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DOE OSTI2021

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗

DOE OSTI2026

Structural coherence model for predicting molten salt thermal conductivity informed by the pair distribution function

To enable thermal behavior prediction and design optimization of molten salt reactors, thermal conductivity of molten salts must be characterized in terms of salt composition and temperature. Current theoretical models fail to provide consistent approximations for all halide mixtures, particularly actinide-bearing melts. This study aims to link the short-range order structure of molten salts to the mean free path of energy carriers through a simple structural coherence model informed by the partial pair distribution function. The proposed method is used to predict the thermal conductivity of 33 alkali and alkaline earth halide salts. Predictions approximate experimental measurements with a mean absolute error of 15.7% for dissociating, complexing, and actinide salts, including unary LiCl, NaCl, and MgCl 2 as well as mixtures LiF–NaF–KF (FLiNaK), LiF–BeF 2 (FLiBe), and NaCl–UCl 3 . The work provides evidence for the validity of energy carrier descriptions of molecular-level heat transfer in molten salts, with implications for improved theories of liquid energy transport in general.

Actinide mixtures↗

DOE OSTI2021

Determining the Structure-Property Relationships of Quasi-Two-Dimensional Semiconductor Nanoplatelets.

We report a theoretical study of CdSe nanoplatelets aimed at identifying the main factors determining their photophysical properties. Using atomic configurations optimized with density functional theory calculations, we computed quasiparticle and exciton binding energies of nanoplatelets with two to seven monolayers. We employed many body perturbation theory at the GW level and solved the Bethe-Salpeter equation to obtain absorption spectra and excitonic properties. Our results, which agree well with recent experiments, were then used to design a model that allows us to disentangle the effects of quantum confinement, strain induced by passivating ligands, and dielectric environment on the electronic properties of nanoplatelets. We found that, for the model to accurately reproduce our first principle results, it is critical to account for surface stress and consider a finite potential barrier and energy-dependent effective masses when describing quantum confinement. Our findings call into question previous assumptions on the validity of an infinite barrier to describe carrier confinement in nanoplatelets, suggesting that it may be possible to optimize interfacial charge transfer and extraction by appropriately choosing passivating ligands. The model developed here is generalizable to core-shell platelets and enables the description of system sizes not yet directly treatable by first-principles calculations.

Greenwood, Arin R.↗

DOE OSTI2026

Tunable Electronic Energy Level Alignment and Exciton Diversity in Organic–Inorganic van der Waals Heterostructures

van der Waals stacking of two-dimensional (2D) materials offers a powerful platform for engineering material interfaces with tailored electronic and optical properties. While most van der Waals multilayers have featured inorganic monolayers, incorporating molecular monolayers introduces additional degrees of tunability and functionality. Here, in this study, we investigate hybrid bilayers composed of atomically thin perylene-based molecular crystals interfaced with monolayer transition metal dichalcogenides (TMDs), specifically MoS 2 and WS 2 . Using the ab initio many-body perturbation theory within the GW approximation and the Bethe-Salpeter equation approach, we predict emergent properties beyond those of the isolated constituent systems. Notably, we find substantial renormalization of monolayer molecular crystal band gap due to TMD-induced polarization. Furthermore, by varying the TMD monolayer, we demonstrate tuning of the energy level alignment of the bilayer and subsequent control over a diversity of lowest-energy excitons, which include strongly bound hybrid excitons and long-lived charge-transfer excitons. These findings establish organic-inorganic van der Waals heterostructures as a promising class of materials for tunable optoelectronic devices and quantum excitonic phenomena, expanding the design space for low-dimensional systems.

