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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A novel shallow bore ground heat exchanger for ground source heat pump applications—Model development and validation

A novel ground heat exchanger design, the Underground Thermal Battery (UTB), was proposed as a low-cost alternative to the conventional vertical bore ground heat exchanger (VBGHE) for the application of ground source heat pumps (GSHPs). The UTB is designed to be installed in a shallow borehole (less than 6 m deep) and thus could cost much less than the conventional VBGHE, which usually is installed in vertical bores 60 m deep. By utilizing natural convection of water and phase change materials, the UTB tempers its temperature change in response to thermal loads, which helps improve the efficiency of GSHPs. A one-dimensional (1D) model of the UTB has been developed and validated with the measured performance data of a small-scale UTB, as well as the simulation results of a more detailed three dimensional (3D) numerical model, which accounts for both heat transfer and fluid dynamics in the UTB. Further, this 1D model is computationally much more efficient than the 3D model and thus can simulate the annual performance of the UTB with acceptable computation time. The 1D model has been used to evaluate the performance of the UTB so that it can be compared with the conventional VBGHE.

42 ENGINEERING↗
DOE OSTI2020

Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy

The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.

Mckee, Evan↗
DOE OSTI2022

A Variable Pressure Multi-Pressure Rail System Design for Agricultural Applications

This paper presents a solution for reducing energy loss in the hydraulic control system of agricultural tractors and their implements. The solution is referred to as a multi-pressure rail (MPR) and provides power to the hydraulic functions following a pressure control logic, as opposed to the traditional flow control logic typical of hydraulic systems used in off-road vehicles. The proposed hydraulic control system allows for elimination of redundant flow control valves in the state-of-the-art system, which cause excessive throttling losses leading to poor overall energy efficiency. Related work on MPR technology targets construction vehicles, where the MPR solution can allow energy recovery during overrunning loads and better engine management. This paper alternatively addresses the case of agricultural applications where functions mostly operate under resistive load conditions with slow dynamics, which offers an opportunity to target throttle losses. For this purpose, the paper introduces a variable pressure control strategy to handle the instantaneous pressure at each rail. To develop both the controller and the hydraulic system architecture, a stationary test rig is conceived and used to validate a numerical simulation model of the MPR system and its control strategy. Particular focus is given to the dynamic behavior of the system during the switches of a function between different pressure rails, which needs to ensure reduced oscillations of the flow provided to each hydraulic function. Once validated, the simulation model is used to predict the energy savings of the MPR solution in an actual application: a 435 hp hydraulic tractor powering a 16-row planter, for which operating features during typical drive cycles were available to the authors. The results show up to 59% total power reduction at the pump shaft, corresponding to 89.8% system efficiency gain.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints

Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher waste loading of 37.5 wt% compared to 34 wt%. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches. Future works with these tools include expanding them with other regression models and UQ methods, and exploration into other optimization problems or dynamic control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Aerodynamic Analysis of Wind Loading on Parabolic Trough Collectors Using High-Fidelity CFD Modeling

Wind loading on parabolic solar collector support structures is the key driver for material costs. As identified by NREL's CSP Best Practices project [1], ability to predict wind-loading in deep-arrays and the impact of edge effects has potential to decrease levelized cost of electricity (LCoE) by 15%. In this study, we present a high-fidelity computational model that can model the flow around deep arrays of parabolic troughs under varying turbulent inflow conditions. Ability to accurately reproduce atmospheric boundary layer (ABL) profiles is first demonstrated through Large Eddy Simulation (LES) simulations and compared against wind tunnel measurements. Thereafter, unsteady turbulent flow past single row and six row configurations is computed using hybrid RANS/LES simulations. Investigations of wind loading, through parametric tilt angle variations of single row and on waked collectors in six-row configuration, are used to highlight design considerations for parabolic troughs only possible through high-fidelity unsteady calculations.

aerodynamics↗
DOE OSTI2022

Assessing the Impact of a Novel TBC Material on Heat Transfer in a Spark Ignition Engine through 3D CFD-FEA Co-Simulation Routine

