Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computational physics applications”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Efficient computation of N -point correlation functions in D dimensions

We present efficient algorithms for computing the N-point correlation functions (NPCFs) of random fields in arbitrary D-dimensional homogeneous and isotropic spaces. Such statistics appear throughout the physical sciences and provide a natural tool to describe stochastic processes. Typically, algorithms for computing the NPCF components have $\mathscr O$(n N ) complexity (for a dataset containing n particles); their application is thus computationally infeasible unless N is small. By projecting the statistic onto a suitably defined angular basis, we show that the estimators can be written in a separable form, with complexity $\mathscr O$(n 2 ) or $\mathscr O$(n g log n g ) if evaluated using a Fast Fourier Transform on a grid of size n g . Our decomposition is built upon the D-dimensional hyperspherical harmonics; these form a complete basis on the (D – 1) sphere and are intrinsically related to angular momentum operators. Concatenation of (N – 1) such harmonics gives states of definite combined angular momentum, forming a natural separable basis for the NPCF. As N and D grow, the number of basis components quickly becomes large, providing a practical limitation to this (and all other) approaches: However, the dimensionality is greatly reduced in the presence of symmetries; for example, isotropic correlation functions require only states of zero combined angular momentum. We provide a Julia package implementing our estimators and show how they can be applied to a variety of scenarios within cosmology and fluid dynamics. The efficiency of such estimators will allow higher-order correlators to become a standard tool in the analysis of random fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Scalable Quantum Cryptography Network for Protected Automation Communication (Final Report)

This is the final report for a CEDS-funded project aimed at developing a new quantum technology for securing utility communication networks used to control and monitor electrical grid equipment. Securing these control networks represents a unique challenge as the performance of the security solution has a direct impact on the stability and reliability of the electrical grid. Traditional, software-based solutions - developed for information networks - are not suitable for utility control networks because they introduce latency, require burdensome maintenance and upgrades, are often incompatible with legacy equipment, and introduce operational complexity that reduces grid reliability. Consequently, many U.S. utilities do not use existing solutions and, instead, protect their critical control networks through the careful isolation and obscuration of their networked equipment. With more utilities embracing grid automation, the attack surface that utilities must defend from hackers has grown to an unmanageable size. To address this situation, Qubitekk and its partners proposed and developed a hardware-based solution that can secure critical control networks without negatively impacting grid performance. This new solution is based on quantum key distribution (QKD) techniques that guarantee secure key generation and distribution across a utility control network. Through deployment and field testing of a prototype QKD system, we have shown that this solution delivers long-term network security, is technically feasible to implement and maintain on a utility’s distribution substation network and does not negatively impact grid operations. In addition, the project has identified and solved key challenges associated with generating, transmitting, and measuring coherent photonic quantum states on a real-world fiber optic network. These additional findings are playing a critical role in advancing quantum networks for quantum computing applications. An overview of the QKD prototype development effort, field testing activities and results, and additional findings relevant to emerging quantum networks are presented in this report.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1998

Current and Future Critical Issues in Rocket Propulsion Systems

The objective of this research was to tackle several problems that are currently of great importance to NASA. In a liquid rocket engine several complex processes take place that are not thoroughly understood. Droplet evaporation, turbulence, finite rate chemistry, instability, and injection/atomization phenomena are some of the critical issues being encountered in a liquid rocket engine environment. Pulse Detonation Engines (PDE) performance, combustion chamber instability analysis, 60K motor flowfield pattern from hydrocarbon fuel combustion, and 3D flowfield analysis for the Combined Cycle engine were of special interest to NASA. During the summer of 1997, we made an attempt to generate computational results for all of the above problems and shed some light on understanding some of the complex physical phenomena. For this purpose, the Liquid Thrust Chamber Performance (LTCP) code, mainly designed for liquid rocket engine applications, was utilized. The following test cases were considered: (1) Characterization of a detonation wave in a Pulse Detonation Tube; (2) 60K Motor wall temperature studies; (3) Propagation of a pressure pulse in a combustion chamber (under single and two-phase flow conditions); (4) Transonic region flowfield analysis affected by viscous effects; (5) Exploring the viscous differences between a smooth and a corrugated wall; and (6) 3D thrust chamber flowfield analysis of the Combined Cycle engine. It was shown that the LTCP-2D and LTCP-3D codes are capable of solving complex and stiff conservation equations for gaseous and droplet phases in a very robust and efficient manner. These codes can be run on a workstation and personal computers (PC's).

