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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 289 records · Page 16

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗

DOE OSTI2023

Open-Source Emulation Platform for Hands-on Cyber Attack Training and Experimentation

Explore the source record for details and available documents.

Hahn, Andrew Stuart↗

NASA NTRS1977

Experimental program for the operational study of data collection platforms in Bolivia

The system developed for the SMS/GOES satellite while learning the limitations possessed by this system with regard to the LANDSAT 1 and 2 satellites with respect to the transmission distance and horizon angle is investigated. The advantages possessed by this system in comparison with conventional methods are evaluated so as to permit studying the feasibility of introducing it into this country in the near future.

Source record↗

DOE OSTI2022

Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. Here, in this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

NEFTSec: Networked federation testbed for cyber-physical security of smart grid: Architecture, applications, and evaluation

As today's power grid is evolving into a densely interconnected cyber-physical system (CPS), a high fidelity and multifaceted testbed environment is needed to perform cybersecurity experiments in a realistic grid environment. Traditional standalone CPS testbeds lack the ability to emulate complex cyber-physical interdependencies between multiple smart grid domains in a real-time environment. Therefore, there are ongoing research and development (R&D) efforts to develop an interconnected CPS testbed by sharing geographically dispersed testbed resources to perform distributed simulation while analysing simulation fidelity. This paper presents a networked federation testbed for cybersecurity evaluation of today's and emerging smart grid environments. Specifically, it presents two novel testbed architectures, including cyber federation and cyber-physical federation, identifies R&D applications, and also describes testbed building blocks with experimental case studies. It also presents a novel co-simulation interface algorithm to facilitate distributed simulation within cyber-physical federation. The resources available at the PowerCyber CPS security testbed at Iowa State University (ISU) and the US Army Research Laboratory are utilised to develop this platform for performing multiple experimental case studies pertaining to wide-area protection and control applications in power system. Finally, experimental results are presented to analyse the simulation fidelity and real-time performance of the testbed federation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Examining experimental nitrogen-based emissions trends from ammonia/diesel and ammonia/hydrogen/diesel combustion

Ammonia has garnered interest as an alternative fuel for power sectors with heavy payload and distance requirements, such as shipping. In this study, ammonia was used in a dual-fuel compression-ignition combustion strategy to overcome some of its technical barriers, using a diesel pilot to ignite a premixed mixture of ammonia and air. Mixtures of premixed ammonia and hydrogen were also explored to evaluate whether the inclusion of hydrogen improves nitrogen-based emissions from the combustion process. A single-cylinder version of a Cummins ISB 6.7 L engine platform was used to experimentally study these effects at various global air/fuel ratios and hydrogen energy fractions. Hydrogen inclusion produced pronounced NOx and N2O emissions, while inclusion of trapped residuals increased N2O but reduced NOx. The two most recent and relevant mechanisms available in the literature—those from Xu and Zhang-Ren-Kokjohn—were used in a chemical kinetics analysis to examine the observed differences in the NOx trends from two dual-fuel ammonia/diesel datasets: (1) in which a portion of the premixed ammonia was substituted with hydrogen and (2) in which the effect of hot trapped residuals was evaluated with only ammonia/air premixed mixtures. The analysis with both mechanisms showed agreement with experimental trends; however, contributions from thermal vs. fuel-borne NOx pathways showed disagreement. A reaction pathway analysis showed that the HNO to NO pathway was the key to NO formation in the mixture.

