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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst

For disordered catalysts such as atomically dispersed “single-atom” metals on amorphous silica, the active sites inherit different properties from their quenched-disordered local environments. The observed kinetics are site-averages, typically dominated by a small fraction of highly active sites. Standard sampling methods require expensive ab initio calculations at an intractable number of sites to converge on the siteaveraged kinetics. We present a new method that efficiently estimates the site-averaged turnover frequency (TOF). The new estimator uses the same importance learning algorithm [Vandervelden et al., React. Chem. Eng. 5, 77 (2020)] that we previously used to compute the siteaveraged activation energy. We demonstrate the method by computing the site-averaged TOF for a simple disordered lattice model of an amorphous catalyst. The results show that with the importance learning algorithm, the site-averaged TOF and activation energy can now be obtained concurrently with orders of magnitude reduction in required ab initio calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Waterhammer Transient Simulation and Model Anchoring for the Robotic Lunar Lander Propulsion System

Waterhammer transients have the potential to adversely impact propulsion system design if not properly addressed. Waterhammer can potentially lead to system plumbing, and component damage. Multi-thruster propulsion systems also develop constructive/destructive wave interference which becomes difficult to predict without detailed models. Therefore, it is important to sufficiently characterize propulsion system waterhammer in order to develop a robust design with minimal impact to other systems. A risk reduction activity was performed at Marshall Space Flight Center to develop a tool for estimating waterhammer through the use of anchored simulation for the Robotic Lunar Lander (RLL) propulsion system design. Testing was performed to simulate waterhammer surges due to rapid valve closure and consisted of twenty-two series of waterhammer tests, resulting in more than 300 valve actuations. These tests were performed using different valve actuation schemes and three system pressures. Data from the valve characterization tests were used to anchor the models that employed MSCSoftware.EASY5 v.2010 to model transient fluid phenomena by using transient forms of mass and energy conservation. The anchoring process was performed by comparing initial model results to experimental data and then iterating the model input to match the simulation results with the experimental data. The models provide good correlation with experimental results, supporting the use of EASY5 as a tool to model fluid transients and provide a baseline for future RLL system modeling. This paper addresses tasks performed during the waterhammer risk reduction activity for the RLL propulsion system. The problem of waterhammer simulation anchoring as applied to the RLL system is discussed with results from the corresponding experimental valve tests. Important factors for waterhammer mitigation are discussed along with potential design impacts to the RLL propulsion system.

Stein, William B.↗
NASA NTRS1984

Stellar fibril magnetic systems. I - Reduced energy state

The remarkable fibril structure of the magnetic fields at the surface of the sun (with fibrils compressed to 1,000-2,000 gauss) lies outside existing statistical theories of magnetohydrodynamic turbulence. The total energy of the fibril field is enhanced by a factor of more than 100 above the energy for the mean field in a continuum state. The magnetic energy density within a fibril is of the order of 100 times the local kinetic energy density, so that no simple application of equipartition principles is possible. It is pointed out that the total energy of the atmosphere (thermal + gravitational + magnetic) is reduced by the fibril state of the field by avoiding the magnetic inhibition of the convective overturning, suggesting that the formation of the observed intense fibril state may be in response to the associated energy reduction. Calculation of the minimum total energy of a polytropic atmosphere permeated by magnetic fibrils yields theoretical fibril fields of the order of 1-5 kilogauss when characteristics appropriate to the solar convective zone are introduced, in rough agreement with the actual fields of 1-2 kilogauss. The polytrope model, although crude, establishes that a large reduction in total energy is made possible by the fibril state.

Parker, E. N.↗
NASA NTRS1993

Higher-Order Ionospheric Effects on the GPS Observables and Means of Modeling Them

Based on realistic modeling of the electron density of the ionosphere and using a dipole moment approximation for the earth magnetic field, we are able to estimate the effect of the ionosphere on the GPS signal for a ground user. Our study implies reduction of the second-order ionospheric error to less than 2 mm on the average and, therefore, potential improvement of orbit determination and baseline solutions.

ionosphere↗
DOE OSTI2026

Efficient data-driven regression for reduced-order modeling of spatial pattern formation

We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learned non-intrusively from available training data of physically accurate numerical simulations. The method can be applied to general nonlinear systems through the use of polynomial model form, while not requiring knowledge of the underlying physical model, governing equations, or numerical solvers. The process of learning ROMs is posed as a low-cost least-squares problem in a reduced-order subspace identified via Proper Orthogonal Decomposition (POD). Numerical experiments on classical pattern-forming systems–including the Schnakenberg and Mimura–Tsujikawa models–demonstrate that higher-order surrogate models significantly improve prediction accuracy while maintaining low computational cost. The proposed method provides a flexible, non-intrusive model reduction framework, well suited for the analysis of complex spatio-temporal pattern formation phenomena.

