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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Provable Security and Resilience in Critical Infrastructure – Next Steps

With the conclusion of the Laboratory Directed Research and Development (LDRD) project on Provable Security and Resilience (PSaR) in Critical Infrastructure, we present forward-looking technical concepts and strategies that build on the project’s outcomes and INL’s long-standing expertise in infrastructure protection. The challenge is to protect critical infrastructure and functions much more efficiently at scale than capable adversaries can attack at scale. After summarizing progress and ongoing work we’ll discuss what are the challenges that remain and what are new/emerging technologies, strategies, and processes to meet those challenges. Finally, we’ll layout concepts that integrate with other protection work in the coming year and beyond. For example, building secure function-specific platforms based on the seL4 microkernel, and considering the successes of Cyber-Informed Engineering as a model for engage, collaboration, and adoption. We look forward to your feedback and collaboration as we refine and expand this vision.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A detailed modeling study of radiative heat transfer in a heavy-duty diesel engine

In recent years, the importance of radiative heat transfer in combustion has been increasingly recognized. Detailed models have become available that accurately represent the complex spectral radiative properties of reacting gas mixtures and soot particles, and new methods have been developed to solve the radiative transfer equation (RTE). At the same time, the trends toward higher operating pressures and higher levels of exhaust-gas recirculation in compression-ignition engines, together with the demand for higher quantitative accuracy from in-cylinder CFD models, has led to renewed interest in radiative transfer in engines. In this report an in-depth investigation of radiative heat transfer is performed for a heavy-duty diesel truck engine over a range of operating conditions. Results from 10 different combinations of turbulent combustion models, spectral radiation property models, and RTE solvers are compared to provide insight into the global influences of radiation on energy redistribution in the combustion chamber, heat losses, and engine-out pollutant emissions (NO and soot). Also, the relative importance of the individual contributions of molecular gas versus soot radiation, the spectral model, the RTE solver, and unresolved turbulent fluctuations in composition and temperature (turbulence–radiation interactions – TRI) are investigated. Local instantaneous temperatures change by as much as 100 K with consideration of radiation, but the global influences of radiation on heat losses and engine-out emissions are relatively small (in the 5–10% range). Molecular gas radiation dominates over soot radiation, consideration of spectral properties is essential for accurate predictions of reabsorption, a simple RTE solver (a first-order spherical harmonics – P1 – method) is sufficient for the conditions investigated, and TRI effects are small (less than 10%). While the global influences of radiation are relatively small, it is nevertheless desirable to explicitly account for radiation in in-cylinder CFD. To that end, a simplified CFD radiation model has been proposed, based on the findings reported here.

42 ENGINEERING↗
NASA NTRS2008

Intelligent Elements for ISHM

There are a number of architecture models for implementing Integrated Systems Health Management (ISHM) capabilities. For example, approaches based on the OSA-CBM and OSA-EAI models, or specific architectures developed in response to local needs. NASA s John C. Stennis Space Center (SSC) has developed one such version of an extensible architecture in support of rocket engine testing that integrates a palette of functions in order to achieve an ISHM capability. Among the functional capabilities that are supported by the framework are: prognostic models, anomaly detection, a data base of supporting health information, root cause analysis, intelligent elements, and integrated awareness. This paper focuses on the role that intelligent elements can play in ISHM architectures. We define an intelligent element as a smart element with sufficient computing capacity to support anomaly detection or other algorithms in support of ISHM functions. A smart element has the capabilities of supporting networked implementations of IEEE 1451.x smart sensor and actuator protocols. The ISHM group at SSC has been actively developing intelligent elements in conjunction with several partners at other Centers, universities, and companies as part of our ISHM approach for better supporting rocket engine testing. We have developed several implementations. Among the key features for these intelligent sensors is support for IEEE 1451.1 and incorporation of a suite of algorithms for determination of sensor health. Regardless of the potential advantages that can be achieved using intelligent sensors, existing large-scale systems are still based on conventional sensors and data acquisition systems. In order to bring the benefits of intelligent sensors to these environments, we have also developed virtual implementations of intelligent sensors.

