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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards 5G-Enabled Operational Technology for Process Monitoring and Network Slicing

Cyber-Physical Systems (CPS) are deployed to monitor physical processes in critical cyber-enabled services like power generation. However, CPS ecosystems are typically designed without robust security. While it is important to ensure optimal performance of the Operational Technology (OT) environments, security cannot be overlooked. To modernize traditional OT services, 5G technology is being integrated. 5G technology offers low latency and high availability, making it a suitable infrastructure for managing and monitoring physical processes. How-ever, integrating 5G mechanisms into large-scale OT networks introduces new implementation and performance challenges. Therefore, this paper presents a 5G-enabled CPS architecture (5G-CPS) that describes the necessary components, services, and communication protocols and conducts feasibility study to integrate 5G technology in industrial control system networks to understand the performance merits. The 5G-CPS architecture aims to minimize implementation and operational challenges associated with integrating 5G technology into constrained OT.

Aguayo, Jared M.↗
DOE OSTI2021

Compensated Contacts for Three-Terminal Transistor Solar Cells

The heterojunction bipolar transistor solar cell (HBTSC) is an alternative to conventional double-junction solar cells that combines the advantages of three-terminal architectures with a compact design. A critical part of the HBTSC fabrication is the implementation of the middle contact, the one associated to the base layer in the transistor structure, because this layer can be damaged by the diffusion of metallic species during contact annealing. As a solution we propose to implement a "compensated contact", that is, to leave a thin (< 200 nm) portion of emitter or collector between the base layer and the contact metals and compensate its doping during contact annealing. Using an Au/Zn/Au metal structure to compensate a 150 nm thick portion of the collector, we obtain a specific contact resistance ~8 10-4 ..omega.. cm2. This value is fairly invariant for annealing temperatures between 400 and 460 degrees C and annealing times between 2 and 5 minutes, and it does not improve by adding Pd to the metallic stack. Optimizing the compensated contact technology opens the path to the realization of III-V-material-based HBTSCs with open-circuit voltages and fill factors comparable to those of conventional multijunction architectures.

contact resistance↗
DOE OSTI2024

ARQUIN: Architectures for Multinode Superconducting Quantum Computers

Many proposals to scale quantum technology rely on modular or distributed designs wherein individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, internode gates in these systems may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require improvements in entanglement generation, use of entanglement distillation, and optimized software and compilers. Still, it remains unclear what performance is possible with current hardware and what performance algorithms require. In this article, we employ a systems analysis approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. We show how to navigate tradeoffs in entanglement generation and distillation in the context of algorithm performance, lay out how compilers and software should balance between local and internode gates, and discuss when noisy quantum internode links have an advantage over purely classical links. Here, we find that a factor of 10–100× better link performance is required and introduce a research roadmap for the co-design of hardware and software towards the realization of early MNQCs. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Interpenetrated Structures for Enhancing Ion Diffusion Kinetics in Electrochemical Energy Storage Devices

The architectural design of electrodes offers new opportunities for next-generation electrochemical energy storage devices (EESDs) by increasing surface area, thickness, and active materials mass loading while maintaining good ion diffusion through optimized electrode tortuosity. However, conventional thick electrodes increase ion diffusion length and cause larger ion concentration gradients, limiting reaction kinetics. We demonstrate a strategy for building interpenetrated structures that shortens ion diffusion length and reduces ion concentration inhomogeneity. This free-standing device structure also avoids short-circuiting without needing a separator. The feature size and number of interpenetrated units can be adjusted during printing to balance surface area and ion diffusion. Starting with a 3D-printed interpenetrated polymer substrate, we metallize it to make it conductive. This substrate has two individually addressable electrodes, allowing selective electrodeposition of energy storage materials. Using a Zn//MnO 2 battery as a model system, the interpenetrated device outperforms conventional separate electrode configurations, improving volumetric energy density by 221% and exhibiting a higher capacity retention rate of 49% compared to 35% at temperatures from 20 to 0 °C. Our study introduces a new EESD architecture applicable to Li-ion, Na-ion batteries, supercapacitors, etc.