GW-BSE calculations↗

DOE OSTI2018

Electronic Excitations in Complex Molecular Environments: Many-Body Green’s Functions Theory in VOTCA-XTP

Many-body Green’s functions theory within the GW approximation and the Bethe-Salpeter Equation (BSE) is implemented in the open-source VOTCA-XTP software, aiming at the calculation of electronically excited states in complex molecular environments. Based on Gaussian-type atomic orbitals and making use of resolution of identity techniques, the code is designed specifically for nonperiodic systems. Application to a small molecule reference set successfully validates the methodology and its implementation for a variety of excitation types covering an energy range from 2 to 8 eV in single molecules. Further, embedding each GW-BSE calculation into an atomistically resolved surrounding, typically obtained from Molecular Dynamics, accounts for effects originating from local fields and polarization. Using aqueous DNA as a prototypical system, different levels of electrostatic coupling between the regions in this GW-BSE/MM setup are demonstrated. Particular attention is paid to charge-transfer (CT) excitations in adenine base pairs. It is found that their energy is extremely sensitive to the specific environment and to polarization effects. The calculated redshift of the CT excitation energy compared to a nucelobase dimer treated in vacuum is of the order of 1 eV, which matches expectations from experimental data. Predicted lowest CT energies are below that of a single nucleobase excitation, indicating the possibility of an initial (fast) decay of such an UV excited state into a binucleobase CT exciton. Furthermore, the results show that VOTCA-XTP’s GW-BSE/MM is a powerful tool to study a wide range of types of electronic excitations in complex molecular environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Predictions of Line Chilldown Boiling Regime Transitions by a Coupled CFD-Sub-Grid Boiling Model Validated against 1G LN2 Experiments

Propellant storage and transfer during future long-duration missions will involve fuel depot operations in which a donor depot tank is used to fill a receiver spacecraft tank with a cryogenic propellant. Prior to the on-Orbit filling operation both the receiver tank and the transfer line must be cooled. The line chilldown process involves transition between boiling regimes in microgravity that will be quite different from their 1g ground-based counterparts. Since the cryogenic propellant itself will be used to perform the chilldown process, the time constants to cool the wall and the amount of fuel that will be used become important design considerations. In this light, the focus of the present work is to capture the flow boiling characteristics of a cryogenic fluid, namely, liquid nitrogen, during the chill-down of a transfer line using CFD modelling and simulations. The cryogenic chill-down process involves different flow boiling regimes: film boiling, transition boiling and nucleate boiling. The prediction of transition between these regimes in a CFD framework is new and challenging. The present work addresses this challenge by employing a volume-of-fluid (VOF) based methodology with Lee phase change model to predict the film boiling regime of the chill-down process in ANSYS Fluent®. The transition and nucleate boiling regimes are predicted by incorporating a sub-grid model that accounts for bubble nucleation, growth, shedding frequency, and departure diameter. The sub-grid model is implemented into Fluent via a user-defined function for wall-fluid heat flux calculations. The sub-grid model is similar in formulation to the well-known Rensselaer Polytechnic Institute (RPI) boiling model. The CFD model is validated against published experimental data for liquid nitrogen chill-down of a heated stainless-steel pipe in 1g. Predicted results shows good agreement of wall temperature, rewetting temperature, and transition between film and nucleate boiling with the experimental measurements for several different LN2 flowrates in the vertical orientation. The strengths and weaknesses of this modeling approach are presented and discussed.

Cryogenic Fluid Management↗

DOE OSTI2023

Economic competitiveness of pultruded fiber composites for wind turbine applications

Pultrusion manufacturing of fiber reinforced polymers has been shown to yield some of the highest mechanical properties for unidirectional composites, having a high degree of fiber alignment with consistent performance. Pultrusions offer a low-cost manufacturing approach for producing unidirectional composites with a constant cross-section and are used in many applications, including spar caps of wind turbine blades. However, as an intermediate processing step for wind blades, the additional cost of manufacturing pultrusions must be accompanied by sufficient increases in mechanical performance and system benefits. Wind turbine blades are manufactured using vacuum-assisted resin transfer molding with infused unidirectional fiberglass or carbon pultrusions for the spar cap. Infused fiberglass composites are among the most cost-effective structural materials available and replacing this material in the cost-driven wind industry has proven challenging, where infused fiberglass spar caps are still the predominant material system in use. To evaluate alternative material systems in a pultruded composite form, it is necessary to understand the costs for this additional manufacturing step which are shown to add 33%–55% on top of the material costs. A pultrusion cost model has been developed and used to quantify cost sensitivities to various processing parameters. The mechanical performance for pultruded composites is improved versus resin-infusion manufacturing with a 17% increase in design strength at a constant fiber volume fraction, but also enables higher achievable fiber volume fractions. The cost-specific mechanical performance is compared as a function of processing parameters for pultruded composites to identify the opportunities for alternative material and manufacturing approaches for wind turbine spar caps. Finally, four materials are compared in a representative wind turbine blade model to assess the performance of pultruded carbon fiber systems and pultruded fiberglass relative to infused fiberglass, where the pultruded systems produce lower weight blades with various cost distinctions.

