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Results for “Reversible computation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Magnetohydrodynamics using path or stream functions

Magnetization in highly conductive plasmas is ubiquitous to astronomical systems. Flows in such media can be described by three path functions Λ{sub α}, or, for a steady flow, by two stream functions λ{sub κ} and an additional field such as mass density ρ, velocity v, or travel time Δt. While typical analyses of a frozen magnetic field B are problem-specific and involve nonlocal gradients of the fluid element position x(t), we derive the general, local (in Λ or λ space) solution B=(∂x/∂Λ{sub α}){sub t} B-tilde {sub α}ρ/ ρ-tilde , where Lagrangian constants denoted by a tilde are directly fixed at a boundary hypersurface H-tilde on which B is known. For a steady flow, ρ-tilde B/ρ=(∂x/∂λ{sub κ}){sub Δt} B-tilde {sub κ}+v B-tilde {sub 3}/ v-tilde ; here the electric field E∼( B-tilde {sub 2}∇λ{sub 1}− B-tilde {sub 1}∇λ{sub 2})/ ρ-tilde depends only on λ{sub κ} and the boundary conditions. Illustrative special cases include compressible axisymmetric flows and incompressible flows around a sphere, showing that viscosity and compressibility enhance the magnetization and lead to thicker boundary layers. Our method is especially useful for directly computing electric fields, and for addressing upstream magnetic fields that vary in spacetime. We thus estimate the electric fields above heliospheres and magnetospheres, compute the draping of magnetic substructure around a planetary body, and demonstrate the resulting inverse polarity reversal layer. Our analysis can be immediately incorporated into existing hydrodynamic codes that are based on stream or path functions, to passively evolve the electromagnetic fields in a simulated flow. Furthermore, in such a prescription the electromagnetic fields are frozen onto the grid, so it may be developed into a fully magnetohydrodynamic, efficient simulation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Hybrid Energy: Combining Nuclear and Other Energy Sources

The leading cause of global climate change is generally accepted to be growing emissions of greenhouse gas (GHG) as a result of increased use of fossil fuels [1]. Among various sources of GHG, the global electricity supply sector generates the largest share of GHG emissions (37.5% of total CO2 emissions) [2]. Since the current electricity production heavily relies on fossil fuels, it is envisioned that bolstering generation technologies based on non-emitting energy sources, i.e., nuclear and/or renewables could reduce future GHG emissions. Integrated nuclear-renewable hybrid energy systems HES) are very-low-emitting options, but they are capital-intensive technologies that should operate at full capacities to maximize profits. Hence, electricity generators often pay the grid to take electricity when demand is low, resulting in negative profits for many hours per year. Instead of wasting an excess generation capacity at negative profit during off-peak hours when electricity prices are low, nuclear-renewable HES could result in positive profits by storing and/or utilizing surplus thermal and/or electrical energy to produce useful storable products to meet industrial and transportation demands. Consequently, it is necessary (1) to identify key integrated system options based on specific regions and (2) to propose optimal operating strategy to economically produce products on demand. In prioritizing region-specific HES options, available resources, markets, existing infrastructures, and etc. need to be researched to identify attractive system options. For example, the scarcity of water (market) and the availability of abundant solar radiation make solar energy (resource) a suitable option to mitigate the water deficit the Central-Southern region of the U.S. Thus, a solar energy-driven desalination process would be an attractive option to be integrated into a nuclear power plant to support the production of fresh water in this region. In this work, we introduce a particular HES option proposed for a specific U.S. region and briefly describe our modeling assumptions and procedure utilized for its analysis. Preliminary simulation results are also included addressing several technical characteristics of the proposed nuclear-renewable HES.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Computational Studies of Electrode Materials in Sodium-Ion Batteries

Sodium-ion batteries have gained extensive interest as a promising solution for large-scale electrochemical energy storage, owing to their low cost, materials abundance, good reversibility, and decent energy density. For sodium-ion batteries to achieve comparable performance to current lithium-ion batteries, significant improvements are still required in cathode, anode, and electrolyte materials. Understanding the functioning and degradation mechanisms of the materials is essential. Computational techniques have been widely applied in tandem with experimental investigations to provide crucial fundamental insights into electrode materials and to facilitate the development of materials for sodium-ion batteries. In this research, the authors review computational studies on electrode materials in sodium-ion batteries. The authors summarize the current state-of-the-art computational techniques and their applications in investigating the structure, ordering, diffusion, and phase transformation in cathode and anode materials for sodium-ion batteries. The unique capability and the obtained knowledge of computational studies as well as the perspectives for sodium-ion battery materials are discussed in this review.

