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DOE OSTI2025

2024 Incubator Final Report: Active Hybrid Mooring

This Incubator project has defined and developed a representation of an active hybrid mooring (AHM) system in Simulink to assess whether the speed and accuracy of AHM is sufficient to warrant further investigation in a larger project. We first built a representative mooring system for the VolturnUS-S 15MW floating offshore wind turbine (FOWT) in 1,000-meter water depth to provide baseline loads and displacements needed in the AHM system, with the results showing off-the-shelf actuators would be practical. We then defined model configurations for the numerical and physical substructures, using standalone MoorDyn v2 with a novel input strategy and chained hydraulic actuators represented in Simulink Simscape, respectively. The remainder of the project has focused on verifying the existing models for speed and accuracy against OpenFAST simulations, increasing their complexity to enable six degree of freedom (6DOF) operation, and connecting the numerical and physical substructures into a single holistic model. The verification phase of the project has shown promising results, though further refinement is needed to resolve accuracy discrepancies and for robust performance across a wider range of metocean conditions. The numerical model we developed is computationally stable and operates faster than real time using a time step of 0.01 seconds or shorter, indicating the model is likely fast enough for our AHM design to operate successfully. Additionally, the Simscape model of a hydraulic actuator operates with less than 0.3% relative error when actuating mooring loads from an OpenFAST simulation of the VolturnUS-S 1,000-meter mooring system. The aforementined refinements will be performed in the next year as a Continuing Incubator, with key remaining tasks include programming the predictor-corrector loop to minimize movement error, testing against more complex nonlinear wave cases, and expanding to enable loads from connected shared mooring lines.

17 WIND ENERGY↗

DOE OSTI2016

Oil-Soluble Silver–Organic Molecule for in Situ Deposition of Lubricious Metallic Silver at High Temperatures

A major challenge in lubrication technology is to enhance lubricant performance at extreme temperatures that exceed conventional engine oil thermal degradation limits. Soft noble metals such as silver have low reactivity and shear strength, which make them ideal solid lubricants for wear protection and friction reduction between contacting surfaces at high temperatures. However, achieving adequate dispersion in engine lubricants and metallic silver deposition over predetermined temperatures ranges presents a significant chemical challenge. Here we report the synthesis, characterization, and tribological implementation of the trimeric silver pyrazolate complex, [Ag(3,5-dimethyl-4-n-hexyl-pyrazolate)]3 (1). This complex is oil-soluble and undergoes clean thermolysis at ~310 °C to deposit lubricious, protective metallic silver particles on metal/metal oxide surfaces. Temperature-controlled tribometer tests show that greater than 1 wt % loading of 1 reduces wear by 60% in PAO4, a poly-α-olefin lubricant base fluid, and by 70% in a commercial fully formulated 15W40 motor oil (FF oil). This silver–organic complex also imparts sufficient friction reduction so that the tribological transition from oil as the primary lubricant through its thermal degradation, to 1 as the primary lubricant, is experimentally undetectable.

Desanker, Michael↗

DOE OSTI2018

Ultra-Stable and High-Cobalt-Loaded Cobalt@Ordered Mesoporous Carbon Catalysts: All-in-One Deoxygenation of Ketone into Alkylbenzene

Catalytic deoxygenation represents a straightforward and core methodology for fine-chemical production and biomass upgrading. Generally, the application of homogeneous metal complexes or heterogeneous noble-metal catalysts prevails in academia and the chemical industry. We introduce cobalt@ordered mesoporous carbon (Co@OMC) catalysts, which are constructed conveniently by a mechanochemical coordination self-assembly based on a renewable tannin precursor. Importantly, the Co@OMC catalysts with a high loading of in situ confined Co species promote the selective deoxygenation of various ketones, aldehydes, and alcohols efficiently into the corresponding alkanes under mild conditions. Therefore, a simple, inexpensive, and heterogeneous catalyst for selective deoxygenation can be expected, meanwhile the solid-state synthesis affords a green, rapid, and scalable pathway to Co@OMC catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Monodomain Liquid‐Crystal Elastomer Lattices for Broad Strain‐Rate Mechanical Damping

