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At least 973 records · Page 54

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DOE OSTI2020

Localised electromagnetic waves in a rhombic waveguide array with competing nonlinearities

We consider a model of a discrete photonic system representing a quasi-one-dimensional rhombic array of waveguides, where, in addition to the positive cubic nonlinearity, the negative quintic nonlinearity is taken into account for the material of which the central chain of the waveguides is made. The other two waveguide chains are made of an optically linear material. A continual approximation is used to obtain a solution for a system of coupled waves, which describes a wave localised in the transverse direction. In a certain special case, the competition of nonlinearities leads to the formation of a step-shaped distribution of the field intensities over the waveguides. (integrated optics)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Understanding the Role of Hydroxyl Functionalization in Linear Poly(Ethylenimine) for Oxidation‐Resistant Direct Air Capture of CO 2

Aminopolymer-based adsorbents are a prominent class of materials being used for direct air capture of CO 2 at the industrial scale. However, improving their working lifetime, specifically by increasing their resilience to oxidative degradation, remains an ongoing challenge. Toward this end, functionalization of aminopolymers with non-amine functionalities such as hydroxyls has emerged in recent years as a promising strategy toward improving adsorbent lifetime. Although there is a growing body of work demonstrating the effectiveness of this approach and investigating the origin of this improved stability, studies to date have primarily focused on branched aminopolymer systems such as branched poly(ethylenimine). In this work, hydroxyl-functionalized linear poly(ethylenimine) is used to continue to probe the underlying protective mechanism of this strategy. A combination of thermogravimetric analysis, NMR relaxometry, differential scanning calorimetry, and computational simulations is used to better understand the relationship between the extent of chemical functionalization, physical properties, and adsorbent performance.

amine-based adsorbent↗

DOE OSTI2022

Absolute colors and phase coefficients of asteroids

Context: We use the phase curves of small bodies to measure absolute magnitudes and, together with complementary theoretical and laboratory results, to understand the micro- and macroscopic properties of their surfaces. Although we can observe asteroids up to phase angles of about 30 deg, the range of phase angles covered by outer Solar System objects usually does not go further than 7-10 deg for centaurs and 2 deg for trans-Neptunian objects, and a linear relation between magnitude and phase angle may be assumed. Aims: We aim to directly compare data taken for objects in the inner Solar System (inside the orbit of Jupiter) with data for centaurs and trans-Neptunian objects. Methods: We use the SLOAN Moving Objects Catalog (MOC) data to construct phase curves restricted to phase angles of less than or equal to 7.5 deg, which is compatible with the angles observed for the trans-Neptunian and Centaur population. We assume a linear model for the photometric behavior to obtain absolute magnitudes and phase coefficients in the ugirz, V, and R filters. Results: We obtain absolute magnitudes in seven filters for over 4000 objects. Our comparison with outer Solar System objects suggests the surfaces have a common property: intrinsically redder objects become blue with increasing phase angle, while the opposite happens for intrinsically bluer objects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

MoorDyn V2: New Capabilities in Mooring System Components and Load Cases

MoorDyn, an open-source mooring dynamics model, is being expanded with capabilities for additional mooring system features and load cases. As floating wind turbine technology matures, mooring systems are becoming more sophisticated and more complex scenarios need to be considered in the design process. Mooring systems may have synthetic line materials, ballast/buoyancy bodies along the lines, or interconnections between platforms. Failure modes may involve multiple cascading line failures that depend on mooring system dynamics. Features recently added to MoorDyn aim to address these emerging needs. MoorDyn’s linear elasticity model has been supplemented to support user-defined stress-strain curves, which can be adjusted to represent synthetic mooring materials. Rigid six-degree-of-freedom bodies in the mooring system can now be modeled using two new model objects. “Rod” objects provide an option for rigid cylindrical bodies. They use the existing Morison equation-based hydrodynamics model and can be connected to mooring lines at either end. “Body” objects provide a generic six-degree-of-freedom rigid-body representation based on a lumped-parameter model of translational and rotational properties. Rod objects can be added to Body objects and mooring lines can be attached at any location, allowing a wide variety of submerged structures to be integrated into the mooring system. Lastly, a means of dynamically simulating mooring line failures has been implemented. These new features, currently in the C++ version of MoorDyn, are described and then demonstrated on a two-turbine shared-mooring array. A qualitative view of the results suggests the new features are functioning as expected.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2020