25 ENERGY STORAGE↗
DOE OSTI2018

Effective-field theory for one-dimensional nonrelativistic particles with contact interaction

We consider a field theory that describes interacting nonrelativistic particles of two types, which map to each other under time reversal, with point-like interaction. We identify a new type of interaction which depends on the relative velocity between the particles. We compute the renormalization group running of the coupling constants and find a fixed point and a fixed line. We show that the scattering amplitudes can be expressed in terms of three parameters. The result matches with a quantum mechanical analysis and represents the most general point-like interaction consistent with unitarity and time reversal invariance.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem

We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of Christianson (1994) for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enabling larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving performance. Finally, the methods introduced here will be of value to other efforts applying AD tools to ice models, particularly ones which solve a hybrid shallow ice/shallow shelf approximation to the Stokes equations.

58 GEOSCIENCES↗
DOE OSTI2023

Dynamic and reversible transformations of subnanometre-sized palladium on ceria for efficient methane removal

Reversibly adjusting the active structures of supported metal catalysts in response to dynamic working conditions has long been pursued. Here we report the reaction-environment-modulated transformations of subnanometre-sized Pd on CeO 2 for efficient methane removal, leveraging the reaction environments at different stages of automotive exhaust aftertreatment. During the cold start of vehicles, inactive Pd 1 single atoms are readily transformed into PdO $x$ subnanometre clusters by CO even at room temperature with excess O 2 , resulting in boosted low-temperature CH 4 oxidation. At elevated temperatures, dispersion of PdO $x$ cluster into Pd 1 against metal sintering renders outstanding hydrothermal stability to the catalyst, to be activated during the next vehicle start. Combined experimental and computational studies elucidate the dynamically evolved Pd speciation on CeO 2 at an atomic level. In conclusion, modulating the reversible nature of supported metals helps overcome the long-existing trade-off between low-temperature activity and high-temperature stability, also providing a new paradigm for designing intelligent catalysts that brings single-atom/cluster catalysts closer to real applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

De novo design of pH-responsive self-assembling helical protein filaments

Abstract Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Reverse image search for scientific data within and beyond the visible spectrum

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computational Optimization of Room Temperature Usable Capacity for Hydrogen Storage in MFU-4-Type Metal–Organic Frameworks via Pairwise Metal Substitutions

The efficient storage of hydrogen is a critical challenge in the quest for sustainable energy solutions. Current adsorbent-based methods achieve satisfactory storage densities predominantly under cryogenic temperatures and/or high pressures, which imposes problems with cost-efficient and safe implementation of this technology. Materials that can bind hydrogen gas reversibly at ambient temperatures and more moderate pressures could play a pivotal role in enabling hydrogen-powered technologies. In this study, we use reliable computational modeling to investigate two synthetically feasible paths for tuning the enthalpy of H2 binding in MFU-4-type metal–organic frameworks (MOFs), aiming to maximize usable capacity. This study examines MIM4 IICl3(bta)6 (bta– = benzotriazolate) Kuratowski-type clusters as a model for strong binding sites in MFU-4l frameworks. We systematically evaluate the impact of separately tuning the central MII metal ion (which plays a structural role) and the peripheral MI metal ion (which binds the substrate) on the energetics of H2 binding. Our computational study reveals that H2 binding at an MI site mostly follows the trend AgI < CuI < NiI < CoI < AuI while a larger central MII site generally weakens the H2 binding at a MI site. Importantly, we have identified three new combinations of MI and MII to achieve high fractional usable capacities of the total H2 adsorbed under a pressure swing from 5 to 100 bar at room temperature. Additionally, we examine the nature of the binding interaction between the peripheral metal atom and the hydrogen molecule. While charge transfer predominantly induces this interaction, for several atom combinations, a change in the polarization (associated with variations in the ionic radius of the MI binding atom) is another important factor for adjusting the strength of the interaction. We suggest that the proposed compositions of Kuratowski-type clusters are highly desirable synthetic targets for future laboratory study.