Thermal barrier coatings (TBCs) have been of interest since the 1970s for application in internal combustion (IC) engines. Thin TBCs exhibit a temperature swing phenomenon wherein wall temperatures dynamically respond to the transient working-gas temperature throughout the engine cycle, thus reducing the temperature difference driving the heat transfer. Determining these varying wall temperatures is necessary to evaluate and study the effect of coatings on wall heat transfer. This study focuses on developing a 3D computational fluid dynamics (CFD)-finite element analysis (FEA) coupled simulation, or co-simulation, routine to determine the wall temperatures of a piston coated with a thin TBC layer subject to spark ignition combustion heat flux. A CONVERGE 3D-CFD model was used to simulate the combustion process in a single-cylinder, light-duty experimental spark ignition (SI) engine. Transient piston heat transfer analysis was conducted using ABAQUS, a FEA package, under the simulated combustion heat flux load. The effect of the temperature swing phenomenon due to this TBC layer was observed in a CFD simulation by implementing the FEA results as the piston thermal boundary conditions. The boundary conditions were passed between the CFD and FEA tools until a quasi-steady state solution was achieved. Furthermore, a reduction in wall heat transfer was observed due to a reduced temperature difference between the wall and the working gas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Modeling airborne pathogen transport and transmission risks of SARS-CoV-2

An integrated modeling approach has been developed to better understand the relative impacts of different expiratory and environmental factors on airborne pathogen transport and transmission, motivated by the recent COVID-19 pandemic. Computational fluid dynamics (CFD) modeling was used to simulate spatial-temporal aerosol concentrations and quantified risks of exposure as a function of separation distance, exposure duration, environmental conditions (e.g., airflow/ventilation), and face coverings. The CFD results were combined with infectivity models to determine probability of infection, which is a function of the spatial-temporal aerosol concentrations, viral load, infectivity rate, viral viability, lung-deposition probability, and inhalation rate. Uncertainty distributions were determined for these parameters from the literature. Probabilistic analyses were performed to determine cumulative distributions of infection probabilities and to determine the most important parameters impacting transmission. This modeling approach has relevance to both pathogen and pollutant dispersion from expelled aerosol plumes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Data-Driven Algorithm for Enabling Delay Tolerance in Resilient Microgrid Controls Using Dynamic Mode Decomposition

The increased implementation of smart grid technologies in the power distribution grid presents unique opportunities that enable resiliency, but also brings challenges motivating needs for novel solutions and mitigation techniques. The bi-directional power and data flow allow for the grid to operate with increased resiliency, which is the ability to avoid discontinuity of service to end-use loads during extreme events. However, in applications where control of the distribution grid or microgrid relies on communication networks, the degradation of communication systems in the form of loss or high latency can cause maloperation and result in loss of end-use loads. Here this paper presents a novel framework to enable delay tolerance of centralized microgrid control schemes to mitigate communication system latency impacts and guarantee successful control action. We demonstrate the delay tolerance on a control scheme that operates a battery energy storage system (BESS) to offset the sudden loss of generation and maintain system frequency. During periods of severely degraded communication system performance, the proposed delay-tolerant algorithm compensates for the latency by utilizing a data-driven model generated at the device level using dynamic mode decomposition (DMD) to determine the performance of the communications. The DMD technique predicts the system’s frequency using device-level terminal measurements and provides updated control signals. The HELICS cosimulation platform evaluates the cyber-physical interaction of the power system model in GridLAB-D, the centralized control agent in Python, and the discrete network model in NS-3. The framework is tested and validated on the IEEE-123 node system modified to represent a networked remote microgrid model, and the results show an improvement in the dynamic performance

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Three-Dimensional Electromagnetic and Structural Analysis of Disruptions in the COMPASS Upgrade Vacuum Vessel

Structural analyses of disruptions and the electromagnetic (EM) analyses to support them typically use symmetry to produce a manageable model size. Nonaxisymmetric halo loads require larger analysis models. Here, in this paper, the results of transient EM analyses of halo strikes during a vertical displacement event are presented. The plasma motions and halo characteristics are prescribed based on analyses performed by the Institute of Plasma Physics of the Czech Republic. The time transient EM solution provides loads to a full three-dimensional transient dynamic analysis of the vacuum vessel. The responses to large lateral halo loads are altered and mitigated by the mode responses of the vessel. Dynamic load factors are computed for specific locations in the vessel and supports.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Adaptive Reinforcement Learning (ARL) Control of a Multi-port Resonant Converter in UAV Systems

This study presents an adaptive reinforcement learning (ARL) control framework for a multi-port resonant converter used in hybrid unmanned aerial vehicle (UAV) power systems. The converter integrates high-frequency half-bridge input ports connected to a rectified engine–generator set and a battery energy storage system, along with a semi-bridgeless active rectifier supplying the propulsion load. A deep RL agent is trained to dynamically regulate inter-port phase-shift commands in real time based on flight conditions and load power demand. The ARL controller autonomously identifies phase-shift combinations that maximize conversion efficiency while maintaining stable and coordinated power flow, even under rapidly varying operating scenarios. This data-driven approach eliminates the need for explicit system modeling or extensive manual tuning and enables coordinated control among multiple power ports without inter-port communication. Experimental results validate that the ARL based strategy achieves reliable power sharing and consistently high-efficiency operation across diverse UAV operating conditions.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2022

