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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Design and Analysis of a Wake Steering Controller with Wind Direction Variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values. For a spacing of 5 rotor diameters and a turbulence intensity of 10%, robust yaw offsets optimized for variable wind directions yielded an energy gain improvement of 128%. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗
DOE OSTI2022

Modeling the thermodynamics of the FeTi hydrogenation under para-equilibrium: An ab-initio and experimental study

We report FeTi-based hydrides have recently re-attracted attention as stationary hydrogen storage materials due to favorable reversibility, good sorption kinetics and relatively low costs compared to alternative intermetallic hydrides. Employing the OpenCalphad software, the thermodynamics of the (FeTi) 1-x H x (0 ≤ x ≤ 1) system were assessed as a key basis for modeling hydrogenation of FeTi-based alloys. New thermodynamic data were acquired from our experimental pressure-composition-isotherm (PCI) curves, as well as first-principles calculations utilizing density functional theory (DFT). The thermodynamic phase models were carefully selected based on critical analysis of literature information and ab-initio investigations. Key thermodynamic properties such as dissociation pressure, formation enthalpies and phase diagrams were calculated in good agreement to our performed experiments and literature-reported data. This work provides an initial perspective, which can be extended to account for higher-order thermodynamic assessments and subsequently enables the design of novel FeTi-based hydrides. In addition, the assessed thermodynamic data can serve as key inputs for kinetic models and hydride microstructure simulations.

08 HYDROGEN↗
DOE OSTI2020

Novel Technique for Developing Linearized Convex System Models from Experimentally Derived Data

This paper presents a novel technique for generating a convex system model from an experimentally derived data set which features variance among repeated measurements. The convex system model developed as a test case characterizes the dynamic system losses of a vanadium redox flow battery as a function of the active power output and the battery state of charge. The technique hinges on a pre-cleaning via clustering procedure which precedes the formation of a planar convex hull comprised of triangular simplices. The clustering procedure efficiently reduces the experimental data set while mitigating variance among repeated measurements and removes outliers. Ultimately, the lower evelope of the planar convex hull serves as the desired convex system model. The proposed technique reduces systematic model error which is otherwise present when directly developing a planar convex hull model based on an unreduced data set.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

GridLAB-D Technical Support Document: Residential Equivalent Thermal Parameter Model

GridLAB-D is a power distribution systems simulation and analysis tool developed by Pacific Northwest National Laboratory. Although GridLAB-D and its underlying residential load model has been used in a variety of power systems analyses, comprehensive documentation of the derivation of the residential house model has not been formally published. The purpose of this technical support document is to serve as a comprehensive resource for the thermal dynamics modeling and house definition implemented in GridLAB-D. It covers model derivation, parameter definitions, and implementation steps in the codebase.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Direct Feed High-Level Waste APPS Model Glass Testing (DFHLW APPS) Matrix

This report summarizes the data collected during the batching and melting of the Direct Feed High-Level Waste APPS Model Glass Matrix (DFHLW APPS) to serve as a quality-assured validation of the Aspen Process Performance Simulation (APPS) formulation method. Of 15 glasses tested, 12 satisfied all target property constraints. Two glasses, APPS-05 and -06, formed nepheline on canister centerline cooling heat-treatment and failed the Product Consistency Test response limits. Glass APPS-07-2 formed unacceptably high concentrations of crystals (primarily Na3Nd(PO4)2) when heat treated at 950 °C. All other glasses were found to be satisfactory. The measured property values were compared to predicted values from a set of current models. In many cases the current models were found to be inadequate for design of DFHLW glasses. These models are being adjusted to correct for mispredictions. Other models, e.g., density, toxicity characteristic leaching procedure, and sulfur solubility, are adequate for formulation of DFHLW glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Inflation and Dark Energy from Spectroscopy at $z > 2$

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at $z < 1$, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at $2 < z < 5$, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $\sigma(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗
DOE OSTI2022

Neural network based fast prediction of β N limits in HL-2M

Artificial neural networks (NNs) are trained, based on the numerical database, to predict the no-wall and ideal-wall β N limits, due to onset of the n = 1 ( n is the toroidal mode number) ideal external kink instability, for the HL-2M tokamak. The database is constructed by toroidal computations utilizing both the equilibrium code CHEASE (Lütjens et al 1992 Comput. Phys. Commun. 69 287) and the stability code MARS-F (Liu et al 2000 Phys. Plasmas 7 3681). The stability results show that (1) the plasma elongation generally enhances both β N limits, for either positive or negative triangularity plasmas; (2) the effect is more pronounced for positive triangularity plasmas; (3) the computed no-wall β N limit linearly scales with the plasma internal inductance, with the proportionality coefficient ranging between 1 and 5 for HL-2M; (4) the no-wall limit substantially decreases with increasing pressure peaking factor. Furthermore, both the NN model and the convolutional neural network (CNN) model are trained and tested, producing consistent results. The trained NNs predict both the no-wall and ideal-wall limits with as high as 95% accuracy, compared to those directly computed by the stability code. Additional test cases, produced by the Tokamak Simulation Code (Jardin et al 1993 Nucl. Fusion 33 371), also show reasonable performance of the trained NNs, with the relative error being within 10%. The constructed database provides effective references for the future HL-2M operations. The trained NNs can be used as a real-time monitor for disruption prevention in the HL-2M experiments, or serve as part of the integrated modeling tools for ideal kink stability analysis.

