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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

A Three-Dimensional Parallel Time-Accurate Turbopump Simulation Procedure Using Overset Grid System

The objective of the current effort is to provide a computational framework for design and analysis of the entire fuel supply system of a liquid rocket engine, including high-fidelity unsteady turbopump flow analysis. This capability is needed to support the design of pump sub-systems for advanced space transportation vehicles that are likely to involve liquid propulsion systems. To date, computational tools for design/analysis of turbopump flows are based on relatively lower fidelity methods. An unsteady, three-dimensional viscous flow analysis tool involving stationary and rotational components for the entire turbopump assembly has not been available for real-world engineering applications. The present effort provides developers with information such as transient flow phenomena at start up, and nonuniform inflows, and will eventually impact on system vibration and structures. In the proposed paper, the progress toward the capability of complete simulation of the turbo-pump for a liquid rocket engine is reported. The Space Shuttle Main Engine (SSME) turbo-pump is used as a test case for evaluation of the hybrid MPI/Open-MP and MLP versions of the INS3D code. CAD to solution auto-scripting capability is being developed for turbopump applications. The relative motion of the grid systems for the rotor-stator interaction was obtained using overset grid techniques. Unsteady computations for the SSME turbo-pump, which contains 114 zones with 34.5 million grid points, are carried out on Origin 3000 systems at NASA Ames Research Center. Results from these time-accurate simulations with moving boundary capability are presented along with the performance of parallel versions of the code.

Kiris, Cetin↗
DOE OSTI2021

Modeling of particle transport, neutrals and radiation in magnetically-confined plasmas with Aurora

In this work, we present Aurora, an open-source package for particle transport, neutrals and radiation modeling in magnetic confinement fusion plasmas. Aurora's modern multi-language interface enables simulations of 1.5D impurity transport within high-performance computing frameworks, particularly for the inference of particle transport coefficients. A user-friendly Python library allows simple interaction with atomic rates from the Atomic Data and Atomic Structure database as well as other sources. This enables a range of radiation predictions, both for power balance and spectroscopic analysis. We discuss here the superstaging approximation for complex ions, as a way to group charge states and reduce computational cost, demonstrating its wide applicability within the Aurora forward model and beyond. Aurora also facilitates neutral particle analysis, both from experimental spectroscopic data and other simulation codes. Leveraging Aurora's capabilities to interface SOLPS-ITER results, we demonstrate that charge exchange is unlikely to affect the total radiated power from the ITER core during high performance operation. Finally, we describe the ImpRad module in the one modeling framework for integrated task framework, developed to enable experimental analysis and transport inferences on multiple devices using Aurora.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Simulating Transonic Buffet Aerodynamics for the Boeing Transonic Truss-Braced Wing Aircraft

The flow past the Boeing Transonic Truss-Braced Wing (TTBW) aircraft is simulated with a Hybrid Reynolds-Averaged Navier Stokes Large-Eddy Simulations (HRLES) turbulence modeling approach to study transonic buffet onset and evaluate the predictive capability of the numerical approach. All simulations used structured overset curvilinear grids for the free-air configuration, with all simulations performed using the Launch, Ascent, and Vehicle Aerodynamics (LAVA) computational framework. Aerodynamic loads and surface pressure obtained from the HRLES solutions are compared to results from unsteady RANS (URANS) and experiments conducted at NASA Ames 11-by 11-Foot Transonic Wind Tunnel. Overall good agreement is obtained in the predicted loads and surface pressure. Unsteady pressure data from HRLES and URANS simulations is utilized to compute power spectral density (PSD) to predict transonic buffet onset and compared to the Kulite data obtained from the experiment. Comparisons of the PSD spectrum also show reasonable agreement with the wind tunnel data, with HRLES and URANS predicting buffet onset at a slightly earlier angle-of-attack than the experiment.

