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DOE OSTI2026

Trilinos: Enabling Scientific Computing across Diverse Hardware Architectures at Scale

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to “An Overview of the Trilinos project” by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397–423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This article also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and contributors.

Heterogeneous Hardware Architectures↗

DOE OSTI2019

Liquid-State Dewetting of Pulsed-Laser-Heated Nanoscale Metal Films and Other Geometries

Metal films of nanoscale thickness, deposited on substrates and exposed to laser heating, provide systems that involve several interesting multiphysics effects. In addition to fluid mechanical aspects associated with a free boundary setup, other relevant physical effects include phase change, thermal flow, and liquid–solid interactions. Such films are challenging to model, in particular because inertial effects may be relevant, and large contact angles require care when considering the long-wave formulation. Applications of nanoscale metal films are numerous, and the materials science community is actively pursuing more complex setups involving templated films and substrates, bimetallic films and alloys, and a variety of elemental film geometries. The goal of this review is to discuss our current understanding of thin metal film systems, while also providing an overview of the challenges in this research area, which stands at the intersection of fluid mechanics, materials science, and thermal physics.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Imaging and Modeling of Passive Water Management in a Miniature Fuel Cell

A small microwatt fuel cell stack with 8 cells was designed, fabricated, and tested for passive operation with pure H 2 and O 2 to provide continuous power for multiple decades with uncontrolled fluctuating ambient conditions. The stack was designed to operate with dead ended gas flows with water removal via passive membrane diffusion to notches in the bipolar plates. Two stacks with active areas of 0.2 cm 2 , one with 200 nm of membrane and the other with 400 urn, were assembled and tested at varying temperatures and current conditions. Temperatures ranged -55 °C to 70 °C, with focus on the lower temperatures. Factors affecting water production and removal included temperature, gas crossover, and membrane thickness. Currents were applied from 16 uA to 10 mA after the cells were stabilized at temperature, followed by a period of drying to observe water transport to the external peripheries of the cell. Finally, a two-dimensional multiphysics model was developed to further explore the water management system in varying conditions.

25 ENERGY STORAGE↗

DOE OSTI2023

Direct Measurements of Ionic Transport Behavior of Dual-Layer Porous Electrodes

To improve power and cycling performance of lithium-ion batteries, dual-layer or porosity-gradient electrodes have been proposed. By engineering a higher porosity close to the separator, the intention is to improve ion transport where it is most impactful. In this research, MacMullin numbers of two dual-layer anode samples are tested using an impedance measurement technique developed previously. To characterize the microstructure of each layer independently, we developed an improved transmission-line model that accounts for each layer's properties.Virtual experiments using COMSOL Multiphysics to simulate impedance measurements are used to examine and improve the accuracy of the numerical inversion procedure. The results for the two dual-layer anodes studied show that MacMullin numbers follow expected trends, though the anodes are quite different from each other.

Dual-Layer Electrodes↗

DOE OSTI2023

Electrochemical Residence Time Distribution as a Diagnostic Tool for Redox Flow Batteries

The fluid dynamic and electrochemical performance of redox flow batteries (RFBs) stems from the relationship between the flow field and the porous electrode, whose interplay determines how active species move and react during device operation. While characterization techniques, such as residence time distribution, offer insights into species mobility within a reactive volume for a traditional chemical reactor, electrochemical reactors also enable simultaneous measurement of the redox reactions, unlocking another dimension of analysis. Herein, we demonstrate how potentiodynamic measurements, using injections of electrolyte examined through moment analysis, can provide electrode-specific performance scaling relationships across a matrix of carbon paper and cloth electrodes with flow through and interdigitated flow fields. We further combine experimental campaigns with multiphysics simulations to demonstrate how electrode surface area can be estimated with this technique, which we then validate with activated and unactivated commercial carbon cloth electrodes. These studies reveal the multiscale observations that potentiodynamic measurements afford, augmenting existing electrochemical techniques for holistic electrochemical reactor diagnostics.

Electrochemistry↗

DOE OSTI2023

Relative-Humidity Dependence of Electrochemically Active Surface Area in Porous Carbon Catalyst Layers

Polymer-electrolyte fuel cells (PEFCs) utilize porous catalyst layers (CLs) formed of carbon supports on which Pt particles are deposited and ionomer films are distributed. Carbon supports themselves have varying degrees of porosity, where high-surface-area carbon (HSC) supports possess nanometer-sized interior pores that are suitable for Pt nanoparticle deposition but prevent deleterious ionomer penetration. However, this requires protons to transport through water pathways inside the pores. To understand the generation of such pathways, we examine the various mechanisms of water uptake by PEFC CLs, and the subsequent impact of water uptake on Pt utilization through developing a multiphysics model of the water wetting phenomena as a function of relative humidity. The model details water uptake via ionomer absorption, capillary condensation in the hydrophilic pores, and surface adsorption using molecular potential that account for various water and surface dipole interactions. The results quantify how mesoporous carbons with highly hydrophilic pores increase Pt utilization through the development of wetted layers, which at the same time enable optimized gas-transport pathways. It also demonstrates the impact of pore-size distribution (PSD) and physical and chemical parameters on the water uptake phenomena, allowing for future CL particle and structure optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