MoorDyn V2: New Capabilities in Mooring System Components and Load Cases: Preprint

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2020

Gradient-Based Multi-Area Distribution System State Estimation

The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates

Efficient operation of battery energy storage systems requires that battery temperature remains within a specific range. Current techno-economic models neglect the parasitic loads heating and cooling operations have on these devices, assuming they operate at constant temperature. In this work, these effects are investigated considering the optimal sizing of battery energy storage systems when deployed in cold environments. Here, a peak shaving application is presented as a linear programming problem which is then formulated in the PYOMO optimization programming language. The building energy simulation software EnergyPlus is used to model the heating, ventilation, and air conditioning load of the battery energy storage system enclosure. Case studies are conducted for eight locations in the United States considering a nickel manganese cobalt oxide lithium ion battery type and whether the power conversion system is inside or outside the enclosure. The results show an increase of 42% to 300% in energy capacity size, 43% to 217% in power rating, and 43% to 296% increase in capital cost dependent on location. This analysis shows that the heating, ventilation, and air conditioning load can have a large impact on the optimal sizes and cost of a battery energy storage system and merit consideration in techno-economic studies.

25 ENERGY STORAGE↗

DOE OSTI2020

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2022

High-Temperature Linear Receiver Enabled by Multicomponent Aerogels

Concentrating solar thermal (CST) technology has significant potential mainly due to its dispatchability and low cost of storage. However, to compete with other sources, including utility-scale solar PV, its final cost (cents/kWh) still needs to be lowered. Cost reduction can be achieved by improving the system level efficiency of the CST plants through the deployment of advanced power cycles, which operate at high temperatures of ~700°C. However, optical, and thermal losses pose a major challenge to the efficiency of such CST systems. The overall aim of this project is to investigate and de-risk a linear solar receiver concept called an Aerogel Insulated Receiver (AIR) that generates high temperatures (up to 700°C) at a low solar concentration ratio (<100) and with a high collection efficiency (optical × receiver). Our prior work has demonstrated the thermal stability1 and optical and heat-insulating properties2,3 of transparent aerogel insulation at a one-inch scale. The focus of this work is on (1) co-optimization of the geometry of the aerogel tile and receiver enclosure to fit a standard parabolic collector (PTC), (2) scale-up of aerogels into 4-inch tiles while preserving key properties, (3) experimental measurement of receiver heat loss (W/m) in a >70-cm test stand and validation of anticipated receiver performance at high temperatures. Regarding (1), appropriate optical and thermal models for a parabolic trough receiver (PTR) are developed and validated. The geometry of the aerogels and the receiver enclosure are co-optimized to maximize the collection efficiency. The model predicts a 54% collection efficiency at 700°C for an AIR design based on flat aerogels. By combining the collection efficiency with the power block efficiency of supercritical CO 2 cycles, we predict >10% improvements in peak plant efficiency relative to existing line-focusing CST systems. The application of curved plasmonic aerogels is predicted to further increase the collection efficiency to 64%. Regarding (2), we demonstrate the successful development of 6-inch-long refractory aerogel tiles with optical, thermal, and stability characteristics consistent with our prior work. This scale-up requires a transition to a larger ALD station and modifying the ALD process variables such as exposure time and the number of precursor doses. Regarding (3), we design and develop an AIR test stand measuring 3 feet in length. Heat loss performance analysis is carried out using the test stand. The results indicate that aerogel insulation can significantly reduce receiver thermal losses at the high operating temperatures required for next-generation PTRs. The experimental results agree with the heat loss performance predicted by our receiver model. Lastly, we conducted preliminary failure mode and effects (FMEA) and techno-economic (TEA) analyses to identify failure mitigation strategies and commercial opportunities, respectively. Overall, this project identifies key opportunities and challenges in deploying aerogel insulating receivers in next-generation line-focusing CST technologies.

