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At least 631 records · Page 35

Co-synthesis of Hydrogen and High-Value Carbon Products from Methane Pyrolysis

The ARPA-E Methane Pyrolysis Project successfully developed a scalable technology for hydrogen production with low-CO x emission through methane pyrolysis, co-producing high-value carbon nanotubes (CNTs). The project focused on optimizing reactor design, enhancing catalyst performance, and assessing techno-economic feasibility to create a commercially viable and environmentally sustainable process. The fluidized bed reactor achieved over 90% methane (CH 4 ) conversion by using a 5% CO 2 co-feed, which stabilizes carbon yields and minimizes catalyst deactivation. This setup allowed for continuous operation across ten cycles, each consisting of a 14-minute pyrolysis phase followed by a 10-minute dislodging phase to remove a fraction of the accumulated carbon, resulting in stable performance and high-quality CNT production. In parallel, monolith reactors coated with Fe demonstrated a sustained methane conversion of 73% while producing CNTs with high crystallinity. Although promising for continuous operation, monolith reactors face challenges in coating durability and scalability, highlighting areas for further optimization in commercial applications. Catalyst formulation played a key role in enhancing process efficiency. The core catalyst used was 5%Fe/Al 2 O 3 (wt%), optimized through wet impregnation, which improved CNT morphology, yielding longer and more uniform CNTs. The catalyst's performance was further enhanced by adding promoters: 2.5 wt% Ni increased methane conversion close to the thermodynamic limit, while 2.5 wt% Mn improved CNT alignment and crystallinity, and 1.5 wt% NaCl boosted CNT morphology but slightly lowered methane conversion. These adjustments allowed the reactor to maintain high methane conversion while producing high-quality CNTs, enabling stable performance over multiple cycles. To address carbon buildup and ensure uninterrupted operation, a pneumatic conveying tube was implemented for effective carbon dislodging in the fluidized bed configuration. CO 2 and H 2 O co-feeds were also introduced to enhance carbon removal, with CO 2 boosting CNT yield by approximately 15%. This setup enabled stable reactor operation across multiple cycles, preventing clogging and minimizing catalyst wear, making the process suitable for industrial scaling. Techno-economic analysis (TEA) projected hydrogen production costs between $\$$1.00 and $\$$1.64 per kilogram, with CNT values assumed at $\$$375/ton and $\$$100/ton. The life cycle assessment showed that CO 2 emissions could be as low as 0.64 kg CO 2 e/kg H 2 at 95% methane conversion assuming an electricity input of 50 kg CO 2 e/MWh. Even at 50% methane conversion, emissions remained below 1 kg CO 2 e/kg H 2 , demonstrating the process's low-emission potential and making it a viable alternative to traditional steam methane reforming. Overall, the results from this project demonstrate the feasibility of a pyrolysis process where carbon is continuously removed from the catalyst surface and hydrogen is continuously produced until a catalyst regeneration step is required to fully clean the catalyst surface and renew catalyst performance. Major open challenges are related to avoiding the loss of catalyst material in the dislodged carbon during fluidized bed conditions, since our best result demonstrated a carbon purity of ~70 wt. % (rest being iron and alumina). A monolith reactor was used to favor dislodgement of carbon compared to fluidized bed conditions but our results do not demonstrate an advantage of the monolith configuration. Catalyst performance was similar to fluidized bed conditions with slower deactivation rates overall, but we could not observe carbon dislodging in any of the tens of experiments that were run at Stanford. Our results show that the most relevant areas of improvement are related to the fundamental understanding of the iron-carbon interface for dislodging, and the development of catalyst that can produce CNTs via a base-growth mechanism such that catalyst is not lost in the dislodgement steps. The final report documents all findings and methodologies in detail, providing a valuable resource for the scientific community. By building on these results, researchers can further advance methane pyrolysis technology, moving toward a more sustainable, scalable pathway for hydrogen production. This work lays the foundation for future research and commercial efforts to reduce emissions in hydrogen production while generating valuable carbon products.

08 HYDROGEN↗

LuGo: An enhanced quantum phase estimation implementation

Quantum Phase Estimation (QPE) is a cardinal algorithm in quantum computing that plays a crucial role in various applications, including cryptography, molecular simulation, and solving systems of linear equations. However, the standard implementation of QPE faces challenges related to time complexity and circuit depth, which limit its practicality for large-scale computations. We introduce LuGo, a novel framework designed to enhance the performance of QPE by reducing circuit duplication, as well as using parallelization techniques to achieve faster generation of the QPE circuit and gate reduction. We validate the effectiveness of our framework by generating quantum linear solver circuits, which require both QPE and inverse QPE, to solve linear systems of equations. LuGo achieves significant improvements in both computational efficiency and hardware requirements without compromising on accuracy. Compared to a standard QPE implementation, LuGo reduces time consumption to generate a circuit that solves a 2 6 × 2 6 system matrix by a factor of 50.68 and over 31× reduction of quantum gates and circuit depth, with no fidelity loss on an ideal quantum simulator. Furthermore, we demonstrated the versatility and scalability of LuGo enabled HHL algorithm by simulating a canonical Hele-Shaw fluid problem using a quantum simulator. With these advantages, LuGo paves the way for more efficient implementations of QPE, enabling broader applications across several quantum computing domains.