High-Resolution Nanoprinting Approach through Self-Driven Electrodeposition

A novel high-resolution nanoprinting method through self-driven electrodeposition is proposed and certain aspects are confirmed with known copper electrochemistry through multiphysics modeling. A series of simulations are conducted involving a nanopipette filled with concentrated CuSO 4 solution introduced close to the working electrode (WE) in the cell of diluted CuSO 4 electrolyte, where Cu 2+ ions diffuse from nanopipette to the electrode and form a limited region with more concentrated cations compared to the rest of the cell. Meanwhile, this process leads to a more positive equilibrium potential of copper redox reaction and thereby local negative surface overpotential on WE. The negative surface overpotential drives the Cu 2+ electrodeposition in the form of a nano-protrusion on the target location.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Understanding the Dynamics of Primary Zn-MnO 2 Alkaline Battery Gassing with Operando Visualization and Pressure Cells

The leading cause for safety vent rupture in alkaline batteries is the intrinsic instability of Zn in the highly alkaline reacting environment. Zn and aqueous KOH react in a parasitic process to generate hydrogen gas, which can rupture the seal and vent the hydrogen along with small amounts of electrolyte, and thus, damage consumer devices. Abusive conditions, particularly deep discharge, are known to accelerate this “gassing” phenomena. In order to understand the fundamental drivers and mechanisms for such gassing behavior, the results from multiphysics modeling, ex-situ microscopy and operando measurements of cell potential, pressure and visualization have been combined. Operando measurements were enabled by the development a new research platform that enables a cross-sectional view of a cylindrical Zn-MnO 2 primary alkaline battery throughout its discharge and recovery. A second version of this cell can actively measure the in-cell pressure during the discharge. It is shown that steep concentration gradients emerge during the cell discharge through a redox electrolyte mechanism, leading to the formation of high surface area Zn deposits that experience rapid corrosion when the cell rests to its open circuit voltage. Such corrosion is paired with the release of hydrogen and high cell pressure – eventually leading to cell rupture.

25 ENERGY STORAGE↗

DOE OSTI2017

Elucidating Performance Limitations in Alkaline-Exchange- Membrane Fuel Cells

Water management is a serious concern for alkaline-exchange-membrane fuel cells (AEMFCs) because water is a reactant in the alkaline oxygen-reduction reaction and hydroxide conduction in alkaline-exchange membranes is highly hydration dependent. Here in this article, we develop and use a multiphysics, multiphase model to explore water management in AEMFCs. We demonstrate that the low performance is mostly caused by extremely non-uniform distribution of water in the ionomer phase. A sensitivity analysis of design parameters including humidification strategies, membrane properties, and water transport resistance was undertaken to explore possible optimization strategies. Furthermore, the strategy and issues of reducing bicarbonate/carbonate buildup in the membrane-electrode assembly with CO 2 from air is demonstrated based on the model prediction. Overall, mathematical modeling is used to explore trends and strategies to overcome performance bottlenecks and help enable AEMFC commercialization.

25 ENERGY STORAGE↗

DOE OSTI2020

(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

42 ENGINEERING↗

DOE OSTI2020

Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO2R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. Here, we specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Quinoa, Version 0.2

Quinoa is a set of computational tools that enables research and numerical analysis in fluid dynamics. At this time it remains a test-bed to experiment with various algorithms using fully asynchronous runtime systems. Currently, Quinoa consists of the following tools: (1) Walker, a numerical integrator for systems of stochastic differential equations in time. It is a mathematical tool to analyze and design the behavior of stochastic differential equations. It allows the estimation of arbitrary coupled statistics and probability density functions and is currently used for the design of statistical moment approximations for multiple mixing materials in variable-density turbulence. (2) Inciter, an overdecomposition-aware finite element field solver for partial differential equations using 3D unstructured grids. Inciter is used to research asynchronous mesh-based algorithms and to experiment with coupling asynchronous to bulk-synchronous parallel code. Two planned new features of Inciter, compared to the previous release (LA-CC-16-015), to be implemented in 2017, are (a) a simple Navier-Stokes solver for ideal single-material compressible gases, and (b) solution-adaptive mesh refinement (AMR), which enables dynamically concentrating compute resources to regions with interesting physics. Using the NS-AMR problem we plan to explore how to scale such high-load-imbalance simulations, representative of large production multiphysics codes, to very large problems on very large computers using an asynchronous runtime system. (3) RNGTest, a test harness to subject random number generators to stringent statistical tests enabling quantitative ranking with respect to their quality and computational cost. (4) UnitTest, a unit test harness, running hundreds of tests per second, capable of testing serial, synchronous, and asynchronous functions. (5) MeshConv, a mesh file converter that can be used to convert 3D tetrahedron meshes from and to either of the following formats: Gmsh, http://www.geuz.org/gmsh, Netgen, http://sourceforge.net/apps/mediawiki/netgen-mesher, ExodusII, http://sourceforge.net/projects/exodusii, HyperMesh, http://www.altairhyperworks.com/product/HyperMesh.

