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DOE OSTI2022

Sierra/SolidMechanics 5.10 User's Guide

Sierra/SolidMechanics (Sierra/SM) is a Lagrangian, three-dimensional code for finite element analysis of solids and structures. It provides capabilities for explicit dynamic, implicit quasistatic and dynamic analyses. The explicit dynamics capabilities allow for the efficient and robust solution of models with extensive contact subjected to large, suddenly applied loads. For implicit problems, Sierra/SM uses a multi-level iterative solver, which enables it to effectively solve problems with large deformations, nonlinear material behavior, and contact. Sierra/SM has a versatile library of continuum and structural elements, and a large library of material models. The code is written for parallel computing environments enabling scalable solutions of extremely large problems for both implicit and explicit analyses. It is built on the SIERRA Framework, which facilitates coupling with other SIERRA mechanics codes. This document describes the functionality and input syntax for Sierra/SM.

42 ENGINEERING↗

DOE OSTI2023

Sierra/Solid Mechanics 5.16 User's Guide

36 MATERIALS SCIENCE↗

DOE OSTI2024

Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production

One of the main differences between floating offshore wind turbines (FOWTs) and fixed-bottom turbines is the angular and translational motions of FOWTs. When it comes to planning a floating wind farm (FWF), the translational motions introduce an additional layer of complexity to the FWF layout. The ability of a FOWT to relocate its position represents an opportunity to mitigate wake losses within an FWF. By passively relocating downwind turbines out of the wake generated by upwind turbines, we can reduce wake-induced energy losses and enhance overall energy production. The translational movements of FOWTs are governed by the mooring system attached to it. The way a FOWT relocates its position changes if the design of the mooring system attached to it changes. Additionally, the translational motion of a FOWT attached to a given mooring system is different for different wind directions. Hence, we can tailor a mooring system design for a FOWT to passively control its motions according to the wind direction. In this work, we present a new self-adjusting FWF layout design and assess its performance using both static and dynamic methods. The results show that relocating the FOWTs in an FWF can increase the energy production by 3 % using a steady-state wake model and 1.4 % using a dynamic wake model at a wind speed of 10 m s -1 . Moreover, we compare the fatigue and ultimate loads of the mooring systems of the self-adjusting FWF layout design to the mooring systems in a current state-of-the-art FWF baseline design. The comparison shows that with smaller mooring system diameters, the self-adjusting FWF design has similar fatigue damage compared to the baseline design with bigger mooring system diameters at rated wind speed. Finally, the ultimate loads on the mooring systems of the self-adjusting FWF design are lower than those on the mooring systems of the baseline design.

17 WIND ENERGY↗

DOE OSTI2020

Ion-Conducting Dynamic Solid Polymer Electrolyte Adhesives

Cross-linked polymer electrolytes containing structurally dynamic disulfide bonds have been synthesized to investigate their combined ion transport and adhesive properties. Dynamic network polymers of varying cross-link densities are synthesized via thiol oxidation of a bisthiol monomer, 2,2'-(ethylenedioxy)diethanethiol, and tetrathiol cross-linker, pentaerythritol tetrakis(3-mercaptopropionate). At optimal loading of lithium bis(trifluoromethane-sulfonyl-imide) (LiTFSI) salt, the ionic conductivities (σ) at 90 °C are about 1 × 10–4 and 1 × 10–5 S/cm at the lowest and highest cross-linking, respectively. Notably, in comparison to the equivalent nondynamic network, the dynamic network shows a positive deviation in σ above 90 °C, which suggests the enhancement of ion transport occurs from the difference in structural relaxation on account of the dissociation of disulfide bonds. Lap shear adhesion and conductivity tests on ITO-coated glass substrates reveal the dynamic network exhibits a higher adhesive shear strength of 0.2 MPa (vs 0.03 MPa for the nondynamic network) and higher σ after the application of external stimulus (UV light or heat). The adhesive strength and σ are stable over multiple debonding/rebonding cycles and, thus, demonstrating the utility of these structurally dynamic networks as solid polymer electrolyte adhesives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