Quantum algorithm↗

A general approach to seismic inversion with automatic differentiation

Imaging Earth structure or seismic sources from seismic data involves minimizing a target misfit function, and is commonly solved through gradient-based optimization. The adjoint-state method has been developed to compute the gradient efficiently; however, its implementation can be time-consuming and difficult. We develop a general seismic inversion framework to calculate gradients using reverse-mode automatic differentiation. The central idea is that adjoint-state methods and reverse-mode automatic differentiation are mathematically equivalent. Here, the mapping between numerical PDE simulation and deep learning allows us to build a seismic inverse modeling library, ADSeismic, based on deep learning frameworks, which supports high performance reverse-mode automatic differentiation on CPUs and GPUs. We demonstrate the performance of ADSeismic on inverse problems related to velocity model estimation, rupture imaging, earthquake location, and source time function retrieval. ADSeismic has the potential to solve a wide variety of inverse modeling applications within a unified framework.

58 GEOSCIENCES↗

Semicoherent symmetric quantum processes: Theory and applications

Discovering pragmatic and efficient approaches to construct ε-approximations of quantum operators such as real (imaginary) time-evolution propagators in terms of the basic quantum operations (gates) is challenging. Prior ε-approximations are invaluable, in that they enable the compilation of classical and quantum algorithm modeling of, e.g., dynamical and thermodynamic quantum properties. In parallel, symmetries are powerful tools concisely describing the fundamental laws of nature; the symmetric underpinnings of physical laws have consistently provided profound insights and substantially increased predictive power. In this work, we consider the interplay between the ε-approximate processes and the exact symmetries in a semicoherent context—where measurements occur at each logical clock cycle. Here we draw inspiration from Pascual Jordan's groundbreaking formulation of nonassociative, but commutative, symmetric algebraic form. Our symmetrized formalism is then applied in various domains such as quantum random walks, real-time evolutions, variational algorithm ansatzes, and efficient entanglement verification. Our work paves the way for a deeper understanding and greater appreciation of how symmetries can be used to control quantum dynamics in settings where coherence is a limited resource.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Unraveling kinking: A plasticity enhancing failure mode in high strength nano metallic laminates

Kinking is an important and plasticity-enhancing deformation/failure mode in numerous mechanically anisotropic materials including high-strength nano metallic laminates (NMLs). However, our current limited understanding of the mechanics of kinking and its dependence on microstructural attributes is insufficient for thoroughly comprehending and eventually being able to control failure behaviors of materials. In this study, we investigate kinking dependencies on microstructural attributes in NMLs via in situ micropillar compression, multiscale microstructure characterization, dislocation dynamic simulations, and crystal plasticity modeling. Additionally, by examining several NML systems (Cu/Fe, Ag/Fe, Al-4Mg/Fe), we demonstrate that the development of internal stresses during loading activates local layer-parallel glide triggering kinking in NMLs. Furthermore, this work reveals the effect of key microstructural features including layer thickness, layer waviness, interface barrier strength, and work hardening capacity on kink band formation in NMLs. More broadly, our efforts represent a generically applicable approach for probing large-strain deformation behavior of complex materials via synergetic modeling and experimental efforts.

36 MATERIALS SCIENCE↗

Porting hypre to heterogeneous computer architectures: Strategies and experiences

We report that linear systems are occurring in many applications, and solving them can take a large amount of the total simulation time. The high performance library hypre provides a variety of interfaces and linear solvers, including various multigrid methods, that have achieved good scalability on a variety of homogeneous parallel computer architectures. Heterogeneous architectures with nodes that have both CPUs and accelerators provide new challenges, since they require more fine-grained parallelism and reduced data movement between different memories on a single node as well as across nodes. We will discuss our experiences and strategies to port hypre to heterogeneous computers with accelerators, including the design of a new memory model, the use of abstractions, the BoxLoop macros in the structured and semi-structured interfaces, and the restructuring of algebraic multigrid (AMG) into modular components. We present numerical experiments comparing CPU and GPU performance for several test problems.