Data-driven modeling↗
DOE OSTI2023

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

Abstract. This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFSE can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of a glacier. The problem size and complexity were chosen to reflect the challenges posed by future continental-scale studies while still facilitating a computationally feasible investigation of MFSE methods. When quantifying uncertainty introduced by a high-dimensional parameterization of the basal friction field, MFSE was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single-fidelity approach that only used the highest-fidelity model. This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice-flow fields predicted by the high-fidelity model. The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. Consequently, our results suggest that MFSE could be highly useful for reducing the cost of computing continental-scale probabilistic projections of sea-level rise due to ice-sheet mass change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

The effect of a gravity wave drag parameterization scheme on GLA fourth order GCM forecasts

Ten-day forecast experiments have been performed to determined whether the introduction of a simple orographic gravity wave drag scheme into a fourth-order GCM would reduce the climate drift of the fine resolution model and improve the model's medium range predictive skill. Error reduction due to the gravity waves is found in stratospheric predictions, where the improvement is confined mainly to the zonal mean component. Improvements are noted in the Northern Hemisphere climatology, where low level westerlies are weakened and shifted poleward, and in the Southern Hemisphere, where the roaring forties and fifties are better simulated.

Helfand, H. M.↗
NASA NTRS2000

Gravity and Nonconservative Force Model Tuning for the GEOSAT Follow-On Spacecraft

The US Navy's GEOSAT Follow-On spacecraft was launched on February 10, 1998 and the primary objective of the mission was to map the oceans using a radar altimeter. Three radar altimeter calibration campaigns have been conducted in 1999 and 2000. The spacecraft is tracked by satellite laser ranging (SLR) and Doppler beacons and a limited amount of data have been obtained from the Global Positioning Receiver (GPS) on board the satellite. Even with EGM96, the predicted radial orbit error due to gravity field mismodelling (to 70x70) remains high at 2.61 cm (compared to 0.88 cm for TOPEX). We report on the preliminary gravity model tuning for GFO using SLR, and altimeter crossover data. Preliminary solutions using SLR and GFO/GFO crossover data from CalVal campaigns I and II in June-August 1999, and January-February 2000 have reduced the predicted radial orbit error to 1.9 cm and further reduction will be possible when additional data are added to the solutions. The gravity model tuning has improved principally the low order m-daily terms and has reduced significantly the geographically correlated error present in this satellite orbit. In addition to gravity field mismodelling, the largest contributor to the orbit error is the non-conservative force mismodelling. We report on further nonconservative force model tuning results using available data from over one cycle in beta prime.

Lemoine, Frank G.↗
DOE OSTI2024

Low-Cost Heliostat for High-Flux Small-Area Receivers (Final Technical Report)

