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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

3D printing synthesis of catalysts

The catalyst industry generates approximately $20 billion every year globally and plays a major role in the energy production sector. Traditional industrial catalysts (e.g., pellets) typically have mass and heat transfer limitations, and cause a pressure drop in continuous-flow reactors, lowering the efficiency of the catalytic processes. 3D printing technology has evolved rapidly over the past decade, and the 3D printing of catalysts with desired geometries can address many of the above-stated challenges with traditional catalysts. However, challenges remain to be addressed and opportunities remain to be explored before the full potential in the design and 3D printing of novel catalysts can be realized. Herein this article reviews the recent development in the 3D printing of catalysts. It summarizes the 3D printing design, dimension, property, and performance of 3D printed catalysts. The applications of 3D printed catalysts, such as reforming, wastewater treatment, and CO 2 capture and removal, are discussed, with a techno-economic analysis and life cycle analysis. Future research directions and opportunities for 3D printing of catalysts are also highlighted.

25 ENERGY STORAGE↗
DOE OSTI2017

Nitrogen removal from digested slurries using a simplified ammonia stripping technique

Highlights: • A novel application of N stripping for anaerobic digestates was studied. • The studied process combines mixing, pH adjustment and continuous air flow. •High ammonia removal efficiency (87%) was achieved at pH 9 and 40 {sup °}C. • Acceptable ammonia removal efficiency (69%) was achieved without pH adjustment. • Technique is a viable option for farms especially combined with biogas plants. - Abstract: This study assessed a novel technique for removing nitrogen from digested organic waste based on a slow release of ammonia that was promoted by continuous mixing of the digestate and delivering a continuous air stream across the surface of the liquid. Three 10-day experiments were conducted using two 50-L reactors. In the first two, nitrogen removal efficiencies were evaluated from identical digestates maintained at different temperatures (30 °C and 40 °C). At the start of the first experiment, the digestates were adjusted to pH 9 using sodium hydroxide, while in the second experiment pH was not adjusted. The highest ammonia removal efficiency (87%) was obtained at 40 °C with pH adjustment. However at 40 °C without pH adjustment, removal efficiencies of 69% for ammonia and 47% for total nitrogen were obtained. In the third experiment two different digestates were tested at 50 °C without pH adjustment. Although the initial chemical characteristics of the digestates were different in this experiment, the ammonia removal efficiencies were very similar (approximately 85%). Despite ammonia removal, the pH increased in all experiments, most likely due to carbon dioxide stripping that was promoted by temperature and mixing. The technique proved to be suitable for removing nitrogen following anaerobic digestion of livestock manure because effective removal was obtained at natural pH (≈8) and 40 °C, common operating conditions at typical biogas plants that process manure. Furthermore, the electrical energy requirement to operate the process is limited (estimated to be 3.8 kW h m{sup −3} digestate). Further improvements may increase the efficiency and reduce the processing time of this treatment technique. Even without these advances slow-rate air stripping of ammonia is a viable option for reducing the environmental impact associated with animal manure management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Molecular shear heating and vortex dynamics in thermostatted two dimensional Yukawa liquids

It is well known that two-dimensional macroscale shear flows are susceptible to instabilities leading to macroscale vortical structures. The linear and nonlinear fate of such a macroscale flow in a strongly coupled medium is a fundamental problem. A popular example of a strongly coupled medium is a dusty plasma, often modelled as a Yukawa liquid. Recently, laboratory experiments and molecular dynamics (MD) studies of shear flows in strongly coupled Yukawa liquids indicated the occurrence of strong molecular shear heating, which is found to reduce the coupling strength exponentially leading to the destruction of macroscale vorticity. To understand the vortex dynamics of strongly coupled molecular fluids undergoing macroscale shear flows and molecular shear heating, MD simulation has been performed, which allows the macroscopic vortex dynamics to evolve, while at the same time “removes” the microscopically generated heat without using the velocity degrees of freedom. We demonstrate that by using a configurational thermostat in a novel way, the microscale heat generated by shear flow can be thermostatted out efficiently without compromising the large scale vortex dynamics. In the present work, using MD simulations, a comparative study of shear flow evolution in Yukawa liquids in the presence and absence of molecular or microscopic heating is presented for a prototype shear flow, namely, Kolmogorov flow.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A time-resolved imaging system for the diagnosis of x-ray self-emission in high energy density physics experiments

