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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗
DOE OSTI2020

Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs

Hydrocarbon recovery from a hydraulic-fractured unconventional reservoir can be enhanced by replacing slickwater with non-Newtonian high-viscosity gel as a fracturing fluid. However, in the literature, the role of gel rheology on hydrocarbon production from a fractured reservoir is not very well understood. This lack of understanding makes it difficult for the industry to choose appropriate viscosity parameters of fracturing gels. Motivated by these limitations, first, we have developed a high-fidelity model of non-Newtonian fluid flow to simulate fracture propagation, fluid leak-off, and gel flowback processes to predict the oil production from a hydraulic fractured reservoir. Simulation results of the high-fidelity model show how fracture geometry, formation damage, and gel cleanup process varies with the fracturing fluid rheology. Therefore, the developed model is used to understand the effect of gel rheology on hydrocarbon production via a sensitivity analysis, which provides a set of rheological parameters that maximize the cumulative amount of oil production. Finally, utilizing this optimal high-viscosity gel, a model predictive controller is designed to obtain an optimal pumping schedule necessary for producing a target fracture geometry and proppant concentration. The closed-loop simulation results demonstrate that the obtained pumping schedule successfully achieved the desired fracture geometry and proppant concentration at the end of pumping, leading to the maximum oil production from an unconventional reservoir.

13 HYDRO ENERGY↗
DOE OSTI2021

An adjoint method for determining the sensitivity of island size to magnetic field variations

An adjoint method to calculate the gradient of island width in stellarators is presented and applied to a set of magnetic field configurations. The underlying method for calculation of the island width is that of Cary & Hanson ( Phys. Fluids B, vol. 3, issue 4, 1991, pp. 1006–1014) (with a minor modification), and requires that the residue of the island centre be small. Therefore, the gradient of the residue is calculated in addition. Both the island width and the gradient calculations are verified using an analytical magnetic field configuration introduced by Reiman & Greenside ( Comput. Phys. Commun. , vol. 43, issue 1, 1986, pp. 157–167). The method is also applied to the calculation of the shape gradient of the width of a magnetic island in a National Compact Stellarator Experiment (NCSX) vacuum configuration with respect to positions on a coil. A gradient-based optimization is applied to a magnetic field configuration studied by Hanson & Cary ( Phys. Fluids , vol. 27, issue 4, 1984, pp. 767–769) to minimize stochasticity by adding perturbations to a pair of helical coils. Although only vacuum magnetic fields and an analytical magnetic field model are considered in this work, the adjoint calculation of the island width gradient could also be applied to a magnetohydrodynamic (MHD) equilibrium if the derivative of the magnetic field, with respect to the equilibrium parameters, is known. Using the island width gradient calculation presented here, more general gradient-based optimization methods can be applied to design stellarators with small magnetic islands. Moreover, the sensitivity of the island size may itself be optimized to ensure that coil tolerances, with respect to island size, are kept as high as possible.

Physics↗
DOE OSTI2021

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub

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

Low-efficiency-droop c -plane InGaN/GaN light-emitting diodes through the use of thick single quantum wells and doped barriers

Efficiency droop at high current densities is a problem for InGaN-based light-emitting diodes (LEDs), especially for conventional c-plane devices. The large internal electric fields in c-plane quantum wells (QWs) lead to an increase in the active region carrier density (n), causing the electrical efficiency droop onset to occur at low current densities. Here, we present an approach to reduce the internal electric fields (Eint) in c-plane QWs by placing doped p-type and n-type GaN barriers close to the QW. The reduced Eint also allows a thick QW active region design, which helps to lower n to further reduce the droop. The concept of using doped barriers to control Eint is explained using theory and device simulations. Following that, multiple series of thick single QW (SQW) LEDs were grown and characterized. Key parameters in the epitaxial design such as the doping levels and the relative position of the doped barriers were systematically studied and optimized. In conclusion, by using optimized doped barriers and a thick SQW, c-plane LEDs with a low-efficiency droop of 14% at 300 A/cm2 [with respect to the peak external quantum efficiency (EQE)] and a high peak EQE of 49% were demonstrated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Dynamic evolution of ultracold electron beams from nanostructured cathodes

Dynamic Coulomb expansion of dense, nonuniform particle beamlets can lead to causticlike formations in the charge density profile of the resultant beam. Such features only occur for cold beams where thermal diffusion of the particles is minimized. These phenomena have been observed experimentally for select cases with ion beams but not yet for electron beams as the electron dynamics occur over far faster timescales. We simulate the interaction of electron bunches while varying the initial transverse temperature and density profiles to determine the thresholds that characterize this pattern formation. Furthermore, we extend the simulations to geometric arrangements that are more pertinent to the nanotip cathode application and consider the effects of asymmetries and the impact of a low-density halo on the overall process. The simulations are conducted using a previously documented high-fidelity algorithm for collisional particle dynamics. These studies identify the temperature and density thresholds required for caustic formation and provide guidance for optimizing the design of nanoscale structured cathodes. We also demonstrate that the correlation between the final density patterns and the underlying beam parameters enables a virtual diagnostic tool for measuring ultralow emittance beams solely based on a transverse charge density profile.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

