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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tree-based solvers for adaptive mesh refinement code FLASH – III: a novel scheme for radiation pressure on dust and gas and radiative transfer from diffuse sources

Radiation is an important contributor to the energetics of the interstellar medium, yet its transport is difficult to solve numerically. We present a novel approach towards solving radiative transfer of diffuse sources via backwards ray tracing. Here, we focus on the radiative transfer of infrared radiation and the radiation pressure on dust. The new module, TREERAY/RADPRESSURE, is an extension to the novel radiative transfer method TREERAY implemented in the grid-based Magneto-Hydrodynamics code FLASH. In TREERAY/RADPRESSURE, every cell and every star particle is a source of infrared radiation. We also describe how gas, dust, and radiation are coupled via a chemical network. This allows us to compute the local dust temperature in thermal equilibrium, leading to a significantly improvement over the classical grey approximation. In several tests, we demonstrate that the scheme produces the correct radiative intensities as well as the correct momentum input by radiation pressure. Subsequently, we apply our new scheme to model massive star formation from a collapsing, turbulent core of 150 M ⊙ . We include the effects of both, ionizing and infrared radiation on the dynamics of the core. We find that the newborn massive star prevents fragmentation in its proximity due to radiative heating. Over time, dust and radiation temperature equalize, while the gas temperature can be either warmer due to shock heating or colder due to insufficient dust–gas coupling. Compared to gravity, the effects of radiation pressure are insignificant for the stellar mass on the simulated time-scale in this work.

(ISM:) HII regions↗
DOE OSTI2023

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. Further, the present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

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

Quantum Interior Point Methods for Semidefinite Optimization

We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more similar to a classical solution algorithm, computes an inexact search direction and is not guaranteed to explore only feasible points; the second scheme uses a nullspace representation of the Newton linear system to ensure feasibility even with inexact search directions. The second is a novel scheme that might seem impractical in the classical world, but it is well-suited for a hybrid quantum-classical setting. We show that both schemes converge to an optimal solution of the semidefinite optimization problem under standard assumptions. By comparing the theoretical performance of classical and quantum interior point methods with respect to various input parameters, we show that our second scheme obtains a speedup over classical algorithms in terms of the dimension of the problem n , but has worse dependence on other numerical parameters.

Augustino, Brandon↗
DOE OSTI2020

Multiphysics for nuclear energy applications using a cohesive computational framework

With the recent development of advanced numerical algorithms, software design, and low-cost high-performance computer hardware, reliance on coupled multiphysics to predict the behavior of complex physical systems is beginning to become standard practice. This is especially true in nuclear energy applications where strong nonlinear interdependencies exist between reactor physics, radiation transport, multi-scale nuclear fuels performance, thermal fluids, etc. Resolving these nonlinear dependencies requires choices in multiphysics software approaches. Two main multiphysics modeling and simulation approaches have emerged. The first is based upon "code coupling" where disparate physics codes of different software design, code languages, and spatial and temporal integration schemes are coupled together with relatively complex data passing interfaces. The second multiphysics software approach is to employ a "cohesive" framework where all physics applications are developed with a common software design, i.e., data structures, syntax, input format, integrated spatial and temporal discretization schemes, etc. In this paper we present the Multiphysics Object-Oriented Simulation Environment (MOOSE) development and runtime framework and describe the framework's cohesive modeling and simulation multiphysics approach. Then, a "cohesive-like" extension of the MOOSE framework is presented where MOOSE-based physics software applications are efficiently coupled to non-MOOSE (external) physics codes to form multiphysics applications using MOOSE's unique interface capabilities. Finally, several examples of MOOSE's cohesive and cohesive-like multiphysics applications will be demonstrated. These multiphysics demonstrations will incorporate both MOOSE-based applications and external codes, including Nek5000, RELAP-7, TRACE, BISON, and Pronghorn.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies

The Biosphere-Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land-surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah-MP LSM framework through evaluation and optimization of the Noah-MP BATS ground snow albedo formulation using 2-band (visible and near-infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah-MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh-snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central-southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh-snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh-snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh-snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time-varying formulation for fresh-snow albedo.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Historical total ozone radiative forcing derived from CMIP6 simulations