14 SOLAR ENERGY↗

DOE OSTI2022

A Comparison of Machine Learning Methods for Frequency Nadir Estimation in Power Systems

An increasing penetration level of inverter-based renewable energy resources changes the inertia of power systems, posing challenges for maintaining the desired system frequency stability. An accurate frequency nadir estimation is crucial for power system operators to prepare preventive actions against large frequency excursions. In this paper, five machine learning methods - linear regression, gradient boosting, support vector regression, an artificial neural network, and XGBoost - are applied to two different datasets, i.e., 1) the unit generation dataset and 2) the system total inertia and headroom dataset, for the prediction of the frequency nadir. The training and testing datasets are generated through extensive generation scheduling simulations using Multi-timescale Integrated Dynamic and Scheduling (MI-DAS) toolbox on the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. Numerical results show that all five machine learning methods perform well in predicting the nadir frequency of the system. Among them, the gradient boosting and the XGBoost are clear winners yielding the best prediction accuracy in terms of four evaluation metrics.

data driven↗

DOE OSTI2025

Minimum GHG emissions and energy consumption of U.S. PET and polyolefin packaging supply chains in a circular economy

There is a wide agreement on the urgency of transforming linear management of plastics towards a circular economy model. However, no clear pathways exist as to required recycling technologies involved and system-wide environmental impacts. This study explores such pathways in the U.S. for the most commonly used packaging plastics through a combination of mechanical and emerging advanced recycling technologies. A system optimization model aimed at minimizing environmental impacts was developed to determine optimal end-of-life (EOL) management and locations of existing and emerging U.S. recycling infrastructures. Our study includes material flows from virgin resin production through semi-manufacturing processes to existing EOL disposal and recycling processes. An optimized circular plastics packaging system achieved greenhouse gas (GHG) emission savings of up to 28% and cumulative energy demand (CED) savings of up to 46%, compared to the linear economy. Moreover, these savings of GHG emissions and CED impacts represent a reduction of 0.16% and 0.49% compared to annual U.S. GHG emissions and energy consumption in 2022, respectively. The optimal recycling rates and systems-level circularity ranged from 78–99% and 57–75%, respectively. Increased energy savings led to increased GHG emissions showing a potential trade-off between GHG emissions and energy. Analysis of 40 scenarios showed the importance of material collection distances, blend limit of mechanically recycled resins, process yields, and mandated recycling rates for achieving a sustainable circular economy of plastics.

09 - BIOMASS FUELS↗

DOE OSTI2024

FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings (FTL) v1.0

Fusion Transfer Learning (FTL) model provides a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. The knowledge transfer process leverages a pre-trained neural encoder-decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL's capacity to capture transitional behaviors and dynamical features in plasma dynamics -- a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics (MHD) modes.

Bai, Zhe↗

DOE OSTI2020

Dynamics of poly[n]catenane melts

Inspired by advances in the chemical synthesis of interlocking polymer architectures, extensive molecular dynamics simulations have been conducted to study the dynamical properties of poly[n]catenanes-polymers composed entirely of interlocking rings-in the melt state. Both the degree of polymerization (number of links) and the number of beads per ring are systematically varied, and the results are compared to linear and ring polymers. Herein, a simple Rouse-like model is presented, and its analytical solution suggests a decomposition of the dynamics into "ring-like" and "linear-like" regimes at short and long times, respectively. In agreement with this picture, multiple sub-diffusive regimes are observed in the monomer mean-squared-displacements even though interchain entanglement is not prevalent in the system. However, the Rouse-type model does not account for the topological effects of the mechanical bonds, which significantly alter the dynamics at intermediate length scales both within the rings and at the chain segment scales. The stress relaxation in the system is extremely rapid and may be conveniently separated into ring-like and linear-like contributions, again in agreement with the Rouse picture. However, the viscosity has a non-monotonic dependence on the ring size for long chains, which disagrees strongly with theoretical predictions. This unexpected observation cannot be explained in terms of chain disentanglement and is inconsistent with other measures of polymer relaxation. Possible mechanisms for this behavior are proposed and implications for materials design are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗

DOE OSTI2023

Verification of Spent Fuel Inside Dry Storage Casks by Cask Top Fast Neutron Mapping (FY2023 Mid-Year Report)