Schmalzel, John L.↗
NASA NTRS1993

Liquid propellant rocket engine combustion simulation with a time-accurate CFD method

Time-accurate computational fluid dynamics (CFD) algorithms are among the basic requirements as an engineering or research tool for realistic simulations of transient combustion phenomena, such as combustion instability, transient start-up, etc., inside the rocket engine combustion chamber. A time-accurate pressure based method is employed in the FDNS code for combustion model development. This is in connection with other program development activities such as spray combustion model development and efficient finite-rate chemistry solution method implementation. In the present study, a second-order time-accurate time-marching scheme is employed. For better spatial resolutions near discontinuities (e.g., shocks, contact discontinuities), a 3rd-order accurate TVD scheme for modeling the convection terms is implemented in the FDNS code. Necessary modification to the predictor/multi-corrector solution algorithm in order to maintain time-accurate wave propagation is also investigated. Benchmark 1-D and multidimensional test cases, which include the classical shock tube wave propagation problems, resonant pipe test case, unsteady flow development of a blast tube test case, and H2/O2 rocket engine chamber combustion start-up transient simulation, etc., are investigated to validate and demonstrate the accuracy and robustness of the present numerical scheme and solution algorithm.

Chen, Y. S.↗
DOE OSTI2021

Evaluation of residual gas fraction estimation methods for cycle-to-cycle combustion variability analysis and modeling

Cycle-to-cycle combustion variability in spark-ignition engines during normal operation is mainly caused by random perturbations of the in-cylinder conditions such as the flow velocity field, homogeneity of the air-fuel distribution, spark energy discharge, and turbulence intensity of the flame front. Such perturbations translate into the variability of the energy released observed at the end of the combustion process. During normal operating conditions, the cycle-to-cycle variability (CCV) of the energy release behaves as random uncorrelated noise. However, during diluted combustion, in either the form of exhaust gas recirculation (EGR) or excess air (lean operation), the CCV tends to increase as dilution increases. Moreover, when the ignition limit is reached at high dilution levels, the combustion CCV is exacerbated by sporadic occurrences of incomplete combustion events, and the uncorrelation assumption no longer holds. The low or null energy released by partial burns and misfires has an impact on the following combustion event due to the residual gas that carries burned and unburned gases, which contributes to the deterministic coupling between engine cycles. Many residual gas fraction estimation methods, however, only address the nominal case where complete combustion occurs and combustion events are uncorrelated. Here we evaluate the efficacy of such methods on capturing the effects of partial burns and misfires on the residual gas estimate for high-EGR operation. The advantages and disadvantages of each method are discussed based on their ability to generate cycle-to-cycle estimates. Finally, a comparison between the different estimation techniques is presented based on their usefulness for control-oriented modeling.

42 ENGINEERING↗
DOE OSTI2017

An Integrated Approach to Scaling Task-Based Runtime Systems for Next Generation Engineering problems

The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. Such problems may have complex coupled multi-physics models, as well as computationally and communication intensive algorithms not often seen in standard academic problems, while also needing high-demand I/O for analysis purposes. In such cases even codes with good existing scaling properties may need significant changes, addressed in a cross-cutting way to solve such problems at extreme scales. These challenges relate to system components that were not previously problematic on less complex problems and/or at smaller computational scales. This paper illustrates these challenges for Uintah, a highly scalable asynchronous many-task runtime system being applied to the modeling of a 1000 MWe ultra-supercritical coal boiler. In order to model this formidable, production engineering problem, not only was an integrated approach needed that built upon existing scalable components in areas such as complex stencil calculations and linear algebra, but the significant challenges of high demand I/O, complex task graphs, and infrastructure inefficiencies also had to be addressed. This integrated approach allowed this problem to run on 256K CPU cores on Mira, with good weak scaling to 512K CPU cores and similar strong scaling, except for radiation. The need for strong scaling of a new ray tracing-based radiation model required substantial Uintah infrastructure improvements before 119K CPU cores and 7.5K GPUs on Titan could be used, with scaling out to 256K CPU cores and 16K GPUs. The resulting code demonstrates not only excellent overall scalability but a significant improvement in overall performance for the full-scale, production boiler problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Uncertainty and sensitivity analysis of fission gas behavior in engineering-scale fuel modeling

