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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Real‐Time Optical Process Monitoring for Structure and Property Control of Aerosol Jet Printed Functional Materials

Abstract Aerosol jet printing is a popular digital additive manufacturing method for flexible and hybrid electronics, but it lacks sophisticated real‐time process control schemes that would enable more widespread adoption in manufacturing environments. Here, an optical measurement system is introduced to track the aerosol density upstream of the printhead. The measured optical extinction, combined with the aerosol flow rate, is directly related to deposition rate and accurately predicts functional materials properties such as the electrical resistance of printed graphene films. This real‐time system offers a compelling solution for process drift and batch‐to‐batch variability, rendering it a valuable tool for both real‐time control of aerosol jet printing and fundamental studies of the underlying process science.

Tafoya, Rebecca R.↗
DOE OSTI2020

Skyrmion lattice creep at ultra-low current densities

Abstract Magnetic skyrmions are well-suited for encoding information because they are nano-sized, topologically stable, and only require ultra-low critical current densities j c to depin from the underlying atomic lattice. Above j c skyrmions exhibit well-controlled motion, making them prime candidates for race-track memories. In thin films thermally-activated creep motion of isolated skyrmions was observed below j c as predicted by theory. Uncontrolled skyrmion motion is detrimental for race-track memories and is not fully understood. Notably, the creep of skyrmion lattices in bulk materials remains to be explored. Here we show using resonant ultrasound spectroscopy—a probe highly sensitive to the coupling between skyrmion and atomic lattices—that in the prototypical skyrmion lattice material MnSi depinning occurs at $${j}_{c}^{* }$$ j c * that is only 4 percent of j c . Our experiments are in excellent agreement with Anderson-Kim theory for creep and allow us to reveal a new dynamic regime at ultra-low current densities characterized by thermally-activated skyrmion-lattice-creep with important consequences for applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

IMAGINE BioSecurity: Mesocosm-Based Methods to Evaluate Biocontainment Strategies and Impact of Industrial Microbes Upon Native Ecosystems

Project Goals: The Integrative Modeling and Genome-scale Engineering for Biosystems Security (IMAGINE BioSecurity) SFA project seeks to establish an understanding of the behavior of engineered microbes in controlled versus environmental conditions to predictively devise new strategies for responding to biological escape. To this end, the IMAGINE Team has established a plant-soil mesocosm platform to track and quantify the fate of industrial microbes in environmental systems and assess the efficacy of biocontainment constraints upon genetically engineered microbe escape frequency and the impact of industrial microbes upon native ecological microbiomes. Abstract Text: Genetically modified industrial production microbes and their associated bioproducts have emerged as an integral component of a sustainable bioeconomy. However, the rapid development of these innovative technologies raises biosecurity concerns, namely, the risk of environmental escape. Thus, the realization of a bioeconomy hinges not only on the development and deployment of microbial production hosts, but also on the development of secure biosystems and biocontainment designs. Current laboratory-based biocontainment testing systems do not accurately reflect complexities found in natural environments, necessitating an environmentally relevant analysis pipeline that allows for the detection of rare escapees, the effect of associated bio-products, and the impact on native ecologies. To this end, we have developed an approach that utilizes soil mesocosms and integrated systems analyses to evaluate the efficacy of novel biocontainment strategies and to assess the impact of production systems upon terrestrial microbiome dynamics. We demonstrate the utility of this approach by modeling a contamination with industrial microbial chasses versus their biocontained counterparts. Here we demonstrate the broad utility of this system by highlighting findings from both strains of Saccharomyces cerevisiae that are contained with an inducible toxin anti-toxin system, and stains of Escherichia coli that are contained via genomic recoding. The resultant data demonstrate that this system has broad utility across diverse microbial chassis and biocontainment strategies, enables us to track the fate of our contaminating microbe with high sensitivity in the soil, as well as monitor broader impacts of the perturbation on the underlying soil system. The findings presented here support the use of this mesocosm-based approach to assess the environmental impact of industrial microbes and to validate biocontainment strategies.