Navaz, Homayun K.↗
DOE OSTI2023

Four-Stage Multi-Physics Simulations to Assist Temperature Sensor Design for Industrial-Scale Coal-Fired Boiler

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

42 ENGINEERING↗
DOE OSTI2024

Generating and processing optical waveforms using spectral singularities

Here, we show that a laser at threshold can be utilized to generate the class of coherent and transform-limited waveforms (vt — z) m e i(kz—ωt) at optical frequencies. We derive these properties analytically and demonstrate them in semiclassical time-domain laser simulations. We then utilize these waveforms to expand other waveforms with high modulation frequencies and demonstrate theoretically the feasibility of complex-frequency coherent absorption at optical frequencies, with efficient energy transduction and cavity loading. This approach has potential applications in quantum computing, photonic circuits, and biomedicine.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Expanding Benchmarks for Nuclear Data Validation [Slides]

The presentation discusses benchmarking in comparison to experimental truth, the integral experiments that were performed, and that benchmarks are evaluated experiments. It also discusses validation testing, and that the ultimate goal of the validation end product is to improve evaluated nuclear data for applications It does state that validation highlights errors in the Nuclear Data Pipeline that affect applications, for example: Missing Cd Capture Gammas in ENDF/B-VII.1. It acknowledges that additional types of experiments are needed to test data used in applications, and that validation experiments do not have to be complicated and expensive. The presentation also discusses different types of measurements including activation foil and fission chamber measurements, reactor kinetics measurements, and subcritical measurements. The presentation concludes with an experiment summary and states that all applications need validation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1980

Sensor/actuator failure detection and isolation for airbreathing propulsion systems

In this paper, the Generalized Likelihood Ratio (GLR) test is used to detect and isolate sensor and/or actuator failures when a digital computer simulation model of the physical system is available. The input to the GLR detector is an innovation sequence formed by subtracting the model outputs from the sensed outputs. Application of the GLR detector to the General Electric QCSEE turbofan engine demonstrates the utility of the proposed procedure.

Leininger, G. G.↗
NASA NTRS1982

Assessment Planning and Evaluation of Renewable Energy Resources: an Interactive Computer Assisted Procedure

Adaptation and derivation were used to develop a procedure for assessing the availability of renewable energy resources on the landscape while simultaneously accounting for the economic, legal, social, and environmental issues required. Done in a step-by-step fashion, the procedure can be used interactively at the computer terminals. Its application in determining the hydroelectricity, biomass, and windpower in a 40,000 acre study area of Western Massachusetts shows that: (1) three existing dam sites are physically capable of being retrofitted for hydropower; (2) each of three general areas has a mean annual windspeed exceeding 14 mph and is conductive to windpower; and (3) 20% of the total land area consists of prime agricultural biomass while 30% of the area is prime forest biomass land.

Aston, T. W.↗
NASA NTRS1989

Toward a CFD nose-to-tail capability - Hypersonic unsteady Navier-Stokes code validation

Computational fluid dynamics (CFD) research for hypersonic flows presents new problems in code validation because of the added complexity of the physical models. This paper surveys code validation procedures applicable to hypersonic flow models that include real gas effects. The current status of hypersonic CFD flow analysis is assessed with the Compressible Navier-Stokes (CNS) code as a case study. The methods of code validation discussed to beyond comparison with experimental data to include comparisons with other codes and formulations, component analyses, and estimation of numerical errors. Current results indicate that predicting hypersonic flows of perfect gases and equilibrium air are well in hand. Pressure, shock location, and integrated quantities are relatively easy to predict accurately, while surface quantities such as heat transfer are more sensitive to the solution procedure. Modeling transition to turbulence needs refinement, though preliminary results are promising.

Edwards, Thomas A.↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Geant4 Introduction and Applications Workshop

This workshop talk overviews "Introduction to Geant4 simulation toolkit" which is consist of 1) Geant4 physics coverage, 2) Connecting to external physics engines such as Penelope and FLUKA and 3) Prospect toward heterogeneous computing with GPUs.

43 PARTICLE ACCELERATORS↗
NASA NTRS2018

Hybrid Reduced Order Model for N2-N2 Interactions for Application to Dissociation and Energy Transfer Processes

This work presents a general framework for model reduction of non-equilibrium energy transfer and dissociation processes. The multi-group maximum-entropy method is coupled with the quasi-classical trajectory method to directly construct a reduced order model for chemical non-equilibrium. Kinetic data is calculated by applying detailed balance at a microscopic level, overcoming the limitation of calculating recombination kinetic data. This approach enables the construction of a reduced order model for kinetics which bypasses the need to compute state-to-state kinetic data. This physics based reduced order model ensures that an equilibrium distribution is reached given infinite time, while allowing for non-equilibrium distributions during the relaxation and dissociation processes. A proof-of-concept test case demonstrates the applicability of this model by comparison with state-to-state kinetic data for the N2(Chi (sup 1) Sigma ((sub g), (sup plus sign)) minus N ((exp 4) S (sub u)) system with excellent agreement.