42 ENGINEERING↗

DOE OSTI2020

Fouling modeling and prediction approach for heat exchangers using deep learning

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

42 ENGINEERING↗

DOE OSTI2022

Single-Atom Pt Boosting Electrochemical Nonenzymatic Glucose Sensing on Ni(OH) 2 /N-Doped Graphene

Conventional nanomaterials in electrochemical non-enzymatic sensing face huge challenge due to their complex size-, surface- and composition- dependent catalytic properties and low active site density. In this work, we designed a single-atom Pt supported on Ni(OH) 2 nanoplates/nitrogen-doped graphene (Pt 1 /Ni(OH) 2 /NG) as the first example for constructing a single-atom catalyst based electrochemical non-enzymatic glucose sensor. The resulting Pt 1 /Ni(OH) 2 /NG exhibited a low anode peak potential of 0.48 V and high sensitivity of 220.75 μA mM -1 cm -2 towards glucose, which are 45 mV lower and 12 times higher than those of Ni(OH) 2 , respectively. The catalyst also showed excellent selectivity for several important interferences, short response time of 4.6 s, and high stability over 4 weeks. Experimental and density functional theory (DFT) calculated results reveal that the improved performance of Pt 1 /Ni(OH) 2 /NG could be attributed to stronger binding strength of glucose on single-atom Pt active centers and their surrounding Ni atoms, combined with fast electron transfer ability by the adding of the highly conductive NG. Finally, this research sheds light on the applications of SACs in the field of electrochemical non-enzymatic sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

In situ Infrared Spectroelectrochemistry for Understanding Structural Transformations of Precisely Defined Ions at Electrochemical Interfaces

Understanding the intrinsic properties of electroactive species at electrode-electrolyte interfaces (EEIs) is essential to the rational design of high-performance solid-state energy conversion and storage systems. In situ spectroscopy combined with cyclic voltammetry (CV) provides insights into structural changes of electroactive species at functioning EEIs. Ion soft-landing enables precisely-controlled deposition of massand charge-selected ions onto electrode surfaces thereby avoiding the contamination inherent with conventional electrode preparation techniques. In this contribution, we describe a new approach for the simultaneous electrochemical and spectroscopic characterization of soft-landed ions at operating solid-state EEIs. The technique exploits a specially-fabricated three-electrode cell that is compatible with in situ infrared reflection absorption spectroscopy (IRRAS) characterization of the softlanded ions. Keggin polyoxometalate (POM) anions, PW12O40 3-, were selected as a model system for these experiments due to their multielectron redox activity, structural stability, and well-characterized IRRAS spectrum. In situ CV measurements indicated continuous multielectron transfer processes of the soft-landed PW12O40 3- anions over a large potential range of -2.1 to -0.3 V. A distinct shift in the wavenumber of the terminal W=Ot stretching vibration in the IRRAS spectra was observed during the multielectron reduction process. The results demonstrate the capabilities of the in situ spectroelectrochemical approach for examining structural changes of well-defined electroactive species during electron transfer processes at operating solid-state EEIs.

Su, Pei↗

DOE OSTI2023

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Machine learning atomistic potentials trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. The curation of the DFT datasets can be extensive in size and time-consuming to train and refine. In this study, we focus on addressing these barriers by developing minimalistic and flexible datasets for many elements in the periodic table regardless of their mass, electronic configuration, and ground state lattice. These DFT datasets have on average, ~4000 different structures and 27 atoms per structure, which we found sufficient to maintain the predictive accuracy of DFT properties and notably with high transferability. We envision these highly curated training sets as starting points for the community to expand, modify, or use with other machine learning atomistic potential models, whatever may suit individual needs, further accelerating the utilization of machine learning as a tool for material design and discovery.