BASIC BIOLOGICAL SCIENCES,INORGANIC, ORGANIC, PHYS↗
DOE OSTI2021

Experimental results of wake steering using fixed angles

In this article, the authors present a test of wake steering at a commercial wind farm. A single fixed yaw offset, rather than an optimized offset schedule, is alternately applied to an upstream wind turbine, and the effect on downstream turbines is analyzed. This experimental design allows for comparison with engineering wake models independent of the controller's ability to track a varying offset and correctly measure wind direction. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions. Results of the article from collected data show good agreement with the FLOw Redirection and Induction in Steady State (FLORIS) engineering model and offer support for the asymmetry of wake steering predicted by newer models, such as the Gauss–curl hybrid model.

17 WIND ENERGY↗
DOE OSTI2020

A Virtual Space Vector Model Predictive Control for a Seven-Level Hybrid Multilevel Converter

This paper proposes a virtual space vector (VSV) model predictive control (MPC) for a three-phase seven-level (7L) hybrid multilevel converter (HMC), where each phase consists of an active-neutral-point-clamped converter with a floating H-bridge. To achieve the best current tracking, which is the primary goal of the proposed algorithm, a novel geometrical positioning approach is proposed to select the optimal voltage vector among all the realspace vectors and VSVs. Then, all the possible switching sequences that belong to the optimal voltage vector are evaluated to realize the dc capacitor voltage balancing and common-mode voltage reduction. Through an external modulator, the optimal voltage vector can be synthesized by using either one-, three-, or seven-segment switching sequence. Compared with the conventional MPC, the proposed VSV-MPC can reduce not only the computational burden but also the current THD. Both simulation and experimental results obtained on silicon carbide based 7L-HMC prototype are presented to validate the feasibility and effectiveness of the proposed VSVMPC strategy.

42 ENGINEERING↗
DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A Generic Multivector Model Predictive Control With Symmetric Pulse Pattern for Hybrid Multilevel Converters

This article presents a generic multivector model predictive control (MV-MPC) method that has the potential to be applied to all hybrid multilevel converters (HMCs). It first locates the reference voltage vector in the 120° oblique coordinate to select the three adjacent voltage vectors to be applied over one control cycle. Then, the current tracking is guaranteed through duty cycle optimization and dc capacitor voltages are balanced by evaluating possible switching sequences that belong to the voltage vectors with optimal duty cycles. At last, the optimal switching sequence with a symmetric five- or seven-segment pulse pattern is generated using an external modulator. Here, the proposed generic MV-MPC can significantly improve the quality of the output current, while achieving a constant equivalent switching frequency at the same time. Experimental studies on an all silicon carbide HMC prototype, i.e., an active neutral point clamped converter with cascaded H-bridge, are presented to validate the effectiveness of the proposed MV-MPC strategy.

42 ENGINEERING↗
DOE OSTI2020

A Simplified Model Predictive Control Strategy for a Nine-Level Hybrid Multilevel Converter

This paper proposes a simplified model predictive control (SMPC) for a 9-level hybrid multilevel converter based on the active-neutral-point-clamped (ANPC) topology. The proposed SMPC firstly identifies the voltage vector that has optimal current tracking performance by a novel geometrical positioning approach in the complex plane, which can dramatically reduce the computational burden. Then it evaluates the switching states that are subject to the same voltage vector and selects the optimal switching state to balance the dc capacitor voltages. Both simulation and experimental results are carried out to validate the feasibility and effectiveness of the proposed control strategy.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluation of PV Module Packaging Strategies of Monofacial and Bifacial PERC Using Degradation Pathway Network Modeling

As the PV industry is rapidly expanding, it is important to thoroughly investigate the long-term impact of packaging strategies on the performance of PV modules. In this study, the variants in sets differ on the basis of manufacturer (A/B), encapsulant (EVA/POE), rear encapsulant (UV-cutoff/opaque/transparent), module architecture (GB/DG) and cell type (monofacial/bifacial). The minimodules were exposed for 2520 hours in modified damp heat, with or without full spectrum light. Every 504 hours, stepwise electrical characterization techniques were employed to track changes in minimodules. Degradation pathway modeling using network structural equation modeling was employed to study pairwise relationships between variables and service lifetime prediction in minimodules. Through this study, differences in quality control are identified in minimodules made by different manufacturers. Minimodules with UV-cutoff rear encapsulant show relatively better stability, whereas the ones with opaque rear encapsulant show greater power loss. In addition, GB having UV-cutoff rear encapsulation and GB with POE having opaque rear encapsulation were identified to be stable as they lack a best model fit. Here, the primary power loss mechanism in degrading variants is interconnect corrosion.