A diagnostic capable of recording spatially and temporally resolved x-ray self-emission data was developed to characterize experiments on the MAGPIE pulsed-power generator. The diagnostic used two separate imaging systems: a pinhole imaging system with two-dimensional spatial resolution and a slit imaging system with one-dimensional spatial resolution. The two-dimensional imaging system imaged light onto the image plate. The one-dimensional imaging system imaged light onto the same piece of image plate and a linear array of silicon photodiodes. This design allowed the cross-comparison of different images, allowing a picture of the spatial and temporal distribution of x-ray self-emission to be established. The design was tested in a series of pulsed-power-driven magnetic-reconnection experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Meeting the Challenges of Modeling Astrophysical Thermonuclear Explosions: Castro, Maestro, and the AMReX Astrophysics Suite

We describe the AMReX suite of astrophysics codes and their application to modeling problems in stellar astrophysics. Maestro is tuned to efficiently model subsonic convective flows while Castro models the highly compressible flows associated with stellar explosions. Both are built on the block-structured adaptive mesh refinement library AMReX. Together, these codes enable a thorough investigation of stellar phenomena, including Type Ia supernovae and X-ray bursts. We describe these science applications and the approach we are taking to make these codes performant on current and future many-core and GPU-based architectures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

First inverse kinematics measurement of resonances in Be 7 ( α , γ ) C 11 relevant to neutrino-driven wind nucleosynthesis using DRAGON

A possible mechanism to explain the origin of the light $\textit{p}$ nuclei in the Galaxy is the nucleosynthesis in the proton-rich neutrino-driven wind ejecta of core-collapse supernovas via the $\textit{νp}$ process. However, this production scenario is very sensitive to the underlying supernova dynamics and the nuclear physics input. As far as the nuclear uncertainties are concerned, the breakout from the $\textit{pp}$ chains via the 7 Be(α,γ) 11 C reaction has been identified as an important link which can influence the nuclear flow and, therefore, the efficiency of the $\textit{νp}$ process. However, its reaction rate is poorly known over the relevant temperature range, $\textit{T}$ = 1.5–3 GK. We report on the first direct measurement of two resonances of the 7 Be(α,γ) 11 C reaction with previously unknown strengths using an intense radioactive 7 Be beam from the Isotope Separator and Accelerator (ISAC-I) Center facility and the DRAGON recoil separator in inverse kinematics. In this work, we have decreased the 7 Be(α,γ) 11 C reaction rate uncertainty to ≈9.4–10.7% over the relevant temperature region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence

In this study, we present a nonintrusive reduced order modeling (ROM) framework for large-scale quasistationary systems. The framework proposed in this work exploits the time series prediction capability of long short-term memory (LSTM) recurrent neural network architecture such that (1) in the training phase, the LSTM model is trained on the modal coefficients extracted from the high-resolution data snapshots using proper orthogonal decomposition (POD) transform, and (2) in the testing phase, the trained model predicts the modal coefficients for the total time recursively based on the initial time history. Hence, no prior information about the underlying governing equations is required to generate the ROM. To illustrate the predictive performance of the proposed framework, the mean flow fields and time series response of the field values are reconstructed from the predicted modal coefficients by using an inverse POD transform. As a representative benchmark test case, we consider a two-dimensional quasigeostrophic ocean circulation model which, in general, displays an enormous range of fluctuating spatial and temporal scales. We first demonstrate that the conventional Galerkin projection-based reduced order modeling of such systems requires a high number of POD modes to obtain a stable flow physics. In addition, ROM-Galerkin projection (ROM-GP) does not seem to capture the intermittent bursts appearing in the dynamics of the first few most energetic modes. However, the proposed nonintrusive ROM framework based on LSTM (ROM-LSTM) yields a stable solution even for a small number of POD modes. We also observe that the ROM-LSTM model is able to capture quasiperiodic intermittent bursts accurately, and yields a stable and accurate mean flow dynamics using the time history of a few previous time states, denoted as the lookback time window in this paper. We show several features of ROM-LSTM framework such as significantly higher accuracy than ROM-GP, and faster performance using larger time step size. Throughout the paper, we demonstrate our findings in terms of time series evolution of the field values and mean flow patterns, which suggest that the proposed fully nonintrusive ROM framework is robust and capable of predicting chaotic nonlinear fluid flows in an extremely efficient way compared to the conventional projection-based ROM framework.

