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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Improving Energy Efficiency of Wireless Communication Circuitry in Miscellaneous Electric Loads (Final Report)

The overarching objective of this project is to reduce phantom power in miscellaneous electric loads (MELs). We achieve this power reduction with a wireless Connectivity Module that uses ultra-low power (ULP) custom wakeup receivers (WRXs) paired with an ULP node controller (NC) chip. These components can remain always-on at power levels much lower than the inherent standby power of MELs, allowing them to cut off the phantom power to the MELs device while still preserving responsiveness of the MELs devices with low latency when they are needed, either via a wireless wakeup signal received by the WRX or a prediction that the device is needed based on a model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Smart Contracts for Power Grid Applications Using the Advanced DLT Cyber Grid Guard Testbed

In this study is presented two power system applications with distributed ledger technology (DLT) and smart contracts (SC) that were assessed in a Cyber-Grid-Guard System (CGGS) advanced testbed, with protective relays, power meters, communication devices, DLT devices, synchronized time source, clock displays and real time simulator. This CGGS testbed was set in the Advanced Protection Lab, 252 lab space of the Grid Research Integration and Deployment Center (GRID- C), at Oak Ridge National Laboratory. In power grids, customer-owned distributed energy resources (DERs) are more frequent than in the past, and the numbers of points of interconnection (POI) with customer-owned DERs have increased. Disruptive operation from DERs presents a risk to grid operations, and protective relays located at the POI are used to isolate out-of-tolerance or poorly behaving of DERs. Ensuring the integrity of data from the relays at the POI, and DLT could enhance the security of the power grids. The first application is a SC to define and control the allowable total power factor (TPF) of the DER (wind farm) output, and the terms of the SC are implemented using DLT with a CGGS for a customer-owned DER. The TPF SC was implemented by the CGGS using DLT. The experimental model was performed with a real-time simulator using a CGGS and relay in-the-loop. The data collected from the CGGS were used to execute the TPF SC. The TPF limits were between +0.9 and +1.0, and the breakers’ operation in the POI was controlled by the relay using the SC. The events were collected from the real-time simulator, CGGS, and SEL 700GT relay to validate a successful application of the TPF SC using DLT. The second application is a SC to measure and control the allowable voltage service limits (VSL) by the CGGS using DLT. The tests were performed by using a real-time simulator, CGGS and relay in-the-loop. The data was collected from the CGGS that executed the SC. The main constraints were defined based on ANSI C84.1 service voltage limits, and the operation of the breakers in the POI. The events were collected from the CGGS, and SEL 700GT relay to assess a successful operation of the VSL SC using DLT.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Impact of front-end parameters of the ARCADIA MD3 on charged particle detection

The ARCADIA INFN R&D project developed a Fully Depleted Monolithic Active Pixel Sensor (FD-MAPS) using a customized LFoundry 110 nm CIS process. The first in-beam characterization of the ARCADIA Main Demonstrator 3 (MD3) sensor with 200 $μ$m active thickness has been performed at the Fermilab Test Beam Facility with a 120 GeV proton beam. The Device Under Test (DUT) is tested with a trigger-less telescope composed of two ARCADIA MD3 tracking planes. This early study investigates the effect of the front-end parameters on the tracking performance.

Pantouvakis, C. [Padua U.; INFN, Padua]↗
DOE OSTI2023

Feedback lock-in: A versatile multi-terminal measurement system for electrical transport devices

Here, we present the design and implementation of a measurement system that enables parallel drive and detection of small currents and voltages at numerous electrical contacts to a multi-terminal electrical device. This system, which we term a feedback lock-in, combines digital control-loop feedback with software-defined lock-in measurements to dynamically source currents and measure small, pre-amplified potentials. The effective input impedance of each current/voltage probe can be set via software, permitting any given contact to behave as an open-circuit voltage lead or as a virtually grounded current source/sink. This enables programmatic switching of measurement configurations and permits measurement of currents at multiple drain contacts without the use of current preamplifiers. Our 32-channel implementation relies on commercially available digital input/output boards, home-built voltage preamplifiers, and custom open-source software. With our feedback lock-in, we demonstrate differential measurement sensitivity comparable to a widely used commercially available lock-in amplifier and perform efficient multi-terminal electrical transport measurements on twisted bilayer graphene and SrTiO 3 quantum point contacts. The feedback lock-in also enables a new style of measurement using multiple current probes, which we demonstrate on a ballistic graphene device.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Droplet bioprinting of acellular and cell-laden structures at high-resolutions