Physics↗
DOE OSTI2018

Separating the effects of nucleotide and EB binding on microtubule structure

Microtubules (MTs) are polymers assembled from αβ-tubulin heterodimers that display the hallmark behavior of dynamic instability. MT dynamics are driven by GTP hydrolysis within the MT lattice, and are highly regulated by a number of MT-associated proteins (MAPs). How MAPs affect MTs is still not fully understood, partly due to a lack of high-resolution structural data on undecorated MTs, which need to serve as a baseline for further comparisons. Here we report three structures of MTs in different nucleotide states (GMPCPP, GDP, and GTPγS) at near-atomic resolution and in the absence of any binding proteins. These structures allowed us to differentiate the effects of nucleotide state versus MAP binding on MT structure. Kinesin binding has a small effect on the extended, GMPCPP-bound lattice, but hardly affects the compacted GDP-MT lattice, while binding of end-binding (EB) proteins can induce lattice compaction (together with lattice twist) in MTs that were initially in an extended and more stable state. We propose a MT lattice-centric model in which the MT lattice serves as a platform that integrates internal tubulin signals, such as nucleotide state, with outside signals, such as binding of MAPs or mechanical forces, resulting in global lattice rearrangements that in turn affect the affinity of other MT partners and result in the exquisite regulation of MT dynamics.

Science & Technology - Other Topics↗
DOE OSTI2025

Resolution requirements for numerical modeling of neutrino quantum kinetics

Neutrino quantum kinetics is a rapidly evolving field in computational astrophysics, with a primary focus on collective neutrino oscillations in core-collapse supernovae and postmerger phases of binary neutron star mergers. In recent years, there has been considerable debate concerning resolution dependence in numerical simulations. In this paper, we conduct a comprehensive resolution study in both angular- and spatial directions by using two independent schemes of quantum kinetic neutrino transport: finite volume and pseudospectral methods. We complement our discussion by linear stability analysis including inhomogeneous modes. Our result suggests that decreasing spatial resolutions underestimates the growth of flavor instability, and then leads to wrong asymptotic states of flavor conversions, which potentially has a critical impact on astrophysical consequences. We further delve into numerical results of low resolution simulations, that reveals the underlying mechanism responsible for numerical artifacts caused by insufficient resolutions. Finally, this study settles the debate on requirements of resolutions and serves as a guideline for numerical modeling of quantum kinetic neutrino transport.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Right-handed neutrino dark matter in the classically conformal $\mathrm{U(1)'}$ extended standard model

Here we consider the dark matter (DM) scenario in the context of the classically conformal U(1)′ extended standard model (SM), with three right-handed neutrinos (RHNs) and the U(1)′ Higgs field. The model is free from all of the U(1)′ gauge and gravitational anomalies in the presence of the three RHNs. We introduce a Z 2 parity in the model, under which an odd parity is assigned to one RHN, while all of the other particles are assigned to be Z 2 even, and hence the Z 2 -odd RHN serves as a DM candidate. In this model, the U(1)′ gauge symmetry is radiatively broken through the Coleman-Weinberg mechanism, by which the electroweak symmetry breaking is triggered. There are three free parameters in our model—the U(1)′ charge of the SM Higgs doublet (x H ), the new U(1)′ gauge coupling (g X ), and the U(1)′ gauge boson (Z′) mass (m Z′ )—which are severely constrained in order to solve the electroweak vacuum instability problem, and satisfy the LHC Run-2 bounds from the search for the Z′ boson resonance. In addition to these constraints, we investigate the RHN DM physics. Because of the nature of classical conformality, we find that a RHN DM pair mainly annihilates into the SM particles through Z′ boson exchange. This is the so-called Z′-portal DM scenario. Combining the electroweak vacuum stability condition, the LHC Run-2 bounds, and the cosmological constraint from the observed DM relic density, we find that all constraints work together to narrow the allowed parameter regions and, in particular, exclude m Z′ ≲ 3.5 TeV. For the obtained allowed regions, we calculate the spin-independent cross section of the RHN DM with nucleons. We find that the resultant cross section is well below the current experimental upper bounds.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