The role of uncertainties in fission gas behavior calculations as part of engineering-scale nuclear fuel modeling is investigated using the BISON fuel performance code and a recently implemented physics-based model for the coupled fission gas release and swelling. Through the integration of BISON with the DAKOTA software, a sensitivity analysis of the results to selected model parameters is carried out based on UO2 single-pellet simulations covering different power regimes. The parameters are varied within ranges representative of the relative uncertainties and consistent with the information from the open literature. The study leads to an initial quantitative assessment of the uncertainty in fission gas behavior modeling with the parameter characterization presently available. Also, the relative importance of the single parameters is evaluated. Moreover, a sensitivity analysis is carried out based on simulations of a fuel rod irradiation experiment, pointing out a significant impact of the considered uncertainties on the calculated fission gas release and cladding diametral strain. The results of the study indicate that the commonly accepted deviation between calculated and measured fission gas release by a factor of 2 approximately corresponds to the inherent modeling uncertainty at high fission gas release. Nevertheless, higher deviations may be expected for values around 10% and lower. Implications are discussed in terms of directions of research for the improved modeling of fission gas behavior for engineering purposes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1992

Combustion instability analysis for liquid propellant rocket engines

The multi-dimensional numerical model has been developed to analyze the nonlinear combustion instabilities in liquid-fueled engines. The present pressure-based approach can handle the implicit pressure-velocity coupling in a non-iterative way. The additional scalar conservation equations for the chemical species, the energy, and the turbulent transport quantities can be handled by the same predictor-corrector sequences. This method is time-accurate and it can be applicable to the all-speed, transient, multi-phase, and reacting flows. Special emphasis is given to the acoustic/vaporization interaction which may act as the crucial rate-controlling mechanism in the liquid-fueled rocket engines. The subcritical vaporization is modeled to account for the effects of variable thermophysical properties, non-unitary Lewis number in the gas-film, the Stefan flow effect, and the effect of transient liquid heating. The test cases include the one-dimenisonal fast transient non-reacting and reacting flows, and the multi-dimensional combustion instabilities encountered in the liquid-fueled rocket thrust chamber. The present numerical model successfully demonstrated the capability to simulate the fast transient spray-combusting flows in terms of the limiting-cycle amplitude phenomena, correspondence between combustion and acoustics, and the steep-fronted wave and flame propagation. The investigated parameters include the spray initial conditions, air-fuel mixture ratios, and the engine geometry. Stable and unstable operating conditions are found for the liquid-fueled combustors. Under certain conditions, the limiting cycle behavior of the combusting flowfields is obtained. The numerical results indicate that the spray vaporization processes play an important role in releasing thermal energy and driving the combustion instability.

Kim, Y. M.↗
DOE OSTI2017

Particle tracking acceleration via signed distance fields in direct-accelerated geometry Monte Carlo

Computer-aided design (CAD)-based Monte Carlo radiation transport is of value to the nuclear engineering community for its ability to conduct transport on high-fidelity models of nuclear systems, but it is more computationally expensive than native geometry representations. This work describes the adaptation of a rendering data structure, the signed distance field, as a geometric query tool for accelerating CAD-based transport in the direct-accelerated geometry Monte Carlo toolkit. Demonstrations of its effectiveness are shown for several problems. The beginnings of a predictive model for the data structure's utilization based on various problem parameters is also introduced.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

