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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modular, Multifunction, Multiport and Medium Voltage Utility Scale SiC PV Inverter (Final Report)

This project is to develop and demonstrate a Modular, Multi-function, Multiport and Medium Voltage utility scale SiC solar inverter (M4 Inverter). The M4 Inverter is a next generation utility scale PV + storage inverter that can substantially reduce the levelized cost of energy (LCOE) while providing grid friendly ancillary services. Conventional utility scale solar inverters includes three parts, a megawatt rated solar inverter, a low frequency transformer (LFT) and a medium voltage switch gear. The proposed M4 Inverter directly converts the DC output of solar panels to medium voltage AC, eliminating the bulky and costly LFT. The M4 Inverter also has a DC port to interface with an additional energy storage device. This plus its bidirectional power flow capability, allows multiple functions in addition to PV maximum power tracking be achieved. These additional functionalities include but not limited to reactive power support, peak shaving, fast frequency regulation and synthetic inertia. Predicted LCOE reductions with these added functionalities, improved efficiency and longer lifetime time are more than 30% compared with the 2017 utility scale solar LCOE. A 1MVA SiC M4 Inverter was developed and tested at high power level. The project has also advanced the state of the art in controller hardware in the loop simulation capability. Power electronic innovations introduced in the M4 Inverter includes 1700V SiC MOSFET application in 1500V PV system, soft switching three-port power converters and modular converter architecture.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Critical Review of Radiolysis Issues in Water-Cooled Fission and Fusion Reactors: Part I, Assessment of Radiolysis Models

A critical review is presented on modeling of the radiolysis of the coolant water in nuclear power reactors with emphasis on ITER. The review is presented in two parts: In Part I, we assess previous work in terms of compliance with important chemical principles and conclude that no model proposed to date is completely satisfactory, in this regard. Thus, some reactions that have been proposed in various radiolysis models are not elementary in nature and can be decomposed into two or more elementary reactions, some of which are already included in the models. These reactions must be removed in formulating a viable model. Furthermore, elementary reactions between species of like charge are also commonly included, but they can be discounted upon the basis of Coulombic repulsion under the prevailing conditions (T < 350 °C) and must also be removed. Likewise, it is concluded that the current state of knowledge with respect to radiolytic yields (i.e., G-values) is also unsatisfactory. More work is required to ensure that the yields used in radiolysis models are truly “primary” yields corresponding to a time scale of nanoseconds or less. This is necessary to ensure that the impact of the reactions that occur outside of the spurs (ionizing particle tracks in the medium) are not counted twice. In Part II, the authors review the use of the radiolysis models coupled with electrochemical models to predict the water chemistry, corrosion potential, crack growth rate in Type 304 SS, and accumulated damage in the coolant circuits of boiling water reactors, pressurized water reactors, and the test fusion reactor, ITER. Based on experience with fission reactors, the emphasis should be placed on the control of the electrochemical corrosion potential because it is the parameter that best describes the state of corrosion in coolant circuits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Application of NEAMS Multiphysics Framework for Species Tracking in Molten Salt Reactors

This report from Idaho National Laboratory (INL) summarizes the key modeling and simulation activities conducted under the Department of Energy (DOE) Molten Salt Reactor (MSR) Campaign during the Fiscal Year 2023 (FY23). The focus of the work was to leverage state-of-the-art modeling capabilities from the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) codes to enable novel multiphysics and multiscale modeling and simulation of MSRs. Through collaboration with NEAMS code developers, advanced multiphysics analysis capabilities for MSR systems were demonstrated by coupling depletion, thermal-hydraulics, and thermochemistry into an innovative framework for chemical species transport in MSRs. As a result, the framework can track nuclides throughout their lifetimes in the core, from production (depletion) to advection throughout the salt volume (thermal-hydraulics) and off-gassing or precipitation outside of the salt (thermochemistry). This work supports the near-term deployment of MSRs by integrating the synergistic efforts between the DOE’s MSR Campaign and NEAMS program. The resulting framework will help better connect system design modelers with experimentalists to better understand and predict complex physical behaviors in MSRs. Researchers and MSR developers alike can now leverage these new modeling and simulation capabilities to perform novel analyses with applications including: • MSR dynamics during normal operational transients and accident scenarios • Off-gas system design and performance for fuel cycle and depletion analysis • Corrosion and active chemistry control for reactor component health and lifetime determination • Source term, decay heat and activity determination in accident scenarios • Special nuclear material accountancy and chemical forensic analysis for safeguards • Digital twin development of experiments and experimental reactor demonstrations • Measurement requirements for instrumentation and control design • Uncertainty and sensitivity analysis of missing data to inform future experimental data collection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Contrasting geochemical and fungal controls on decomposition of lignin and soil carbon at continental scale

Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Signal Temporal Logic Control for Residential HVAC Systems to Accommodate High Solar PV Penetration

This paper proposes a new signal temporal logic (STL) control for ON/OFF residential buildings' Heating Ventilation and Air Conditioning (HVAC) systems. STL is used to control indoor temperatures while consuming most of the generated solar photovoltaic (PV) power locally to minimize its impact on the grid and reduce the need for large energy storage devices. In contrast to most, if not all, control mechanisms such as the traditional model predictive control (MPC), STL control allows for including temporal constraints in the control formulation to further relax indoor temperatures' constraints and allow them to exceed the comfort band limits for a prespecified (short) period of time. This relaxation allows to consume an additional PV power by the HVAC systems, which prevents such an unwanted intermittent power from affecting the grid. We formulate the MPC-based STL control mechanism to implement the objective. Simulation results show that the PV tracking performance has been improved while employing the proposed STL controller.

Wu, Tumin↗
DOE OSTI2023

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Dynamic Motions of Topological Defects in Nematic Liquid Crystals under Spatial Confinement

Abstract Topological defects (TDs) have become a sensational topic due to their significant influence on the unusual optical and physiochemical characteristics of the material. To facilitate their application across a wide range of disciplines it is desirable to analyze and gain fundamental understanding of TDs in both equilibrium and nonequilibrium systems. Liquid crystals (LCs) are considered an ideal system for study given the direct visualization of TDs and a straightforward analyzation process. In addition to the equilibrium morphology of LC TDs, it is also of great interest to track and control the formation and annihilation of defects during thermodynamic processes. However, controlling the dynamic behavior of formed defects remains a challenge. Here, nematic LCs confined in a cell exhibiting surfaces with periodic anchoring conditions containing surface topography are relied on. The effects of patterned surface characteristics such as width, periodicity, and degree of curvature on defects dynamic motion, stabilization, and annihilation are explored. The computational experiments recapitulate the TDs transition path and provide free energy‐based predictions of critical distances for defect annihilation. Taken together, this simple approach offers a promising opportunity to control the dynamics of TDs in LCs through chemical patterned surfaces with topography.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Graph-Learning-Assisted State and Event Tracking for Solar-Penetrated Power Grids with Heterogeneous Data Sources

Unlike transmission systems, distribution systems do not typically contain sufficient metering to enable real-time state estimation. The lack of sufficient real-time measurements prohibits accurate and timely monitoring of the state of distribution systems. As a result, control and optimal operation of distribution systems, especially those containing large numbers of renewable generation units are not possible without proper data and information about the current state of the system. The main motivation of this project is to address this shortcoming by developing an approach which provides “predicted” real-time measurements so that they can be used to execute a distribution system state estimator. Thus, the objective of the project is to make the distribution systems fully observable, such that the hosting capacity for solar generation can be accurately estimated, and unnecessary solar curtailments can be avoided. In order to accomplish this goal, the project investigated the use of a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams obtained from AMI meters, SCADA as well as PMU measurements and created synchronous measurement snapshots for the state estimator (SE); and developed a hybrid robust SE which provides not only accurate state estimates but also real-time feedback for the ML model refinement.