LAF-Net: A Deep Residual and Cross-Attention Framework for Day-Ahead Load Forecasting: Preprint

Accurate day-ahead load forecasting is essential for reliable power system operations and market efficiency. System operators such as the Midcontinent Independent System Operator (MISO) rely on forecasts from multiple vendors, yet combining them effectively remains a persistent challenge due to vendor-specific biases. This paper presents a novel LSTM-Attention Fusion Network with Error Representation (LAF-Net) that enhances day-ahead hourly load forecasting through deep residual learning and multi-modal cross-attention. The proposed model builds a historical error memory from past vendor performance and dynamically queries it with future hour context to generate adaptive, hour-specific trust weights for each vendor. A bounded residual correction further refines forecasts by mitigating systematic and temporally localized errors. Tested on real MISO LBA data with multi-vendor forecasts, LAF-Net consistently outperforms the best vendor baseline across all 38 LBAs, achieving more than a 40% reduction in system-level mean absolute error (MAE) during peak load hours relative to the best vendor baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

17 WIND ENERGY↗

DOE OSTI2023

Final Report DE-FE0031785 Mohsen Ahmadian, Ph.D. Demonstration of Proof of Concept of a Multiphysics Approach for Real-Time Remote Monitoring of Dynamic Changes in Pressure and Salinity in Hydraulically Fractured Networks

Hydraulic fracturing has evolved into a multistep process with varying flow rates, carrier fluids (e.g., gel or slickwater), proppant loadings, and proppant grain sizes. As a result, primary recovery from a hydraulically fractured tight-oil reservoir is often a tiny fraction of the original oil in place, ranging between 5 and 10%. As stated in the FOA1990, “part of this problem is due to the inability of current well completion processes to effectively stimulate the entire reservoir volume in contact with the wellbore. Innovative technologies are needed that can help improve the effectiveness of reservoir completion methods, maximize stimulated reservoir volumes, and optimize recovery over the entire producing life span of a well”. We first need to enhance the current fracture diagnostic techniques to improve a well-completion design. However, detecting and delineating a subsurface hydraulic fracture is extremely difficult because the induced fracture network is only fractionally propped, and these propped fractures are generally very thin. Microseismic and tiltmeter monitoring techniques can provide information on the fracture extent but provide little or no information on the movement and final distribution of proppant or production fluids. On the other hand, electromagnetic (EM) imaging has shown the capability to monitor proppant distribution throughout the fracture area, especially in the presence of Electrically Active Proppants (EAPs). A previous EM survey of hydraulic fracturing at the Devine Fracture Pilot Site (DFPS) and subsequent EM code developments demonstrated this survey as a robust technique to remotely interrogate the extent of the EAP-filled hydraulic fracture during its propagation. The objectives of the project were threefold: (1) to capitalize on the material properties of an EAP to demonstrate remote monitoring of relative changes in pressure, pressure, and flow that are commonly encountered during production from a hydraulically fractured reservoir; (2) to evaluate EM imaging tools, to achieve Objective 1 in near real-time; and (3) to develop a multi-physics joint inversion approach to precisely predict flow patterns and physiochemical changes within an EAP-filled fracture network. This research project was built upon our previous work at the Devine Test Site managed by the Bureau of Economic Geology (BEG) at The University of Texas at Austin (UT-Austin). It also leveraged a significant investment from the Advanced Energy Consortium (AEC) to address the DOE's interest in subsurface flow, containment, and characterization by multiple signals. This three-year and three-month project succeeded in demonstrating the feasibility of a real-time dynamic fluid flow mapping technique at Technology Readiness Level 5 (TRL5) by utilizing a commercially available surface-based Controlled-Source Electromagnetic (CSEM) method (Objectives 1, 2). We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, the observed electric field in our study is influenced by bottomhole pressure, flow rate, and salinity, which is demonstrated by comparing these parameters with the electrical field potentials. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce the whole measured electric field magnitudes. Preliminary estimates showed that including Streaming Potential (SP) in our geophysical model is likely needed to reduce the simulation misfit.