Turbomachinery noise

Summarized here are key advances in experimental techniques and theoretical applications which point the way to a broad understanding and control of turbomachinery noise. On the experimental side, the development of effective inflow control techniques makes it possible to conduct, in ground based facilities, definitive experiments in internally controlled blade row interactions. Results can now be valid indicators of flight behavior and can provide a firm base for comparison with analytical results. Inflow control coupled with detailed diagnostic tools such as blade pressure measurements can be used to uncover the more subtle mechanisms such as rotor strut interaction, which can set tone levels for some engine configurations. Initial mappings of rotor wake-vortex flow fields have provided a data base for a first generation semiempirical flow disturbance model. Laser velocimetry offers a nonintrusive method for validating and improving the model. Digital data systems and signal processing algorithms are bringing mode measurement closer to a working tool that can be frequently applied to a real machine such as a turbofan engine. On the analytical side, models of most of the links in the chain from turbomachine blade source to far field observation point have been formulated. Three dimensional lifting surface theory for blade rows, including source noncompactness and cascade effects, blade row transmission models incorporating mode and frequency scattering, and modal radiation calculations, including hybrid numerical-analytical approaches, are tools which await further application.

Groeneweg, John F.↗
DOE OSTI2021

Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report)

This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

42 ENGINEERING↗
NASA NTRS2001

Modeling of Multi-Tube Pulse Detonation Engine Operation

The present paper explores some preliminary issues concerning the operational characteristics of multiple-tube pulsed detonation engines (PDEs). The study is based on a two-dimensional analysis of the first-pulse operation of two detonation tubes exhausting through a common nozzle. Computations are first performed to assess isolated tube behavior followed by results for multi-tube flow phenomena. The computations are based on an eight-species, finite-rate transient flow-field model. The results serve as an important precursor to understanding appropriate propellant fill procedures and shock wave propagation in multi-tube, multi-dimensional simulations. Differences in behavior between single and multi-tube PDE models are discussed, The influence of multi-tube geometry and the preferred times for injecting the fresh propellant mixture during multi-tube PDE operation are studied.

Ebrahimi, Houshang B.↗
DOE OSTI2025

Modelling pulsed field magnetization of iron-based bulk superconductors

Abstract Bulk superconductors can be used as super-strength quasi-permanent magnets capable of providing magnetic flux densities considerably superior to conventional permanent magnets. This makes them attractive for several engineering applications that rely on strong magnetic fields like rotating machines, NMR/MRI and magnetic drug delivery systems. Recently, the authors reported a record trapped magnetic field in an iron-based bulk superconductor: 2.83 T was trapped in potassium-doped barium iron arsenide (Ba, K)Fe 2 As 2 (or Ba122) at 5 K. Of particular significance is that the strength and temporal stability of this magnetic field exceeds the requirements of MRI machines, indicating iron-based bulks can now perform at levels demanded by engineering applications. One crucial challenge for their practical use, however, is the need to apply and remove an external magnetic field to magnetize them. Pulsed field magnetization (PFM) shows great promise as a practical method of magnetizing bulks, but the process generates heat in the bulk that is detrimental to its superconducting performance and ability to act as a super-strength magnet. In this paper, coupled electromagnetic–thermal numerical models are used to simulate the PFM of iron-based bulk superconductors. Here we focus on the recent-record-breaking, fine-grain polycrystalline K-doped Ba122 bulks. The impact that the specific J c ( B ) characteristics and thermal properties of the Ba122 material—all of which have been experimentally measured from state-of-the-art samples—have on the magnetic flux dynamics and thermal behaviour during PFM, including the final trapped field, is investigated. We show that because the thermal properties are similar to those of REBa 2 Cu 3 O 7 −δ bulks, a similar response to pulsed fields is obtained. A maximum trapped field of ∼0.81 T (∼43.3% of the maximum trapped field capability under ideal, field-cooling conditions) was simulated at 5 K, with a magnetization efficiency of ∼54%. The modelling framework provides a fast and flexible tool for optimising the practical PFM process at different operating temperatures to maximise the trapped field in state-of-the-art Ba122 bulks and to guide the design of future experiments.

bulk superconductors↗
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗
DOE OSTI2021

Evaluating causal‐based feature selection for fuel property prediction models

Abstract In‐silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench‐scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation‐based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal‐based feature selection might have on both model performance and identification of key molecular substructures. We found that causal‐based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.