42 ENGINEERING↗

DOE OSTI2019

Key role of antibonding electron transfer in bonding on solid surfaces

The description of the chemical bond between a solid surface and an atom or a molecule is the fundamental basis for understanding surface reactivity and catalysis. Despite considerable research efforts, the physics that rules the strength of such chemical bonds remains elusive, especially on semiconductor surfaces. Widespread understandings are mostly based on the degree of filling of antibonding surface-adsorbate states that weaken the surface adsorption. The unoccupied antibonding surface-adsorbate states are often considered to have no effects on surface bonding. Here in this paper, we show that the energy levels of unoccupied antibonding surface-adsorbate states relative to the Fermi-level play a critical role in determining the trends in variations of surface adsorption energies. The electrons that would occupy those high-energy antibonding states are transferred to the Fermi-level, leading to an energy gain that largely controls surface bonding. To illustrate this picture, as a validating case, we study the hydrogen evolution reaction (HER) catalyzed by MoS 2 from density functional theory calculations. We find that the majority of antibonding surface-hydrogen states are positioned well above the Fermi-energy. A clear linear relationship between the energy gain from antibonding electron transfer and the adsorption energy is identified for hydrogen binds to either molybdenum or sulfur atoms at different sites. The antibonding-electron transfer energy can thus serve as a primary catalytic activity descriptor. The emerging picture identifies the origin of HER on MoS 2 , which is related to the empty in-gap states induced by sulfur vacancies or edges. Under this picture, the effects of surface inhomogeneity (e.g., defects, step edges) on surface bonding strength can be understood. This antibonding electron transfer picture also offers a physically different explanation for the well-known d-band theory for hydrogen adsorption on transition metal surfaces. The results provide guidelines for understanding and optimizing catalyst performance and designing new solid catalysts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1993

Flight testing and frequency domain analysis for rotorcraft handling qualities characteristics

A demonstration of frequency domain flight testing techniques and analyses was performed on a U.S. Army OH-58D helicopter in support of the OH-58D Airworthiness and Flight Characteristics Evaluation and the Army's development and ongoing review of Aeronautical Design Standard 33C, Handling Qualities Requirements for Military Rotorcraft. Hover and forward flight (60 knots) tests were conducted in 1 flight hour by Army experimental test pilots. Further processing of the hover data generated a complete database of velocity, angular rate, and acceleration frequency responses to control inputs. A joint effort was then undertaken by the Airworthiness Qualification Test Directorate (AQTD) and the U.S. Army Aeroflightdynamics Directorate (AFDD) to derive handling qualities information from the frequency response database. A significant amount of information could be extracted from the frequency domain database using a variety of approaches. This report documents numerous results that have been obtained from the simple frequency domain tests; in many areas, these results provide more insight into the aircraft dynamics that affect handling qualities than to traditional flight tests. The handling qualities results include ADS-33C bandwidth and phase delay calculations, vibration spectral determinations, transfer function models to examine single axis results, and a six degree of freedom fully coupled state space model. The ability of this model to accurately predict aircraft responses was verified using data from pulse inputs. This report also documents the frequency-sweep flight test technique and data analysis used to support the tests.

Ham, Johnnie A.↗

DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗

DOE OSTI2026

Effect of Initiator Density, Catalyst Concentration, and Surface Curvature on the Uniformity of Polymers Grafted from Spherical Nanoparticles

Polymer-grafted nanoparticles (PGNPs) are versatile hybrid materials whose properties critically depend on brush dimensions, uniformity, and grafting density. Herein, we systematically investigated how initiator density, catalyst concentration, and nanoparticle curvature govern the growth of poly(methyl methacrylate) (PMMA) brushes grafted from spherical SiO 2 nanoparticles via surface-initiated activators regenerated by electron transfer atom transfer radical polymerization (SI-ARGET ATRP). By tuning the initiator density through a combination of “active” and “dummy” silane initiators anchored on the nanoparticles’ surface and controlling the catalyst concentration, we reveal that increased initiator crowding and smaller surface curvature amplify steric hindrance, leading to decreased initiation efficiency and broader molecular weight distributions. Correlation with the corresponding unattached chains by ARGET ATRP suggests the presence of permanently inaccessible (“buried”) initiation sites, which are a characteristic of surface-grafted systems. At sufficient Cu catalyst concentrations, uniform brush growth is attained across different initiator densities, whereas decreased catalyst concentrations accentuate nonconcurrent initiation and propagation. These findings provide mechanistic insights into the interplay of initiator density, catalyst concentration, and surface curvature, offering design principles for tailoring the PGNP architecture. These results can guide the structural engineering of densely grafted surfaces, including nanoparticles and flat substrates, for applications in nanocomposites, photonics, and functional coatings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Dynamic Wind Loading on CSP Collectors Caused by Turbulent Wind Fluctuations: Insights from a 2-Year Field Campaign