02 PETROLEUM↗

DOE OSTI2023

Measurements and Prediction of Ash Deposition in a Cyclone-Fired Boiler Operating under Variable Load Conditions

Measurements of ash deposition rates were made between the secondary superheater and reheater sections of a 450 MW cyclone-fired lignite boiler as the operational load varied from 33 to 100%. Significant reductions in deposition rates with a decrease in operational load were observed. To uncover the causative mechanisms behind these observations, operational data from the power plant were used to carry out computational fluid dynamic (CFD) simulations of the boiler. After ascertaining that the gas temperatures and velocities at various sections within the boiler were being represented adequately, decoupled simulations of the ash deposition process on the deposit probe were carried out using a finely resolved boundary layer mesh. Fly ash particle size distribution (PSD) and its concentration for the decoupled calculations were determined from stand-alone cyclone barrel simulations. The ash partitioning (mass %) between the fly ash and slag was found to be ~50:50, which was in line with previous field observations, and it did not vary significantly across different cyclone loads. The predicted PSD of the deposit ash was concentrated in the size range 10–30 microns, which was in agreement with cross-sectional images of the deposit obtained from the measurements. At lower loads, sharp variations in the deposition rates were predicted in the gas temperature range 950–1150 K. The particle kinetic energy—particle viscosity-based capture methodology utilized in this study in conjunction with appropriate ash compositions, ash viscosity models and gas temperature estimates can help estimate slagging propensities at different loads reasonably well in these systems.

Krishnamoorthy, Gautham (ORCID:0000000255205092)↗

DOE OSTI2021

Analysis of the Amorphous and Interphase Influence of Comononomer Loading on Polymer Properties toward Forwarding Bioadvantaged Copolyamides

In this paper we present an approach for selectively modifying the properties of semicrystalline polymers by introducing “bioadvantaged” counits. With this approach, the unique functionality of biomass can be leveraged to tailor the properties of the amorphous phase of semicrystalline polymers with minimal impact on crystallinity and thermomechanical properties. As a model case, PA 6,6 copolyamides were produced using the bioadvantaged monomer trans-3-hexenedioic acid (t3HDA). The analogous structure of t3HDA to adipic acid, a PA 6,6 monomer, allows for seamless integration. Screening over the entire composition range identified the t3HDA loading (20 mol%) beyond which properties deviate appreciably from Nylon 6,6. Once identified, copolyamides of suitable compositions were upgraded to commercial quality and fully characterized to assess the influence of counit loading and polymer structure on thermal and mechanical properties. Samples were characterized using gel permeation chromatography (GPC), proton nuclear magnetic resonance spectroscopy (1H NMR), heteronuclear single quantum coherence spectroscopy (HSQC), wide-angle X-ray scattering (WAXS), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), dynamic mechanical analysis (DMA), tensile testing, flexural testing, and water absorption testing. t3HDA units were shown to hydrate during the harsh polycondensation to 3-hydroxyhexanedioic acid (3HHDA) and fully incorporate into the polymer backbone. Loading levels up to 20% were shown to have comparable thermal and mechanical properties in the dry state, yet moisture absorption—a known method for improving the toughness, yield strain, and elongation of polyamides—was enhanced by over 100% at 20% loading. This case study on bioadvantaged copolymers elucidates the governing structure-function principles that can be leveraged to forward value-added renewable polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Holistic Small-Signal Stability Analysis for Large-Scale Inverter-Intensive Power Systems with Coupled and Full-Order Dynamics from Control Systems and Power Networks

