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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 757 records · Page 42

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DOE OSTI2025

Impact of dynamics, entanglement and Markovian noise on the fidelity of few-qubit digital quantum simulation

Quantum algorithms have been proposed to accelerate the simulation of the chaotic dynamical systems that are ubiquitous in the physics of plasmas. Quantum computers without error correction might even use noise to their advantage to calculate the Lyapunov exponent by measuring the Loschmidt echo fidelity decay rate. For the first time, digital Hamiltonian simulations of the quantum sawtooth map, performed on the IBM-Q quantum hardware platform, show that the fidelity decay rate of a digital quantum simulation increases during the transition from dynamical localization to chaotic diffusion in the map. The observed error per CNOT gate increases by $1.5{\times }$ as the dynamics varies from localized to diffusive, while only changing the phases of virtual RZ gates and keeping the overall gate count constant. A gate-based Lindblad noise model that captures the effective change in relaxation and dephasing errors during gate operation qualitatively explains the effect of dynamics on fidelity as being due to the localization and entanglement of the states created. Specifically, highly delocalized states that are entangled with random phases show an increased sensitivity to dephasing and, on average, a similar sensitivity to relaxation as localized states. In contrast, delocalized unentangled states show an increased sensitivity to dephasing but a lower sensitivity to relaxation. This gate-based Lindblad model is shown to be a useful benchmarking tool by estimating the effective Lindblad coherence times during CNOT gates and finding a consistent $2\unicode{x2013}3{\times }$ shorter $T_2$ time than reported for idle qubits. Thus, the interplay of the dynamics of a simulation with the noise processes that are active can strongly influence the overall fidelity decay rate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2001

Task Assignment Heuristics for Distributed CFD Applications

CFD applications require high-performance computational platforms: 1. Complex physics and domain configuration demand strongly coupled solutions; 2. Applications are CPU and memory intensive; and 3. Huge resource requirements can only be satisfied by teraflop-scale machines or distributed computing.

Lopez-Benitez, N.↗

DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗

DOE OSTI2024

HomPINNs: Homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions

Not provided.

Computer Science↗

DOE OSTI2024

Data-driven Quasi-static Surrogate Model for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), energy manifold (EM), balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational overhead and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The findings shed light on the accuracy and applicability of the surrogate models in practical scenarios, demonstrating their potential to expedite the analysis of nuclear-powered IES and inform future research and development efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1996

Dynamics of Nuclear Regions of Galaxies

Current research carried out with the help of the ASEE-NASA Summer Faculty Program, at NASA-Ames, is concentrated on the dynamics of nuclear regions of galaxies. From a dynamical point of view a galaxy is a collection of around 10(sup 11) stars like our Sun, each of which moves in the summed gravitational field of all the remaining stars. Thus galaxy dynamics becomes a self-consistent n-body problem with forces given by Newtonian gravitation. Strong nonlinearity in the gravitational force and the inherent nonlinearity of self-consistent problems both argue for a numerical approach. The technique of numerical experiments consis of constructing an environment in the computer that is as close as possible to the physical conditions in a real galaxy and then carrying out experiments much like laboratory experiments in physics or engineering, in this environment. Computationally, an experiment is an initial value problem, and a good deal of thought and effort goes into the design of the starting conditions that serve as initial values. Experiments are run at Ames because all the 'equipment' is in place-the programs, the necessary computational power, and good facilities for post-run analysis. Our goal for this research program is to study the nuclear regions in detail and this means replacing most of the galaxy by a suitable boundary condition to allow the full capability of numerical experiments to be brought to bear on a small region perhaps 1/1000 of the linear dimensions of an entire galaxy. This is an extremely delicate numerical problem, one in which some small feature overlook, can easily lead to a collapse or blow-up of the entire system. All particles attract each other in gravitational problems, and the 1/r(sup 2) force is: (1) nonlinear; (2) strong at short range; (3) long-range, and (4) unscreened at any distance.

Miller, Richard H.↗

DOE OSTI2025

SDA: a symbolic differential algebra package in C++

Truncated Power Series Algebra (TPSA), or Differential Algebra (DA), is a well-established tool in accelerator physics, commonly used for generating high-order maps of dynamic systems, as well as in symplectic tracking, normal form analysis, verified integration, optimization, and fast multipole methods. This package is the first to perform symbolic DA computations, enabling traceability of initial condition contributions and runtime reduction for repeated DA calculations, potentially expanding DA’s applications.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Unsteady airloads due to separated flow on airfoils and wings

Experimental and computational studies of airloads due to separated flows over airfoils and wings conducted at the NASA Langley Research Center are surveyed. Results are presented for cases involving local flow separation such as shock-induced separation, for the initiation of leading-edge vortex flows, and for cases involving unsteady airloads due to flows separating over remote aircraft components. Good correlation is obtained between experiment and computation for cases of locally separating flow and steady computations of vortex flow over delta wings and complex forebody geometries are shown. Physical flow modeling issues and computational requirements for the case of vertical tail buffeting are developed.

