perturbation typically drives a resonant response at the rational surfaces from the core to the edge of tokamak plasmas, due to strong mode coupling and amplification. In this paper, we present a method to isolate the edge from core resonant fields using the ideal perturbed equilibrium code and to design an edge-localized resonant magnetic perturbation (RMP) for effective edge localized mode (ELM) control. A robust feature of the edge-localized RMP is the curtailed response to the field at the low-field-side (LFS) midplane, as opposed to typical RMPs which strongly resonate with the LFS fields. This emphasizes the importance of off-midplane coils to improve ELM control without provoking a large core response that could lead to devastating instabilities. The conceptual design of new ELM control coils based on the edge-localized RMP in KSTAR shows how this new insight can be utilized to enhance the efficiency of our ELM suppression capabilities. Simple window-pane coils matching the edge-localized resonant mode structure substantially expand in the ELM suppression window beyond the existing coil. Further optimization using the flexible optimized coils using space-curves code leads to additional enhancement in the edge-localized control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Control over emergence and alignment of transient blisters in thermo-responsive gels using hierarchically patterned substrates

Surfaces with tunable microscale textures are vital in a large variety of technological applications, including heat transfer, antifouling and adhesion. To facilitate such broad-scale use, there is a need to create surfaces that undergo reconfigurable changes in topology and thus, enable switchable functionality. To date, there is a relative dearth of methods for engineering surfaces that can be actuated to change topography over a range of length scales, and hence, form tunable hierarchically structured layers. Combining modeling and experiments, we design a geometrically patterned, thermo-responsive poly (N-isopropylacrylamide) gel film that undergoes controllable hierarchical changes in topology with changes in temperature. At the bottom, the film is covalently bound to a solid, curved substrate; at the top, the film encompasses longitudinal rectangular ridges that are oriented perpendicular to the underlying cylindrical curves. At temperatures below lower critical solution temperature (LCST), the swollen gel exhibits 3D variations in polymer density and thickness defined by the gel's top and bottom topography. As the temperature rises above LCST, the interplay between the upper ridges and lower curves in the gel drives non-uniform, directional solvent transport, the nucleation and propagation of a phase-separated higher-density skin layer, and the resulting pressure buildup within the film. These different, interacting kinetic processes lead to an instability, which produces transient microscopic blisters in the film. Through simulations, we show how tuning the width of the ridges modifies the propagation of a skin layer and creates localized pressure build-up points, which enables control over the emergence, distribution, and alignment of the microscopic blisters. Futhermore, we provide a simple argument to predict the size of such microscopic features. Experiments confirm our predictions and further highlight how our computational model enables the rational design of topographical transitions in these tunable films. The development of actuatable, hierarchically structured films provides new routes for achieving switchable functionality in actuators, drug release systems and adhesives.

42 ENGINEERING↗

DOE OSTI2024

A Particle-in-Cell Method for Plasmas with a Generalized Momentum Formulation, Part II: Enforcing the Lorenz Gauge Condition

In a previous paper Christlieb et al. (A particle-in-cell method for plasmas with a generalized momentum formulation, part I: Model formulation, 2024), we developed a new particle-in-cell (PIC) method for the relativistic Vlasov–Maxwell system in which the electromagnetic fields and the equations of motion for the particles were cast in terms of scalar and vector potentials through a Hamiltonian formulation. This new method evolved the potentials under the Lorenz gauge using integral equation methods. New methods to construct spatial derivatives of the potentials that converge at the same rates as the fields were also presented. The new particle method was compared against standard explicit discretizations, including the well-known FDTD-PIC method, for a range of applications involving sheaths and particle beams. Here, this paper extends this new class of methods by focusing on the enforcement the Lorenz gauge condition in both exact and approximate forms using co-located meshes. A time-consistency property of the proposed field solver for the vector potential form of Maxwell’s equations is established, which is shown to preserve the equivalence between the semi-discrete Lorenz gauge condition and the analogous semi-discrete continuity equation. Using this property, we present three methods to enforce a semi-discrete gauge condition. The first method introduces an update for the continuity equation that is consistent with the discretization of the Lorenz gauge condition. Both the finite difference and spectral implementations satisfy this discrete gauge condition to machine precision. The second approach we propose enforces a semi-discrete continuity equation using the boundary integral solution to the field equations. The potential benefit of this approach is that it eliminates spatial derivatives that appear on the particle data, namely the current density, which is often calculated by linear combinations of low-order spline basis functions. This method is ideally suited to boundary integral equation methods that invert multi-dimensional operators without dimensional splitting techniques and will be the subject of future work. The third approach introduces a gauge correcting method that makes direct use of the gauge condition to modify the scalar potential and uses local maps for both the charge and current densities. This results in a gauge error, as the maps do not enforce the continuity equation. The vector potential coming from the current density is taken to be exact, and using the Lorenz gauge, we compute a correction to the scalar potential that makes the two potentials satisfy the gauge condition. This method also enforces the gauge condition to machine precision. We demonstrate two of the proposed methods in the context of periodic domains. Problems defined on bounded domains, including those with complex geometric features remain an ongoing effort. However, this work shows that it is possible to design computationally efficient methods that can effectively enforce the Lorenz gauge condition in a non-staggered PIC formulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Parametric optimization of PCM-enhanced underground thermal energy storage for buildings in moderate cold climates

