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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Noise Prediction of Multi-stream Internally Mixed Jets with External and Internal Plugs using Large-Eddy Simulation

This paper presents results from ongoing research on jet noise prediction using wall-modeled large eddy simulations (WMLES) conducted within the Launch Ascent and Vehicle Aerodynamics (LAVA) computational framework. The study primarily focuses on the aeroacoustic implications of multi-stream nozzle configurations with internal mixing and both internal and external plugs, at a Reynolds number of 1×10 6 based on the nozzle exit diameter. While internal mixing nozzles have long been considered for jet noise reduction, the complexities of their impact on overall noise levels remain insufficiently understood. This research applies established best practices for WMLES to these complex nozzle configurations, aiming to assess their efficacy and identify limitations in accurately predicting noise behaviors. Through detailed comparisons with experimental data obtained from NASA’s Glenn Research Center, initial findings underscore the challenges inherent in current simulation practices when confronted with intricate geometric and operational conditions. Responding to these challenges, the study explores innovative computational approaches to rectify discrepancies noted between experimental outcomes and CFD predictions. Once example is the introduction of a resonating sound source in the simulation environment to mimic potential unobserved acoustic phenomena.A second example is the strategic modifications to the geometry of external plugs to account for real-world deformations caused by heating and gravity. These novel strategies aim to enhance the accuracy and reliability of noise predictions from supersonic jets, advancing our understanding and capability to effectively reduce jet noise in commercial supersonic aircraft

CST↗
DOE OSTI2017

Evaluating the Practicability of Multiphase CFD for BWR Fuel Assembly Analysis

Multiphase computational fluid dynamics (M-CFD) models of a boiling water reactor (BWR) fuel assembly have been generated in the commercial CFD code, STAR-CCM+ Ver.11.04.010-R8. The Eulerian multiphase approach was employed, in combination with a baseline ‘Zero Closure model’ for two-phase flow boiling, applicable to high void fraction regimes. The capability and practicability of MCFD for BWR fuel assembly were evaluated against the international OECD/NRC BWR Full-size Finemesh Bundle Test (BFBT) benchmark data. The benchmark selected, BFBT-4101-61, is a steady-state full power case, with high void fraction conditions. Geometrical modeling and mesh construction of BWR assemblies introduce challenges in generating high quality computational meshes, while retaining a low cell count to allow acceptable runtimes. In order to evaluate the scalability of the CFD code, parallel CFD simulations were carried out with the High Performance Computing (HPC) system of the Idaho National Laboratory. M-CFD results display good agreement with the experimental data for both local void fraction distribution and exit quality. The parallel speedup factor is linearly proportional to the number of processors up to 500 cores, and retains over 92% efficiency with 1000 cores. The execution time of the simulation decreased exponentially with increasing number of processors while the cost of the simulation increased linearly. The proposed baseline closure, in combination with the parallel performance of the STAR-CCM+ code demonstrated promising capabilities for analyzing BWR fuel assemblies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Road to Useful Quantum Computers

Building a useful quantum computer is a grand science and engineering challenge, currently pursued intensely by teams around the world. In the 1980s, Richard Feynman and Yuri Manin observed independently that computers based on quantum mechanics might enable better simulations of quantum phenomena. Their vision remained an intellectual curiosity until Peter Shor published his famous quantum algorithm for integer factoring, and shortly thereafter a proof that errors in quantum computations can be corrected. Since then, quantum computing R&D has progressed rapidly, from small-scale experiments in university physics laboratories to well-funded industrial efforts and prototypes. Hype notwithstanding, quantum computers have yet to solve scientifically or practically important problems -- a target often called quantum utility. In this article, we describe the capabilities of contemporary quantum computers, compare them to the requirements of quantum utility, and illustrate how to track progress from today to utility. We highlight key science and engineering challenges on the road to quantum utility, touching on relevant aspects of our own research.