Bakosi, Jozsef↗

DOE OSTI2018

PV TOMCAT

PV TOMCAT predicts the outdoor operating temperature and output power of a PV module based on measured physical properties and outdoor conditions. It includes a ray tracing model of a PV module, which runs in the commercial SunSolve ray tracer. Output from this model is combined with meteorological time series data to produce a combined input file for a finite-element model of heat conduction and radiation. Finally, this finite-element model is run in the commercial COMSOL Multiphysics software, producing time series outputs of temperature and power.

Deceglie, Michael↗

DOE OSTI2019

SCPACK vR1.0

User-defined elements in ANSYS enable programming of the finite element method to simulate the unique behavior of superconducting magnets. A set of electromagnetic and thermal user elements incorporate both quench and interfilament coupling current (IFCC) effects in a consistent manner, allowing for simulation of strongly coupled, multiphysics behavior typical of superconducting devices.

Brouwer, Lucas↗

DOE OSTI2018

Kelvin

Kelvin is a multiphysics sintering simulation software that uses the material point method to simulate the thermomechanical behavior of metal parts.

Billings, JayJ↗

DOE OSTI2020

Concurrent Relaxation through Accelerated Deep Learning

CRADL captures performance metrics of machine learning algorithms operating on mesh data from multiphysics codes This proxy application is a tool to explore scalability of inference on HPC platforms, and also gather performance metrics for inference on new machine learning specific hardware. CRADL is designed to give users as fine a control as possible over an inference simulation. Users may select the number of cycles, amount of data, and batch size to pass to the accelerator of choice. Additionally the user may select a number of performance optimization libraries and flags. CRADL comes packaged with a repository of anonymized multi-physics simulation data, as well as a pretrained model for inference. The code allows a user to load their own pre-trained model and data if they wish. The code can operate in multiple parallelization schemes, with performance enhancing options such as half-precision libraries, PyTorch benchmarking, and pinned memory with non-blocking data transfers.

Zieb, KristoferJ.↗

DOE OSTI2021

CARDINAL

Cardinal is a code that automates the workflow for iteratively solving neutral particle transport via the OpenMC code and computational fluid dynamics via the NekRS code within the Multiphysics Object Oriented Simulation Environment (MOOSE), a finite element framework for solving general partial differential equations. Cardinal establishes a mapping between the Monte Carlo geometry representation used in OpenMC and the spectral element mesh used in NekRS to other physics applications built upon the MOOSE framework, allowing high-resolution particle transport and fluid dynamics physics feedback to the MOOSE application ecosystem. Heat generation rates as solved by OpenMC are used as heat sources in MOOSE, while the temperature and density fields from NekRS are used as conjugate heat transfer boundary conditions and/or source fields in MOOSE. OpenMC, Nek5000, and MOOSE are open source, community-developed simulation codes.

NOVAK, APRILJEAN RODGERS↗

DOE OSTI2023

Multiscale Ecosystem for solving Maxwell-Schrodinger equations of open quantum systems (OpenMS)

Light-matter interactions play an important role in many branches of physics, chemistry, energy, and materials science. In the strong coupling regime, light-matter interactions are able to tune the materials properties via the formation of new quasiparticles, such as plasmons and polaritons. However, theoretical and numerical modeling of the light-matter interaction-mediated processes remain a big challenge because light-matter interactions are fundamentally multiscale and multiphysics problems involving multiple interactions between electrons, nuclei, and photons at different time/length scales. In the current community, light-matter interactions were treated at different levels of theoretical complexity in quantum chemistry and quantum optics. In quantum optics or quantum photonics, the matter is usually simplified as a few-level system, and the light is treated quantum-mechanically. On the other hand, quantum chemistry explores first-principles methods, including both single-particle and many-body-based techniques, to describe the electronic properties of matter in detail. However, light is usually prescribed as a classical electromagnetic field, and the light-matter interaction is taken into account as an external potential via classical approximations. This software is designed to fill current modeling shortcomings by delivering a first-ever scalable multiscale platform for simulating light-matter interactions in realistic electromagnetic environments. The software solves Maxwell and Schrodinger equations self-consistent on the heterogeneous platforms. It implements HF/DFT, TDDFT, and coupled-cluster counterparts for light-matter interactions and adopts modular programming to offload massively parallel algorithms on a large number of CPU/GPUs.

Zhang, Yu↗