Designing structures that effectively dissipate energy across a broad range of mechanical loading rates, including those from compression, shock, and vibration, poses a significant engineering challenge. In this study, liquid‐crystal elastomers (LCEs), which possess anisotropic properties due to the alignment of their polymer backbone, are explored. As a result, LCEs exhibit a soft elastic response under mechanical loading, making them ideal for energy dissipation. Advances in additive manufacturing (AM) enable simple fabrication of foamlike dissipative structures with complex lattice geometries. Herein, direct ink write 3D printing, an extrusion AM method, is used to fabricate aligned, monodomain LCE lattice structures for broad strain‐rate mechanical damping. In this work, it is shown that these structures can dissipate strain energy in quasi‐static environments, comparable to traditional elastomeric lattices, and provide improved damping under high strain‐rate drop testing due to LCE soft elasticity. Additionally, under dynamic mechanical vibration, monodomain LCE lattices enhance damping at structural natural frequencies and provide high‐frequency attenuation. Finally, in these findings, a promising method is presented for fabricating mechanical damping devices that effectively dissipate energy across a broad range of loading rates.

3D printings↗

DOE OSTI2022

Extreme fast charge aging: Correlation between electrode scale and heterogeneous degradation in Ni-rich layered cathodes

Extreme fast charging (XFC) is a key requirement for the adoption of battery-based electric vehicles by the transportation sector. However, XFC has been shown to accelerate degradation, causing the capacity, life, and safety of batteries to deteriorate. There are no systematic studies in the open literature regarding aging modes in Ni-rich Li y Ni 0.a Mn 0.b Co 0.c O 2 (NMCabc) cathodes caused by fast charging. Herein, we report the effects of cathode composition and electrode loading in pouch cells containing NMC532, 622 and 811 paired with graphite and cycled under XFC conditions. The relative anisotropic volume change in the unit cell increases with Ni content in low-loading cells, while it levels up for all three NMC cathodes in high-loading cells because of substantial Li plating. The amounts of lithium plating and heterogeneity on the anode, determined by crystallographic phase quantification, were found to be correlated with electrode loading and cathode heterogeneity. The concentrations of the transition metals deposited on the anodes depend on NMCabc composition in a complex way. More particle cracking and surface degradation was found in NMC811. Here, the findings in this work provide a new understanding of the failure mechanisms and their practical implications for compositional tuning of future high-Ni NMCabc cathode materials during fast charging.

25 ENERGY STORAGE↗

DOE OSTI2018

Optimal Discrete Net-Load Balancing in Smart Grids with High PV Penetration

Mitigating supply-demand mismatch is essential for smooth power grid operation. Traditionally, load curtailment techniques such as demand response have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for smart grids with high PV penetration. These grids sometimes exhibit supply surplus, causing overvoltages. Currently, these are mitigated using voltage manipulation techniques such as Volt-Var Optimizations, which are computationally expensive, thereby increasing the complexity of grid operations. Taking advantage of recent technological developments that enable rapid selective connection of PV modules of an installation to the grid, we develop a unified net-load balancing framework that performs both load and solar curtailment. We demonstrate that when the available curtailment values are discrete, this problem is NP-hard and we develop bounded approximation algorithms. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation, while ensuring that practical constraints such as fairness, network capacity limits, and so forth are satisfied. We also develop an online algorithm that performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions that are close to optimal in a small amount of time.

14 SOLAR ENERGY↗

DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Ion-beam radiation-induced Eshelby transformations: The mean and variance in hydrostatic and shear residual stresses

Ion beam plays a pivotal role in ion implantations and the fabrication of nanostructures. However, there lacks a quantitative model to describe the residual stresses associated with the ion-beam radiation. Radiation-induced residual stress/transformation strain have been mostly recognized in the hydrostatic sub strain space. Here, in this study, we use molecular dynamics (MD) simulations to show that the response of a material to irradiation is generally anisotropic that depends on the ion-beam direction, and should be described using tensorial quantities. We demonstrate that accelerator-based ion beam irradiation, combined with the intrinsic lattice anisotropy and externally induced anisotropy (such as anisotropic mechanical loadings), causes radiation-actuated shear transformation strains in addition to hydrostatic expansion. We map out these complex correlations for several materials. Radiation-induced defects are shown to be responsible for residual shear stresses in the manner of Eshelby inclusion transformation. We propose such tensorial response model should be considered for accurate nanoscale fabrication using ion-beam irradiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Tailoring Interfacial Interactions via Polymer-Grafted Nanoparticles Improves Performance of Parts Created by 3D Printing