Edwards, John W.↗

NASA NTRS1990

Unsteady airloads due to separated flow on airfoils and wings

Edwards, John W.↗

NASA NTRS1999

Portable Parallel Programming for the Dynamic Load Balancing of Unstructured Grid Applications

The ability to dynamically adapt an unstructured -rid (or mesh) is a powerful tool for solving computational problems with evolving physical features; however, an efficient parallel implementation is rather difficult, particularly from the view point of portability on various multiprocessor platforms We address this problem by developing PLUM, tin automatic anti architecture-independent framework for adaptive numerical computations in a message-passing environment. Portability is demonstrated by comparing performance on an SP2, an Origin2000, and a T3E, without any code modifications. We also present a general-purpose load balancer that utilizes symmetric broadcast networks (SBN) as the underlying communication pattern, with a goal to providing a global view of system loads across processors. Experiments on, an SP2 and an Origin2000 demonstrate the portability of our approach which achieves superb load balance at the cost of minimal extra overhead.

Biswas, Rupak↗

NASA NTRS1990

Welding arc plasma physics

The problems of weld quality control and weld process dependability continue to be relevant issues in modern metal welding technology. These become especially important for NASA missions which may require the assembly or repair of larger orbiting platforms using automatic welding techniques. To extend present welding technologies for such applications, NASA/MSFC's Materials and Processes Lab is developing physical models of the arc welding process with the goal of providing both a basis for improved design of weld control systems, and a better understanding of how arc welding variables influence final weld properties. The physics of the plasma arc discharge is reasonably well established in terms of transport processes occurring in the arc column itself, although recourse to sophisticated numerical treatments is normally required to obtain quantitative results. Unfortunately the rigor of these numerical computations often obscures the physics of the underlying model due to its inherent complexity. In contrast, this work has focused on a relatively simple physical model of the arc discharge to describe the gross features observed in welding arcs. Emphasis was placed of deriving analytic expressions for the voltage along the arc axis as a function of known or measurable arc parameters. The model retains the essential physics for a straight polarity, diffusion dominated free burning arc in argon, with major simplifications of collisionless sheaths and simple energy balances at the electrodes.

Cain, Bruce L.↗

NASA NTRSDate not supplied

Mixed-Domain Charge Transport in S-Se Alloys as a Li-S Battery Cathode Material

Lithium-sulfur batteries are emerging candidate systems for the next-generation long-range electric aircraft application owing to their potential to deliver high energy density per weight. Their cathodes are to be made primarily with sulfur, but sulfur by itself has much too low electron conductivity to serve as a useful cathode. An idea that has been suggested to overcome this bottleneck is to alloy sulfur with selenium in the hope that electron mobility, which is thought to improve charge transport at some expense of increased weight and reduced energy density. However, understanding of these alloy structures and their transport mechanism is insufficient, and electron mobility at varying degrees of selenium content is not well charted, which are critical for determining the optimum selenium content and testing whether this strategy is generally feasible. The difficulty of computationally characterizing these alloy systems is rooted in the structure. The structures of both sulfur and selenium exhibit eight-atom rings that pack together in various orientations to form their respective crystals. For one, because these crystals (and presumably their alloys as well) feature multiple polymorphs such that their structural coherence is rather unclear. Secondly, these are also relatively large systems (32 atoms per cell) with low-symmetry, which complicate computation both in terms of accuracy and efficiency. Thirdly, such semi-molecular, semi-crystalline structures lead to a combination of band-transport characteristics and hopping-transport characteristics, each of which is a domain with its own physics and set of computational, theoretical challenges. In addition, there is a general dearth of experimental transport data for sulfur-selenium alloys. In this study, we make a comprehensive attempt to tackle this problem using a wide array of first-principles methods. We generate special quasirandom structures to simulate alloy structures, and compute their first-principles Raman spectra for comparison with experimental data to ensure their structural soundness. We then proceed to use recently developed, state-of-the-art tool (AMSET) in order to efficiently compute electronic mobilities under band transport of pure sulfur, pure selenium, as well as their alloys of various compositions at a reasonable accuracy. We then sample numerous dimer configurations of nearest-neighbor eight-atom-ring-pairs and compute electronic hopping rates between them, which yields hopping mobility. A combination of these efforts lead to a general mapping of electron transport behaviors throughout the alloy range. Preliminary results show that introduction of selenium into sulfur initially damages electron mobility due to disorder but eventually improves it beyond that of pure sulfur with additional selenium content, ultimately peaking at the pure-selenium limit. Band transport dominates for holes in sulfur and electrons in selenium, but in all other cases, hopping transport is dominant. Ongoing efforts include determination of charge concentration and conductivity, as well as performing multiphysics modeling to determine the optimum selenium content for best tradeoff between conductivity and energy density for aircraft range.