14 SOLAR ENERGY↗
DOE OSTI2020

Simplified Two-Stage Model Predictive Control for a Hybrid Multilevel Converter With Floating H-Bridge

This paper proposes a simplified two-stage model predictive control (ST-MPC) for a hybrid multilevel converter, which is an active-neutral-point-clamped converter with floating H-bridge (ANPC-H). The objective of the first stage is to select the voltage vector that has the optimal current tracking performance by using a novel geometrical positioning approach in the complex plane. The second stage selects the best switching state among all the available switching states that belong to the same voltage vector obtained in the first stage, to balance dc capacitor voltages and reduce the common mode voltage. The proposed ST-MPC can dramatically reduce the computational burden and ensure the best current tracking by the two-stage structure, such that the execution time is much shorter compared with the conventional MPC. In addition, the geometrical positioning approach in the first stage is generic and can be applicable for any multilevel converters with N-level output; thus, this ST-MPC can be applied for both sevenand nine-level operation of the hybrid ANPC-H converter under different dc voltage ratios. Both simulation results and experimental results obtained on a silicon carbide hybrid ANPC-H converter prototype validate the feasibility and effectiveness of the proposed ST-MPC strategy.

42 ENGINEERING↗
DOE OSTI2021

A control oriented comprehensive degradation model for battery energy storage system life prediction

In order to ensure that stationary battery energy storage systems (BESSs) provide reliable energy buffering, both for power quality and economic purposes, the degradation must be considered. Cell degradation involves various side reactions and is highly dependent on its operating conditions. To accurately track cell degradation and predict its impact on battery behavior, a comprehensive physics-based degradation model based on an electrolyte phase-enhanced single particle (SP) model is developed. Key degradation physics, namely solid electrolyte interphase (SEI) layer formation and growth, Li plating on the graphite anode, and Mn dissolution on cathode of nickel-cobalt-manganese oxide (NMC622) are considered. The model is validated against experimental capacity data. The results reveal that the deposition rate of both SEI layer and Li metal increase as the charge voltage increases. At the cathode side, the solvent oxidation rate determines the Mn dissolution rate. As a result, the volume fraction of NMC622 in the cathode continually decreases at a gradually increasing rate.

25 ENERGY STORAGE↗
DOE OSTI2022

A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads

This paper uses a two-level model predictive control-based approach for the coordinated control and energy management of an integrated system that includes photovoltaic (PV) generation, energy storage, and building loads. Novel features of the proposed local controller include (1) the ability to simultaneously manage building loads and energy storage to achieve different operational objectives such as energy efficiency, economic cost efficiency, demand response and grid optimization through the design of specific power trajectory tracking performance functionals, (2) an energy trim function that minimizes the impact of solar forecasting errors on system performance, and (3) the design of a state of charge controller that uses day-ahead forecast of solar power and building loads to intialize energy storage at the start of each day. The local controller is tested in simulation using an exemplary system with PV generation, energy storage and dispatchable building loads. Two sample days with different PV forecasts and multiple case scenarios are considered, and the performance of the algorithm in managing the real and reactive net building load trajectories and the ramp rate of PV injections into the utility network are evaluated. The simulations are based on actual forecasted and measured PV data, and the results show that the local controller meets the tracking requirements for real and reactive power within the operating constraints of the building.