Tkachenko, Nikolay V↗
DOE OSTI2025

Graph-based Reversible Evaluation and Tangents Library

GRETL is a C++ library for evaluation, re-evaluation and algorithmic differentiation of functional operations on an arbitrary computational graph with limited memory usage. Similar to popular machine learning frameworks in Python, like PyTorch and JAX, it tracks and stores both operations and output data as functions are evaluated. Once this composition of functions is built up, the entire chain of operations can be back propagated to compute sensitivities of the final result with respect to any number of inputs. In contrast to most machine learning applications, memory usage becomes the bottleneck for back propagation in many physics applications, especially for time-dependent PDEs. Dynamic check pointing becomes essential. An important distinguishing feature of GRETL is its ability to limit the maximum memory usage by automatically dynamic checkpointing the data output for each graph operation (see Wang, Moin, Iaccarino, 2009). During backpropagation, parts of the graph that are no longer in memory are automatically re-evaluated from upstream checkpointed states as needed for derivative sensitivity calculations (or more precisely, for vector-Jacobian products). GRETL is particularly beneficial for applications, such as coupled multi-physics, where deriving adjoint-based sensitivities and managing checkpoint memory across modules becomes onerous. Cases which can be readily handled by the GRETL library include: different time-integration algorithms per physics (e.g., coupled predictor-corrector algorithms, IMEX, etc.), sub-cycling, asynchronous integrators, state dependent timestep sizes, iterative solvers and coupling algorithms, controller algorithms, and more.

Tupek, MichaelR [Lawrence Livermore National Labor↗
DOE OSTI2026

Chirality reversal at finite magnetic impurity strength and local signatures of a topological phase transition

Here, we study the honeycomb lattice with a single magnetic impurity modeled by adding imaginary next-nearest-neighbor hopping 𝑖⁢ℎ on a single hexagon. This Haldane defect gives a topological mass term to the gapless Dirac cones and generates chirality. For a small density of defects, Neehus et al. [Phys. Rev. Lett. 135, 126604 (2025)] found that the system's chirality reverses at a critical ℎ 𝑐 ≈ 0.95 associated with an unexpected tricritical point of Dirac fermions at zero defect density. We investigate this zero-density limit by analyzing a single defect and computing two experimentally relevant measures of chirality: (1) orbital magnetization via local Chern marker, a bulk probe of all occupied states; and (2) electronic currents of low-energy states. Both probes show a chirality reversal at a critical ℎ 𝑐 ≈ 0.9–1.0. Motivated by this consistency, we propose a defect-scale toy model whose low-energy states reverse their chirality at ℎ$^{'}_{c}$ ≈ 0.87. Remarkably, the same pair of zero-energy bound states also generates the critical point ℎ 𝑐 in the full impurity projected T-matrix. Our results show how the chirality reversal produced by an impurity can be observed either in local probes or in the global topology, and suggest a possible role of the microscopic defect structure at the critical point.

Chern insulators↗
DOE OSTI2022

Physics constrained learning for data-driven inverse modeling from sparse observations

Deep neural networks (DNN) have been used to model nonlinear relations between physical quantities. Those DNNs are embedded in physical systems described by partial differential equations (PDE) and trained by minimizing a loss function that measures the discrepancy between predictions and observations in some chosen norm. This loss function often includes the PDE constraints as a penalty term when only sparse observations are available. As a result, the PDE is only satisfied approximately by the solution. However, the penalty term typically slows down the convergence of the optimizer for stiff problems. We present a new approach that trains the embedded DNNs while numerically satisfying the PDE constraints. We develop an algorithm that enables differentiating both explicit and implicit numerical solvers in reverse-mode automatic differentiation. This allows the gradients of the DNNs and the PDE solvers to be computed in a unified framework. We demonstrate that our approach enjoys faster convergence and better stability in relatively stiff problems compared to the penalty method. Furthermore, our approach allows for the potential to solve and accelerate a wide range of data-driven inverse modeling, where the physical constraints are described by PDEs and need to be satisfied accurately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Signal propagation in reversible digital mechanics

Digital mechanics explores information processing through binary, mechanical circuits. This work demonstrates a flexural, mechanical integrated circuit (m-IC) that achieves reversible, non-reciprocal signal propagation through integrated AND logic and memory. Our approach exploits sequential bistable transitions with symmetric energy wells, tunable stiffness, impedance matching, and AND gate non-linearity, to enable signal propagation, repeatability, and reversibility. We present a generalized model of logic kinematics and energetics, validated experimentally, to study energy flows, quantify energetic limits, and identify operating regimes for reversible logic. Macro-scale experiments confirm propagation dynamics, and new fabrication methods extend the architecture to micro-scale devices. By achieving controlled, reversible signal transmission across interconnected logic and memory, this work establishes a scalable platform for robust mechanical computing and adaptive sensing.