Advances in digital light projection(DLP) based (bio) printers have made printing of intricate structures at high resolution possible using a wide range of photosensitive bioinks. A typical setup of a DLP bioprinter includes a vat or reservoir filled with liquid bioink, which presents challenges in terms of cost associated with bioink synthesis, high waste, and gravity-induced cell settling, contaminations, or variation in bioink viscosity during the printing process. Here, we report a vat-free, low-volume, waste-free droplet bioprinting method capable of rapidly printing 3D soft structures at high resolution using model bioinks and model cells. A multiphase many-body dissipative particle dynamics model was developed to simulate the dynamic process of droplet-based DLP printing and elucidate the roles of surface wettability and bioink viscosity. Process variables such as light intensity, photo-initiator concentration, and bioink formulations were optimized to print 3D soft structures (∼0.4–3 kPa) with a typical layer thickness of 50 µm, an XY resolution of 38 ± 1.5 μm and Z resolution of 237 ± 5.4 µm. To demonstrate its versatility, droplet bioprinting was used to print a range of acellular 3D structures such as a lattice cube, a Mayan pyramid, a heart-shaped structure, and a microfluidic chip with endothelialized channels. Droplet bioprinting, performed using model C3H/10T1/2 cells, exhibited high viability (90%) and cell spreading. Additionally, microfluidic devices with internal channel networks lined with endothelial cells showed robust monolayer formation while osteoblast-laden constructs showed mineral deposition upon osteogenic induction. Overall, droplet bioprinting could be a low-cost, no-waste, easy-to-use, method to make customized bioprinted constructs for a range of biomedical applications.

DLP↗
DOE OSTI2023

Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor

Neuromorphic processors have garnered considerable interest in recent years for their potential in enabling energy-efficient and high-speed computing. The Locally Competative Algorithm (LCA) has been utilized for power efficient sparse coding on neuromophic processors, including the first Loihi processor \cite{appletospikes, loihi1}. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible \cite{loihi2}. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is faster and orders of magnitude more efficient, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent,autonomous, real-time processing on small robots, satellite where there are strict SWaP (small, lightweighr, and low-power) requirement. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.

Parpart, Gavin G.↗
DOE OSTI2023

19.28% Efficiency and Stable Polymer Solar Cells Enabled by Introducing an NIR–Absorbing Guest Acceptor

Here, a near-infrared (NIR)-absorbing small-molecule acceptor (SMA) Y-SeNF with strong intermolecular interaction and crystallinity is developed by combining selenophene-fused core with naphthalene-containing end-group, and then as a custom-tailor guest acceptor is incorporated into the binary PM6:L8-BO host system. Y-SeNF shows a 65 nm red-shifted absorption compared to L8-BO. Thanks to the strong crystallinity and intermolecular interaction of Y-SeNF, the morphology of PM6:L8-BO:Y-SeNF can be precisely regulated by introducing Y-SeNF, achieving improved charge-transporting and suppressed non-radiative energy loss. Consequently, ternary polymer solar cells (PSCs) offer an impressive device efficiency of 19.28% with both high photovoltage (0.873 V) and photocurrent (27.88 mA cm –2 ), which is one of the highest efficiencies in reported single-junction PSCs. Notably, ternary PSC has excellent stability under maximum-power-point tracking for even over 200 h, which is better than its parental binary devices. The study provides a novel strategy to construct NIR-absorbing SMA for efficient and stable PSCs toward practical applications.

14 SOLAR ENERGY↗
DOE OSTI2020

Free-Surface Liquid Lithium Flow Modeling and Stability Analysis for Fusion Applications

Liquid metal plasma facing components are considered an attractive design choice for fusion devices including pilot plants. Virtual prototyping of such devices includes modeling of free-surface flow of the electrically conductive liquid, which requires computational fluid dynamics (CFD) and magnetohydrodynamics (MHD) simulations. Numerical tools capable of simulating flows and heat transfer in the free-surface MHD flow were developed at PPPL based on the customized ANSYS CFX. Here, MHD is introduced using a magnetic vector potential approach. Free-surface flow capabilities are available in the code and were tested. Special stabilization procedures were derived and applied to improve convergence of the momentum equations with the source terms due to the Lorentz force and surface tension. Important characteristics of the fusion-relevant liquid metal flow is free surface smoothness and stability. Heat flux from the plasma impacts the liquid surface at a very acute angle, so any change of the free surface from axisymmetry can dramatically increase the local heat flux density and thus create excessive evaporation of liquid lithium into the plasma, which is detrimental to operations. Stability analysis of the liquid metal film flow was performed to determine applicable flow regimes. Thin film flow along horizontal wall is considered. Effects of gravity, magnetic field, and surface tension are included in the analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Gate-based quantum computing for protein design

Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover’s algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm’s oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover’s algorithm over classical methods (i.e.,).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Scalable/Secure Cooperative Algorithms and Framework for Extremely-high Penetration Solar Integration (SolarExPert) (Final Technical Report)

This SolarExPert project has developed a Sustainable Grid Platform (SGP) with scalable architecture of distributed control and optimization. The SGP consists of the following major functions: 1) an advanced grid architecture with hierarchical and distributed communication and control, combined with the OpenFMB standard and implemented on the Multi-Agent OpenDSS (MA-OpenDSS) platform; 2) an online distributed stochastic optimal power flow; 3) an online distributed system state estimation algorithm; 4) the distributed Volt/VAR optimization and frequency control algorithms; 5) the distributed distribution system restoration strategy. The developed SGP together with advanced functions are tested in 1 million (1M)-node distribution system on the MA-OpenDSS platform. Furthermore, the models and algorithms are tested in 100,000-node system HiL simulation, and also in P-HiL implementation with 100 physical devices. The developed functions haven been validated and tested on the selected actual distribution feeder with the data collected from the field of Maui Meadows in Hawaii. The distributed PV hosting capacities with cooperative Volt/VAR and Volt/VAR/Watt control are estimated and compared to provide recommendations for customers and the utility company.