High-Current Light-Ion Cyclotron for Applications in Nuclear Security and Radioisotope Production

In this article, we propose the conceptual design, with supporting beam dynamics results, of a normal conducting, separated-sector cyclotron with a strong-focusing field gradient designed to accelerate light ions with a charge-to-mass ratio of 1/2 up to 15–20 MeV/u. The design can support a host of applications for therapy, radiobiology, material science, and instrumentation development. The light-ion species, which can include a mixed ion beam, can be dynamically chosen to provide a range of characteristic signals appropriate for specific material identification such as special nuclear materials. A conservative baseline concept is presented which has been optimized for radioisotope production of alpha emitters and theranostic radiopharmaceuticals. The modular design is also demonstrated to be scalable in gross physical parameters by a factor of 2–3, thereby reducing the size, weight, and power requirements (SWaP) and enabling near-term security applications.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Predictive Modeling of Electrodeposition in a Single Pore Flow-Through Electrode: From Electronucleation to Coating Thickness Uniformity

The emergence of advanced manufacturing methods capable of producing porous three-dimensional structures has expanded the design space for next-generation functional components. The ability to fabricate ordered 3D foams for use in electrocatalysis reactors has increased the need for controlled deposition of catalytic metals onto porous support materials, such as carbon. However, there is a lack of clear design guidelines for electrodeposition onto 3D substrates, due to the geometric complexity and multi-scale nature of the problem. Furthermore, electro-nucleation phenomena are often overlooked in macro-scale models of current distribution during deposition. Here, a graphite flow-through electrode (FTE) is used as a model system for copper deposition within a single pore. Potential distributions and electro-nucleation phenomena are coupled in a continuum level model by incorporating nucleation size and density as a function of overpotential, determined experimentally using in situ atomic force microscopy (AFM). The model predictions are validated by measuring the coating uniformity in the pore using micro-computed X-ray tomography (μCT). A scaling analysis comprising dimensionless parameters such as the Wagner number is presented. The simplified scaling relationship framework can guide the electrodeposition process and electrode design to optimize plating of porous substrates under fluid flow conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

REBOUND: Reverse Engineering Bidirectional Outflow Under Non-Equilibrium Diffusion

Rare-earth elements (REEs) are essential for electronics, renewable energy, and defense technologies. However, the current supply of REEs relies on mining concentrated in a few countries and energy-intensive separations. DOE’s Basic Energy Sciences (BES) program has launched a grand challenge which aims to ensure a sustainable supply of critical REEs by developing innovative and environmentally friendly separation methods. As an alternative to costly and harmful traditional methods, the Non-Equilibrium Transport Driven Separations (NETS) initiative has created a microfluidic Y-channel co-flow method that applies external fields to exploit magneto- and electrohydrodynamic effects for separating dilute REE ions from complex feedstocks. Computational fluid dynamics (CFD) studies have identified a few operating conditions with promising ion selectivity and separation efficiency. However, challenges remain regarding Y-channel versatility across feedstocks and accurate incorporation of physical phenomena into CFD models. In this work, we develop a multi-fidelity modelling approach which integrates experimental results with CFD simulation to build a surrogate model for the dependence of separation efficiency to variation of design parameters. The surrogate model enables a reinforcement learning (RL) method to adaptively launch CFD and experimental runs, improving model fidelity around optimal Y-channel parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

LAMP Technical Readiness Evaluation Report: LAMP-ENG-RPT-003 (Revision 2)

An internal preliminary evaluation of Critical Technology Elements (CTEs) for the LANSCE Modernization Project (LAMP) was completed in 2023. This included determining corresponding Technical Readiness Levels (TRLs) for all subsystems using the criteria of DOE G 413.3-4A, Technical Readiness Assessment Guide. This revised report includes a summary of the recent design modifications required to meet the project Key Performance Requirements (KPPs), some of which may reduce technical risks to the project. These recent design modifications are based on the LAMP Conceptual Design which is the design evaluated in this document and include: • Further optimization of the low-energy and medium-energy beam transport regions (LEBT and MEBT, respectively), including relocation of various functional elements (i.e. choppers, kickers, and bunchers). • An additional 100-keV H - ion source to separate ion-source function based on beam delivery requirements. • A high-repetition-rate pulsed kicker magnet to select/merge the two H - ion beams into a common low-energy beam transport. • Modification and further optimization to a more conventional 3-MeV Radio Frequency Quadrupole (RFQ) design. Performance of the RFQ has been optimized to deliver the required three types of beams while meeting the project Key Performance Parameters (KPPs). • The addition of a second chopper in the medium-energy beam transport (MEBT) line to reduce the required pulser voltages.