AATT↗
DOE OSTI2024

Early Inference of Nuclear Technology-Directed Research Activities of Authors from Scientific Publications

Nuclear research articles can provide information about early nuclear proliferation indicators such as influential research entities and technology capability levels of a country, but detection of nuclear activities typically occurs after they have started. We investigate the extent to which nuclear research articles can be used to infer whether a research entity will acquire or develop a nuclear technology before it happens. Early detection of nuclear proliferation or technology development indicators from data is challenging due to partial observability, sparse and unlabeled information, and confounding signals from multiple concurrent activities. This paper presents the early detection problem as a sequential decision-making, goal inference problem, where the objective is to characterize and predict an individual’s, organization’s, or a country’s intent (unobserved goal-directed behavior) towards developing a nuclear capability from partially observed sequences of their research publications, using inverse reinforcement learning and Bayesian goal inference methods. A computational framework is presented, and its application demonstrated using 29,196 Scopus records for a case study related to a civil nuclear capability. The case study results serve as a proof-of-concept demonstration for inference of technology-directed research activity of authors who publish in the nuclear domain. The inference method, combined with advanced computing, may be used to assess and monitor activities pertaining to early developmental stages of a nuclear technology or capability, which in turn can help to identify and prioritize activities with nuclear proliferation potential for further investigation.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Unitary Qubit Lattice Algorithms for Plasma Physics

This final technical report summarizes research conducted under DOE Award DE-SC0021653 to develop unitary Quantum Lattice Algorithms for modeling electromagnetic wave propagation and scattering in complex media, including plasmas. The project developed and validated quantum-inspired formulations of Maxwell's equations that preserve unitary evolution and can be evaluated on classical high-performance computing systems while providing a foundation for future quantum-computing implementations. Major accomplishments include the development of two- and three-dimensional algorithms for electromagnetic scattering; scalable, distributed-memory implementations demonstrated on the Perlmutter supercomputer; formulations for nonlinear lossless fluid dynamics and cold, lossless, inhomogeneous magnetized plasmas; and an explicit quantum algorithm for a time-discretized Lorenz model. Simulations reproduced a range of characteristic wave phenomena, including transient effects that are not readily apparent in conventional frequency-domain studies, demonstrating the effectiveness of the proposed approach for modeling complex electromagnetic and plasma systems. The work establishes a unified theoretical and computational framework for quantum and quantum-inspired simulation and provides a foundation for future implementation on fault-tolerant quantum systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2002

Micro-Mechanical Modeling of Ductile Fracture in Welded Aluminum-Lithium Alloys

This study describes a 3-D computational framework to model stable extension of a macroscopic crack under mode I conditions in ductile metals. Material separation occurs through a local fracture mechanism based on the growth and coalescence of micro voids dispersed in the material. The Gurson-Tvergaard plasticity model for void materials describes the eventual loss of material stress capacity under sustained loading. Fixed-sized computational cell elements defined on a thin layer adjacent to the crack plane provide an implicit length scale for continuum damage model. Outside this layer, the material remains undamaged by void growth, consistent with metallurgical observations. The key micro-mechanics parameters are D, the thickness of cell layer, and f 0 , the initial cell porosity.

Ahmed Ibrahim↗
NASA NTRS1998

The Role of Multiphysics Simulation in Multidisciplinary Analysis

This article describes the applications of the Spectrum(Tm) Solver in Multidisciplinary Analysis (MDA). Spectrum, a multiphysics simulation software based on the finite element method, addresses compressible and incompressible fluid flow, structural, and thermal modeling as well as the interaction between these disciplines. Multiphysics simulation is based on a single computational framework for the modeling of multiple interacting physical phenomena. Interaction constraints are enforced in a fully-coupled manner using the augmented-Lagrangian method. Within the multiphysics framework, the finite element treatment of fluids is based on Galerkin-Least-Squares (GLS) method with discontinuity capturing operators. The arbitrary-Lagrangian-Eulerian method is utilized to account for deformable fluid domains. The finite element treatment of solids and structures is based on the Hu-Washizu variational principle. The multiphysics architecture lends itself naturally to high-performance parallel computing. Aeroelastic, propulsion, thermal management and manufacturing applications are presented.

Rifai, Steven M.↗
DOE OSTI2022

Pathways for Electron Transfer at MgO–Water Interfaces from Ab Initio Molecular Dynamics