Maximum Entropy↗
DOE OSTI2021

Variational Monte Carlo Calculations of A ≤ 4 Nuclei with an Artificial Neural-Network Correlator Ansatz

Here, the complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrodinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of A ≤ 4 nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually exact Green's function Monte Carlo results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Neural Network for Principle of Least Action

The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager–Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the “ground-truth” molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ 38 from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications.

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

Alloying Effects on the Transport Properties of Refractory High-entropy Alloys

Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. Here we validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluating diffusion and the thermodynamic factor for binary ionic mixtures

Molecular dynamics (MD) simulations are a powerful tool for the calculation of transport properties in mixtures. Not only are MD simulations capable of treating multicomponent systems, they are also applicable over a wide range of temperatures and densities. In plasma physics, this is particularly important for applications such as inertial confinement fusion. While many studies have focused on the effect of plasma coupling on transport properties, here we focus on the effects of mixing. We compute the thermodynamic factor, a measure of ideal/non-ideal mixing, for three binary ionic mixtures. Here, we consider mixtures of hydrogen and carbon, hydrogen and argon, and argon and carbon, each at 500 randomly generated state points in the warm dense matter and plasma regimes. The calculated thermodynamic factors indicate different mixing behavior across phase space, which can significantly affect the corresponding mutual diffusion coefficients. As MD simulations are still computationally expensive, we apply modern data science tools to predict the thermodynamic factor over a large phase space. Further, we propose a more accurate approximation to the mutual diffusion coefficient than the commonly applied Darken relation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Ab initio many-fermion structure calculations on a quantum computer

To overcome the limitations of existing algorithms for solving self-bound quantum many-body problems—such as those encountered in nuclear and particle physics—that access only a restricted subset of energy levels and provide limited structural information, we introduce and demonstrate a novel quantum-classical approach capable of resolving the complete bound-state spectrum. This method also provides the total angular momentum 𝐽 associated with each eigenstate. Here, our approach is based on expressing the Hamiltonian in second-quantized form within a novel input model combined with a scan scheme, enabling broad applicability to configuration-interaction calculations across diverse fields. We apply this hybrid method to compute, for the first time, the bound-state spectrum together with corresponding 𝐽 values of 20 O using a realistic strong-interaction Hamiltonian. Our approach applies to hadron spectra and 𝐽 values solved in the relativistic basis light-front quantization approach.

Du, Weijie [Chinese Academy of Sciences (CAS), Lan↗
NASA NTRS1997

Efficient Multidisciplinary Analysis Procedure Using Multi-Level Parallelization Approach

Multidisciplinary applications are suitable for parallel computing environment by adopting the domain decomposition method. Immediately, a multidisciplinary application can be parallelized by solving each discipline separately. In order to perform coupled multidisciplinary analysis, coupling of each discipline can be accomplished by exchanging boundary data at the interfaces. This is regarded as discipline-level parallelization. Next level could be a "coarse-grain" parallelization of each discipline, which mainly depends on the physical geometry and nature of each discipline. For example, it is almost impossible for structured-grid based computational fluid dynamics codes to do flow analysis of an aircraft by using a single grid because of the complexity of its configuration. Thus, multi-block grid is commonly used to describe the details of complex geometry. Similarly, in structural analysis, the structure is frequently subdivided into substructures. Thus, the computation of each subdomain can be easily parallelized since each subdomain is solved separately independent of other domains. The parallelization is accomplished by solving each subdomain separately on a separate processor and exchanging the boundary conditions at domain interfaces periodically. However, the physical decomposition of the domain introduces explicit boundary conditions at the domain interfaces. This is not desirable for critical areas such as those containing shock waves or flow separations. Thus, a "fine-grain" parallelization is introduced to overcome this problem. The "fine-grain" parallelization is one that solves exactly the same system of equations of a subdomain by using more than one processors without introducing any explicit boundary conditions. An efficient multidisciplinary analysis procedure can be accomplished by successfully combining the above multi-level parallelism. A multidisciplinary analysis code, ENSAERO developed at NASA Ames Research Center is used in this study to implement the proposed approach. The communication data structure required for the proposed approach will be studied in detail. This work will demonstrate the feasibility of using multi-level parallelization approach in multidisciplinary analysis applications.

Byun, Chansup↗