Emerging Technologies (cs.ET)↗
DOE OSTI2019

De novo design of a homo-trimeric amantadine-binding protein

The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the C 3 symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A phase-field fracture formulation for generalized standard materials: The interplay between thermomechanics and damage

Accurately modeling fracture of ductile materials poses open challenges in the field of computational mechanics due to the multiphysics nature of their failure processes. Integrating the interplay between thermodynamics and damage into ductile fracture models is vital for predicting critical failure modes. Here, in this paper, we develop a versatile phase-field (PF) framework for modeling ductile fracture, taking into account finite-strain elasto-plasticity. The framework stems from a variational formulation of constitutive relations for generalized standard materials (GSMs), whose response is described by a Helmholtz free energy and a dissipation pseudo-potential. Its variational structure is based on a minimum principle for a functional that expresses the sum of power densities for reversible and irreversible processes. By minimizing this functional with a constraint on a von Mises yield function, we derive the evolution equation for the equivalent plastic strain and an associative flow rule. This constrained optimization problem is analytically solved for a wide class of thermo-viscoplasticity models. The key innovations of the current work include (i) a cubic plastic degradation function that accounts for a non-vanishing damage-dependent yield stress, (ii) closed-form expressions of the Helmholtz free energy and dissipation pseudo-potential for three thermo-viscoplasticity models, (iii) an extended Johnson–Cook plasticity model with a nonlinear hardening law, and (iv) a plastic work heat source that depends on the plastic degradation function and a variable Taylor–Quinney (TQ) coefficient. The capabilities of the proposed framework are tested with the aid of four ductile fracture problems, including the Sandia Fracture Challenge. In each of these problems, we examine the evolution of relevant field variables such as the PF order parameter, the equivalent plastic strain, the temperature, and the internal power dissipation density, in addition to the overall structural response quantified by the force–displacement curve. These numerical studies demonstrate that the proposed framework effectively represents ductile fracture, yielding computational results that exhibit good agreement with experimental data.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Applied Operations Research: Augmented Reality in an Industrial Environment

Augmented reality is the application of computer generated data or graphics onto a real world view. Its use provides the operator additional information or a heightened situational awareness. While advancements have been made in automation and diagnostics of high value critical equipment to improve readiness, reliability and maintenance, the need for assisting and support to Operations and Maintenance staff persists. AR can improve the human machine interface where computer capabilities maximize the human experience and analysis capabilities. NASA operates multiple facilities with complex ground based HVCE in support of national aerodynamics and space exploration, and the need exists to improve operational support and close a gap related to capability sustainment where key and experienced staff consistently rotate work assignments and reach their expiration of term of service. The initiation of an AR capability to augment and improve human abilities and training experience in the industrial environment requires planning and establishment of a goal and objectives for the systems and specific applications. This paper explored use of AR in support of Operation staff in real time operation of HVCE and its maintenance. The results identified include identification of specific goal and objectives, challenges related to availability and computer system infrastructure.

Cole, Stuart K.↗
DOE OSTI2014

Simulation at Extreme-Scale: Co-Design Thinking and Practices

The current trend of computer architecture evolving towards exaflop/s is the fast increasing floating point performance (the so-called “free” flops) accompanied by much slowly improving the bandwidth of memory and network. Numerical simulation would undergo the challenge posed by the unbalanced increase in the compute power and the capability of data movement. In this paper, after reviewing the challenges of hardware and software in moving towards exascale computing, we present co-design thinking for selecting, optimizing, and developing a numerical algorithm and a simulation tool to meet the challenge of simulation at extreme scale. Examples are presented to demonstrate the new way of thinking and its effectiveness on the emerging architecture.