25 ENERGY STORAGE↗
DOE OSTI2014

Early career: Templating of liquid crystal microstructures by reversible addition-fragmentation chain transfer polymerization

This research has shown that the microstructure of self-assembled copolymers can be decoupled from the polymer chemistry. The simplest polymer architecture, linear block copolymers, is valuable for a broad range of applications, including adhesives and coatings, medical devices, electronics and energy storage, because these block copolymers reproducibly self-assemble into microphase separated nanoscale domains. Unfortunately, the self-assembled microstructure is tuned by polymer composition, thus limiting the potential to simultaneously optimize chemical, mechanical, and transport properties for desired applications. To this end, much work was been put into manipulating block copolymer self-assembly independently of polymer composition. These efforts have included the use of additives or solvents to alter polymer chain conformation, the addition of a third monomer to produce ABC triblock terpolymers, architectures with mixed blocks, such as tapered/gradient polymers, and the synthesis of other nonlinear molecular architectures. This work has shown that the microstructures formed by linear ABC terpolymers can be altered by controlling the architecture of the polymer molecules at a constant monomer composition, so that the microstructure is tuned independently from the chemical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Automated Work Package: Conceptual Design and Data Architecture

The automated work package (AWP) is one of the U.S. Department of Energy’s (DOE) Light Water Reactor Sustainability Program efforts to enhance the safety and economics of the nuclear power industry. An AWP is an adaptive and interactive work package that intelligently drives the work process according to the plant condition, resources status, and users progress. The AWP aims to automate several manual tasks of the work process to enhance human performance and reduce human errors. Electronic work packages (eWPs), studied by the Electric Power Research Institute (EPRI), are work packages that rely to various extent on electronic data processing and presentation. AWPs are the future of eWPs. They are envisioned to incorporate the advanced technologies of the future, and thus address the unresolved deficiencies associated with the eWPs in a nuclear power plant. In order to define the AWP, it is necessary to develop an ideal envisioned scenario of the future work process without any current technology restriction. The approach followed to develop this scenario is specific to every stage of the work process execution. The scenario development resulted in fifty advanced functionalities that can be part of the AWP. To rank the importance of these functionalities, a survey was conducted involving several U.S. nuclear utilities. The survey aimed at determining the current need of the nuclear industry with respect to the current work process, i.e. what the industry is satisfied with, and where the industry envisions potential for improvement. The survey evaluated the most promising functionalities resulting from the scenario development. The results demonstrated a significant desire to adopt the majority of these functionalities. The results of the survey are expected to drive the Idaho National Laboratory (INL) AWP research and development (R&D). In order to facilitate this mission, a prototype AWP is needed. Since the vast majority of earlier efforts focused on the frontend aspects of the AWP, the backend data architecture was researched and developed in this effort. The backend design involved data architecture aspects. It was realized through this effort that the key aspects of this design are hierarchy, data configuration and live information, data templates and instances, the flow of work package execution, the introduction of properties, and the means to interface the backend to the frontend. After the backend design was developed, a data structure was built to reflect the developed data architecture. The data structure was developed to accommodate the fifty functionalities identified by the envisioned scenario development. The data structure was evaluated by incorporating an example work order from the nuclear power industry. The implementation resulted in several optimization iterations of the data structure. In addition, the rearrangement of the work order information to fit the data structure highlighted several possibilities for improvement in the current work order design, and significantly reduced the size of the work order.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Optimizing transmit field inhomogeneity of parallel RF transmit design in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B+1) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B+1 field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient’s presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B+1 fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B+1 fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method’s advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.