14 SOLAR ENERGY↗
DOE OSTI2022

Distributed model predictive control for coordinated, grid-interactive buildings

Continued focus on reducing carbon emissions and improving energy efficiency requires buildings to become grid-interactive and not just behave as static consumers. A distributed model predictive control (DMPC) algorithm known as Limited-Communication (LC) DMPC is modified to enable grid-interactive buildings. A grid-aggregator subsystem is added that allows for a bulk grid power reference signal to be followed while the individual building subsystems also achieve their local comfort objectives. The LC-DMPC algorithm is applied for the first time to systems with multiple buildings. Adequate power tracking is shown for different simulation scenarios involving heterogeneous buildings, and next steps are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Robust Data-Driven Approach for Mechanical Serial Sectioning

Mechanical serial sectioning (MSS) provides detailed microstructural information across large length scales. By repeatedly removing thin layers of material and imaging the exposed surface, a 3D representation of a specimen’s internal structure can be constructed, enabling failure analysis and feature identification that are otherwise inaccessible via conventional 2D or nondestructive evaluation techniques. Achieving consistent and accurate material removal can be challenging due to system variability, requiring an experienced operator to manually adjust parameters, prolonging data collection times and necessitating post-processing routines to standardize the data. Here, to address these challenges, this paper presents the employment of a one-step model predictive control (MPC) framework tailored to a run-to-run (R2R) controller. The R2R-MPC controller automates the parameter selection process, improving the consistency of material removal through iterative feedback for disturbance rejection and accurate tracking of the target removal rate. Using a data-driven approach, the controller robustly adapts to changing material characteristics. The effectiveness of the R2R-MPC controller is demonstrated through simulation and experimental results and compared to previous data collection procedures.

3D Materials Science↗
DOE OSTI2023

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Energy & Fuels↗
DOE OSTI2021

Theoretical Control-Centric Modeling for Precision Model-Based Sliding Mode Control of a Hydraulic Artificial Muscle Actuator

Artificial muscles (AMs) typically rely on pneumatic sources of fluid power. The use of hydraulics can increase the power and force to weight and volume ratios of AM actuators. This paper develops a control-centric third-order single-input single-output (SISO) lumped-parameter dynamic model and sliding mode position controller based on Filippov's principle of equivalent dynamics for a braided hydraulic artificial muscle (HAM) actuator. The model predicts the nonlinear behavior of the HAM free contraction and captures the fluid and actuator nonlinear dynamic interactions in addition to the braid deformation. Model simulations are compared to experimental results for quasi-static pressurization, isometric pressurization, and open-loop square wave commands at 0.25, 0.5, and 1 Hz. Experiments of sine wave tracking at 0.25, 0.5, and 1 Hz and continuous square wave tracking at 0.067 Hz are conducted using a sliding mode controller (SMC) derived from the model. The SMC achieves a steady-state error of 6 μm at multiple setpoints within the actuator's 17 mm stroke. Compared to a proportional-integral-derivative (PID) controller, the SMC root-mean-square (RMS) error, mean error, and absolute maximum error are reduced on average by 53%, 61%, and 44%, respectively, demonstrating the benefit of model-based approaches for controlling HAMs.

42 ENGINEERING↗
DOE OSTI2022

Nonresonant particle acceleration in strong turbulence: Comparison to kinetic and MHD simulations

Collisionless, magnetized turbulence offers a promising framework for the generation of nonthermal high-energy particles in various astrophysical sites. Yet, the detailed mechanism that governs particle acceleration has remained subject to debate. By means of 2D and 3D particle-in-cell, as well as 3D (incompressible) magnetohydrodynamic (MHD) simulations, we test here a recent model of nonresonant particle acceleration in strongly magnetized turbulence, which ascribes the energization of particles to their continuous interaction with the random velocity flow of the turbulence, in the spirit of the original Fermi model. To do so, we compare, for a large number of particles that were tracked in the simulations, the predicted and the observed histories of particles momenta. The predicted history is that derived from the model, after extracting from the simulations, at each point along the particle trajectory, the three force terms that control acceleration: the acceleration of the field line velocity projected along the field line direction, its shear projected along the same direction, and its transverse compressive part. Overall, we find a clear correlation between the model predictions and the numerical experiments, indicating that this nonresonant model can successfully account for the bulk of particle energization through Fermi-type processes in strongly magnetized turbulence. Additionally we also observe that the parallel shear contribution tends to dominate the physics of energization in the particle-in-cell simulations, while in the magnetohydrodynamic incompressible simulation, both the parallel shear and the transverse compressive term provide about equal contributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