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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 577 records · Page 32

Reliable p K a Prediction through Efficient Incorporation of Anharmonicity within the Nuclear–Electronic Orbital Framework

Accurate pK a prediction is critical for understanding chemical reactivity and molecular properties across a wide range of applications. Computational approaches usually invoke a harmonic treatment of the vibrational modes for zero-point energies, as well as thermal and entropic contributions. Herein, we present a general protocol for relative pK a prediction that incorporates the significant anharmonic effects using nuclear–electronic orbital (NEO) theory. This protocol is validated against experimental data for a range of molecules in acetonitrile, including protonated nitrogen bases, nitrophenols, anilines, and diamines, as well as cobalt electrocatalysts. For simple acids, the NEO approach offers only a slight improvement over conventional density functional theory with the standard harmonic vibrational treatment, whereas for hydrogen-bonded acids, the NEO approach offers more significantly improved performance at a comparable computational cost. This accessible methodology provides a practical route for accurate pKa prediction in challenging systems and is extendable to related thermodynamic properties such as hydricities and proton-coupled redox potentials.

Density functional theory↗

An analysis code for the Rapid Engineering Estimation of Momentum and Energy Losses (REMEL)

Nonideal behavior has traditionally been modeled by defining efficiency (a comparison between actual and isentropic processes), and subsequent specification by empirical or heuristic methods. With the increasing complexity of aeropropulsion system designs, the reliability of these more traditional methods is uncertain. Computational fluid dynamics (CFD) and experimental methods can provide this information but are expensive in terms of human resources, cost, and time. This report discusses an alternative to empirical and CFD methods by applying classical analytical techniques and a simplified flow model to provide rapid engineering estimates of these losses based on steady, quasi-one-dimensional governing equations including viscous and heat transfer terms (estimated by Reynold's analogy). A preliminary verification of REMEL has been compared with full Navier-Stokes (FNS) and CFD boundary layer computations for several high-speed inlet and forebody designs. Current methods compare quite well with more complex method results and solutions compare very well with simple degenerate and asymptotic results such as Fanno flow, isentropic variable area flow, and a newly developed, combined variable area duct with friction flow solution. These solution comparisons may offer an alternative to transitional and CFD-intense methods for the rapid estimation of viscous and heat transfer losses in aeropropulsion systems.

Dechant, Lawrence J.↗

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗

Wind Loading on CSP Collectors

The project significantly enhanced the community's understanding of the fundamental physics drivers underlying the wind-loading experienced by concentrating solar power (CSP) collector structures (i.e., parabolic troughs and heliostats) as well as their support structures. This project had two overarching objectives: (1) detailed measurements to characterize the prevailing wind conditions and resulting operational loads on collector structures, and (2) development and validation of a computationally efficient, high-fidelity modeling tool capable of predicting wind-loading in deep-array installations. Over three years, we conducted comprehensive at-scale field measurements of the atmospheric turbulent wind conditions, and the resulting wind loads on parabolic troughs and heliostats. Two at-scale measurement campaigns yielded first-of-its-kind, high-resolution, long-term datasets that are used to characterize the complex flow field and wind loading on parabolic-troughs and heliostats in operational power plants. The high-resolution measurements collected during these campaigns were used to validate the high-fidelity computational models developed at NREL. These open-source computationally efficient models were shown to be accurate in predicting wind-driven loads on collectors without the need for a large supercomputer.

14 SOLAR ENERGY↗

A 3DHZETRN Code in a Spherical Uniform Sphere with Monte Carlo Verification

The computationally efficient HZETRN code has been used in recent trade studies for lunar and Martian exploration and is currently being used in the engineering development of the next generation of space vehicles, habitats, and extra vehicular activity equipment. A new version (3DHZETRN) capable of transporting High charge (Z) and Energy (HZE) and light ions (including neutrons) under space-like boundary conditions with enhanced neutron and light ion propagation is under development. In the present report, new algorithms for light ion and neutron propagation with well-defined convergence criteria in 3D objects is developed and tested against Monte Carlo simulations to verify the solution methodology. The code will be available through the software system, OLTARIS, for shield design and validation and provides a basis for personal computer software capable of space shield analysis and optimization.

Wilson, John W.↗

Ion cyclotron anisotropy instabilities in the magnetosheath - Theory and simulations

Second-order theory and 1D hybrid computer simulations are used to examine the nonlinear properties of proton and helium cyclotron anisotropy instabilities in the terrestrial magnetosheath at relatively weak fluctuations levels and at propagation parallel to the background magnetic field. The simulations confirm the second-order predictions that both instabilities yield efficient wave-particle interactions; the rate at which the driving species anisotropy is reduced is much greater than the rate at which that species loses kinetic energy. It is suggested that these instabilities should saturate at relatively low levels. An approximate expression is derived for the fluctuating field energy at saturation of the fastest growing modes, and it is found that it is in fair agreement with three computer simulations.