The increasing penetration of inverter-based resources (IBRs) into the existing power systems introduces tremendous benefits for enhanced sustainability but also poses inevitable challenges in terms of insufficient inertia, potential instability, and complex network dynamics, among others. However, the additional coupling introduced by the interactions among gridfollowing (GFL) and grid-forming (GFM) IBRs and the other components (i.e., synchronous generators [SGs], loads, and network, etc.) has not been clearly explored. A holistic, scalable, and quantitative stability analysis framework with the control systems and power networks is still missing. Here, in this paper, to fill in the technical gaps, a holistic small-signal model of the entire system with both rotating generation units and IBRs is established. An extended power flow model with operation dynamics from both generator control schemes and power networks is proposed to provide the varying steady-state operating points for small-signal modeling. The proposed method is compared with MATLAB solvers, and the results show that the proposed approach has a minimum calculation time, which can be less than 12 seconds for a large-scale power system with up to 2,000 buses. Furthermore, a quantitative method is developed to identify the impacts of IBRs on system performance with emphases on the potential stability issues with GFL IBRs, additional benefits of employing GFM IBRs, the feasibility of replacing SGs with GFM IBRs, and the impact of penetration level of different kinds of generation units. Finally, a field island power system is used to verify the proposed approach, and hardware-in-the-loop (HIL) tests are provided to further demonstrate the effectiveness of the proposed analysis.

14 SOLAR ENERGY↗

DOE OSTI2024

Near wake evolution of a tidal stream turbine due to asymmetric sheared turbulent inflow with different integral length scales

Tidal stream turbines deployed at highly energetic open water sites are subjected to sheared inflow in the rotor plane. The inflow shear is expected to cause asymmetric loading on the rotor blades and affect the downstream wake. In the current study, two different turbulent inflow conditions, static-high shear and dynamic shear, were generated via an active-grid turbulence generator. A 1:20 scaled three-bladed horizontal axis tidal turbine model was tested in those conditions. The results were compared to a quasi-laminar case with no imposed turbulence or shear. The results show that the high shear reduces the average performance, with a drop of up to 16% in the optimal power coefficient. Besides, the shear profiles increase torque fluctuations and induce significant differences in wake hydrodynamics between the high-speed (upper) and low-speed (lower) regions. The large integral length scales further enhance the load fluctuations perceived by the rotor but have a negligible effect on the mean wake field quantities and the wake recovery. The lower half region featured a faster breakdown of tip vortex structure and a rapid drop of swirl number, a phenomenon conjectured to be a consequence of the strong turbulence intensities and Reynolds stresses in the lower half region. Furthermore, the sheared turbulent inflow also results in a very intensive energy redistribution process towards large-scale, low-frequency motions, which is important to the downstream turbines.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Influence of biogenic emissions from boreal forests on aerosol–cloud interactions

Boreal forest acts as a carbon sink and contributes to the formation of secondary organic aerosols via emission of aerosol precursor compounds. However, these influences on the climate system are poorly quantified. Here we show direct observational evidence that aerosol emissions from the boreal forest biosphere influence warm cloud microphysics and cloud-aerosol interactions in a scale-dependent and highly dynamic manner. Analyses of in situ and ground-based remote-sensing observations from the SMEAR II station in Finland, conducted over eight months in 2014, reveal substantial increases in aerosol load over the forest one to three days after aerosol-poor marine air enters the forest environment. We find that these changes are consistent with secondary organic aerosol formation and, together with water-vapour emissions from evapotranspiration, are associated with changes in the radiative properties of warm, low-level clouds. Here, the feedbacks between boreal forest emissions and aerosol-cloud interactions and the highly dynamic nature of these interactions in air transported over the forest over timescales of several days suggest boreal forests have the potential to mitigate climate change on a continental scale. Our findings suggest that even small changes in aerosol precursor emissions, whether due to changing climatic or anthropogenic factors, may substantially modify the radiative properties of clouds in moderately polluted environments. Emissions from the boreal forest biosphere can substantially increase aerosol load above the forest and influence the radiative properties of clouds, according to analysis of observations from a monitoring station in Finland.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Damage initiation and evolution in Al-Si layered microstructures under shock loading conditions at atomic scales