Radiative forcing (RF) time series for total ozone from 1850 up to the present day are calculated based on historical simulations of ozone from 10 climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, RF is calculated for ozone fields prepared as an input for CMIP6 models without chemistry schemes and from a chemical transport model simulation. A radiative kernel for ozone is constructed and used to derive the RF. The ozone RF in 2010 (2005–2014) relative to 1850 is 0.35 W m -2 [0.08–0.61] (5–95% uncertainty range) based on models with both tropospheric and stratospheric chemistry. One of these models has a negative present-day total ozone RF. Excluding this model, the present-day ozone RF increases to 0.39 W m -2 [0.27–0.51] (5–95% uncertainty range). The rest of the models have RF close to or stronger than the RF time series assessed by the Intergovernmental Panel on Climate Change in the fifth assessment report with the primary driver likely being the new precursor emissions used in CMIP6. The rapid adjustments beyond stratospheric temperature are estimated to be weak and thus the RF is a good measure of effective radiative forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ab initio many-fermion structure calculations on a quantum computer

To overcome the limitations of existing algorithms for solving self-bound quantum many-body problems—such as those encountered in nuclear and particle physics—that access only a restricted subset of energy levels and provide limited structural information, we introduce and demonstrate a novel quantum-classical approach capable of resolving the complete bound-state spectrum. This method also provides the total angular momentum 𝐽 associated with each eigenstate. Here, our approach is based on expressing the Hamiltonian in second-quantized form within a novel input model combined with a scan scheme, enabling broad applicability to configuration-interaction calculations across diverse fields. We apply this hybrid method to compute, for the first time, the bound-state spectrum together with corresponding 𝐽 values of 20 O using a realistic strong-interaction Hamiltonian. Our approach applies to hadron spectra and 𝐽 values solved in the relativistic basis light-front quantization approach.

Du, Weijie [Chinese Academy of Sciences (CAS), Lan↗
DOE OSTI2024

The Compensating Common-Gate Frontend Circuit Used as Current Source Receiver

A very low power frontend circuit using the compensating common-gate scheme is described. It combines features of the regular common-gate topology and the trans-impedance amplifier (TIA) schemes allowing designers to achieve low input impedance and sufficient bandwidth with small power consumption. It can be used as an interface between detectors and the pseudo-thyristors we developed to complete a full frontend signal chain. Simulation shows that in a 65 nm process, it is possible to achieve an input impedance <1K Ohms allowing a charge detection <6fC with 3pF parasitic capacitance, using static power below 20 micro-Watts/channel.

Fu, T.↗
DOE OSTI2022

Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study

Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely, Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict the future behavior of dynamic systems over long periods based on training data from relatively short periods. The multi-dimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. Three numerical examples, including one mathematical example and two nonlinear engineering analysis models, are used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning (DL)-based methods for the three particular examples studied. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.

42 ENGINEERING↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗
DOE OSTI2022

Novel Quadratic High Gain Boost Converter With Adaptive Soft-Switching Scheme and Reduced Conduction Loss

In this article, an improved soft-switching quadratic boost converter is proposed. Instead of inserting an additional active clamp or auxiliary zero-voltage transition circuit at the switching node, the proposed topology connects the high-voltage switching node to the input diode node by replacing one of the input diodes with a low-rated switch. The proposed topology can attain soft-switching condition for all the switches and input diode turns- off under zero-current switching (ZCS). The operation of input-side switch not only aids zero-voltage switching (ZVS) turn- on for the main switch but also helps in reducing the conduction loss. Also, the input-side switch operates under ZCS turn- on and ZVS turn- off , making it a loss economical solution. An adaptive timing scheme for driving the input switch is proposed, which can ensure soft-switching condition under varying gain and load range. The detailed operational modes, analysis, and design considerations of the proposed topology are presented. A 250 W hardware prototype is built to validate the performance of the proposed converter operating at 100 kHz switching frequency. Results with adaptive soft-switching scheme shows that the converter is modulated to achieve its best efficiency condition under various system conditions. Furthermore, a peak efficiency of 96.1% at 155 W and efficiencies above 95.75% over a wide load range are achieved using all Si devices.