Gary, S. P.↗

Real-time process monitoring and automated control for direct ink write 3D printing of frontally polymerizing thermosets

Additive manufacturing (AM) enables the fabrication of complex geometries, yet its application to thermosets remains limited by post-processing requirements. Frontal ring-opening metathesis polymerization (FROMP) offers a promising alternative, enabling energy-efficient, in situ curing of freestanding thermoset structures. This study presents a real-time process monitoring and automated control system for direct ink writing (DIW) of FROMP thermosets. By integrating thermochromic leuco dyes and computer vision, we enable real-time polymerization front tracking, allowing autonomous printing parameter adjustments for consistent geometries across resin formulations. The system’s accuracy was validated against manual tracking, demonstrating precise front velocity detection. Its adaptability was confirmed by printing freestanding mechanical springs with different resins, achieving consistent geometries and mechanical properties despite front velocity variations. These findings highlight the potential of automated DIW control for scalable, repeatable, and material-agnostic 3D printing of thermosets.

Mejia, Edgar Brian [Sandia National Laboratories (↗

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Equilibrium Core Model for Micro Pebble Bed Reactors Using OpenMC

Estimating the equilibrium state for pebble bed reactors (PBRs) presents complex challenges as it requires simultaneous consideration of changes in the pebbles’ movement as well as their fuel compositions. Whereas traditional approaches use multigroup diffusion codes for neutronics calculations of PBRs’ equilibrium state, the double-heterogeneity of PBRs complicates neutron cross-section generation. Continuous-energy Monte Carlo (MC) methods are better suited for detailed PBR analysis because of their natural handling of double-heterogeneity, but they demand substantially more computational resources. Here, this study introduces a novel method for efficiently estimating the equilibrium state in small and micro PBRs with reduced computational cost. The method is anticipated to accelerate the processes of core design and performing parametric studies for utilizing advanced fuel and structural materials. The HTR-10 reactor design was used for validating the method’s predictions and evaluating its computational efficiency. When compared to reference calculation values from the literature, criticality (k-effective) was predicted to be approximately within the margin of error of the MC transport calculation, average core power density (in megawatts per cubic meter) was predicted within 2.5% relative error, and maximum thermal flux (10 13 n/cm 2 .s −1 ) was predicted within 1.8% relative error. The calculated inventory of fission products and fuel composition in the equilibrium core were within 15% and 16.6%, respectively, when compared to reported values from the literature. The difference is attributed to variance in the considered values of the core temperature, which was found to significantly affect the depletion analyses.

Equilibrium core↗

Vibrational Power Flow Analysis of Rods and Beams

A new method to model vibrational power flow and predict the resulting energy density levels in uniform rods and beams is investigated. This method models the flow of vibrational power in a manner analogous to the flow of thermal power in a heat conduction problem. The classical displacement solutions for harmonically excited, hysteretically damped rods and beams are used to derive expressions for the vibrational power flow and energy density in the rod and beam. Under certain conditions, the power flow in these two structural elements will be shown to be proportional to the energy density gradient. Using the relationship between power flow and energy density, an energy balance on differential control volumes in the rod and beam leads to a Poisson's equation which models the energy density distribution in the rod and beam. Coupling the energy density and power flow solutions for rods and beams is also discussed. It is shown that the resonant behavior of finite structures complicates the coupling of solutions, especially when the excitations are single frequency inputs. Two coupling formulations are discussed, the first based on the receptance method, and the second on the travelling wave approach used in Statistical Energy Analysis. The receptance method is the more computationally intensive but is capable of analyzing single frequency excitation cases. The traveling wave approach gives a good approximation of the frequency average of energy density and power flow in coupled systems, and thus, is an efficient technique for use with broadband frequency excitation.

Wohlever, James Christopher↗

Auroral modeling of the 3371 A emission rate - Dependence on characteristic electron energy

An efficient two-stream auroral electron model is used to study the deposition of auroral energy and the dependence of auroral emission rates on characteristic energy. This model incorporates the concept of average energy loss to reduce the computation time. This simple two-stream model produces integrated emission rates that are in excellent agreement with the much more complex multistream model of Strickland et al. (1983) but disagrees with a recent study by Rees and Lummerzheim (1989) that indicates that the N2 second positive emission rate is a strongly decreasing function of the characteristic energy of the precipitating flux. These calculations reveal that a 10 keV electron will undergo approximately 160 ionizing collisions, with an average energy loss per collision of 62 eV before thermalizing. The secondary electrons are created with an average energy of 42 eV. When all processes including the backscattered escape fluxes are taken into account, the average energy loss per electron-ion pair is 35 eV in good agreement with laboratory results.