Tyrewala, Daanish [ORNL] (ORCID:0000000208599324)↗

DOE OSTI2020

Distribution Feeder-Scale Fast Frequency Response via Optimal Coordination of Net-load Resources Part I: Solution Design

This work is the first of a two-part series that develops and experimentally demonstrates a first-of-its-kind hierarchical control solution for optimally dispatching thousands of deferrable loads and distributed energy resources (DERs) across a distribution feeder to provide fast frequency response (FFR) within 500 ms to the bulk power system. This approach rapidly coordinates resources online after a frequency event occurs, allowing fast-changing, behind-the-meter (BTM) resources to be incorporated and aggregate FFR power set points to be achieved more quickly and accurately than existing approaches. We also present a solution for determining the optimal amount of headroom to operate solar inverters with to minimize opportunity cost while ensuring the FFR response viability of a building with the inverter and deferrable loads. In Part I, we develop practical algorithms for fast, cost-based optimal dispatch at multiple aggregation scales (single building, multiple buildings, and full distribution feeder), establish their optimality, and demonstrate via simulation that they are faster than state-of-the-art, coordinated frequency response approaches. In Part II, the entire platform is implemented and experimentally verified using a unique power hardware-in-the-loop demonstration, including more than 100 powered loads and DERs connected to a real-world distribution network model and over 10,000 net-load resources dispatched.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Wafer-scale remote epitaxy of III-V semiconductors and applications

In conventional epitaxy of single-crystalline semiconductor materials, it is challenging to separate the grown layer with the substrate due to the strong bonding at the interface. Remote epitaxy is a recently discovered method to grow single-crystalline thin films on graphene, wherein the grown film can be exfoliated at the graphene interface to form freestanding membranes. Here, we present our recent development on remote epitaxy of III-V semiconductors. We show that directly growing 2D materials on III-V substrates as a remote epitaxy template is an ideal pathway that can eliminate transfer process-related defects and can realize wafer-scale process of remote epitaxy and substrate reuse. We present the strategies to grow 2D materials on the surface of III-V materials, which is much more challenging than thermally robust substrates such as SiO2/Si or sapphire. The nucleation of III-V on 2D material-coated III-V platforms via remote interaction is investigated both experimentally and theoretically. Lastly, we show advanced remote epitaxial platforms and optoelectronic applications enabled by remote epitaxy, and the capability to recycle the III-V substrates for repeated remote epitaxy and production of freestanding III-V thin films.

Kim, Hyunseok↗

DOE OSTI2021

Wafer-scale remote epitaxy of III-V semiconductors and applications

Kim, Hyunseok↗

DOE OSTI2024

Performance Evaluation of Various Configurations for Domestic Refrigerators with R600a

The increasing global focus on mitigating global warming has driven a change in refrigerant selection for domestic refrigerators. While most existing refrigerators use R-134a as their working fluid, there is a gradual shift towards R600a in newly manufactured products to minimize environmental impact. In order to design a more energy-efficient isobutane refrigerator, various configurations, including both single-evaporator and dual-evaporator cycles, were investigated. The study was conducted using a high-fidelity simulation platform, the Heat Pump Design Model (HPDM), developed and experimentally validated by the US DOE/ORNL. This platform includes detailed segment-to-segment heat exchanger model, fan model, and compressor model, utilizing refrigerant properties from REFPROP 10. A quasi-steady-state approach was employed to simulate the transient behavior and performance of a domestic refrigerator. The performance of various configurations was compared, and the effects of the refrigerant properties and operating conditions were also analyzed and discussed.

Yang, Cheng-Min↗

DOE OSTI2026

A multi-backend autotuning study of feature selection on GPUs

Abstract Feature selection is an important step in machine learning that can benefit from GPU acceleration. As the number of GPU vendors increases, it is imperative to adapt algorithms such as the minimum Redundancy Maximum Relevance (mRMR) feature selection method to different backends that support several GPU architectures. This work presents a multi-backend implementation of mRMR across CUDA, HIP, and SYCL, and studies its performance when combined with Bayesian optimization and transfer learning to automatically tune execution parameters for different platforms and datasets. Our experimental results show that when tuned, CUDA and HIP achieve comparable performance on NVIDIA architectures, while SYCL exhibits a moderate performance gap. Overall, this work highlights the impact of backend choice and autotuning on GPU-accelerated feature selection and provides insights into deploying mRMR across heterogeneous environments.

