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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Mass Inferencing Model Creation And Deployment To Lunar Excavation Robot, RASSOR

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.

ROS↗
DOE OSTI2022

Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control

This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel global-local policy search method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity

Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simu-lations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-di?usion equations using a non-negative finite element formulation for di?erent input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion strength/contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(107) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.

Mudunuru, Maruti K.↗
DOE OSTI2023

Assessment of Combustion Residual Leachate Volume, Composition, and Treatment Costs

Combustion residuals and the resulting leachate from storage sites represent a large volume of wastewater in the United States (U.S.) that has not been quantified. Here this work estimates the constituents present, volume of wastewater, and costs of treatment for both combustion residual landfill leachate and the leachate from surface impoundment closures. Combustion residual landfill leachate produced from contact with bituminous coal combustion byproducts is generally predicted to be higher in lithium and manganese, whereas landfill leachate produced from contact with subbituminous coal combustion byproducts is generally predicted to be higher in mercury and vanadium. The annual volume of a single landfill with combustion residual leachate can reach more than 800,000 cubic meters. This leachate represents an annual volume of 26.8–42.8 million cubic meters nationally. Closing surface impoundments can yield between 830 and 1040 cubic meters of leachate nationally for a three-year closure period. Costs as low as $1.5/m 3 or as high as $95/m 3 are observed. Treatment trains will need to remove 72% of total suspended solids (TSS), 87% of arsenic, and 64% of mercury from landfill leachate. When applied to impoundments, these treatment trains would need to remove 97% of arsenic.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Scalable Predictive And Risk Technologies

The research involves developing scalable technologies for risk-informed predictive analytics to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop technologies to scale risk-informed predictive analytic algorithms across different plant assets at the plant site and across the nuclear fleet. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation using plant operation data 2. Federated and Transfer learning model 3. Feature group based Multi-kernel SVM 4. Three state markov model

Manjunatha, KoushikAraseethota↗
NASA NTRS1972

Apollo 15 mission report. Supplement 2: Service propulsion system final flight evaluation

The command and service module 112 service propulsion system performance for the Apollo 15 mission was evaluated and found to be satisfactory. The following items were considered: (1) the steady-state performance as determined from analysis of the third and eight burns; (2) techniques, problems, and assumptions; (3) flight analysis results as compared to the preflight predicted performance; (4) the propellant utilization and gaging system operation; (5) the pressurization system performance; (6) transient data; and (7) revision of estimated propellant consumption.

Smith, R. J.↗
NASA NTRS2012

Accurate ab initio Quartic Force Fields of Cyclic and Bent HC2N Isomers

Highly correlated ab initio quartic force field (QFFs) are used to calculate the equilibrium structures and predict the spectroscopic parameters of three HC2N isomers. Specifically, the ground state quasilinear triplet and the lowest cyclic and bent singlet isomers are included in the present study. Extensive treatment of correlation effects were included using the singles and doubles coupled-cluster method that includes a perturbational estimate of the effects of connected triple excitations, denoted CCSD(T). Dunning s correlation-consistent basis sets cc-pVXZ, X=3,4,5, were used, and a three-point formula for extrapolation to the one-particle basis set limit was used. Core-correlation and scalar relativistic corrections were also included to yield highly accurate QFFs. The QFFs were used together with second-order perturbation theory (with proper treatment of Fermi resonances) and variational methods to solve the nuclear Schr dinger equation. The quasilinear nature of the triplet isomer is problematic, and it is concluded that a QFF is not adequate to describe properly all of the fundamental vibrational frequencies and spectroscopic constants (though some constants not dependent on the bending motion are well reproduced by perturbation theory). On the other hand, this procedure (a QFF together with either perturbation theory or variational methods) leads to highly accurate fundamental vibrational frequencies and spectroscopic constants for the cyclic and bent singlet isomers of HC2N. All three isomers possess significant dipole moments, 3.05D, 3.06D, and 1.71D, for the quasilinear triplet, the cyclic singlet, and the bent singlet isomers, respectively. It is concluded that the spectroscopic constants determined for the cyclic and bent singlet isomers are the most accurate available, and it is hoped that these will be useful in the interpretation of high-resolution astronomical observations or laboratory experiments.