Understanding Phase and Interfacial Effects of Spall Fracture in Additively Manufactured Ti-5Al-5V-5Mo-3Cr

Additive manufactured Ti-5Al-5V-5Mo-3Cr (Ti-5553) is being considered as an AM repair material for engineering applications because of its superior strength properties compared to other titanium alloys. Here, we describe the failure mechanisms observed through computed tomography, electron backscatter diffraction (EBSD), and scanning electron microscopy (SEM) of spall damage as a result of tensile failure in as-built and annealed Ti-5553. We also investigate the phase stability in native powder, as-built and annealed Ti-5553 through diamond anvil cell (DAC) and ramp compression experiments. We then explore the effect of tensile loading on a sample containing an interface between a Ti-6Al-V4 (Ti-64) baseplate and additively manufactured Ti-5553 layer. Post-mortem materials characterization showed spallation occurred in regions of initial porosity and the interface provides a nucleation site for spall damage below the spall strength of Ti-5553. Preliminary peridynamics modeling of the dynamic experiments is described. Finally, we discuss further development of Stochastic Parallel PARticle Kinteic Simulator (SPPARKS) Monte Carlo (MC) capabilities to include the integration of alpha (α)-phase and microstructural simulations for this multiphase titanium alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The influence of wind veer and drivetrain flexibility on fatigue loading for large floating wind turbines

To reduce costs, offshore wind turbines are expected to be designed with significantly increased rotor diameters. Larger turbines become more flexible and span a larger portion of the atmospheric boundary layer. With these changes, the validity of traditional modeling assumptions should be investigated. This work challenges two common assumptions: (1) that the drivetrain can be considered rigid (except in torsion) and does not couple with the rotor and tower and 2) that wind directional change with height (veer) does not greatly influence the fatigue damage in the tower, blades and drivetrain. Two large semi-submersible floating wind turbines are considered: a 15 and a 22 MW reference turbine. Both use direct-drive generators. Aero-hydro-servo-elastic simulations are performed using OpenFAST, with drivetrain bending flexibility and main bearing response implemented in the coupled analysis. The turbines are subjected to a set of load cases at below-, near- and above-rated mean wind speeds, assembled based on the 3 km Norwegian reanalysis (NORA3) hourly wind and wave hindcast data for Utsira Nord, off the coast of Norway. In each load case, conditions with and without veer are simulated to evaluate the influence of veer on damage equivalent loads (DELs) of the turbine tower, blades and main bearings. Further, these load cases are applied to evaluate the influence of drivetrain flexibility on aero-elastic turbine response. The results indicate that, depending on the veer gradient, mean wind speed, operating regime and turbine size, veer can be very important for tower-top DELs and the fluctuations of main bearing radial loads, while main bearing and blade-root flapwise DELs are less affected. Considering these specific load cases and turbine models, drivetrain flexibility is found to significantly influence tower-top DELs of the largest turbine: the tower-top fore-aft and torsional damage equivalent moments of the 22 MW turbine are reduced by more than 20 % at near-rated wind speeds when the drivetrain is modeled as flexible.

17 WIND ENERGY↗
DOE OSTI2026

Modeling, Validation, and Control of the IEA‐15 MW Reference Wind Turbine and VolturnUS‐S Platform

This paper presents the acausal modeling, validation, and control of floating offshore wind turbines (FOWTs). The model simulates the IEA‐15 MW reference turbine and the semi‐submersible VolturnUS‐S platform utilizing a Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS), which integrates the key coupled aero‐hydro‐elasto‐servo dynamics and is being developed by authors at the University of Central Florida. Verification and validation are conducted using numerical data from the industry‐standard simulation platform OpenFAST and experimental data from the Floating Offshore‐wind and Controls Advanced Laboratory (FOCAL) project, in which the authors were involved. Numerical results demonstrate the model's ability to qualitatively capture loads and responses across various load cases, highlighting the impact of the control system under different wind and wave conditions and opening new opportunities for optimizing FOWT designs. This paper provides wind turbine researchers with valuable insights into system characteristics, system frequencies, damping effects, and internal reaction forces, serving as a reference for future studies in FOWT modeling and control.