Beceiro, Bieito (ORCID:0000000333014890)↗

DOE OSTI2021

Low-frequency and Moiré–Floquet engineering: A review

Highlights: • Review of theoretical and experimental advances in Floquet engineering. • Review of theories to derive effective Floquet Hamiltonians, with a focus on the low-frequency regime. • Review of the theoretical effects of light in Moiré materials. We review recent work on low-frequency Floquet engineering and its application to quantum materials driven by light, focusing on van der Waals systems hosting Moiré superlattices. These non-equilibrium systems combine the twist-angle sensitivity of the band structure with the flexibility of light drives. The frequency, amplitude, and polarization of light can be tuned in experimental setups, leading to platforms with on-demand properties. First, we review recent theoretical developments to derive effective Floquet Hamiltonians, emphasizing the low-frequency regime. We then review applications of some of these theories to study twisted graphene and transition metal dichalcogenide systems irradiated by light in free space and inside a waveguide. We study the changes induced in the quasienergies and steady-states, which can lead to topological transitions. Next, we consider van der Waals magnetic materials driven by low-frequency light pulses in resonance with the phonons. We discuss the phonon dynamics induced by the light and the resulting magnetic transitions from a Floquet perspective. We finish by outlining new directions for Moiré–Floquet engineering in the low-frequency regime and their relevance for technological applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Unraveling the Nature of Lasing Emission from Hybrid Silicon Nitride and Colloidal Nanocrystal Photonic Crystals with Low Refractive Index Contrast

Silicon nitride is used for its low optical loss and high thermal stability, making it a suitable platform for visible-light applications in integrated photonic devices. However, its application has been limited due to inefficient light emission, a problem addressed by integrating various types of light emitters onto the platform. In particular, the integration of solution-processable colloidal nanocrystals (NCs) as optical gain materials onto the silicon nitride platform is a promising route but requires a more solid theoretical footing. By leveraging 2D surface-emitting photonic crystal structures combined with NCs, we effectively confine and manipulate light to achieve lasing from green to red. Building on this, we model the light–matter interactions of the low index contrast NC/nitride platform, validated by extensive experimental validations through Fourier imaging techniques, revealing the full photonic band structure and showing clear mode congestion. Finally, these comprehensive studies confirm the potential of hybrid NC-based structures for fully integrated on-chip laser applications and indicate routes for further improvement.

BIC Lasing↗

DOE OSTI2024

Side chain engineering in indacenodithiophene- co -benzothiadiazole and its impact on mixed ionic–electronic transport properties

Organic semiconductors are increasingly being decorated with hydrophilic solubilising chains to create materials that can function as mixed ionic–electronic conductors, which are promising candidates for interfacing biological systems with organic electronics. While numerous organic semiconductors, including p- and n-type materials, small molecules and polymers, have been successfully tailored to encompass mixed conduction properties, common to all these systems is that they have been semicrystalline materials. Here, we explore how side chain engineering in the nano-crystalline indacenodithiophene-co-benzothiadiazole (IDTBT) polymer can be used to instil ionic transport properties and how this in turn influences the electronic transport properties. This allows us to ultimately assess the mixed ionic–electronic transport properties of these new IDTBT polymers using the organic electrochemical transistor as the testing platform. Using a complementary experimental and computational approach, we find that polar IDTBT derivatives can be infiltrated by water and solvated ions, they can be electrochemically doped efficiently in aqueous electrolyte with fast doping kinetics, and upon aqueous swelling there is no deterioration of the close interchain contacts that are vital for efficient charge transport in the IDTBT system. Despite these promising attributes, mixed ionic–electronic charge transport properties are surprisingly poor in all the polar IDTBT derivatives. Albeit a “negative” result, this finding clearly contradicts established side chain engineering rules for mixed ionic–electronic conductors, which motivated our continued investigation of this system. We eventually find this anomalous behaviour to be caused by increasing energetic disorder in the polymers with increasing polar side chain content. We have investigated computationally how the polar side chain motifs contribute to this detrimental energetic inhomogeneity and ultimately use the learnings to propose new molecular design criteria for side chains that can facilitate ion transport without impeding electronic transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Localization dynamics in a centrally coupled system