Inostroza, Natalia↗
NASA NTRS1982

An estimation of aerodynamic forces and moments on an airplane model under steady state spin conditions

The problem of estimating the aerodynamic characteristics of a wing body configuration of a spinning airplane at extreme angles of attack and spin rates is approached by the application of strip theory. Semiempirical methods are used to estimate the aerodynamic force distributions over each component using static, nonrotational wind-tunnel test data. To these predictions, corrections are applied to account for the primary rotational flow effects which are significant at large spin rates. The results of this modified strip theory are shown to be in good agreement with spin tunnel rotary balance test data.

Pamadi, B. N.↗
NASA NTRS2018

A Momentum-Based Indicator for Predicting the Peak Opening Load of Supersonic Parachutes

In this paper, a new empirical indicator for predicting the peak opening loads of supersonic parachutes is presented. The proposed indicator is proportional to twice the free-stream dynamic pressure and the projected area of the parachute, which is equivalent to estimating the opening load as a percentage of the free-stream momentum flux through the projected area at the moment of peak inflation. The form of this expression is motivated by a classical control volume analysis of the aerodynamic forces acting on a parachute during inflation, under the simplifying assumptions of quasi-static and one-dimensional flow. For parachute geometries and flight conditions typical of Mars Entry, Descent, and Landing systems, the largest contribution to the total drag is shown to be a momentum flux term that is associated with the entrainment of atmosphere within the inflating parachute volume. Using this new method, empirical constants are calculated from existing flight reconstruction data and are shown to have a smaller standard deviation than similar constants determined using the customary indicator form, which is based on the steady-state subsonic drag and proportional to the parachute reference area. These empirical constants are also compared to an analytic estimate, derived from the control volume analysis, and shown to have excellent agreement across a wide range of Mach numbers and dynamic pressures for several parachute geometries. While opening loads estimated using both methods produce similar results at low supersonic Mach numbers typical of past inflations, the proposed method predicts notably larger loads at higher Mach numbers, those above Mach 2.0, due to the omission of any Mach Efficiency Factor. Several current Mars EDL projects have adopted this new indicator.

Way, David W.↗
DOE OSTI2023

Spatial Bayesian models project shifts in suitable habitat for Pacific Northwest tree species under climate change

Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir ( Abies procera ), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir ( Pseudotsuga menziesii var. menziesii ) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km 2 ) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak ( Quercus garryana ) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak ( Quercus douglasii ) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak ( Quercus kelloggii ), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.

Kralicek, Karin↗
DOE OSTI2025

Heavy flavour jet substructure

We present a comprehensive study of energy correlation functions and jet angularities for heavy-flavour QCD jets. In particular, we discuss the possibility of using these observables to expose the dead cone effect, i.e. the suppression of collinear QCD radiation around massive quarks, and to investigate the sensitivity of different observable definitions to the presence of quark masses. Our calculations are presented as all-order resummed predictions at next-to-leading-logarithmic accuracy, matched to (partial) fixed-order results to obtain a better description of the transition around the dead cone threshold. We also compare our analytic results with Pythia, Herwig and Sherpa Monte Carlo predictions to estimate the impact of non-perturbative contributions such as hadronisation, underlying events and B-hadron decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD

Here, the Deep Operator Network (DeepONet) is a neural network architecture used to approximate operators, including the solution operator of parametric PDEs. DeepONets have shown remarkable approximation ability. However, the performance of DeepONets deteriorates when the training data is polluted with noise, a scenario that occurs in practice. To handle noisy data, we propose a Bayesian DeepONet based on replica exchange Langevin diffusion (reLD). Replica exchange uses two particles. The first particle trains a DeepONet to exploit the loss landscape and make predictions. The other particle trains a different DeepONet to explore the loss landscape and escape local minima via swapping. Compared to DeepONets trained with state-of-the-art gradient-based algorithms (e.g., Adam), the proposed Bayesian DeepONet greatly improves the training convergence for noisy scenarios and accurately estimates the uncertainty. To further reduce the high computational cost of the reLD training of DeepONets, we propose (1) an accelerated training framework that exploits the DeepONet's architecture to reduce its computational cost up to 25% without compromising performance and (2) a transfer learning strategy that accelerates training DeepONets for PDEs with different parameter values. Finally, we illustrate the effectiveness of the proposed Bayesian DeepONet using four parametric PDE problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2022

Measurement and simulation of small cryogenic neon pellet Ne-I 640 nm photon efficiency during ablation in DIII-D plasma

Small (~1 mm) neon pellet fragments are fired into DIII-D H-mode plasmas, and resulting trajectory-averaged photon efficiencies (neutral neon ionization events for every photon emitted) of S/XB ≈ 85 are estimated for Ne-I 640 nm by dividing the estimated initial pellet fragment mass by the measured number of emitted Ne-I photons. The experiments are modeled by running the Lagrangian particle (LP) fluid/magneto-hydrodynamic pellet code to estimate axial ablation plume neon density profiles and temperature profiles at each pellet position. These solutions are then fed into the PrismSPECT collisional-radiative code, which calculates resulting neon charge states and photon emission rates, giving a profile-average of S/XB ≈ 109. The burnthrough plasma minor radius predicted by LP (ρ ≈ 0.63) is reasonably close to the experimental observation ρ ≈ 0.6. Furthermore, the modeling indicates that local S/XB is not constant along the pellet trajectory but tends to increase with increasing ablation rate. Non-equilibrium kinetics are predicted to be very important, while line trapping is predicted to be relatively unimportant (for Ne-I 640 nm S/XB).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Wind Farm Layout Optimization with Loads Considerations

The objective of this paper is to improve the annual energy production of a wind farm by optimizing the layout of a wind farm, while considering fatigue loads on turbines. In this paper, the loads are estimated using the edgewise bending moment computed using CCBlade, a steady-state blade element momentum code. The edgewise bending moment is then used to calculate fatigue damage using Miner's rule. The fatigue damage is used to constrain the layout optimization problem. We show that our method can predict blade root damage with similar trends to damage calculated with other methods, such as a complex, computationally expensive large-eddy simulation and unsteady aeroelastic code. We also optimize wind farm layouts and show that, for a simple problem with two wind directions and ten turbines, the fatigue damage can be constrained without sacrificing wind farm power production.

17 WIND ENERGY↗
NASA NTRS1977

Prediction of the bulk temperature in spur gears based on finite element temperature analysis

The temperature distribution in spur gears operating in a state of thermal equilibrium is solved by using a finite element method. The effects of various dimensionless parameters on bulk temperature are shown. A table is provided which can be used to predict the bulk temperature on gear teeth, once the heat transfer coefficients and frictional heat input is estimated. Theoretical results for estimating heat transfer coefficients and frictional heat are also summarized.

Patir, N.↗
NASA NTRS1987

Volcanic control of the Io atmosphere and neutral and plasma torus

Rate equations for the column density of Io's atmosphere and the number density of neutral atoms and ions in the torus have been formulated in terms of surface sublimation and venting of volcanic gases, sputtering efficiency, thermal escape, and the plasma processes that take place in the torus which included electron impact ionization, charge exchange, and transport. In the limit of low volcanic activity, estimates of the densities can be obtained, and the baseline configuration that would exist in the absence of volcanic activity is found. Solution of the coupled steady state rate equations shows that the densities are sensitive, to a varying degree, to the level of Io volcanism. The steady state densities predicted by the model are consistent with a volcanic flux of no more than 10 to the 12th/sq cm per sec. This is significantly lower than the values obtained on the basis of resurfacing rates and appears to imply that surface lava flows play a major role in the resurfacing process.

Eviatar, Aharon↗