42 ENGINEERING↗
DOE OSTI2022

Development and Validation of Resistance-Capacitance Model for Phase Change Material Embedded in Porous Media

A simple and effective way to enhance PCM thermal conductivity is by embedding porous materials with high thermal conductivities. In this work, CFD was used to evaluate the melting of PCM embedded in metal foam supplied with constant heat flux. Good agreement between experimental and numerical results has been found with a mean PCM local temperature deviation of 1.62K and 2.97K in two locations. The CFD simulation time was 56 hours for 6.5 hours of flow time. A computationally efficient resistance capacitance-based model (RCM) was developed to improve simulation speed. The proposed RCM exhibited mean local temperature deviations of 1.86K and 2.96K when compared against the experimental data for two thermocouple positions, however, it was approximately 2.5x10 5 times faster than CFD. The charging time deviation was less than 1% compared to CFD, and no significant change in thermal energy stored was observed since the temperature differences were small enough to result in insignificant sensible heat deviation. A simulation cost-effectiveness index (CEI) considering accuracy and computational affordability was introduced; the CEI of the RCM solver is 10 3 times better compared to CFD for the prediction of local PCM temperature.

25 ENERGY STORAGE↗
DOE OSTI2023

Development of a 300 MWe Utility Scale Oxy-Fuel sCO2 Turbine

A 300 MWe direct-fired supercritical carbon dioxide (sCO2) oxy-fuel turbine is being developed that will burn natural gas-fired, coal syngas and even hydrogen mixtures capable of 1,150ºC turbine inlet temperature at 300 bar. This design will significantly improve the state-of-the-art for thermal efficiency and results in a high-pressure stream of CO2 with 98%+ carbon capture, making the power plant near emission-free and more efficient than Natural Gas Combined Cycle (NGCC) plants with carbon capture. This power plant will be capable of burning coal through gasification and cleanup of the synthesis gas (syngas). sCO2 power cycles are a transformational technology for the energy industry, providing higher efficiency heat source energy conversion for conventional and alternative energy sources. This novel cycle significantly reduces capital costs because of smaller equipment footprints, design modularity, and allows for rapid cyclic load and source following to balance solar and wind energy power swings. Oxy-fuel sCO2 cycles take these advantages even further, utilizing higher firing temperatures, improved efficiency, and simpler carbon capture strategies. This turbine will require cooled turbine nozzles and blades as well as advanced thermal management systems to accommodate these high temperatures. The cooled blade heat transfer correlations required new test programs at higher Reynolds number than air-breathing gas turbines for impingement, serpentine, and pin-fin regions. Novel blade optimization was performed to maximize aerodynamic efficiency, while minimizing cooling flows. A turbine layout was generated utilizing individual combustor cans with cooled liners feeding into a 6-stage axial flow turbine with cooled stator nozzles and turbine blades. The case design and thermal management preliminary design will be described.

Moore, Jeffrey↗
DOE OSTI2019

An Evolve-Filter-Relax Stabilized Reduced Order Stochastic Collocation Method for the Time-Dependent Navier--Stokes Equations

In this paper, we propose a filter-based stabilization of reduced order models (ROMs) for uncertainty quantification (UQ) of the time-dependent Navier--Stokes equations in convection-dominated regimes. Here we propose a novel high-order ROM differential filter and use it in conjunction with an evolve-filter-relax (EFR) algorithm to attenuate the numerical oscillations of standard ROMs. We also examine how stochastic collocation methods can be combined with the EFR algorithm for efficient UQ of fluid flows. We test the new framework in the numerical simulation of a two-dimensional flow past a circular cylinder with a random viscosity that yields a random Reynolds number with mean Re = 100.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

The allocation of a variable series reactor considering AC constraints and contingencies

