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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Field Sensitivity Analysis of Turbulence Model Parameters for Flow Over a Wing

Reynolds-averaged-Navier-Stokes (RANS) turbulence models are a critical tool in computational-fluid-dynamics simulations of aerodynamic systems, but simulation results can be highly sensitive to RANS-model parameter choices. Sensitivity analysis can be used to quantify these impacts, and the objective of this study is to demonstrate field sensitivity analysis with respect to ten parameters in the 2003 Menter shear-stress-transport (SST) turbulence model. Here, the analysis is demonstrated for an application relevant to wind energy, namely, flow over a NACA 0015 wing at 12 degree angle of attack and a Reynolds number of 1.5 x 10 6 . We quantify sensitivity using Sobol indices and the mean-squared gradient, which are estimated using polynomial chaos and active subspace models, respectively. Our results indicate that there are substantial spatial variations in parameter sensitivities, with different sets of most-sensitive parameters near the wing, as well as in the downstream wake, consistent with the physical interpretations of the turbulence model inputs. We show that, for this particular turbulence model and flow, simultaneous dimension reduction is possible across all quantities of interest, enabling efficient exploration of model outcomes. Ultimately, this analysis provides new insights into turbulence model parameter sensitivities in incompressible flows, and also demonstrates the implementation of field sensitivity analysis for applications relevant to aerodynamics simulations.

17 WIND ENERGY↗
DOE OSTI2025

High-current-density electrosynthesis of formate from captured CO 2 solution by MOF-derived bismuth nanosheets

Greenhouse gas emissions present a significant challenge to humanity, and utilizing renewable electricity to convert emitted CO 2 into value-added products offers a promising solution; however, traditional CO 2 capture and regeneration processes remain energy-intensive, restricting the overall system efficiency and decarbonization efficacy. In this study, an advanced direct reduction of captured CO 2 with large current densities for formate electrosynthesis was demonstrated without the need for CO 2 regeneration or compression. The bismuth nanosheet (DRM-BiNS) was synthesized by direct reduction of a Bi-based MOF, representing a new class of catalytic materials with a large surface area and interconnected pores, suitable for the direct reduction of captured CO 2 . By seamlessly combining experimentation and simulation, insights into the structure-parameter-performance relation were acquired in a flow cell setting, including critical membrane-electrode distance, cell orientation, and pumping flow rate. Important flow-cell components, such as catholyte volume, electrode substrate, membrane choice, and ionomer type, were also carefully examined to enhance the cell performance. In sharp contrast to prior studies limited to current densities below 20 mA/cm 2 in bicarbonate-based captured CO 2 solutions, this work demonstrates a remarkable current density of 300 mA/cm 2 with an FE to formate comparable to the case with gas-fed CO 2 reduction. Moreover, the process sustained an FE above 50% at a high current density of 500 mA/cm 2 . The DRM-BiNS catalyst exhibited outstanding selectivity, activity, and stability, significantly outperforming oxide-derived bismuth nanosheets (OD-BiNS) in captured CO 2 reduction. Furthermore, these findings offer critical insights into the development of sustainable and scalable CO 2 utilization technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1987

Adaptive control and noise suppression by a variable-gain gradient algorithm

An adaptive control system based on normalized LMS filters is investigated. The finite impulse response of the nonparametric controller is adaptively estimated using a given reference model. Specifically, the following issues are addressed: The stability of the closed loop system is analyzed and heuristically established. Next, the adaptation process is studied for piecewise constant plant parameters. It is shown that by introducing a variable-gain in the gradient algorithm, a substantial reduction in the LMS adaptation rate can be achieved. Finally, process noise at the plant output generally causes a biased estimate of the controller. By introducing a noise suppression scheme, this bias can be substantially reduced and the response of the adapted system becomes very close to that of the reference model. Extensive computer simulations validate these and demonstrate assertions that the system can rapidly adapt to random jumps in plant parameters.