The nature of electron transfer across metal oxide–water interfaces depends significantly on the band gap of the oxide and its band edge energies relative to the potentials of relevant aqueous redox couples. Here we focus on the water interface with MgO, a prototypical wide band gap oxide whose conduction band edge is close in energy to that of water. We investigate the behavior of an excess electron at and out of equilibrium near the interface using ab initio molecular dynamics based on hybrid density functional theory. Our simulations show that under equilibrium conditions the excess electron (donated by an Al impurity in MgO) localizes to a midgap defect state comparable in energy and shape to a hydrated electron in bulk water. To characterize the electron transfer from the conduction band of MgO to interfacial product states, we dope near-equilibrium configurations of the pristine MgO–water system with Al and run short trajectories of these instantaneously out-of-equilibrium systems. Here we observe two distinct products associated with the excess electron: a surface-localized electron (e surf –) and an aqueous hydrogen radical (H • ). The H • pathway exhibits a much higher activation barrier despite being more exoergic, making e surf – the kinetic product. Our characterization of the pathways on the basis of Marcus theory is consistent with the poor observed utility of MgO for water radiolysis. Moreover, we anticipate that the computational framework employed here will be broadly applicable to assessing electron transfer mechanisms at aqueous, photocatalytic interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗
DOE OSTI2022

Numerical Modeling of Atmospheric Rime Ice Accretion on an Airfoil Using an Eulerian Approach

This paper presents an approach to numerically simulate the inherently unsteady rime ice accretion problem on a two-dimensional airfoil and elucidate the associated variations under different icing conditions. The airflow field and the water impingement on the airfoil are obtained based on an Eulerian two-phase model. A dynamic mesh strategy is employed to unsteadily account for the changes in the ice profile and its impact on the air and droplet flow by continuously reconstructing the computational grid at each time-step through smoothing and layering mechanisms. All main icing modules including the airflow field, droplet trajectory, icing thickness profile, and mesh management are fully coupled within the same computational framework without resorting to any external tools. Classical icing theory is employed to model the rime ice roughness, and it is assumed that the ice accretes in a direction normal to the airfoil surface. The governing Reynolds-averaged Navier–Stokes (RANS) conservation equations along with the energy and continuity equations are solved to produce the velocity and temperature fields. A convective film heat transfer coefficient is computed based on the surface heat flux and a recovery temperature which takes into account the dissipative heat release in the boundary layer in the vicinity of the airfoil surface. With the implemented strategy and calculating the convective heat transfer coefficient, the water film thickness is also calculated along with the ice shape. Furthermore, the model is validated by comparing the local collection efficiency distribution and ice shape with experimental data, and the results show that the implemented approach provides acceptable predictions of ice accretion profiles and rates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗
DOE OSTI2025

Quantum computation of SU(2) lattice gauge theory with continuous variables

We present a quantum computational framework for pure SU(2) lattice gauge theory, using continuous variables instead of discrete qubits to represent the infinite-dimensional Hilbert space of the gauge fields. We consider a ladder as well as a two-dimensional grid of plaquettes, detailing the use of gauge fixing to reduce the degrees of freedom and simplify the Hamiltonian. We demonstrate how system dynamics, ground states, and energy gaps can be computed using the continuous-variable approach to quantum computing. Our results indicate that it is feasible to study non-Abelian gauge theories with continuous variables, providing new avenues for understanding the real-time dynamics of quantum field theories.

Gauge Symmetry↗
NASA NTRS1991

Simulating motivated cognition

A research effort to develop a sophisticated computer model of human behavior is described. A computer framework of motivated cognition was developed. Motivated cognition focuses on the motivations or affects that provide the context and drive in human cognition and decision making. A conceptual architecture of the human decision-making approach from the perspective of information processing in the human brain is developed in diagrammatic form. A preliminary version of such a diagram is presented. This architecture is then used as a vehicle for successfully constructing a computer program simulation Dweck and Leggett's findings that relate how an individual's implicit theories orient them toward particular goals, with resultant cognitions, affects, and behavior.