This project analyzed a two-stage heliostat concept consisting of a tracking stage and a concentrating stage. The tracking stage uses mirrors mounted on a common drive that move to track the sun. The concentrating stage consists of stationary mirrors that each have a unique angle to direct rays towards a small-area, high-flux, point-focused receiver. By splitting the collection and concentrating process into two stages, multiple small, inexpensive mirrors can share a structure and be controlled by a single drive in the tracking stage. The project effort developed modeling techniques that were specifically relevant to this two-stage heliostat concept. Both field-level and unit-level models were developed. The field-level model does not explicitly consider unit-level losses which are predicted by the unit-level model and then integrated into the field-level model through a correlation referred to as an efficiency modifier. This approach is referred to as the two-model approach; the development and demonstration of this two-model approach for a multi-stage heliostat technology is a key outcome of this work. The field-level model is used to design a field that hits a specific design day power given a set of heliostat design parameters. An oversized field is simulated and then heliostat units are removed based on their annual energy production in order to generate the highest performing field. The field reduction procedure fits a smooth curve fit to annual energy production as a function of position in the field which has the effect of reducing the noise that is otherwise caused by the Monte Carlo ray tracing technique. This approach is referred to as the annual energy fit method and substantially reduces computational run time for a given field level modeling accuracy. The annual energy fit approach enables the selection of a properly sized, high-performing field using orders of magnitude fewer rays than would otherwise be possible and the development of this approach is a second key outcome of this work. These models are used within a genetic optimization algorithm in order to optimize the geometric parameters associated with a heliostat in order to achieve the lowest cost per unit of collected design day power. The cost modeling that underlies the optimization is a simple, scaling type analysis backed up by a much more detailed Design for Manufacture and Assembly (DFMA) analysis. Although the figure of merit used for optimization was not cost per mirror area, this metric is reasonable to use as a means of comparison. The optimally designed 500 kW design has a tracking mirror specific cost of $181.85/m 2 , which is significantly larger than the target value and also larger than the current state of the art. The cost of the torque-tube type linkages contributed substantially to the overall cost. Based on this observation, potentially attractive alternative design configuration utilizing a capstan type actuation system should be investigated. Finally, NREL compared the performance of the two-stage heliostat to the performance of a focused and different sized flat conventional heliostats and showed that, as expected, additional losses versus the convention heliostat caused by a worse cosine efficiency, two stages of reflection, and interstage interactions. The two-stage heliostat requires around 75% more reflective area than a flat 1x1 meter conventional heliostat (similar to a focused heliostat) and 40% more than a flat 2x2 meter conventional heliostat.

14 SOLAR ENERGY↗
DOE OSTI2023

Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computationally expensive. This work proposes a context-aware multi-fidelity Monte Carlo method that optimally balances the costs of training low-fidelity models with the costs of Monte Carlo sampling. It generalizes the previously developed context-aware bi-fidelity Monte Carlo method to hierarchies of multiple models and to more general types of low-fidelity models. When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets. This is in stark contrast to traditional surrogate modeling and model reduction techniques that construct low-fidelity models with the primary goal of approximating well the high-fidelity model outputs and typically ignore the context in which the learned models will be used in upstream tasks. Further, the proposed context-aware multi-fidelity Monte Carlo method applies to hierarchies of a wide range of types of low-fidelity models such as sparse-grid and deep-network models. Numerical experiments with the gyrokinetic simulation code Gene show speedups of up to two orders of magnitude compared to standard estimators when quantifying uncertainties in small-scale fluctuations in confined plasma in fusion reactors. This corresponds to a runtime reduction from 72 days to four hours on one node of the Lonestar6 supercomputer at the Texas Advanced Computing Center.

42 ENGINEERING↗
DOE OSTI2020

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. Here, we propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota’s MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1982

Pole placement and order reduction in two-time-scale control systems through Riccati iteration

A transformation of variables taken from singular perturbations may be applied to two-time-scale linear systems in state space form to reduce the system to block-diagonal form with slow and fast modes decoupled. The transformation is easily computed by applying the new Riccati iteration. The iteration yields a solution to the nonsymmetric algebraic Riccati equation obtained by partitioning the original system matrix A. The numerical procedure is initiated with the trivial iterate L(0) = 0, and is globally convergent to the desired unique time scale decoupling solution. After transformation, the decoupled system may be used in controller design to achieve exact closed loop pole placement in the slow subsystem without altering the poles of the fast subsystem. The decoupled form may also be used to reduce system order by wetting a small parameter to zero. Provided the fast subsystem is stable, the order reduction can be expected to yield a good approximation to the original system. These methods are demonstrated using the 16th order linear model of a turbofan engine.

Anderson, L. R.↗
NASA NTRSDate not supplied

Microstructure Controlled Multi-Layer Porous Material Liner Tested On The Advanced Noise Control Fan

A novel concept of multilayer micro structurally controlled noise mitigation porous material was developed and experimentally validated in the laboratory by normal incidence impedance test. Extensive parametric studies were made to understand various parameters that influence acoustic absorption and transmission loss based on the test specimen size, particle size, porosity, pore size, and its distribution through the thickness. The results were reported in many publications. This work was focused on applying the concept and assessing the validity of the technology to aircraft turbomachine engines. The NASA Glenn Research Center’s Advanced Noise Control Fan (ANCF) low-speed test rig was selected. The liner insert required a 4-ft internal diameter cylinder with a 2-inch thick and 9-inch axial length. The liner was fabricated in eight segments and assembled in an aluminum test rig. The liner was tested for both tonal and broadband noise at the Notre Dame Turbomachinery Lab White Field test facility, where the ANCF is located. The study concludes that: Microstructurally controlled porous material is very effective in dissipating the sound energy; the liner is effective for tonal noise (2 to 13 dB reduction in noise levels) that constitutes the major part of the overall fan noise, especially the blade passing frequency which is generally the dominating component; Also, the liner is effective for broadband noise with reductions in sound pressure level of about 3 dB for the shaft order greater than 20. The proposed two-layer liner model demonstrated the validity of the concept. More studies are required to optimize the design.