Merhav, S. J.↗
NASA NTRS1985

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Murphy, Patrick Charles↗
DOE OSTI2020

Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ 1 -regularization

We develop a new data-driven paradigm for efficient model reduction of a broad class of nonlinear dynamical systems. Our model reduction method directly enables the interpretation of key components of the dynamical system, unlike traditional projection-based model reduction methods that focus on reducing computational complexity more than interpretability. Our method is not application specific and is simple to implement on nonlinear dynamical systems arising from a variety of different fields. It requires minimal parameterization using a single parameter to trade-off between model complexity and estimation error. We use a data-driven paradigm to formulate model reduction as an efficient convex optimization problem that scales polynomially in the original size of the complex system, enabling systems with as many as thousands of components to be reduced in a matter of minutes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance-reduction techniques that directly bias particle direction have historically received limited attention despite being useful for many radiation transport applications such as those in which radiation travels through large regions with a low probability of colliding. One such technique is known as DXTRAN (short for deterministic transport) in the MCNP Monte Carlo radiation transport code. Until now, effectively applying DXTRAN in calculations is largely based on empirical observations of computational performance. Optimal DXTRAN parameters are identified through manual iteration. This work develops new mathematical descriptions of the DXTRAN variance-reduction process and demonstrates a new automated variance-reduction method that applies these mathematical formulations to determine the optimal application of DXTRAN in a given problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1980

Geophysical and astronomical models applied in the analysis of very long baseline interferometry

Very long baseline interferometry presents an opportunity to measure at the centimeter level such geodetic parameters as baseline length and instantaneous pole position. In order to achieve such precision, the geophysical and astronomical models used in data analysis must be as accurate as possible. The Mark-3 interactive data analysis system includes a number of refinements beyond conventional practice in modeling precession, nutation, diurnal polar motion, UT1, solid Earth tides, relativistic light deflection, and reduction to solar system barycentric coordinates. The algorithms and their effects on the recovered geodetic, geophysical, and astrometric parameters are discussed.

Ma, C.↗
NASA NTRSDate not supplied

Real-Time Drag Optimization of Aspect Ratio 13.5 Common Research Model with Distributed Flap System

This paper presents a real-time drag optimization study of the aspect ratio 13.5 Common Research Model (CRM) with a distributed mini-plain flap system. A surrogate aerodynamic model of the aspect ratio 13.5 CRM is developed based on an aerodynamic database computed by a transonic panel method to capture the aerodynamic coefficients as functions of the angle of attack, flap deflections, and Mach number. A recursive least-squares parameter estimation algorithm is designed to estimate the aerodynamic parameters of the surrogate model. The estimated surrogate model is then used in an on-line drag optimization strategy based on an adjoint method. The results of the real-time drag optimization indicate a drag reduction of 2.46% for Mach 0.80, 3.37% for Mach 0.85, and 1.95% for Mach 0.88. The results show the convergence of all parameter estimates.

Drag Optimization↗
DOE OSTI2026

Variance reduction in lattice QCD observables via normalizing flows

Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters. This work implements the approach for observables involving gluonic operator insertions in the SU(3) Yang-Mills theory and two-flavor QCD in four space-time dimensions. Variance reduction by factors of 10–60 is achieved in glueball correlation functions and in gluonic matrix elements related to hadron structure, with demonstrated computational advantages. The observed variance reduction is found to be approximately independent of the lattice volume, so volume transfer can be utilized to minimize training costs.

Abbott, Ryan [Columbia U.; MIT, Cambridge, CTP; IA↗
NASA NTRS1978

Instrumental variables algorithm for modal parameter identification in flutter testing

The paper is concerned with the task of estimating modal parameters from system response measurement in aircraft flutter testing. A frequency-domain derivation of an instrumental-variables algorithm is presented for a linear time-invariant dynamic system of order n. Basically, this algorithm fits a set of poles and zeros to the measured transfer function. An illustrative example is provided regarding the application of the algorithm to aeroelasticity testing. It is shown that the algorithm can be implemented for on-line data reduction with a microcomputer-based analysis system. By using instrumental variables the sensitivity of the modal parameter estimates to noise in the system-response measurements is reduced greatly. The algorithm is expected to be a powerful and valuable tool for on-line estimation of modal parameters in flutter testing and should be useful in control system and structural dynamics tests.