Richards, P. G.↗

Quadratic Programming for Allocating Control Effort

A computer program calculates an optimal allocation of control effort in a system that includes redundant control actuators. The program implements an iterative (but otherwise single-stage) algorithm of the quadratic-programming type. In general, in the quadratic-programming problem, one seeks the values of a set of variables that minimize a quadratic cost function, subject to a set of linear equality and inequality constraints. In this program, the cost function combines control effort (typically quantified in terms of energy or fuel consumed) and control residuals (differences between commanded and sensed values of variables to be controlled). In comparison with prior control-allocation software, this program offers approximately equal accuracy but much greater computational efficiency. In addition, this program offers flexibility, robustness to actuation failures, and a capability for selective enforcement of control requirements. The computational efficiency of this program makes it suitable for such complex, real-time applications as controlling redundant aircraft actuators or redundant spacecraft thrusters. The program is written in the C language for execution in a UNIX operating system.

Singh, Gurkirpal↗

An AI-Based 3D Bat Movement Tracking System at Wind Energy Facilities Using Multi-Thermal Video Cameras

The poster at the 15th Wind Wildlife Research Meeting discusses how to leverage the potential of real-time thermal-imaging methodologies in quantifying nocturnal bat activities at wind turbines, using 3D computer vision techniques within a deep learning framework. This innovation enables the automatic detection and classification of bats, birds, and insects in thermal-imaging videos captured at wind turbine sites, facilitating efficient and accurate data analysis for enhanced understanding and mitigation of bat-wind turbine interactions.

AI↗

Dual Enhancement of Thermostability and Activity of Xylanase through Computer-Aided Rational Design

In the realm of enzyme engineering, the dual enhancement of thermostability and activity remains a challenge. Herein, we employed a computer-aided approach integrating folding free energy calculations and evolutionary analysis to engineer Paecilomyces thermophila xylanase into a hyperthermophilic enzyme for application in the paper and pulp industry. Through the computational rational design, XynM9 with superior thermostability and enhanced activity was designed. Its optimal reaction temperature increases by 10 °C to 85 °C, its T m increases by 10 °C to 93 °C, and its half-life increases 11-fold to 5.8 h. Additionally, its catalytic efficiency improves by 57% to 3926 s –1 mM –1 . Molecular dynamics simulations revealed that XynM9 is stabilized by more hydrogen bonds and salt bridges than wild-type xylanase. The mutant’s narrower catalytic cleft enhances the substrate-binding affinity, thus improving the catalytic efficiency. In harsh conditions at 80 °C and pH 10, using XynM9 significantly reduced both hemicellulose and lignin, which makes it a good candidate for use in the paper and pulp process. Finally, our study presents an accurate and efficient strategy for the dual enhancement of enzyme properties, guiding further improvement of computational tools for protein stabilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Advancing quantum simulations of the nuclear shell model with Gray-code–based resource-efficient protocols

Background: Some of the computational limitations in solving the nuclear many-body problem could be overcome by utilizing quantum computers. The nuclear shell-model calculations providing deeper insights into the properties of atomic nuclei are one such case with high demand for resources, as the size of the Hilbert space grows exponentially with the number of particles involved. Quantum algorithms are being developed to overcome these challenges and advance such calculations. Purpose: To develop quantum circuits for the nuclear shell-model, leveraging the capabilities of noisy intermediate-scale quantum (NISQ) devices. Here, we aim to minimize resource requirements (specifically in terms of qubits and gates) and strive to reduce the impact of noise by employing relevant mitigation techniques. Methods: We achieve noise resilience by designing an optimized Ansatz for the variational quantum eigensolver (VQE) based on Givens rotations and incorporating qubit-ADAPT-VQE in combination with variational quantum deflation (VQD) to compute ground and excited states, incorporating the zero-noise extrapolation mitigation technique. Furthermore, the qubit requirements are significantly reduced by mapping the basis states to qubits using Gray-code encoding and generalizing transformations of fermionic operators to efficiently represent many-body states. Results: By employing the resource-efficient protocols, we achieve the ground and excited state energy levels of 38 Ar and 6 Li with better accuracy. These energy levels are presented for noiseless simulations, noisy conditions, and after applying noise mitigation techniques. Results are compared for Jordan-Wigner and Gray-code encoding using VQE, qubit-ADAPT-VQE, and VQD. Conclusions: Our work highlights the potential of resource-efficient protocols to leverage the full potential of NISQ devices in scaling the nuclear shell model calculations, offering a pathway toward more complex quantum simulations in nuclear physics. This approach establishes a framework for studying other nuclear systems with improved quantum resource efficiency, marking a significant advancement in applying quantum computing to realistic nuclear physics applications.

Physics - Nuclear physics and radiation physics↗

Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies

Abstract The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.

36 MATERIALS SCIENCE↗

Flow annealed importance sampling bootstrap meets differentiable particle physics

High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