The Variable Series Reactors (VSRs) can efficiently control the power flow through the adjustment of the line reactance. When they are appropriately allocated in the power network, the transmission congestion and generation cost can be reduced. Our work proposes a planning model to optimally allocate VSRs considering AC constraints and multi-scenarios including base case and contingencies. The planning model is originally a non-convex large scale mixed integer nonlinear program (MINLP), which is generally intractable. The proposed Benders approach decomposes the MINLP model into a mixed integer linear program (MILP) master problem and a number of nonlinear subproblems. Numerical case studies based on IEEE 118-bus demonstrate the high performance of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

A Laboratory Astrophysical Jet to Study Canonical Flux Tubes

Understanding the interaction between plasma flows and magnetic fields remains a fundamental problem in plasma physics, with important applications to astrophysics, fusion energy, and advanced space propulsion. For example, flows are of primary importance in astrophysical jets even if it is not fully understood how jets become so long without becoming unstable. Theories for the origin of magnetic fields in the cosmos rely on flowing charged fluids that should generate magnetic fields, yet this remains to be demonstrated experimentally. Fusion energy reactors can be made smaller with flows that improve stability and confinement. Advanced space propulsion could be more efficient with collimated and stable plasma flows through magnetic nozzles but must eventually detach from the nozzle. In all these cases, there appears to be a spontaneous emergence of flowing and/or magnetic structures, suggesting a form of self-organization in plasmas. Beyond satisfying simple intellectual curiosity, understanding plasma self-organization could enable the development of methods to control plasma structures for fusion energy, space propulsion, and other applications. The research project has therefore built a theory and an experiment to investigate the interaction between magnetic fields and plasma flows. The theory is called canonical field theory for short, and the experiment is called Mochi after a rice cake filled with surprising, yet delicious fillings.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides]

The ChemCam instrument of Curiosity uses laser-induced breakdown spectroscopy (LIBS). It fires a laser at target and vaporizes rock surfaces, creating a plasma. Three spectrographs divide the plasma light into wavelengths for chemical analysis: ultraviolet, violet, and visible near-infrared. Regression methods (SVR, PCR, CNN) have been employed for calibration (prediction of the elemental composition of samples); however, labeled ChemCam samples are limited. Here, we focus on unsupervised learning and employ generative models from ChemCam analysis. Further, we use labels (supervised) in combination to the generative model to compute uncertainties related to predictions. We report generative modeling can be successfully applied to model real-world data. Normalizing flow models can be efficiently constructed on latent spaces for fast downstream inference. Unsupervised and supervised learning can be combined to form an uncertainty quantification framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

A One-Dimensional Stack Model for Redox Flow Battery Analysis and Operation

Current redox flow battery (RFB) stack models are not particularly conducive to accurate yet high-throughput studies of stack operation and design. To facilitate system-level analysis, we have developed a one-dimensional RFB stack model through the combination of a one-dimensional Newman-type cell model and a resistor-network to evaluate contributions from shunt currents within the stack. Inclusion of hydraulic losses and membrane crossover enables constrained optimization of system performance and allows users to make recommendations for operating flow rate, current densities, and cell design given a subset of electrolyte and electrode properties. Over the range of experimental conditions explored, shunt current losses remain small, but mass-transfer losses quickly become prohibitive at high current densities. Attempting to offset mass-transfer losses with high flow rates reduces system efficiency due to the increase in pressure drop through the porous electrode. The development of this stack model application, along with the availability of the source MATLAB code, allows for facile approximation of the upper limits of performance with limited empiricism. This work primarily presents a readily adaptable tool to enable researchers to perform either front-end performance estimates based on fundamental material properties or to benchmark their experimental results.

Barton, John L. (ORCID:0000000225729127)↗
DOE OSTI2024

When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling

Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

58 GEOSCIENCES↗
NASA NTRS1971

Preliminary performance of the Brayton 4.25 inch radial compressor operating in a helium xenon gas mixture

Compressor performance was mapped during the Brayton cycle power system testing. The range of testing included three shaft speeds: the design speed of 36,000 rpm, 10 percent overspeed (39,600 rpm), and 10 percent underspeed (32,400 rpm). A range of compressor inlet temperatures from 60 F to 120 F and discharge pressures from 20 to 45 psia were included. The effects of turbine inlet temperatures (from 1200 F to 1600 F) on the compressor were also studied. The data presented include plots of weight flow, compressor pressure ratio, efficiency, and temperature-rise ratio.

Asadourian, A. S.↗