Johnson, W.↗
DOE OSTI2021

Prediction of moisture transfer in cement-based materials: Use of a porous network model to access transfer parameters

Knowing the water transport phenomena is fundamental for understanding the durability of cement-based materials. The moisture content within cementitious materials is of primary importance because it has a direct influence on mechanical (shrinkage, creep) and chemical (penetration of aggressive agents) degradation. Many models based on the determination of the transfer properties of the material under study already exist but require fitting of the parameters to find the experimental kinetics. Transfer parameters are generally estimated from the empirical approaches developed for soils by Van Genuchten and the water vapour diffusion reduction factor is often calculated via Millington's semi-empirical model. In this paper, we make a proposal to improve the prediction of moisture transfer, through mechanisms such as drying, capillary imbibition and water vapour permeability in cement-based materials, by using a porous network model to access the transfer parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tuning transition metal nanoparticles on a non-traditional support via experimental design

The ability to control metal nanoparticle size and morphology on supported catalysts is crucial for optimizing catalytic performance in targeted applications. Here, this work presents a systematic approach for tuning Ni particle and crystallite size on an unconventional, low-porosity silica fume support through select thermal treatments. The catalyst was synthesized via the deposition of nickelocene onto silica fume, resulting in well-dispersed Ni nanoparticles. A face-centered central composite design was employed to systematically assess the effects of time, temperature, and sintering gas environment on metal particle growth. The results demonstrate that the sintering gas environment is the primary factor governing particle and crystallite evolution, with temperature as the next most significant influence. Nickel nanoparticles sintered at temperatures of 650 °C and above under inert conditions exhibited substantial growth and polycrystalline structures, whereas samples treated in oxidative environments formed NiO, restricting particle mobility. Minimally oxidative (500 ppm O₂) environments facilitated rapid sintering while effectively removing residual ligands from the one-step nickelocene deposition process. Extensive structural characterization via a combination of scanning transmission electron microscopy, X-ray diffraction, hydrogen temperature programmed reduction, and small-angle X-ray scattering revealed that oxidative treatments enhanced metal-support interactions, as evidenced by increased reduction temperatures and narrower particle size distributions. These findings establish quantitative relationships between sintering parameters and Ni nanoparticle characteristics, providing a framework for rational catalyst design through controlled thermal treatments. This methodology is broadly applicable to other catalytic systems and provides a quantitative foundation for catalyst design.

CVD↗
DOE OSTI2023

Limited-constraint WAAM fixture for hybrid manufacturing

This paper describes a limited-constraint alternative to the traditional over-constrained build plate clamping in metal additive manufacturing. A fixture design is presented that enables build plate thermal growth within its plane while restricting deformation perpendicular to that plane. A prototype fixture is designed, fabricated, and tested for wire arc additive manufacturing (WAAM). Experimental results are presented for depositing 1.2 mm diameter 5356 aluminum wire on a 5052 aluminum build plate. The build plate deformation is measured for various clamping torques and a 77.3% reduction in distortion is achieved using the limited-constraint approach with identical WAAM parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pulse and Polarization Structures in Axion-Converted X-Rays from Pulsars