Junsoo Park↗

NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗

DOE OSTI2025

Dynamical logical qubits in the Bacon-Shor code

The Bacon-Shor code is a quantum error correcting subsystem code composed of weight-2 check operators that admits a single logical qubit, and has distance 𝑑 on a 𝑑×𝑑 square lattice. We show that when viewed as a Floquet code, by choosing an appropriate measurement schedule of the check operators, it can additionally host several dynamical logical qubits. Specifically, we identify a period-4 measurement schedule of the check operators that preserves logical information between the instantaneous stabilizer groups. Such a schedule not only measures the usual stabilizers of the Bacon-Shor code, but also measures and promotes gauge operators of the parent subsystem code to additional temporary stabilizers that protect the dynamical logical qubits against errors. We show that the code distance of these Floquet-Bacon-Shor codes scales as Θ⁢(𝑑/√𝑘) on an 𝑛=𝑑×𝑑 lattice with 𝑘 dynamical logical qubits, along with the logical qubit of the parent subsystem code. Unlike the usual Bacon-Shor code, the Floquet-Bacon-Shor code family introduced here can therefore saturate the subsystem bound 𝑘⁢𝑑=𝑂⁡(𝑛). Moreover, several errors are shown to be self-corrected purely by the measurement schedule itself. This work provides insights into the design space for dynamical codes and expands the known approaches for constructing Floquet codes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2003

Task IV: Development of Circumferential Inlet Distortion through a Representative Eleven Stage High-speed Axial Compressor

The concepts and the procedure developed in Task I and Task III were used to determine the response of an eleven-stage high-speed compressor to an inlet distortion of 180 deg. circumferential extent for contrasting against its performance under uniform inlet flow. Using the computed results at the inlet to and outlet of the compressor, the computed total pressure ratio and efficiency for the clean condition are determined to be 14.22 and 76.9 percent respectively. As for the distorted case, these are determined to be 10.35 and 71.8 percent respectively, showing deterioration 76.9 percent vs 71.8 percent). The physical consistency of the computed flow field was assessed as a means of demonstrating the applicability and utility of the body force representation for inlet distortion computations. Specifically the computed evolution of the distorted pattern in static pressure and total pressure from compressor inlet to exit is examined. For the eleven-stage compressor examined here, the deterioration in performance has been found to be particularly severe in the last 2 stages. This suggests that the last two stages could be redesigned to alleviate the observed deterioration thus making the compressor performance insensitive to circumferential inlet distortion. This can potentially be accomplished by first determining what should the body force distribution of the last two stages should be to achieve minimal or no deterioration in performance in the last two stages. One can then in principle proceed to determine the blade design to yield such a body force distribution.

Tan, Choon-Sooi↗

DOE OSTI2025

Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing

With Moore’s law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. The stable trapped fluxon state directs the incoming flux through different pathways with the flow statistics representing different synaptic weights. We explore implementation of matrix–vector-multiplication (MVM) operations using arrays of these fluxon synapse devices. We investigate the energy efficiency of online-learning of MNIST dataset. Our results suggest that the fluxon synapse array can provide ~100× reduction in energy consumption compared to other state-of-the-art synaptic devices. This work presents a proof-of-concept that will pave the way for development of high-speed and highly energy efficient neuromorphic computing systems based on superconducting materials.

42 ENGINEERING↗

NASA NTRS1978

On the attitude motion of an orbiting rigid body under the influence of gravity gradient torque

An investigation is made of the rotational motion of a rigid body orbiting the earth, under the influence of the geogravity-gradient torque. The attitude dynamics are formulated as perturbations from a nominal case. The perturbed equations are solved asymptotically, by the multiple scales technique. The independent variable, time, is extended into a space of higher dimension by means of new scales, fast, slow, etc. Integration of the equations is carried out separately in the new variables. The rapid and slow aspects of the attitude dynamics are systematically separated, resulting in a more efficient computer implementation and enhanced physical insight. The theory is applied to predict the attitude dynamics of an asymmetric rigid body satellite. A comparison is made of the maximum errors as the step size increases as predicted by the multiple scales solution and direct numerical integration. An improvement in computational speed of an order of magnitude is demonstrated.

Tao, Y. C.↗

NASA NTRS2006

User-Defined Data Distributions in High-Level Programming Languages

One of the characteristic features of today s high performance computing systems is a physically distributed memory. Efficient management of locality is essential for meeting key performance requirements for these architectures. The standard technique for dealing with this issue has involved the extension of traditional sequential programming languages with explicit message passing, in the context of a processor-centric view of parallel computation. This has resulted in complex and error-prone assembly-style codes in which algorithms and communication are inextricably interwoven. This paper presents a high-level approach to the design and implementation of data distributions. Our work is motivated by the need to improve the current parallel programming methodology by introducing a paradigm supporting the development of efficient and reusable parallel code. This approach is currently being implemented in the context of a new programming language called Chapel, which is designed in the HPCS project Cascade.

physically distributed memory↗