17 WIND ENERGY↗
DOE OSTI2024

A Lake Biogeochemistry Model for Global Methane Emissions: Model Development, Site‐Level Validation, and Global Applicability

Abstract Lakes are important sentinels of climate change and may contribute over 30% of natural methane (CH 4 ) emissions; however, no earth system model (ESM) has represented lake CH 4 dynamics. To fill this gap, we refined a process‐based lake biogeochemical model to simulate global lake CH 4 emissions, including representation of lake bathymetry, oxic methane production (OMP), the effect of water level on ebullition, new non‐linear CH 4 oxidation kinetics, and the coupling of sediment carbon pools with in‐lake primary production and terrigenous carbon loadings. We compiled a lake CH 4 data set for model validation. The model shows promising performance in capturing the seasonal and inter‐annual variabilities of CH 4 emissions at 10 representative lakes for different lake types and the variations in mean annual CH 4 emissions among 106 lakes across the globe. The model reproduces the variations of the observed surface CH 4 diffusion and ebullition along the gradients of lake latitude, depth, and surface area. The results suggest that OMP could play an important role in surface CH 4 diffusion, and its relative importance is higher in less productive and/or deeper lakes. The model performance is improved for capturing CH 4 outgassing events in non‐floodplain lakes and the seasonal variability of CH 4 ebullition in floodplain lakes by representing the effect of water level on ebullition. The model can be integrated into ESMs to constrain global lake CH 4 emissions and climate‐CH 4 feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling and Analysis of a Novel Offshore Binary Species Free-Floating Longline Macroalgal Farming System

The investigation of innovative macroalgal cultivation is important and needed to optimize farming operations, increase biomass production, reduce the impact on the ecosystem, and lower system and operational costs. However, most macroalgal farming systems (MFSs) are stationary, which need to occupy a substantial coastal area, require extensive investment in farm infrastructure, and cost high fertilizer and anchoring expenses. This study aims to model, analyze, and support a novel binary species free-floating longline macroalgal cultivation concept. The expected outcomes could provide a basis for the design and application of the novel MFS to improve biomass production, decrease costs, and reduce the impact on the local ecosystem. In this paper, Saccharina latissima and Nereocystis luetkeana were modeled and validated, and coupled with longline to simulate the binary species MFS free float in various growth periods and associated locations along the US west coast. Further, the numerical predictions indicated the possibility of failure on the longline and breakage at the kelp holdfasts is low. However, the large forces due to an instantaneous change in dynamic loads caused by loss of hydrostatic buoyancy when the longline stretches out of the water would damage the kelps. Buoy-longline contact interactions could damage the buoy, resulting in the loss of the system by sinking. Furthermore, the kelp-longline and kelp-kelp entanglements could potentially cause kelp damage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

GLUE Code: A framework handling communication and interfaces between scales

Many scientific applications are inherently multiscale in nature. Such complex physical phenomena often require simultaneous execution and coordination of simulations spanning multiple time and length scales. This is possible by combining expensive small-scale simulations (such as molecular dynamics simulations) with larger scale simulations (such continuum limit/hydro solvers) to allow for considerably larger systems using task and data parallelism. However, the granularity of the tasks can be very large and often leads to load imbalance. Traditionally, we use approximations to streamline the computation of the more costly interactions and this introduces trade-offs between simulation cost and accuracy. In recent years, the available computational power and the advances in machine learning have made computing these scale-bridging interactions and multiscale simulations more feasible. One driving application has been plasma modeling in inertial confinement fusion (ICF), which is fundamentally multiscale in nature. This requires deep understanding of how to extrapolate microscopic information into macroscopically relevant scales. For example, in ICF one needs an accurate understanding of the connection between experimental observables and the underlying microphysics. The properties of the larger scales are often affected by the microscale behavior incorporated usually into the equations of state and ionic and electronic transport coefficients (Liboff, 1959; Rinderknecht et al., 2014; Rosenberg et al., 2015; Ross et al., 2017). Instead of incorporating this information using reliable molecular dynamics (MD) simulations, one often needs to use theoretical models, due to the inability of MD to reach engineering scales (Glosli et al., 2007; Marinak et al., 1998). One approach to resolve this issue is by coupling two MD simulations of different scales via force interpolation, e.g., the AdResS method (Krekeler et al., 2018; Nagarajan et al., 2013). Another approach, which we will pursue in the scope of this work, is by enabling scale bridging between MD simulations and meso/macro-scale models through the development and support of application programming interfaces that these different applications can interact through.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Additional Data Focused on Phase 1 Geared Toward Computational Fluid Dynamics (CFD) validation

A new validation campaign was developed within the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) to better understand the complex interactions between components of a floating wind system (e.g., columns, pontoons, etc.) in a more generic sense, and to validate modeling approaches for a variety of floating designs. A set of cylinders based on the Offshore Code Comparison Collaboration Continuation (OC4)-DeepCwind semidesign were tested under wave loading, both individually and in different combinations (including the attachment of heave plates of different sizes). The cylinders were held rigid, and pressure sensors were used to measure the distributed loading on the structures, while load cells measured the total integrated hydrodynamic loads.

17 WIND ENERGY↗
DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