14 SOLAR ENERGY↗
DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗
DOE OSTI2020

Connecting Hindered Transport in Porous Media across Length Scales: From Single-Pore to Macroscopic

Hindered mass transport is widely observed in various porous media, including filtration membranes and porous rocks. However, there is no universal model capable of predicting transport in porous media due to the heterogeneity of porous structures and the complexity of the underlying microscopic mechanisms, including electrostatic and hydrodynamic effects. Here, we used a highly ordered porous medium (i.e. a silica inverse opal film) as a model system to directly explore the effects of geometric parameters (i.e. pore size, pore throat size and tracer particle size) and microscopic interaction parameters (e.g. controlled by ionic strength) on nanoparticle transport in porous environments using three-dimensional single-particle tracking. We found a linear scaling relation between the macroscopic diffusion coefficient and microscopic diffusion behaviors and a combination of parameters associated with microscopic features and phenomena, including both geometric effects and particle-level interactions. The proportionality coefficient relating micro and macro behaviors was complex and related to the connectivity of the matrix and pore-size variation, which could lead to tortuous diffusion pathways, hindering macroscopic transport. Furthermore, these findings provide important insights into nanoparticles and macromolecules transport in various porous media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Model predictive control for matrix converter operating in current control mode with load current estimation

A matrix converter system operating in current control mode is provided. The matrix converter includes a switching matrix coupled between a low voltage side and a high voltage side, wherein the matrix converter is coupled at its low voltage side to a generator for receiving an input power including an input current and transforming the input power into an output power at its high voltage side, wherein a load is coupled to the high voltage side. The control system includes a load observer and a model predictive controller (MPC). The load observer is configured to estimate a load current that flows to the load from the high voltage side of the matrix converter as a function of a switching state of the switching matrix, an output voltage output at the high voltage side of the matrix converter, and the input current. The MPC is configured to select the switching state of the switching matrix to meet one or more control objectives defined by minimization of a multi-objective function that tracks the output voltage and the input current using the estimated load current.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor

This article proposes an extended state observer based robust model predictive velocity control to decrease system prediction error under parameter uncertainties for permanent magnet synchronous motor (PMSM). We develop a new PMSM model that consists of velocity and acceleration to lump the system information and an external disturbance into a disturbance. The extended state observer (ESO) is designed to estimate the velocity, acceleration, and disturbance. By estimating the state variables and disturbance using the ESO, the model predictive control (MPC) finds the optimal control input by predicting future system behavior. Additionally, the direct current controller is designed so that the direct current converges to zero. Because the proposed method is not designed based on the cascade structure from the viewpoint of velocity control, the optimization control for the velocity and currents can be defined. Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.

42 ENGINEERING↗
DOE OSTI2022

Nanostructures evolution assessment and spectroscopic properties modification induced by electronic energy loss in KTaO 3 crystal

Modifications of micro/nanostructure and photoelectric properties under swift ion irradiation (645 MeV Xe 35+ ) of KTaO 3 crystal with varying electronic energy losses (7.2–31.4 keV/nm) and ion velocities (0.18–5.00 MeV/u) have been studied by combining experimental and calculated approaches. The i-TS calculations combined with molecular dynamics simulations are compared with the experimental observations, revealing the inner track fine structures from individual spherical defects to continuous ion tracks with core–shell morphologies, and a quantitative relationship, including the melting (0.42 eV/atom), damage (0.75 eV/atom), and amorphous (1.71 eV/atom) thresholds, is established to successfully predict the inner disorder/amorphous proportions and track damage morphologies. The surface nanostructures of nanohillocks (isolated or partial overlaps) and nanopits (serious overlaps) directly depend on the combined action of the deposited potential and kinetic energies of incident ions, which induce local melting and sublimation in the near-surface region. Owing to the decreased recombination and the increased separation efficiency between electrons and holes, the higher photogenerated charge carrier mobility enhanced the photocurrent effect, further optimizing the photoconductivity performance in Xe 35+ -irradiated KTaO 3 . Therefore, controlled defect engineering using the ion irradiation technique, as an effective strategy, could design tailored nanostructure systems and regulate photoelectric properties, further promoting the development of novel technological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pulsed power to control growth of silicon nanoparticles in low temperature flowing plasmas