Lu, Zhengyi [Vanderbilt University]↗
DOE OSTI2021

Transient Efficiency Flexibility and Reliability Optimization of Coal-Fired Power Plants (Model-Based Estimation Library Report)

This document pertains to the reporting requirements of DOE contract FE-0031767. The document covers the development of a model-based estimation (MBE) library for generating digital twins of coal-fired power plants (CFPPs). The models used by the MBE are transient and physics-based. Model classes in the library that correspond to different components in CFPPs are presented with physical explanations and representative results. MBE library provides Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) algorithms. The MBE library is generic and can be used to represent a variety of CFPPs. A digital twin of a CFPP will be created as the reduced-order model of that CFPP is embedded in the developed MBE architecture.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2020

Pterodactyl: Development and Comparison of Control Architectures for a Mechanically Deployed Entry Vehicle

The Pterodactyl project, seeks to advance the current state-of-the-art for entry vehicles by developing novel guidance and control technologies for Deployable Entry Vehicles (DEVs) that can be applied to various entry vehicle configurations. This paper details the efforts on the NASA-funded Pterodactyl project to investigate and implement multiple control techniques for an asymmetric mechanical DEV. We design multiple control architectures for a Pterodactyl Baseline Vehicle (PBV) and evaluate their performance in achieving varying guidance commands during entry. The control architectures studied are (i) propulsive control systems such as reaction control systems and (ii) non-propulsive control systems such as aerodynamic control surfaces and internal moving masses. For each system, state-feedback integral controllers based on linear quadratic regulator (LQR) optimal control methods are designed to track guidance commands of either (i) bank angle or (ii) angle of attack and sideslip angle as determined by the desired guidance trajectory. All control systems are compared for a lunar return reference mission and by providing a comparative analysis of these systems, configurations, and performance, the efforts detailed in this paper and the Pterodactyl project as a whole will help entry vehicle system designers determine suitable control architectures for integration with DEVs and other entry vehicle types.

Okolo, Wendy A.↗
DOE OSTI2021

Competitive adsorption within electrode slurries and impact on cell fabrication and performance

We ask why do battery researchers use 10-20 wt% binder in research cells when commercial chemistries use much less? This report seeks to explore this question by understanding the ordering and structure of polyimide binder with a silicon/pitch-carbon black electrode system. Correlating the effect of binder concentration on electrode architecture and electrochemical properties is vital to enhancing silicon anode cycle and calendar life. Using ultra-small angle neutron scattering (USANS) and binder adsorption isotherms an optimal region of polyimide (PI) binder coverage was found. Further, there is a preferential adsorption of PI to carbon black that has to be completed before PI binds to the silicon. Optimizing the concentration of binder leads to a 50% increase in capacity due to the optimization of binder-silicon-carbon interactions.

25 ENERGY STORAGE↗
DOE OSTI2016

Light-trapping and recycling for extraordinary power conversion in ultra-thin gallium-arsenide solar cells

Here, we demonstrate nearly 30% power conversion efficiency in ultra-thin (~200 nm) gallium arsenide photonic crystal solar cells by numerical solution of the coupled electromagnetic Maxwell and semiconductor drift-diffusion equations. Our architecture enables wave-interference-induced solar light trapping in the wavelength range from 300-865 nm, leading to absorption of almost 90% of incoming sunlight. Our optimized design for 200 nm equivalent bulk thickness of GaAs, is a square-lattice, slanted conical-pore photonic crystal (lattice constant 550 nm, pore diameter 600 nm, and pore depth 290 nm), passivated with AlGaAs, deposited on a silver back-reflector, with ITO upper contact and encapsulated with SiO 2 . Our model includes both radiative and non-radiative recombination of photo-generated charge carriers. When all light from radiative recombination is assumed to escape the structure, a maximum achievable photocurrent density (MAPD) of 27.6 mA/cm 2 is obtained from normally incident AM 1.5 sunlight. For a surface non-radiative recombination velocity of 10 3 cm/s, this corresponds to a solar power conversion efficiency of 28.3%. When all light from radiative recombination is trapped and reabsorbed (complete photon recycling) the power conversion efficiency increases to 29%. If the surface recombination velocity is reduced to 10 cm/sec, photon recycling is much more effective and the power conversion efficiency reaches 30.6%.