Abstract not provided

Street, Dayton P.↗

DOE OSTI2024

Morphology of poly-3-hexyl-thiophene blends with styrene–isoprene–styrene block-copolymer elastomers from X-ray and neutron scattering

The nano- and micron scale morphology of poly(3-hexylthiophene) (P3HT) and polystyrene-block-polyisoprene-block-polystyrene (PS–PI–PS) elastomeric blends is investigated through the use of ultra-small and small angle X-ray and neutron scattering (USAXS, SAXS, SANS). It is demonstrated that loading P3HT into elastomer matrices is possible with little distortion of the elastomeric structure up to a loading of ~5 wt%. Increased loadings of conjugated polymer is found to significantly distort the matrix structure. Changes in processing conditions are also found to affect the blend morphology with especially strong dependence on processing temperature. Processing temperatures above the glass transition temperature (T g ) of polystyrene and the melting temperature (T m ) of the conjugated polymer additive (P3HT) creates significantly more organized mesophase domains. P3HT blends with PS–PI–PS can also be flow-aligned through processing, which results in an anisotropic structure that could be useful for the generation of anisotropic properties (e.g. conductivity). Moreover, the extent of flow alignment is significantly affected by the P3HT loading in the PS–PI–PS matrix. The work adds insight to the morphological understanding of a complex P3HT and PS–PI–PS polymer blend as conjugated polymer is added to the system. Here, we also provide studies isolating the effect of processing changes aiding in the understanding of the structural changes in this elastomeric conjugated polymer blend.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Pioneering Heat Pump Project

This project was initiated at a time when ground coupled heat pump systems in this region were limited in size and quantity. There were economic pressures with costs for natural gas and electric utilities that had many organizations considering ground coupled heat pumps; The research has added to the understanding of how ground temperatures fluctuate seasonally and how this affects the performance and operation of the heat pumps. This was done by using a series of temperature sensors buried within the middle of one of the vertical bore fields with sensors located at various depths below grade. Trending of the data showed that there is a lag in ground temperature with respect to air temperatures in the shoulder months, however as full cooling and heating season arrives, the heat rejection and heat extraction from the ground has a significant effect on the ground temps; Additionally it is better understood that while a large community geothermal bore field serving multiple buildings does provide a convenient central plant to use, it introduces complexity of not being able to easily model and predict how each building will contribute to the loads in real time. Additional controllers and programming were added to provide more insight into this real time load profile and allow for intelligent shedding of load via a dry cooler during cool nights in lieu of rejecting to the ground loop. This serves as a means to ‘condition’ the ground loop and mitigate thermal creep of the field, as is typically observed; and It has been observed when compared to traditional heating and cooling equipment, there is still a cost premium to use ground source heat pumps that is driven mostly by the cost for vertical bore holes. Horizontal loop systems are less costly to install, but do not perform as well in this climate zone for heating mode

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Interdisciplinary Education through “Edutainment”: Electric Grid Resilient Control Systems Course

As energy companies and governments attempt to get more from the existing power grid and other critical infrastructures, more automatic control systems are being applied 1-2. With this greater reliance on network-based, digital automation and the stresses of pressing existing infrastructure for greater performance, the power grid and underlying systems have become more susceptible to both malicious attacks and unexpected, natural threats. Governments and other stakeholders have chosen to address infrastructure issues by the implementation of a smarter grid. In the smart grid, operators and control systems supervise power generation, distribution, transmission, and loads to utilize these assets most efficiently3. Such extensive monitoring and control over a distributed system cause complexity that challenge systems designers and human operators in new ways. In addition, cyber vulnerability of these systems has been illustrated in many recent articles on malicious attacks to electric power systems and other similar infrastructure for natural gas, water and communications4. It is therefore critical in the next generation of control systems that resilience plays a large and critical role in the grid design and development. Resilient control systems is a field of research that seeks solutions to the complexity through a holistic approach that combines cognitive science, computer security, communications, and control systems. To enable future researchers and practitioners to assist with designing more resilient systems, science, technology, engineering and mathematics education needs to incorporate interdisciplinary topics. While electrical engineering and computer science programs in the nation include a cyber-security perspective, few if any have focused on the unique control system aspects. Human cognitive aspects are most definitely not addressed in technology education discourse. To this end, a class and education tools in resilient control systems have been created.