43 PARTICLE ACCELERATORS↗
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 OSTI2021

Evaluation of external tubular configurations for a high-temperature chloride molten salt solar receiver operating above 700°C

Next-generation concentrating solar power (CSP) tower technologies target operating temperatures exceeding 700°C to increase the thermal-to-electric conversion efficiency. Molten chloride salts are one possible alternative to current commercial molten nitrate salts to enable the higher operating temperature. This paper analyzes the predicted optical, thermal-fluids, and structural performance of traditional external tubular solar receiver configurations applied with a chloride salt heat transfer fluid (HTF) and inlet/outlet temperatures of 500°C/735°C, and considers sensitivity analysis and optimization relative to receiver sizing, tube sizing, number of panels, flow circuit configurations, and solar flux concentration under constraints on internal velocity, pressure drop, wall thickness, and required creep-fatigue lifetime. The high temperature conditions increase the significance of inelastic deformation mechanisms such as creep relative to that expected in commercial 565°C nitrate salt designs. High-temperature creep and creep-fatigue damage in the metal alloy tubes are the key factors that limit allowable solar flux concentration and achievable receiver thermal efficiency at the near-800°C wall temperature conditions. For a traditional external cylindrical receiver configuration, the design parameters and conditions capable of satisfying all constraints produced, at best, a design point receiver efficiency of 78.2%, or 80.5% when excluding receiver intercept efficiency. Variation in the optimal receiver performance relative to uncertainty in the binding maximum velocity, minimum wall thickness, and minimum lifetime constraints is presented.

14 SOLAR ENERGY↗
DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗
DOE OSTI2021

Investigations into Balancing Peak-to-Average Power Ratio and Mean Power Extraction for a Two-Body Point-Absorber Wave Energy Converter

The power harnessed by wave energy converters (WECs) in oceans is highly variable and, therefore, has a high peak-to-average power (PTAP) ratio. To minimize the cost of a WEC power take off (PTO) system, it is desirable to reduce the PTAP ratio while maximizing the mean power extracted by WECs. The important issue of how PTAP ratio reduction measures (such as adding an inertia element) can affect the mean power extracted in a reference model has not been thoroughly addressed in the literature. To investigate this correlation, this study focuses on the integration of the U.S. Department of Energy’s Reference Model 3, a two-body point absorber, with a slider-crank WEC for linear-to-rotational conversion. In the first phase of this study, a full-scale numerical model was developed that predicts how PTO system parameters, along with an advanced control algorithm, can potentially affect the proposed WEC’s PTAP ratio as well as the mean power extracted. In the second phase, an appropriate scaled-down model was developed, and extracted power results were successfully validated against the full-scale model. Finally, numerical and hardware-in-the-loop (HIL) simulations based on the scaled-down model were designed and conducted to optimize or make trade-offs between the operational performance and PTAP ratio. The initial results with numerical and HIL simulations reveal that gear ratio, crank radius, and generator parameters substantially impact the PTAP ratio and mean power extracted.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Sensitivity of Riparian Buffer Designs to Climate Change—Nutrient and Sediment Loading to Streams: A Case Study in the Albemarle-Pamlico River Basins (USA) Using HAWQS

Riparian buffer zones (RBZs) provide multiple benefits to watershed ecosystems. We aimed to conduct an extensive sensitivity analysis of the RBZ designs to climate change nutrient and sediment loadings to streams. We designed 135 simulation scenarios starting with the six baselines RBZs (grass, urban, two-zone forest, three-zone forest, wildlife, and naturalized) in three 12-digit Hydrologic Unit Code watersheds within the Albemarle-Pamlico river basin (USA). Using the hydrologic and water quality system (HAWQS), we assessed the sensitivity of the designs to five water quality indicator (WQI) parameters: dissolved oxygen (DO), total phosphorous (TP), total nitrogen (TN), sediment (SD), and biochemical oxygen demand (BD). To understand the climate mitigation potential of RBZs, we identified a subset of future climate change projection models of air temperature and precipitation using EPA’s Locating and Selecting Scenarios Online tool. Analyses revealed optimal RBZ designs for the three watersheds. In terms of watershed ecosystem services sustainability, the optimal Urban RBZ in contemporary climate (1983–2018) reduced SD from 61–96%, TN from 34–55%, TP from 9–48%, and BD from 53–99%, and raised DO from 4–10% with respect to No-RBZ in the three watersheds. The late century’s (2070–2099) extreme mean annual climate changes significantly increased the projected SD and BD; however, the addition of urban RBZs was projected to offset the climate change reducing SD from 28–94% and BD from 69–93% in the watersheds. All other types of RBZs are also projected to fully mitigate the climate change impacts on WQI parameters except three-zone RBZ.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