14 SOLAR ENERGY↗
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗
DOE OSTI2023

Time-Dependent Boundary Modeling to Inform Design of SPARC Diagnostic and Actuators

The purpose of this work was to inform the design of diagnostic and actuator systems for a new experimental facility for Commonwealth Fusion Systems (CFS), called SPARC [Creely2020], using time-dependent plasma boundary simulations with the Scrape Off Layer Plasma Simulator (SOLPS) code [Wiesen2015]. The SPARC tokamak is a device that aims to demonstrate fusion energy production and high-field plasma operating scenarios. The developed technologies will be incorporated into a net-electricity pilot that will demonstrate approximately 200MW of electric power. A major difference from other private industry fusion companies is the use of a standard aspect ratio, but at a high magnetic field strength, which provides a well-established physics basis and a modest extrapolation to reactor operation. Time-dependent simulations were used to inform the design of main-chamber plasma facing components, which are protected by feedback control of the heat and particle loads during high power operation. The simulations provide the timescales and magnitudes of plasma and neutral particle response useful for determining the position and operation of diagnostics and actuators that will enable control. The plasma response and phase-space diagrams also provide input into advanced model-based control schemes, which are envisioned for later SPARC operation. Model-based control can reduce the risk of component failure, which would require expensive in-situ repairs and the associated delays, by predicting the system response to actuators over a short time-horizon.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

RAPTURE User's Manual

This User's Guide serves as a brief introduction to the RAPTURE radiation effects analysis code. It includes an overview of the input format, RAPTURE's error- and consistency-checking of the user-provided input files, the automatic-differentiation and convergce-checking schemes employed by RAPTURE, and the RAPTURE output files. A variety of example problems are included in this Guide which collectively demonstrate RAPTURE's current capabilities and provide a suite of test problems and template input files for the user. This Guide includes, for each problem, the problem description, RAPTURE input files, and comparison of the RAPTURE solution with solutiong generated with the Monte Carlo transport code ITS, the legacy deterministic code ADEPT, and, where possible, published experimental results. An appendix includes a description of all keywords and options in the RAPTURE input file.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Model Predictive Control for Urban Traffic Signals with Stability Guarantees

Traditional traffic signal control focuses more on the optimization aspects whereas the stability and robustness of the closed-loop system are less studied. This paper aims to establish the stability properties of traffic signal control systems through the analysis of a practical model predictive control (MPC) scheme, which models the traffic network with the conservation of vehicles based on a store-and forward model and attempts to balance the traffic densities. More precisely, this scheme guarantees the exponential stability of the closed-loop system under state and input constraints when the inflow is feasible and traffic demand can be fully accessed. Practical exponential stability is achieved in case of small uncertain traffic demand by a modification of the previous scheme. Simulation results of a small-scale traffic network validate the theoretical analysis.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2024

Energy-dependent and Energy-integrated Two-moment General-relativistic Neutrino Transport Simulations of a Hypermassive Neutron Star

Abstract We compare two-moment-based energy-dependent and three variants of energy-integrated neutrino transport general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. To study the impacts due to the choice of the neutrino transport schemes, we perform simulations with the same setups and input neutrino microphysics. We show that the main differences between energy-dependent and energy-integrated neutrino transport are found in the disk and ejecta properties, as well as in the neutrino signals. The properties of the disk surrounding the neutron star and the ejecta in energy-dependent transport are very different from the ones obtained using energy-integrated schemes. Specifically, in the energy-dependent case, the disk is more neutron-rich at early times and becomes geometrically thicker at later times. In addition, the ejecta is more massive and, on average, more neutron-rich in the energy-dependent simulations. Moreover, the average neutrino energies and luminosities are about 30% higher. Energy-dependent neutrino transport is necessary if one wants to better model the neutrino signals and matter outflows from neutron star merger remnants via numerical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