97 MATHEMATICS AND COMPUTING↗

Oineus v1.0

A library for multi-threaded computation of persistence diagrams. The algorithm for computing persistence diagrams in a lock-free manner was published in the 'Towards Lock-free Persistent Homology' (D. Morozov, A. Nigmetov, Brief Announcement: SPAA 2020); it scales better than the only other shared-memory parallel implementation of persistent homology computation PHAT. Library includes python bindings to compute persistence diagrams and their vectorizations for lower-star filtrations on grid data. It is intended to be used by scientists working on Topological Data Analysis and its applications in different areas.

Nigmetov, Arnur↗

Parallel simulated annealing with embedded machine learning and multifidelity models for reactor core design

This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multi-constrained combinatorial optimization with the aim of embedding multi-fidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multi-fidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multi-fidelity optimization methods, called ML-PSA, was demonstrated on a continuous multi-fidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multi-fidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Chip-scale frequency combs for data communications in computing systems

Recent developments in chip-based frequency-comb technology demonstrate that comb devices can be implemented in applications where photonic integration and power efficiency are required. The large number of equally spaced comb lines that are generated make combs ideal for use in communication systems, where each line can serve as an optical carrier to allow for massively parallel wavelength-division multiplexing (WDM) transmission. In this review, we summarize the developments in integrated frequency-comb technology for use as a WDM source for communication systems in data centers and high-performance computing systems. We highlight the following three approaches for chip-scale comb generation: semiconductor modelocked lasers, electro-optic combs, and Kerr frequency combs.

Okawachi, Yoshitomo (ORCID:0000000196390549)↗

Efficient derivative computation for unsteady fatigue-constrained nonlinear aero-structural wind turbine blade optimization

Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especially when fatigue constraints are considered. In this work, we systematically compare and evaluate four differentiation techniques, namely algorithmic differentiation, implicit differentiation, sparsity exploitation, and parallelization, to determine their effectiveness in computing accurate gradients through time-domain aero-structural simulations. By integrating these techniques with unsteady nonlinear aerodynamic and structural models, we develop software designed for accurate gradient computation. We show that combining these techniques addresses memory and runtime challenges associated with long simulations required by design load cases. Specifically, the most effective combination reduces derivative computation wall time by over an order of magnitude compared to finite differencing while maintaining superior accuracy. We demonstrate this approach in a proof-of-concept aero-structural optimization of a wind turbine blade that improves the cost of energy by 12.78 %. This comparative study establishes a viable approach for fatigue-aware blade design that balances computational efficiency with modeling accuracy.

17 WIND ENERGY↗

Efficient distributed continual learning for steering experiments in real-time

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input minibatches using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably Dark Experience Replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. Furthermore, we run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

Asynchronous data management↗

Module-Fluidics: Building Blocks for Spatio-Temporal Microenvironment Control

Generating the desired solute concentration signal in micro-environments is vital to many applications ranging from micromixing to analyzing cellular response to a dynamic microenvironment. We propose a new modular design to generate targeted temporally varying concentration signals in microfluidic systems while minimizing perturbations to the flow field. The modularized design, here referred to as module-fluidics, similar in principle to interlocking toy bricks, is constructed from a combination of two building blocks and allows one to achieve versatility and flexibility in dynamically controlling input concentration. The building blocks are an oscillator and an integrator, and their combination enables the creation of controlled and complex concentration signals, with different user-defined time-scales. We show two basic connection patterns, in-series and in-parallel, to test the generation, integration, sampling and superposition of temporally-varying signals. All such signals can be fully characterized by analytic functions, in analogy with electric circuits, and allow one to perform design and optimization before fabrication. Such modularization offers a versatile and promising platform that allows one to create highly customizable time-dependent concentration inputs which can be targeted to the specific application of interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Garnet-Type Solid-State Electrolytes: Materials, Interfaces, and Batteries

Solid-state batteries with desirable advantages, including high-energy density, wide temperature tolerance, and fewer safety-concerns, have been considered as a promising energy storage technology to replace organic liquid electrolyte-dominated Li-ion batteries. Solid-state electrolytes (SSEs) as the most vital component in solid-state batteries largely lead the future battery development. Among different types of solid-state electrolytes, garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolytes have particularly high ionic conductivity (10 –3 to 10 –4 S/cm) and good chemical stability against Li metal, offering a great opportunity for solid-state Li-metal batteries. Since the discovery of garnet-type LLZO in 2007, there has been an increasing interest in the development of garnet-type solid-state electrolytes and all solid-state batteries. Garnet-type electrolyte has been considered one of the most promising and important solid-state electrolytes for batteries with potential benefits in energy density, electrochemical stability, high temperature stability, and safety. In this Review, we will survey recent development of garnet-type LLZO electrolytes with discussions of experimental studies and theoretical results in parallel, LLZO electrolyte synthesis strategies and modifications, stability of garnet solid electrolytes/electrodes, emerging nanostructure designs, degradation mechanisms and mitigations, and battery architectures and integrations. We will also provide a target-oriented research overview of garnet-type LLZO electrolyte and its application in various types of solid-state battery concepts (e.g., Li-ion, Li–S, and Li–air), and we will demonstrate opportunities and perspectives as guides for future development of solid electrolytes and solid-state batteries.