Johnson, Hilary A. [Lawrence Livermore National La↗
DOE OSTI2012

SYNCHROTRON SELF-COMPTON EMISSION AS THE ORIGIN OF THE GAMMA-RAY AFTERGLOW OBSERVED IN GRB 980923

GRB 980923 was one of the brightest bursts observed by the Burst and Transient Source Experiment. Previous studies have detected two distinct components in addition to the main prompt episode, which is well described by a Band function. The first of these is a tail with a duration of {approx_equal} 400 s, while the second is a high-energy component lasting {approx_equal} 2 s. We summarize the observations and argue for a unified model in which the tail can be understood as the early {gamma}-ray afterglow from forward shock synchrotron emission, while the high-energy component arises from synchrotron self-Compton from the reverse shock. Consistency between the main assumption of thick shell emission and agreement between the observed and computed values for fluxes, break energies, starting times, and spectral indices leads to a requirement that the ejecta must be highly magnetized.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

A Simulation Framework for Battery Cell Impact Safety Modeling Using LS-DYNA

The development process of electrified vehicles can benefit significantly from computer-aided engineering tools that predict themultiphysics response of batteries during abusive events. A coupled structural, electrical, electrochemical, and thermal model framework has been developed within the commercially available LS-DYNA software. The finite element model leverages a three-dimensional mesh structure that fully resolves the unit cell components. The mechanical solver predicts the distributed stress and strain response with failure thresholds leading to the onset of an internal short circuit. In this implementation, an arbitrary compressive strain criterion is applied locally to each unit cell. A spatially distributed equivalent circuit model provides an empirical representation of the electrochemical responsewith minimal computational complexity.The thermalmodel provides state information to index the electrical model parameters, while simultaneously accepting irreversible and reversible sources of heat generation. The spatially distributed models of the electrical and thermal dynamics allow for the localization of current density and corresponding temperature response. The ability to predict the distributed thermal response of the cell as its stored energy is completely discharged through the short circuit enables an engineering safety assessment. A parametric analysis of an exemplary model is used to demonstrate the simulation capabilities.

25 ENERGY STORAGE↗
DOE OSTI2021

Pressure Exchanger for Energy Recovery in a Trans-Critical CO2 Refrigeration System

Trans-critical CO2 vapor compression (VC) refrigeration cycles require a high compression ratio, which is associated with high expansion losses. To recover these expansion losses, a pressure exchange process between the low- and high-pressure sides of the VC cycle is proposed and examined in this study. The proposed pressure exchange system is an open type constant volume process where the high- and low-pressure flows mix inside the system. This prototype is inspired by the pressure exchangers used in reverse-osmosis (RO) desalination systems. In this system, a 2D model was generated and modeled using the computational fluid dynamics (CFD) technique. The numerical model ignored any losses due to leakage or hydraulic friction and the process is considered adiabatic. For the modeling, it was assumed that the inlet conditions for the two pressure exchanger flows are similar to the flow conditions at the evaporator and gas cooler outlets in a VC cycle. Two parameters are examined to test the validity of the system and understand their effect on the performance, including the inlet flow rate represented by the inlet velocity and the process time represented by the speed of rotation. A total of nine cases were simulated and analyzed in this study.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Quantum Computing with Majorana Kramers Pairs

Here, we propose a universal gate set acting on a qubit formed by the degenerate ground states of a Coulomb-blockaded time-reversal invariant topological superconductor island with spatially separated Majorana Kramers pairs: the “Majorana Kramers qubit.” All gate operations are implemented by coupling the Majorana Kramers pairs to conventional superconducting leads. Interestingly, in such an all-superconducting device, the energy gap of the leads provides another layer of protection from quasiparticle poisoning independent of the island charging energy. Moreover, the absence of strong magnetic fields—which typically reduce the superconducting gap size of the island—suggests a unique robustness of our qubit to quasiparticle poisoning due to thermal excitations. Consequently, the Majorana Kramers qubit should benefit from prolonged coherence times and may provide an alternative route to a Majorana-based quantum computer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