Concentrating Solar Power (CSP) is a promising solar technology for electricity generation with thermal energy storage and with the additional benefit of industrial heat production. Wind loading on CSP collector structures, such as parabolic troughs or heliostats, is one of the primary drivers of their structural design costs. In particular, dynamic wind loading is a major source of uncertainty in the collector design process, which heavily relies on wind tunnel testing. In the field, the turbulent nature of the incoming wind creates fluctuating loads (support structure loads and resulting mirror deflections) on the collectors, with impacts on fatigue lifetime and optical performance. As is well known, wind tunnel tests cannot entirely reproduce the complex turbulent wind conditions typically observed at full-scale plants. To shed light on this topic, NREL initiated a field campaign at the operational Nevada Solar One (NSO) powerplant that uses parabolic troughs as solar collectors. The aim of the project is a detailed characterization of prevailing wind and turbulence conditions and resulting operational loads on parabolic troughs. We use the published 2-year dataset of high-resolution combined wind and structural loads measurements [1] to characterize the dynamic structural wind response. For quantifying dynamic wind loading, we apply the concept of admittance functions, which are spectral transfer functions that couple the turbulent wind to resulting structural loads (aerodynamic admittance), and to the structural response (mechanical admittance). In practice, aerodynamic admittance describes which turbulent eddy sizes are effective in creating structural loads. The mechanical admittance describes in which frequency ranges these loads are reinforced or dampened by the structure. While these functions are an established concept in civil engineering, their recent application to a single full-scale heliostat [2] proved their broader applicability to CSP collectors. Here, we present a characterization of admittance functions for full-scale parabolic trough collectors and show how wind characteristics (mean wind speed and direction, turbulent kinetic energy, turbulent length scales), the sun-tracking trough angle, and row position alter the admittance functions. Further, we study to which extent the admittance functions are universal for a specific trough geometry and how our findings compare to reported heliostat results. References [1] https://data.openei.org/submissions/5938. [2] Blume, K., Roger, M., and Pitz-Paal, R. 2023b. "Simplified analytical model to describe wind loads and wind-induced tracking deviations of heliostats." Solar Energy, 256, 96-109. https://doi.org/10.1016/j.solener.2023.03.055.

admittance functions↗

DOE OSTI2024

Cryogen-free modular scanning tunneling microscope operating at 4-K in high magnetic field on a compact ultra-high vacuum platform

One of the daunting challenges in modern low temperature scanning tunneling microscopy (STM) is the difficulty of combining atomic resolution with cryogen-free cooling. Further functionality needs, such as ultra-high vacuum (UHV), high magnetic field (HF), and compatibility with μm-sized samples, pose additional challenges to an already ambitious build. We present the design, construction, and performance of a cryogen-free, UHV, low temperature, and high magnetic field system for modular STM operation. An internal vibration isolator reduces vibrations in this system, allowing for atomic resolution STM imaging while maintaining a low base temperature of ∼4 K and magnetic fields up to 9 T. Samples and tips can be conditioned in situ utilizing a heating stage, an ion sputtering gun, an e-beam evaporator, a tip treater, and sample exfoliation. In situ sample and tip exchange and alignment are performed in a connected UHV room temperature stage with optical access. Multisite operation without breaking vacuum is enabled by a unique quick-connect STM head design. A novel low-profile vertical transfer mechanism permits transferring the STM between room temperature and the low temperature cryostat.

Instruments & Instrumentation↗