25 ENERGY STORAGE↗

High-count rate effects in event processing for XRISM/ Resolve X-ray microcalorimeter: I. Ground test

The spectroscopic performance of an X-ray microcalorimeter is compromised at high count rates. We utilize the Resolve X-ray microcalorimeter onboard the XRISM satellite to examine the effects observed during high-count rate measurements and propose modeling approaches to mitigate them. We specifically address the following instrumental effects that impact performance: CPU limit, pile-up, and untriggered electrical cross-talk. Experimental data at high count rates were acquired during ground testing using the flight model instrument and a calibration X-ray source. In the experiment, data processing not limited by the performance of the onboard CPU was run in parallel, which cannot be done in orbit. This makes it possible to access the data degradation caused by limited CPU performance. We use these data to develop models that allow for a more accurate estimation of the aforementioned effects. To illustrate the application of these models in observation planning, we present a simulated observation of GX 13+1. Understanding and addressing these issues is crucial to enhancing the reliability and precision of X-ray spectroscopy in situations characterized by elevated count rates.

47 OTHER INSTRUMENTATION↗

Massively Parallel Capability in Sierra/SD for Simulation Vibration with Piezoelectrics

Sierra/SD is an engineering structural dynamics code that provides Sandia and other customers a tool to model structural and acoustic physics on large complex physical systems using massively parallel processing. This report provides a detailed overview on Sierra/SD’s most recent physics package: coupled electro-mechanical physics. This capability uses the finite element method to model coupled electro-mechanical physics exhibited by piezoelectric materials. This report provides an applications overview, theory overview, and verification examples demonstrating the electro-mechanical physics modeling capabilities of Sierra/SD.

97 MATHEMATICS AND COMPUTING↗

MAESTROeX

MAESTROeX is a massively parallel, finite-volume C++/F90 solver for low Mach number astrophysical flows. The code utilizes a low Mach number equation set allowing for more efficient, long-time integration of highly subsonic flows compared to compressible approaches. The recommended range of applicability is for flows where the Mach number does not exceed~0.1.

Harpole, Alice↗

Giant Apparent Optical Circular Dichroism in Thin Films of Bismuth-Based Hybrid Organic-Inorganic Metal Halide Semiconductor Through Preferred Orientation

Introducing chirality into organic/inorganic hybrid materials can impart chiroptical properties such as circular dichroism. The ability to tune chiroptical properties in self-assembled materials can have important implications for spintronic and optoelectronic applications. Here, a chiral organic cation, (R/S)-4-methoxy-a-methylbenzylammonium, is incorporated to synthesize the bismuth-based hybrid organic-inorganic metal halide semiconductor, (R/S-MeOMePMA)BiI4. Thin films of this Bi-based compound demonstrate large chiroptical responses, with circular dichroism anisotropy (gCD) values up to ˜0.1, close to the highest value observed in another chiral metal-halide semiconductor, (R-MBA2CuCl4). Detailed investigation reveals that this large gCD in (R/S-MeOMePMA)BiI4 is caused by the apparent CD effect. Careful selection of deposition conditions and the concomitant thin-film orientation enables the control of gCD, with maximum value observed when its thin film has a well-crystallized preferred (001) orientation parallel to the substrate. The results support a growing body of evidence that low symmetry plays an important role in achieving unusually large gCD in these chiral metal-halide materials and provides design rules for achieving large chiroptical response via morphology control.

chiroptic response↗

On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control

Here, we study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm exhibits local linear convergence, and that the convergence rate improves exponentially with the overlap size. We also establish global convergence results for a general quadratic programming, which enables the application of the Schwarz scheme inside second-order optimization algorithms (e.g., sequential quadratic programming). The theoretical foundation of our convergence analysis is a sensitivity result of nonlinear OCPs, which we call "exponential decay of sensitivity" (EDS). Intuitively, EDS states that the impact of perturbations at domain boundaries (i.e., initial and terminal time) on the solution decays exponentially as one moves into the domain. Here, we expand a previous analysis available in the literature by showing that EDS holds for both primal and dual solutions of nonlinear OCPs, under uniform second-order sufficient condition, controllability condition, and boundedness condition. We conduct experiments with a quadrotor motion planning problem and a partial differential equations (PDE) control problem to validate our theory, and show that the approach is significantly more efficient than alternating direction method of multipliers and as efficient as the centralized interior-point solver.

42 ENGINEERING↗