Gevarter, William B.↗
NASA NTRS2001

Unsteady Turbopump Flow Simulations

The objective of the current effort is two-fold: 1) to provide a computational framework for design and analysis of the entire fuel supply system of a liquid rocket engine; and 2) to provide high-fidelity unsteady turbopump flow analysis capability to support the design of pump sub-systems for advanced space transportation vehicle. Since the space launch systems in the near future are likely to involve liquid propulsion system, increasing the efficiency and reliability of the turbopump components is an important task. To date, computational tools for design/analysis of turbopump flow are based on relatively lower fidelity methods. Unsteady, three-dimensional viscous flow analysis tool involving stationary and rotational components for the entire turbopump assembly has not been available, at least, for real-world engineering applications. Present effort is an attempt to provide this capability so that developers of the vehicle will be able to extract such information as transient flow phenomena for start up, impact of non-uniform inflow, system vibration and impact on the structure. Those quantities are not readily available from simplified design tools. In this presentation, the progress being made toward complete turbo-pump simulation capability for a liquid rocket engine is reported. Space Shuttle Main Engine (SSME) turbo-pump is used as a test case for the performance evaluation of the hybrid MPI/Open-MP and MLP versions of the INS3D code. Relative motion of the grid system for rotor-stator interaction was obtained by employing overset grid techniques. Time-accuracy of the scheme has been evaluated by using simple test cases. Unsteady computations for SSME turbopump, which contains 106 zones with 34.5 Million grid points, are currently underway on Origin 2000 systems at NASA Ames Research Center. Results from these time-accurate simulations with moving boundary capability and the performance of the parallel versions of the code will be presented.

Centin, Kiris C.↗
DOE OSTI2022

A finite micro-rotation material point method for micropolar solid and fluid dynamics with three-dimensional evolving contacts and free surfaces

This paper introduces an explicit material point method designed specifically for simulating the micropolar continuum dynamics in the finite deformation and finite microrotation regime. The material point method enables us to simulate large deformation problems while circumventing the potential mesh distortion without remeshing. To eliminate rotational motion damping and loss of angular momentum during the projection, we introduce the mapping for microinertia and angular momentum between particles and grids through the affine particle-in-cell approach. The microrotation and the curvature at each particle are updated through zero-order forward integration of the microgyration and its spatial gradient. We show that the microinertia and the angular momentum are conserved during the projections between particles and grids in our formulation. We verify the formulation and implementation by comparing with the analytical dispersion relation of micropolar waves under the small strain and small microrotation, as well as the analytical soliton solution for solids undergoing large deformation and large microrotation. Additionally we also demonstrate the capacity of the proposed computational framework to handle a wide spectrum of simulations that exhibit size effects in the geometrical nonlinear regime through three representative numerical examples, i.e., a cantilever beam torsion problem, a fragment-impact penetration problem, and a micropolar fluid discharging problem.

42 ENGINEERING↗
DOE OSTI2022

Quantification of gas concentrations in NO/NO 2 /C 3 H 8 /NH 3 mixtures using machine learning

We employ machine learning to decode the composition of unknown gas mixtures from the output of an array of four electrochemical sensors. The sensors use metal oxide electrodes paired with a ceramic electrolyte, yttria-stabilized zirconia (YSZ), to produce voltage responses to the presence of gases in complex mixtures. The voltages from the sensor array serve as inputs to a machine learning pipeline which first carries out multi-class classification of mixtures into types based on which gases are present at non-zero concentrations, and subsequently predicts gas concentrations given the mixture type. Thus, our model is able to take a single reading from the sensor array in response to gas mixtures involving NO, NO 2 , C 3 H 8 , and NH 3 , and output a highly accurate prediction of which gases are present in the mixture, along with the concentrations of each constituent gas. Of note, our computational framework can be easily expanded to include additional gases and additional mixture types, allowing it to be used in numerous automotive, industrial and environmental monitoring settings.

47 OTHER INSTRUMENTATION↗
NASA NTRS2018

Implementation of Topology Optimization Using OpenMDAO

Topology optimization is one of the widely known branches among the structural optimization, and it distinguishes itself by a large design domain and versatility. It can determine the optimal design out of an infinite number of configurations, thereby drawing interest from both industry and academia with regards to its applicability to additive manufacturing. However, its implementation is often a daunting task for engineers in practice. One of the issues is the programming effort required whenever the implementation requires any changes, ranging from subtle tweaks to drastic changes in the problem definition, and the derivative computation must be correspondingly updated after any change. The implementation, therefore, is not only time-consuming but also repetitive and susceptible to human-induced errors. In this regard, topology optimization implementations stand to benefit from reusability, ease of restructuring, and modularity. In this work, we propose OpenMDAO, a computational framework for multidisciplinary design optimization (MDO), as a generic platform for topology optimization. Two widely used topology optimization techniquesâ€"density-based and level-setâ€"are implemented as a demonstration. These techniques are implemented in a decomposed manner, with the aid of the modular architecture of OpenMDAO as well as state-of-the-art numerical methods. Variations on the density-based topology optimization approach are shown to demonstrate the modularity and automation for derivative computation that OpenMDAO provides.

Chung, Hayoung↗