This study presents a novel underground thermal energy storage (UTES) system designed for space heating in buildings located in moderate cold climates. The proposed UTES features a borehole with a depth of 25 ft. (7.62 m) and a diameter of 3 ft. (0.91 m), containing two helical pipe loops-one for discharge fluid and another for recharge fluid-and a thermally enhanced phase change material (PCM) layer that provides high energy storage capacity. The system requires daily thermal recharging using a low-grade heat source for a few hours and delivers continuous heating at a nearly constant discharge rate without significant performance degradation over a 24-h period. This study demonstrates that the optimized UTES, when recharged with hot fluid at an inlet temperature of 40 degrees C for 6 h daily, can provide a continuous heat discharge rate of approximately 2.9 kW for 24 h or 3.4 kW if operated under a shorter discharge period of 15 h (17:00-08:00). With a round-trip efficiency of 72-97%, the proposed UTES offers an efficient and reliable solution for short- and long-duration thermal energy storage technology, making it a promising technology for cold climates.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

A simple centrifuge cell method for ex situ quantification of electrical conductivity of slurry electrode materials

We present the design, experimental procedure, and experimental evaluation of a system for fast, simple, and ex situ characterization of electrical conductivity of slurry electrode materials. The system uses a custom-designed electrochemical cell compatible with centrifugation in a swing-bucket centrifuge. The cell features cylindrical graphite electrodes that are partially sheathed so as to expose only 2 mm of the electrode surface to the bottom region of the packed particulate pellet. Also presented is a conduction model that provides a shape factor for estimating effective conductivity. We tested aqueous solutions of carbon black (CB), activated carbon (AC), and mixtures thereof. These particles were dispersed in 0.0 and 0.5 M NaCl solutions. Measurements show that the effective conductivity initially increases linearly with pellet mass and then saturates at higher masses. Notably, CB exhibited a fivefold increase in conductivity than AC at equal pellet masses. CB/AC mixtures at a fixed pellet mass were tested with CB mass fractions of 0 to 100%. Interestingly, the mixture conductivity was found to be a non-monotonic function of CB mass fraction, with a maximum conductivity at about 60 % CB mass fraction. At this maximum, the mixture conductivity is approximately 30 % higher than pure CB. NaCl concentration in the slurry solution had no effects on conductivity. These results highlight the interactions between slurry electrode composition and compaction, offering insights for optimizing slurry electrodes. Furthermore, the system and results may also be applicable to evaluation of particulate materials (including slurries) used for Li-ion batteries, capacitive deionization, fuel cells, and flow electrodes.

Capacitive deionization↗

DOE OSTI2020

Advances and challenges in KSTAR plasma control toward long-pulse, high-performance experiments

An overview of advances and progress on the KSTAR plasma control improvement is given. The enhancement of the KSTAR plasma control system (PCS) continues in order to enable implementations of more sophisticated control algorithms and capabilities of integrated controls on magnets, gas, heating and off-normal event handling. Features and functionalities on the generic magnetic controls, kinetic controls and new fault scheme design are added for the operation space expansion, mainly regarding recent achievements of higher plasma current up to 1.0–1.2MA, high-performance long pulse up to 90s, and discharges relevant to ITER. Technical challenges and prospective upgrades in the near future are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

On the evaluation and selection of network-level traffic control policies: Perimeter control, TUC, and their combination

Perimeter control (PC) of urban traffic networks can be effective in increasing network-wide efficiency. PC operates on the border of a protected region of a traffic network. Most studies thus far considered fixed-time plans for the inner part of these regions. A few studies have shown that combining PC with locally actuated or decentralized traffic control systems may have positive effects on traffic performance, including better-defined Network Macroscopic Fundamental Diagrams (NMFDs), increased network throughput, and reduced delays. The Traffic-responsive Urban Control (TUC) is a real-time network-wide traffic control system with particular design characteristics, such as the balancing of link's occupancies and an inherent gating feature. These characteristics suggest that TUC may enhance the traffic network performance when combined with PC whilst improving the resulting NMFDs and network throughput and delays. Here, in this work, we investigate the effect of feedback perimeter control (FPC), TUC, and their combination on the NMFD and on the traffic conditions of general traffic and public transport in the microsimulation of a realistic model of the Christchurch Central Business District in New Zealand. We perform a thorough investigation of practical aspects of both control strategies and their combination, including parameter tuning and infrastructure requirements, and how they may affect the control system choice. Results show higher throughput and less hysteresis on the NMFDs, particularly when TUC is involved. PC provides benefits concentrated in the protected region which can greatly benefit public transportation if there is an overlap with the transit network. The combination of TUC and FPC boosts network-wide throughput.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Geometry-complete diffusion for 3D molecule generation and optimization

Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