17 WIND ENERGY↗

DOE OSTI2023

The Shr receptor from Streptococcus pyogenes uses a cap and release mechanism to acquire heme–iron from human hemoglobin

Streptococcus pyogenes (group A Streptococcus ) is a clinically important microbial pathogen that requires iron in order to proliferate. During infections, S. pyogenes uses the surface displayed Shr receptor to capture human hemoglobin (Hb) and acquires its iron-laden heme molecules. Through a poorly understood mechanism, Shr engages Hb via two structurally unique N-terminal Hb-interacting domains (HID1 and HID2) which facilitate heme transfer to proximal NEAr Transporter (NEAT) domains. Based on the results of X-ray crystallography, small angle X-ray scattering, NMR spectroscopy, native mass spectrometry, and heme transfer experiments, we propose that Shr utilizes a “cap and release” mechanism to gather heme from Hb. In the mechanism, Shr uses the HID1 and HID2 modules to preferentially recognize only heme-loaded forms of Hb by contacting the edges of its protoporphyrin rings. Heme transfer is enabled by significant receptor dynamics within the Shr–Hb complex which function to transiently uncap HID1 from the heme bound to Hb’s β subunit, enabling the gated release of its relatively weakly bound heme molecule and subsequent capture by Shr’s NEAT domains. These dynamics may maximize the efficiency of heme scavenging by S. pyogenes , enabling it to preferentially recognize and remove heme from only heme-loaded forms of Hb that contain iron.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DERs) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning: Preprint

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2017

Reduced-Order Structure-Preserving Model for Parallel-Connected Three-Phase Grid-Tied Inverters

Next-generation power networks will contain large numbers of grid-connected inverters satisfying a significant fraction of system load. Since each inverter model has a relatively large number of dynamic states, it is impractical to analyze complex system models where the full dynamics of each inverter are retained. To address this challenge, we derive a reduced-order structure-preserving model for parallel-connected grid-tied three-phase inverters. Here, each inverter in the system is assumed to have a full-bridge topology, LCL filter at the point of common coupling, and the control architecture for each inverter includes a current controller, a power controller, and a phase-locked loop for grid synchronization. We outline a structure-preserving reduced-order inverter model with lumped parameters for the setting where the parallel inverters are each designed such that the filter components and controller gains scale linearly with the power rating. By structure preserving, we mean that the reduced-order three-phase inverter model is also composed of an LCL filter, a power controller, current controller, and PLL. We show that the system of parallel inverters can be modeled exactly as one aggregated inverter unit and this equivalent model has the same number of dynamical states as any individual inverter in the system. Numerical simulations validate the reduced-order model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Measuring Flexibility through Reduction Potential

As electrical loads, electric vehicles (EVs) are distinguished in many cases by their substantial flexibility. Harnessing this flexibility, however, requires a precise characterization of its timing and degree. Current approaches to flexibility characterization tend to be limited either by the complexity of their representation or their inability to capture aggregate flexibility across multiple vehicles. In this paper, we present the reduction potential matrix, a novel metric capturing fleet-level EV load flexibility while being both straightforward to calculate and intuitive to interpret. We use the metric to quantify flexibility for two operationally distinct commercial groups: freight vehicles and transit buses. While we find that both vehicle groups have substantial flexibility, its timing, magnitude, and duration varies across the groups. We describe how this variability translates into variation in the each group's role as a grid resource, and conclude with a discussion of how system planners, fleet operators, and other stakeholders can use the novel metric to cultivate symbiosis between vehicles and the grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