14 SOLAR ENERGY↗
DOE OSTI2022

Securing Inverter Communication: Proactive Intrusion Detection and Mitigation System to Tap, Analyze, and Act

The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2022

Controls Optimization Final Report, Controls Algorithms Report, Control Optimization - SPA II: Heaving Buoy Test Results [Three reports]

The over-arching project objective is to fully develop and validate optimal controls frameworks that can subsequently be applied widely to different WEC devices and concepts. Optimal controls of WEC devices represent a fundamental building block for WEC designers that must be considered as an integral part of every stage of device development. Using a building-blocks approach to optimal controls development, this effort will result in the full development of a feed-forward and feed-back control approach and a wave prediction system. Phase I focused primarily on numerical offline optimization and validation using wave tank testing of three industry partners’ WEC devices, including; CalWave, Ocean Energy, and Resolute Marine Energy. These industry partnerships allowed us to identify optimal control strategies for these different WEC topologies at different maturity levels. Phase II focused on demonstrating an integrated control system on an at-sea prototype that is to be custom-built and maturing the HW and SW required to successfully run our advanced controls code frameworks on at-sea systems. A secondary focus during phase II is to adapt our systems identification, controls and wave-prediction frameworks to become more robust and comprehensive in respect to RT capability, robustness, and reliability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Modular Security Apparatus for Managing Distributed Cryptography for Command-and-Control Messages on Operational Technology Networks (Module-OT)

Module-OT is a bump- in- the- wire solution acting as a secure conduit for data between devices or systems across a network. Using the latest in open-source cryptographic libraries, all defined communications undergo authentication, authorization, and encryption. The core system can be easily installed through industry standard processes, and the use of popular open-source packages allows for it to be customizable customized by the developer community or deployed in unique embedded environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The CanBikeCO Full Pilot: Long-Term Results and Analysis From an E-Bike Program in Colorado, USA

Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1-3 miles, and e-bikes, cars, or shared rides for trips of 3-20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Kim, Yoon G.↗
DOE OSTI2023

Distributed Wind-Hybrid Microgrids with Autonomous Controls and Forecasting

Distributed wind-hybrid microgrids have the potential to provide key resilience and economic benefits to both the customers they serve and the utility grids they are connected to. Such microgrids will likely be a key part of the grid of the future, whether connected to large utility grids or linked together in multi-microgrid systems. Through the hybridization of distributed wind and solar photovoltaics, autonomous device-level and system-level controls, battery energy storage systems with smart inverters, and forecasting, these microgrids could maintain local stability and provide grid services - all with renewable power. In the literature, these elements have been considered individually. However, they have not been combined and demonstrated at a high fidelity, which is essential to prove the concept's operation before moving to hardware-in-the-loop and physical demonstrations. In this work, we develop a high-fidelity MATLAB-Simulink model of a real distributed wind-hybrid microgrid that includes all these elements. We demonstrate the microgrid maintaining stability and production in a variety of islanded, grid-connected, and transition scenarios. This includes riding through faults and grid transitions, handling resource variability, and providing grid services. The results demonstrate, at a high fidelity, how distributed wind-hybrid microgrids can operate in an economic and resilient fashion. Finally, we provide recommendations for future research to move advanced distributed wind-hybrid microgrids toward deployment.

ancillary services↗
DOE OSTI2021

A Study of Cost-Saving Potential of Load Flexibility Measures in Grid-Interactive Multifamily Buildings

With recent advances in smart technologies, more and more smart devices are penetrating the residential and commercial buildings market. The introduction of these smart devices is also helping IoT companies emerge with load aggregator roles in the sector. With more utility companies on the track of supporting OpenADR protocols, the aggregators could play a significant role in providing load flexibilities by automatically responding to demand response (DR) events and coordinating load flexibility measures between customers. This would benefit utility companies by reducing stress on the grid during critical peak demand hours as well as customers by allowing them to utilize utility rate structures advantageous to those able to reduce electric usage during high-demand hours. This study evaluates cost and energy savings from adopting multiple load flexibility measures in multifamily buildings. Combinations of different load flexibility measures, including space temperature floating, light dimming, automatic window shading, and water heater temperature floating, are considered. The simulations are performed using OpenStudio®, an open-source U.S. Department of Energy (DOE) simulation platform. For the case study, we used a midrise apartment building with weather conditions from Denver, Colorado. To compare climate zone differences, simulations were also performed for Los Angeles, California, and Chicago, Illinois. Initial results indicate that the application of automated load flexibility measures without careful consideration of dispatching strategies and DR program enrollments could significantly affect the savings. To get meaningful cost savings, aggregators need to encourage tenant awareness to curtail energy usage through occupant behavior in addition to dispatching automatic load flexibility measures. The outcomes from this study are believed to help load aggregators understand the risks and benefits of load flexibility opportunities.

building energy modeling↗