In systems in which interactions couple a central degree of freedom and a bath, one would expect signatures of the bath's phase to be reflected in the dynamics of the central degree of freedom. This has been recently explored in connection with many-body localized baths coupled with a central qubit or a single-cavity mode - systems with growing experimental relevance in various platforms. Such models also have an interesting connection with Floquet many-body localization via quantizing the external drive, although this has been relatively unexplored. Here we adapt the multilayer multiconfigurational time-dependent Hartree (ML-MCTDH) method, a well-known tree tensor network algorithm, to numerically simulate the dynamics of a central degree of freedom, represented by a d-level system (qudit), coupled to a disordered interacting one-dimensional spin bath. ML-MCTDH allows us to reach ≈10 2 lattice sites, a far larger system size than what is feasible with exact diagonalization or kernel polynomial methods. From the intermediate time dynamics, we find a well-defined thermodynamic limit for the qudit dynamics upon appropriate rescaling of the system-bath coupling. The spin system shows similar scaling collapse in the Edward-Anderson spin-glass order parameter or entanglement entropy at relatively short times. At longer timescales, we see slow growth of the entanglement, which may arise from dephasing mechanisms in the localized system or long-range interactions mediated by the central degree of freedom. Similar signs of localization are shown to appear as well with unscaled system-bath coupling.

1-dimensional spin chains↗

DOE OSTI2020

The ePix10k 2-megapixel hard X-ray detector at LCLS

The ePix10ka2M (ePix10k) is a new large area detector specifically developed for X-ray free-electron laser (XFEL) applications. The hybrid pixel detector was developed at SLAC to provide a hard X-ray area detector with a high dynamic range, running at the 120 Hz repetition rate of the Linac Coherent Light Source (LCLS). The ePix10k consists of 16 modules, each with 352 × 384 pixels of 100 µm × 100 µm distributed on four ASICs, resulting in a 2.16 megapixel detector, with a 16.5 cm × 16.5 cm active area and ∼80% coverage. The high dynamic range is achieved with three distinct gain settings (low, medium, high) as well as two auto-ranging modes (high-to-low and medium-to-low). Here the three fixed gain modes are evaluated. The resulting dynamic range (from single photon counting to 10000 photons pixel −1 pulse −1 at 8 keV) makes it suitable for a large number of different XFEL experiments. The ePix10k replaces the large CSPAD in operation since 2011. The dimensions of the two detectors are similar, making the upgrade from CSPAD to ePix10k straightforward for most setups, with the ePix10k improving on experimental performance. The SLAC-developed ePix cameras all utilize a similar platform, are tailored to target different experimental conditions and are designed to provide an upgrade path for future high-repetition-rate XFELs. Here the first measurements on this new ePix10k detector are presented and the performance under typical XFEL conditions evaluated during an LCLS X-ray diffuse scattering experiment measuring the 9.5 keV X-ray photons scattered from a thin liquid jet.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

ESS-DIVE Unoccupied Aerial Systems (UAS) Reporting Format v1

Here we present documentation of the ESS-DIVE reporting format for Unoccupied Aerial System (UAS) data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format provides templates and guidance for the reporting of metadata for UAS experimental campaigns, individual flights, platform and sensor description. To improve data access and discoverability, the reporting format proposes a data description scheme of Levels based on the degree of processing, where Level 0 includes raw data, through to Level 3 being derived data end products. A range of examples of data types for each Level are given, with suggested file naming schemes. The reporting format presented here is intended to form a foundation for future development that will accommodate new UAS technologies and approaches to data access and use in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub at https://github.com/ess-dive-community/essdive-uas. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-uas v1.0. The zip contains the reporting format description, instructions and variable definitions in GitHub markdown language (*.md) and metadata templates in csv format. The reporting format is designed to be compatible with other ESS-DIVE formats, and it is specifically recommended that this reporting format be used in conjunction with the File-level metadata (FLMD) and comma separated values (csv) reporting formats for submission to the ESS-DIVE repository.

54 ENVIRONMENTAL SCIENCES↗