Designing materials with microstructural features like multiphase interfaces have shown significant promise for usage in the next generation defense and nuclear applications. The dynamic response of these interfaces in extreme environments is observed to vary with the deformability of individual phases, which could subsequently alter the favored damage nucleation sites related to spall failure. Here, we investigate the role of spacing of interfaces in a nanocrystalline layered Aluminum-Silicon system on the shock wave propagation behavior, and microstructural and defect evolution using classical molecular dynamics simulations. The simulations are carried out with different hypothetical Al/Si microstructures including variations in the distribution of Si grains as layers in a nanocrystalline Al matrix. The molecular dynamics simulations suggest that spall failure is preferentially initiated at Al/Si interfaces. In addition, for the same system volume and same concentration of Si, a higher number of layers is marked by an increase in shock wave velocity and reduced resistance to spall failure.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Thermal Modelling of Advanced Test Reactor Fuel in a Generalized Dry Storage System

Star-CCM+, a computational fluid dynamics (CFD) software was used to conduct modeling and simulation of the thermal performance of a dry storage configuration consisting of Department of Energy Standardized Canisters (DOESCs) loaded with aluminum-clad spent nuclear fuel (ASNF.) The configuration includes nine DOESCs loaded with Advanced Test Reactor ASNF contained within a stainless steel overcanister centered in a ventilated, concrete overpack. The simulations were used to estimate the maximum temperatures reached by backfill gases inside the overcanister and DOESCs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Google/Makani Energy Kite Modeling (CRADA CRD-18-00569 Final Report)

In support of Makani’s energy kite development program, Makani engaged with NREL to develop, verify, and document a multiphysics engineering model of a megawatt-scale tethered energy kite (named KiteFAST). After the original development of KiteFAST was completed, Makani further engaged NREL to develop a software enhancement of KiteFAST to enable the dynamics modeling of an energy kite tethered to a floating offshore platform (named KiteFAST-OS). Before this project, the capability to model the aero-hydro-servo-elastic dynamics of an airborne wind energy (AWE) system with electricity generation on the flying device (fly gen) and crosswind flight operation did not exist. Such physics-based modeling capability is needed for loads analysis, structural design, and certification of energy kites. Throughout the project, an exclusive license kept KiteFAST and KiteFAST-OS available only to Makani, NREL, and its counterparts. With the ending of Makani, the KiteFAST and KiteFAST-OS software were merged into a single code base (named KiteFAST), the exclusive license was terminated, and the source code, documentation, and example models were released publicly.

17 WIND ENERGY↗

DOE OSTI2025

Operando FTIR investigation of salt dynamics in Li-ion batteries during fast charging

Li-ion batteries, when charged at fast-charging rates ($>$2C), suffer from reduced lifetimes and can undergo catastrophic failure. During high-rate charging, Li-ions are unable to rapidly transport through high-loading electrodes ($>$4 mAh cm −2 ). This results in unequal charge distributions, potentials, and utilization of the active material, which can lead to Li plating. Li-ion concentration polarization, in which Li-ions deplete in the anode and accumulate in the cathode during charging, precedes Li plating. An operando FTIR-ATR graphite/NMC cell developed in this research captured Li-ion concentration polarization in real-time. During fast charging, decreases in Li-ion concentration ($>$95%) were measured at the back of the anode. This is the first verification of complete Li-ion depletion within the anode at high C-rates. The measurements also showed graphite stage transition. A P2D model was developed for comparison to the operando measurements. The measurements agreed with the model in some cases, but disparities existed at high C-rates and loadings. In the experiment, the Li-ion concentration often failed to recover to 1.2 M until several hours after charging, whereas the model Li-ion concentration rapidly recovered. The contrast between the model and experiment results indicates that further investigation is required to improve understanding of Li-ion concentration dynamics during fast charging.

25 - ENERGY STORAGE↗