Neutron stars (NSs) with their strong magnetic fields and hot dense cores could be powerful probes of axions, a classic benchmark of feebly coupled new particles, through abundant production of axions with the axion-nucleon coupling and subsequent conversion into x-rays due to the axion-photon coupling. In this Letter, we point out that the pulsation structures in both the intensity and polarization of x-rays from NSs could provide us additional information about axions and their couplings. We develop new analytical formalisms of pulsation-polarization structure applicable to a wide range of NSs in the axion scenario and argue that they hold in complicated astrophysical environments. As a case study, we apply our formalism to a representative X-ray Dim Isolated Neutron Star, RX J1856.6-3754, with an unexpected hard x-ray excess which might be axion induced. We show with an updated fit that the axion explanation is compatible with both the intensity and pulsation data available, and combining the pulsation data does not shift the posterior by more than 1⁢𝜎. Yet, the preferred parameter space is close to being excluded by other astrophysical constraints. With a 75% reduction of the uncertainties in the pulsation data, we could potentially draw a definite conclusion on the axion-induced x-rays at more than 3⁢𝜎 level.

Axions↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗
DOE OSTI2020

Thermal Management for FPGA Nodes in HPC Systems

The integration of FPGAs into large-scale computing systems is gaining attention. In these systems, real-time data handling for networking, tasks for scientific computing, and machine learning can be executed with customized datapaths on reconfigurable fabric within heterogeneous compute nodes. At the same time, thermal management, particularly battling the cooling cost and guaranteeing the reliability, is a continuing concern. The introduction of new heterogeneous components into HPC nodes only adds further complexities to thermal modeling and management. The thermal behavior of multi-FPGA systems deployed within large compute clusters is less explored. Here, we first show that the thermal behaviors of different FPGAs of the same generation can vary due to their physical locations in a rack and process variation, even though they are running the same tasks. We present a machine learning–based model to capture the thermal behavior of each individual FPGA in the cluster. We then propose two thermal management strategies guided by our thermal model. First, we mitigate thermal variation and hotspots across the cluster by proactive thermal-aware task placement. Under the tested system and benchmarks, we achieve up to 26.4° C and on average 13.3° C system temperature reduction with no performance penalty. Second, we utilize this thermal model to guide HLS parameter tuning at the task design stage to achieve improved thermal response after deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Fast Computational Algorithms for Partial Differential Equations and Uncertainty Quantifications

This project concerned the construction, testing and analysis of computational algorithms for solving parameterized and stochastic partial differential equations. The study and understanding of equations of this type is of fundamental importance in numerous engineering and scientific applications. Examples include simulation of plasma dynamics in models of electric propulsion and nuclear fusion, simulation of multiphase flows, such as the flow of water, gas and oil in reservoirs, and structural analysis of the dependence of structures on materials. Parametrization is used in such settings when properties of the models such as viscosity of fluids or electric resistivity of materials are not precisely understood and instead are treated as random variables. The resulting solutions are themselves random, and having such solutions will enable engineers to use probabilistic methods to assess the likelihood of events, for example, whether a pollutant in a liquid will exceed a limit, and to use such analyses to develop ways to ensure positive outcomes. Construction of accurate (high resolution) computational solutions is expensive, requiring significant computer time and computational resources, and there is need to reduce computational cost to make simulation useful and effective. The aim of the project was to construct algorithms to efficiently compute surrogate solutions to parameterized problems to allow for efficient and accurate simulation. The technical approach used focused on two related strategies, based on rank-reduction methods and reduced-order models. These methods construct surrogate solutions of parameter-dependent models by projection or interpolation into low-dimensional approximation spaces. Cost savings are achieved if the low-dimensional spaces can be identified and constructed efficiently and if the resulting low-dimensional algebraic systems can be solved cheaply. Accomplishments include: Theoretical and empirical demonstration of the effectiveness of fast multigrid solution strategies for computing low-rank representations of parameter-dependent solutions to discrete partial differential equations, including the first proof establishing so-called textbook convergence properties for low-rank methods. Development of efficient solution algorithms for solving nonlinear parameter-dependent partial differential equations used in models of fluid dynamics. Developent of efficient algorithms for low-rank representation of solutions of time-dependent simulations of fluid dynamics using multi-dimensional tensor representations of solutions.

97 MATHEMATICS AND COMPUTING↗