Low-temperature plasmas have seen increasing use for synthesizing high-quality, mono-disperse nanoparticles (NPs). Recent work has highlighted that an important process in NP growth in plasmas is particle trapping—small, negatively charged nanoparticles become trapped by the positive electrostatic potential in the plasma, even if only momentarily charged. In this article, results are discussed from a computational investigation into how pulsing the power applied to an inductively coupled plasma (ICP) reactor may be used for controlling the size of NPs synthesized in the plasma. The model system is an ICP at 1 Torr to grow silicon NPs from an Ar/SiH 4 gas mixture. This system was simulated using a two-dimensional plasma hydrodynamics model coupled to a three-dimensional kinetic NP growth and trajectory tracking model. The effects of pulse frequency and pulse duty cycle are discussed. We identified separate regimes of pulsing where particles become trapped for one pulsed cycle, a few cycles, and many cycles—each having noticeable effects on particle size distributions. For the same average power, pulsing can produce a stronger trapping potential for particles when compared to continuous wave power, potentially increasing particle mono-dispersity. Pulsing may also offer a larger degree of control over particle size for the same average power. Experimental confirmation of predicted trends is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Data for: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial-Aquatic Interfaces

The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes and generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including a large-scale flood manipulation experiment, collectively generating over three million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi-institution projects. This dataset shows example data generated by the sensor network described above. Data output for data loggers connected to groundwater water quality sondes measuring dissolved oxygen, pH, oxidative redox potential (ORP), groundwater elevation, groundwater salinity and temperature, and groundwater elevation; replicate soil moisture and conductivity probes installed at 10 and 30 cm below the ground surface; rainfall and solar radiation; and mean sap flow from 8 replicate probes. Sensors are installed at upland forest (UP), wetlands (W), and transitional locations between these ecosystems (TR). Example datasets are from Goodwin Islands in the Chesapeake Bay and Portage River along the Lake Erie coastline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improvements to PVWatts for Fixed and One-Axis Tracking Systems

This work presents improvements to the widely used NREL PVWatts photovoltaic system energy model to improve modeling accuracy for typical fixed and one axis system designs. The aim is to calculate losses in the PV system assuming typical modern system design practices, while maintaining simplicity by keeping the required set of input parameters small. These improvements allow users to more credibly and quickly evaluate competing system designs in early stage feasibility. Common submodels for module cover, spectral, snow, tracker, transformer, plant controller, and self-shading losses, in addition to a bifacial gain option, are incorporated into the PVWatts model, and are shown to improve PVWatts' system performance prediction capabilities without major impact to ease of use. We anticipate including these improvements in a future release of NREL's open source PVWatts code, and some of the features may become available in the System Advisor Model (SAM) desktop software as well as the popular PVWatts web application.

14 SOLAR ENERGY↗
DOE OSTI2022

Droplet Evaporation-Based Approach for Microliter Fuel Property Measurements

Small-volume, high-throughput screening techniques are sought to enable downselection from a large candidate pool of bio-blendstocks to a select few, having physical properties consistent with requirements of downsized, turbo-boosted internal combustion engines. Herein, this work presents a droplet evaporation-based approach to predict heat of vaporization, vapor pressure, diffusion coefficient, and Lennard–Jones parameters for an unknown fuel. Two different schemes, considering the isothermal evaporation of a moving droplet in ambient air, are proposed, which combine droplet velocity and temperature measurements, with some known properties to predict unknown properties. The schemes utilize an inverse solution of a transient model of droplet evaporation solved in an iterative fashion. A baseline scheme, which only requires droplet size change measurements, is evaluated using test data for three liquid fuels, comprising of alkanes and alcohols, as obtained in a temperature-controlled chamber. Results yield temperature-dependent heat of vaporization and vapor pressure predictions within 10 % and 22 %, respectively, of reference values. The advanced scheme, which additionally requires droplet temperature measurement, is numerically evaluated in the current work and will be experimentally validated in future efforts. The advanced scheme is found to significantly improve prediction quality, with deviations less than 2 % and 1 % for heat of vaporization and vapor pressure, while also predicting diffusion coefficient and Lennard–Jones parameters within 5 % and 8 %, respectively. The combined set of approaches, which primarily track droplet evaporation, can be incorporated into a small-volume, high-throughput fuel screening process.

09 BIOMASS FUELS↗