Nguyen, Bernard↗
DOE OSTI2024

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this chapter, we focus on a particular framework known as Latent Space Dynamics Identification (LaSDI), which transforms the high-fidelity data, governed by a PDE, to simpler and low-dimensional latent-space data, governed by ordinary differential equations (ODEs). These ODEs can be learned and subsequently interpolated to make ROM predictions. Each building block of LaSDI can be easily modulated depending on the application, which makes the LaSDI framework highly flexible. In particular, we present strategies to enforce the laws of thermodynamics into LaSDI models (tLaSDI), enhance robustness in the presence of noise through the weak form (WLaSDI), select high-fidelity training data efficiently through active learning (gLaSDI, GPLaSDI), and quantify the ROM prediction uncertainty through Gaussian processes (GPLaSDI). We demonstrate the performance of different LaSDI approaches on Burgers equation, a non-linear heat conduction problem, and a plasma physics problem, showing that LaSDI algorithms can achieve relative errors of less than a few percent and up to thousands of times speed-ups.

Computational Engineering, Finance, and Science (c↗
NASA NTRS1986

A dynamical model for the central engine of QSOs and active galactic nuclei

A model for the energy generation in quasars and active galaxies is proposed based on (quasi)spherical accretion and a shock as a means for randomizing the inflowing kinetic energy. According to the model, most of the accretion energy is converted into relativistic protons at the shock, which in turn can provide the necessary pressure to self-consistently support it if their energy loss time scale by nuclear collisions is longer than the free fall time scale. The shock can thus be characterized as a 'relativistic proton radiative shock' in analogy with similar accretion shocks on white dwarfs.

Kazanas, Demosthenes↗
DOE OSTI2016

SU-F-T-285: Evaluation of a Patient DVH-Based IMRT QA System

Purpose: To evaluate the clinical performance of a patient DVH-based QA system for prostate VMAT QA. Methods: Mobius3D(M3D) is a QA software with an independent beam model and dose engine. The MobiusFX(MFX) add-on predicts patient dose using treatment machine log files. We commissioned the Mobius beam model in two steps. First, the stock beam model was customized using machine commissioning data, then verified against the TPS with 12 simple phantom plans and 7 clinical 3D plans. Secondly, the Dosimetric Leaf Gap(DLG) in the Mobius model was fine-tuned for VMAT treatment based on ion chamber measurements for 6 clinical VMAT plans. Upon successful commissioning, we retrospectively performed IMRT QA for 12 VMAT plans with the Mobius system as well as the ArcCHECK-3DVH system. Selected patient DVH values (PTV D95, D50; Bladder D2cc, Dmean; Rectum D2cc) were compared between TPS, M3D, MFX, and 3DVH. Results: During the first commissioning step, TPS and M3D calculated target Dmean for 3D plans agree within 0.7%±0.7%, with 3D gamma passing rates of 98%±2%. In the second commissioning step, the Mobius DLG was adjusted by 1.2mm from the stock value, reducing the average difference between MFX calculation and ion chamber measurement from 3.2% to 0.1%. In retrospective prostate VMAT QA, 5 of 60 MFX calculated DVH values have a deviation greater than 5% compared to TPS. One large deviation at high dose level was identified as a potential QA failure. This echoes the 3DVH QA result, which identified 2 instances of large DVH deviation on the same structure. For all DVH’s evaluated, M3D and MFX show high level of agreement (0.1%±0.2%), indicating that the observed deviation is likely from beam modelling differences rather than delivery errors. Conclusion: Mobius system provides a viable solution for DVH based VMAT QA, with the capability of separating TPS and delivery errors.

60 APPLIED LIFE SCIENCES↗