Liner↗
NASA NTRS1995

Intermittent turbulence in solar wind from the south polar hole

The magnetic fields measured by the Ulysses spacecraft are used to study solar wind turbulence in the fast solar wind from the south polar hole. The spacecraft was at about 46 deg south latitude and 3.9 AU. For a magnetic field with a Gaussian distribution the power spectrum (second-order structure function) is sufficient to completely characterize the turbulence. However, the actual distribution is non-Gaussian so that the effects of intermittency must be taken into account. The observed spectral exponents include effects of intermittency and cannot be directly compared with the standard second-order spectral theories such as the Kolmogorov and Kraichnan theories. To permit a better comparison of the observations with the theoretical models, we study the structure characteristics of the data. We find the exponents of the second-order structure functions (power spectra) and the higher-order normalized structure functions for the components of the magnetic fields. We show that these sets of exponents can be approximately described by two basic numbers: the spectral exponent and the intermittency exponent. The intermittency exponent characterizes correlation properties of the energy cascade from large to small scales. Before comparing the observations to the theoretically expected values, a reduction must be made to the observed spectral exponent. The amount of the reduction depends on both the intermittency exponent and the model of the energy cascade assumed in the turbulence theory. We reduce the measured spectral indices according to a simple model for Alfven turbulence that is described here. We then compare our reduced spectral indices with second-order spectral theory. The reduced spectral indices for the period range of 1 min to about a half hour are remarkably constant and in good agreement with the value of 3/2. Thus our treatment is self-consistent. Our tentative conclusion is that the high-frequency turbulence appears to agree with the model of random-phased Alfven waves. This tentative conclusion must be tested by further theoretical and observational work.

Ruzmaikin, A. A.↗
DOE OSTI2020

Critical nuclei at hetero-phase interfaces

Two-step nucleation, in which a metastable intermediate phase acts as a precursor for nucleating a thermodynamically stable phase, has been widely observed in many materials systems and solid-state reactions. Among the advantages of two-step nucleation is that the stable phase may nucleate heterogeneously at the hetero-phase interface between the original and the precursory phases. Although heterogeneous nucleation (HN) theories for homo-phase grain boundaries and inert surfaces are well established, our understanding of HN at reactive hetero-phase interfaces remains incomplete. This deficiency stems from the discontinuity of the chemical potential driving force across the hetero-phase interface, which profoundly affects the fundamental properties of the nucleus in a way that is not properly accounted for in existing models. In this work, we incorporate these effects to extend the classical nucleation theory to HN at hetero-phase interfaces. Our extended model demonstrates that the nucleus shape along the minimum energy path is strongly size-dependent, and this additional degree of freedom can result in the reduction of the critical nucleus volume and associated activation energy barrier by orders of magnitude relative to conventional predictions. The simulation results are used to construct a sensitivity map in the parameter space of interfacial energy and bulk driving force ratios, which quantifies the difference in nucleation barriers predicted by different models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exponential Improvements in the Simulation of Lattice Gauge Theories Using Near-Optimal Techniques

We report a first-of-its-kind analysis on post-Trotter simulation of U(1), SU(2), and SU(3) lattice gauge theories including fermions in arbitrary spatial dimension. We provide explicit circuit constructions as well as T-gate counts and logical qubit counts for Hamiltonian simulation. We find a reduction of up to 25 orders of magnitude in space-time volume over Trotter methods for simulations of non-Abelian lattice gauge theories relevant to the standard model. This improvement results from our algorithm having polynomial scaling with the number of colors in the gauge theory, achieved by utilizing oracle constructions relying on the sparsity of physical operators, in contrast to the exponential scaling seen in state-of-the-art Trotter methods, which employ explicit mappings onto Pauli operators. Our work demonstrates that the use of advanced algorithmic techniques leads to dramatic reductions in the cost of simulating fundamental interactions, bringing it in step with resources required for first-principles quantum simulation of chemistry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