MCNP and GADRAS Comparisons

To facilitate the timely execution of System Threat Reviews (STRs) for DNDO, and also to develop a methodology for performing STRs, LANL performed comparisons of several radiation transport codes (MCNP, GADRAS, and Gamma-Designer) that have been previously utilized to compute radiation signatures. While each of these codes has strengths, it is of paramount interest to determine the limitations of each of the respective codes and also to identify the most time efficient means by which to produce computational results, given the large number of parametric cases that are anticipated in performing STR's. These comparisons serve to identify regions of applicability for each code and provide estimates of uncertainty that may be anticipated. Furthermore, while performing these comparisons, examination of the sensitivity of the results to modeling assumptions was also examined. These investigations serve to enable the creation of the LANL methodology for performing STRs. Given the wide variety of radiation test sources, scenarios, and detectors, LANL calculated comparisons of the following parameters: decay data, multiplicity, device (n,γ) leakages, and radiation transport through representative scenes and shielding. This investigation was performed to understand potential limitations utilizing specific codes for different aspects of the STR challenges.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2013

Modeling X-Ray Thomson Scattering Spectra of Warm Dense Matter

Warm dense matter (WDM) is sometimes described as a \solid density plasma" where neither weakly coupled plasma theory nor condensed matter theory are applicable. More specifically, WDM has densities of ~ 0:1 - 50 times solid density, temperatures of ~ 1 - 100 eV, is partially ionized, globally neutral, has strongly correlated particles (due to interactions), and partial electron degeneracy. In nature, WDM may be found in the interiors of planets such as Jupiter or in the envelopes of white dwarf stars, and in man-made processes such as inertial confinement fusion (ICF), WDM exists as a transient state of matter. To model these physical systems, the equation of state as well as opacity, conductivity, and diffusion coefficients must be known. Experimentally WDM is probed with an experimental technique known as X-Ray Thomson Scattering (XRTS). In such an experiment X-rays photons are scattered off of WDM and the resulting photons are measured at a given solid angle. The resulting spectrum gives information on the density and temperature of WDM, but a model of WDM and the scattering experiment is often required to interpret the results. As a result, these experiments also serve as a method of validation for potential models of WDM. The work presented here focuses on the model of WDM proposed by Starrett and Saumon and a model for scattering based off of Chihara’s work. The model proposed by Starrett and Saumon is an average-atom model that is coupled with a two component plasma model, hereafter referred to as an AA+TCP model. The AA+TCP model is an ab initio model with no free parameters, only requiring knowledge of the element under investigation, the density, and the temperature. The outputs from the model completely determine all the input parameters needed for Chihara’s structure function (chemical potential, elastic feature, screening potential, bound-state wavefunctions, and free wavefunctions), which in turn determines the double differential scattering cross-section of the experiment. This approach to determining the outcome of an XRTS experiment parallels that of Johnson, et al., but expands the work in many respects. For example, the AA+TCP model completely determines all the input parameters to the structure function; hence no ad hoc approximations are necessary to close the system. Also, the free-electron feature and the bound-free feature are modified to include collisions and occupation of free electron states, respectively.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Antiviral Effect of Antimicrobial Peptoid TM9 and Murine Model of Respiratory Coronavirus Infection

New antiviral agents are essential to improving treatment and control of SARS-CoV-2 infections that can lead to the disease COVID-19. Antimicrobial peptoids are sequence-specific oligo-N-substituted glycine peptidomimetics that emulate the structure and function of natural antimicrobial peptides but are resistant to proteases. We demonstrate antiviral activity of a new peptoid (TM9) against the coronavirus, murine hepatitis virus (MHV), as a closely related model for the structure and antiviral susceptibility profile of SARS-CoV-2. This peptoid mimics the human cathelicidin LL-37, which has also been shown to have antimicrobial and antiviral activity. In this study, TM9 was effective against three murine coronavirus strains, demonstrating that the therapeutic window is large enough to allow the use of TM9 for treatment. All three isolates of MHV generated infection in mice after 15 min of exposure by aerosol using the Madison aerosol chamber, and all three viral strains could be isolated from the lungs throughout the 5-day observation period post-infection, with the peak titers on day 2. MHV-A59 and MHV-A59-GFP were also isolated from the liver, heart, spleen, olfactory bulbs, and brain. These data demonstrate that MHV serves as a valuable natural murine model of coronavirus pathogenesis in multiple organs, including the brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Development and Porting of Nuclear Reactor Computational Models for the NRIC Virtual Test Bed in FY23

The U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. In FY23 under the NRIC program, computational models for nuclear reactor analysis were developed and contributed to the VTB Github repository during FY23 by contributors at Argonne National Laboratory. Other models developed outside of NRIC were also ported to the VTB as part of this work, serving a broader mission to make computational reactor analysis models more widely available to the reactor community. The model development activities focused on demonstration of gas-cooled microreactor models including computational fluid dynamics simulations of flow through an industry-inspired air jacket design, and simulation of multiphysics transients for a gas-cooled microreactor assembly. Additionally, development of a molten salt reactor (led at Idaho National Laboratory) was supported through Argonne’s expertise in multigroup cross sections generation. Models for these two reactor types were targeted due to their relevancy to NRIC’s Demonstration of Microreactor Experiments (DOME) and Laboratory for Operation and Testing in the U.S. (LOTUS) physical test beds, which are slated to host microreactor and molten salt reactor experiments. The model porting activities consisted of developing detailed documentation for several physics models which originate from the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. We include only contributions from Argonne National Laboratory in this report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