Computer Science↗
DOE OSTI2025

Certified randomness using a trapped-ion quantum processor

Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers, realizing this potential remains a challenge. An example is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is impossible to achieve solely by classical computation. Here we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the Internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum ‘challenge’ circuits using a small randomness seed, sends them to an untrusted quantum server to execute and verifies the results of the server. We analyse the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 10 18 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of present-day quantum computers.

computer science↗
DOE OSTI2021

Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model using WAVEWATCH III version 6.07

Wind-wave processes have generally been excluded from coupled Earth system models due to the high computational expense of spectral wave models, which resolve a frequency and direction spectrum of waves across space and time. Existing uniform-resolution wave modeling approaches used in Earth system models cannot appropriately represent wave climates from global to coastal ocean scales, largely because of tradeoffs between coastal resolution and computational costs. To resolve this challenge, we introduce a global unstructured mesh capability for the WAVEWATCH III (WW3) model that is suitable for coupling within the US Department of Energy's Energy Exascale Earth System Model (E3SM). The new unstructured WW3 global wave modeling approach can provide the accuracy of higher global resolutions in coastal areas at the relative cost of lower uniform global resolutions. This new capability enables simulation of waves at physically relevant scales as needed for coastal applications.

58 GEOSCIENCES↗
DOE OSTI2019

PRIMA-X - Performance Retargeting of Instrumentation, Measurement, and Analysis Technologies for Exascale Computing

The PRIMA-X (Performance Retargeting of Instrumentation, Measurement, and Analysis Technologies for Exascale Computing) project is the successor of the DOE PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing) project, which addressed the challenge of creating a core measurement infrastructure that would serve as a common platform for both integrating leading parallel performance systems (notably TAU and Scalasca) and developing next-generation scalable performance tools. The PRIMA-X project shifts the focus away from refactorization of robust performance tools towards a re-targeting of the parallel performance measurement and analysis architecture for extreme scales. The massive concurrency, asynchronous execution dynamics, hardware heterogeneity, and multi-objective prerequisites (performance, power, resilience) that identify exascale systems introduce fundamental constraints on the ability to carry forward existing performance methodologies. In particular, there must be a deemphasis of per-thread observation techniques to significantly reduce the otherwise unsustainable flood of redundant performance data. Instead, it will be necessary to assimilate multi-level resource observations into macroscopic performance views, from which resilient performance metrics can be attributed to the computational features of the application. This requires a scalable framework for node-level and system-wide monitoring and runtime analyses of dynamic performance information. Also, the interest in optimizing parallelism parameters with respect to performance and energy drives the integration of tool capabilities in the exascale environment further. Initially, PRIMA-X was a collaborative project between the University of Oregon (lead institution) and the German Research School for Simulation Sciences (GRS). Because Prof. Wolf, the PI at GRS, accepted a position as full professor at Technische Universitaet Darmstadt (TU Darmstadt) starting February 1st, 2015, the work of GRS was continued at TU Darmstadt. This report covers the work at TU Darmstadt after the transition. The first main accomplishment of TU Darmstadt is the development of different techniques to aggregate performance data on the level of threads. TU Darmstadt evaluated different schemes designed at GRS during the first phase of this project and integrated them into Score-P, a widely used performance measurement framework for HPC applications. The second main accomplishment is a substantial increase of Score-P’s scalability, achieved by improving its internal representation of the underlying system. Third, we extended our performance-modeling tool Extra-P to automatically create models with more than one input parameter (e.g., process count and input size), which was previously impossible. These models allow the performance behavior of an application to be extrapolated to larger machines. Specific applications of the new multi-parameter capability realized in this project include modeling the isoefficiency function of task-based applications and a novel co-design methodology. Further extensions of Extra-P comprise online modeling functionality, automatic refinement of the model search space, and segmented performance modeling. Further contributions include new locality metrics for both shared- and distributed-memory applications, a new way of measuring the impact of inter-application inference on the execution time of an application, and finally an automatic method that selects appropriate OpenMP constructs for the parallelization of sequential code and classifies the affected variables based on their sharing semantics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

DevOps Pragmatic Practices and Potential Perils in Scientific Software Development