This project is developing a prototype scanner array verification system for detection of missing fuel assemblies in spent-fuel storage casks. The prototype consists of six fast-neutron scintillator detectors mounted to a linear actuator frame that is placed on the top of a spent fuel cask to scan across all fuel assembly positions. The scanner array was assembled and tested at LLNL in FY2022. A field test schedule has been requested at the Idaho National Laboratory (INL) Cask Farm site for FY2023. Note that the Cask Farm contractor determines this scheduling and not INL directly. Further system automation will be designed and implemented with the goal of obtaining a level of system operation that meets IAEA needs. This includes integration of the scanner array and data-acquisition control software into a single interface for operator use. In addition, commercial operators and the IAEA may have special requirements for portability, shipping, lifting, and installation. Prior to the Field Test at INL, the system will be operated at LLNL to exercise lifting procedure and linear actuators, monitor stability of detector energy and pulse-shape discrimination calibration, and test system software integration efforts. Following the Field Test, we will present results and discuss the technology with the IAEA. We will incorporate additional improvements to the system based on lessons learned from the field test and feedback from the IAEA. If successful, the technology can be transferred to the IAEA or other stakeholders for assessment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

chimes_calculator

The Chebyshev Interaction Model for Efficient Simulation (ChIMES) is a machine-learned interatomic potential targeting chemistry in condensed phase systems. ChIMES models are able to approach quantum-accuracy through a systematically improvable explicitly many-bodied basis comprised of linear combinations of Chebyshev polynomials. Though originally developed as to enable description of organic molecular materials, ChIMES has successfully been applied to systems spanning ambient water to molten carbon, and leveraged as correction for density functional based tight binding simulations. The ChIMES calculator comprises a flexible toolset for evaluating ChIMES interactions (e.g. in simulations, single point calculations, etc). Users have the option of directly imbedding the ChIMES calculator within their codes, or evaluating interactions through the beginner-friendly serial interface, each of which have Python, C++, C, and Fortran API's.

Goldman, Nir↗

DOE OSTI2020

An Experimental Feasibility Study on Applying Neutron Tomography to Encapsulated Spent Nuclear Fuel - 20050

Visual inspection makes easier to ensure the integrity and safety of spent nuclear fuel (SNF) than any classical techniques. Various classical techniques have been applied but there are no reliable methods to qualitatively and quantitatively verify spent fuel in dry storage. Thus, the present authors have developed the prototype safeguards apparatus for dry storage employing the array of He-4 gas scintillation detectors (S670E, Arktis Radiation Detectors Ltd., Switzerland), newly designed to simultaneously measure thermal and fast neutrons without any moderators. The S670E detector has a cylindrical shape with a diameter of 52 mm and active length of 600 mm (total length: 875 mm). The detector is filled by He-4 gas with an approximate pressure of 180 bar for fast neutron detection, and its inner wall is coated by Li-6 for thermal neutron detection. The scintillation lights generated via Li-6 nuclear reaction and elastic scattering are collected by 24 SiPMs linearly paired at the center of the detector. The detector delivers a TTL (Transistor-Transistor Logic) output for pulse readout and UART (Universal Asynchronous Receiver Transmitter) for device control. In order to assess feasibility of the apparatus, an experimental system has been designed, built, and optimized via computational studies. Cf-252 neutron sources and linearly arrayed detectors, working as a single detector, were occupied for this study due to the difficulties in working with SNF. The laboratory scale cask (diameter: 0.67 m, height: 1.5 m), minimized by a factor of 10 compared to the actual thickness of a commercial TN-32 cask, was also manufactured. The detector array was designed to rotate the lab-scale cask and obtain 36 image profiles at every 10 degrees. All profiles were aligned in single frame image called a sinogram, and the cross-sectional image was then fabricated by the inverse radon transform algorithm. These experiments have been repeated with different configurations and numbers of sources. Some gamma-ray sources were also measured with neutron sources in order to distinguish between neutron and gamma-ray pulses. Basically, a He-4 detector is designed to run on Linux OS so it is difficult to directly apply to Windows-based equipment widely used in S. Korea. Therefore, a new data acquisition board working on Windows OS was designed and built. The board mainly consists of FPGA (Field Programmable Gate Array) and SoC (System on Chip) for TTL pulse readout, sorting measured data, and transferring data to a user interface. In conclusion, the tomographic system at lab-scale has shown considerable potential to detect a partial or gross defect of encapsulated assemblies in dry storage. Next steps of this study will be to 1) repeatedly carry out experiments to demonstrate scientific reliability and validity, and 2) numerically integrate signals with weight factors to enhance the image quality since the suggested system based on passive interrogation method requires longer measurement time. Finally, the system will apply to a commercial dry storage phased out soon in S. Korea. (authors)

07 ISOTOPE AND RADIATION SOURCES↗