The DevOps movement, which aims to accelerate the continuous delivery of high-quality software, has taken a leading role in reshaping the software industry. Likewise, there is growing interest in applying DevOps tools and practices in the domains of computational science and engineering (CSE) to meet the ever-growing demand for scalable simulation and analysis. Translating insights from industry to research computing, however, remains an ongoing challenge; DevOps for science and engineering demands adaptation and innovation in those tools and practices. There is a need to better understand the challenges faced by DevOps practitioners in CSE contexts in bridging this divide. To that end, we conducted a participatory action research study to collect and analyze the experiences of DevOps practitioners at a major US national laboratory through the use of storytelling techniques. We share lessons learned and present opportunities for future investigation into DevOps practice in the CSE domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

[Re] Drivers of evapotranspiration from boreal wildfires

Computational reproducibility is a difficult challenge across science. I attempted to use R 3.6.1 to reproduce linear model fits, done originally using v2.6.0 for a 2009 paper on the drivers of large-scale forest evapotranspiration after wildfire. Model outputs were largely identical, aside from minor formatting changes, except for one–out of 12 total– regression in which the median residual value changed very slightly (in the sixth decimal place). I suggest that this essentially successful reproducibility is due to the relative simplicity of the script, its use of only base R functions, and R’s historically conservative approach to breaking changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Diffusion-Welded Microchannel Heat Exchanger for Industrial Processes

The goal of next generation reactors is to increase energy ef?ciency in the production of electricity and provide high-temperature heat for industrial processes. The ef?cient transfer of energy for industrial applications depends on the ability to incorporate effective heat exchangers between the nuclear heat transport system and the industrial process. The need for ef?ciency, compactness, and safety challenge the boundaries of existing heat exchanger technology. Various studies have been performed in attempts to update the secondary heat exchanger that is downstream of the primary heat exchanger, mostly because its performance is strongly tied to the ability to employ more ef?cient industrial processes. Modern compact heat exchangers can provide high compactness, a measure of the ratio of surface area-to-volume of a heat exchange. The microchannel heat exchanger studied here is a plate-type, robust heat exchanger that combines compactness, low pressure drop, high effectiveness, and the ability to operate with a very large pressure differential between hot and cold sides. The plates are etched and thereafter joined by diffusion welding, resulting in extremely strong all-metal heat exchanger cores. After bonding, any number of core blocks can be welded together to provide the required ?ow capacity. This study explores the microchannel heat exchanger and draws conclusions about diffusion welding/bonding for joining heat exchanger plates, with both experimental and computational modeling, along with existing challenges and gaps. Also, presented is a thermal design method for determining overall design speci?cations for a microchannel printed circuit heat exchanger for both supercritical (24 MPa) and subcritical (17 MPa) Rankine power cycles.

42 ENGINEERING↗
DOE OSTI2018

Exemplar for simulation challenges: Large-deformation micromechanics of Sylgard 184/glass microballoon syntactic foams.

Sylgard® 184/Glass Microballoon (GMB) potting material is currently used in many NW systems. Analysts need a macroscale constitutive model that can predict material behavior under complex loading and damage evolution. To address this need, ongoing modeling and experimental efforts have focused on study of damage evolution in these materials. Micromechanical finite element simulations that resolve individual GMB and matrix components promote discovery and better understanding of the material behavior. With these simulations, we can study the role of the GMB volume fraction, time-dependent damage, behavior under confined vs. unconfined compression, and the effects of partial damage. These simulations are challenging and push the boundaries of capability even with the high performance computing tools available at Sandia. We summarize the major challenges and the current state of this modeling effort, as an exemplar of micromechanical modeling needs that can motivate advances in future computing efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

On fast simulation of dynamical system with neural vector enhanced numerical solver

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec’s simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ComPort: Rigorous Testing Methods to Safeguard Software Porting (Final UW Report)

This report summarizes the University of Washington’s contributions to the ComPort project, Rigorous Testing Methods to Safeguard Software Porting, funded under the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR), award number DE-SC0022081. UW’s work addressed numerics-driven correctness challenges arising in heterogeneous and rapidly evolving computing environments. Our efforts focused on developing interactive analysis tools, correctness-preserving rewriting systems, semantic-aware code-transformation infrastructure, and multi-language support for reproducible numerical experiments.

97 MATHEMATICS AND COMPUTING↗