14 SOLAR ENERGY↗
DOE OSTI2024

When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design

This review explores the intersection of bio-plausible artificial intelligence in the form of spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies among algorithms, devices, circuit, and system parameters, crucial for optimal performance. An in-depth analysis leads to the identification of key system-level bottlenecks arising from device limitations, which can be addressed using SNN-specific algorithm–hardware co-design techniques. This review underscores the imperative for holistic device to system design-space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.

Physics↗
DOE OSTI2019

LP-BNN: Ultra-low-Latency BNN Inference with Layer Parallelism

High inference latency seriously limits the deployment of DNNs in real-time domains such as autonomous driving, robotic control, and many others. To address this emerging challenge, researchers have proposed approximate DNNs with reduced precision, e.g., Binarized Neural Networks (BNNs). While BNNs can be built to have little loss in accuracy, latency reduction still has much room for improvement. In this paper, we propose a single-FPGA-based BNN accelerator that achieves microsecond-level ultra-low-latency inference of ImageNet, LP- BNN. We obtain this performance via several design optimizations. First, we optimize the network structure by removing Batch Normalization (BN) functions which leads to significant latency in BNNs without any loss on accuracy. Second, we propose a parameterized architecture which is based on layer parallelism and supports nearly perfect load balancing for various types of BNNs. Third, we fuse all the convolution layers and the first fully connected layer. We process them in parallel through fine- grained inter-layer pipelining. With our proposed accelerator, the inference of binarized AlexNet, VGGNet, and ResNet are completed within 21.5µs, 355µs, and 67.8µs respectively, with no loss in accuracy as compared with other BNN implementations.

Geng, Tong↗
DOE OSTI2014

Optimizing Sparse Matrix-Multiple Vectors Multiplication for Nuclear Configuration Interaction Calculations

Obtaining highly accurate predictions on the properties of light atomic nuclei using the configuration interaction (CI) approach requires computing a few extremal Eigen pairs of the many-body nuclear Hamiltonian matrix. In the Many-body Fermion Dynamics for nuclei (MFDn) code, a block Eigen solver is used for this purpose. Due to the large size of the sparse matrices involved, a significant fraction of the time spent on the Eigen value computations is associated with the multiplication of a sparse matrix (and the transpose of that matrix) with multiple vectors (SpMM and SpMM-T). Existing implementations of SpMM and SpMM-T significantly underperform expectations. Thus, in this paper, we present and analyze optimized implementations of SpMM and SpMM-T. We base our implementation on the compressed sparse blocks (CSB) matrix format and target systems with multi-core architectures. We develop a performance model that allows us to understand and estimate the performance characteristics of our SpMM kernel implementations, and demonstrate the efficiency of our implementation on a series of real-world matrices extracted from MFDn. In particular, we obtain 3-4 speedup on the requisite operations over good implementations based on the commonly used compressed sparse row (CSR) matrix format. The improvements in the SpMM kernel suggest we may attain roughly a 40% speed up in the overall execution time of the block Eigen solver used in MFDn.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

NIF Experiment Editor Redesign. Setting up for the future

The Campaign Management project at the National Ignition Facility is embarking on a major redesign of the software used by experimentalists to setup experiments on the NIF. The current editor was architected over a decade ago and optimized for a different purpose than supporting production science campaigns. It has a steep learning curve and lacks many desirable features. Furthermore, its underlying technology stack and architecture do not support the kind of routine capability evolution that represents current and future growth needs of the Program. The principal stakeholders are the physicists who are users of the editing application, but the entire integrated suite of Campaign Management software has a broad customer base spanning experimentalists, NIF sub-system subject matter experts, planners, and shot operations staff. A recent DOE-mandated review of NIF activity, conducted with the goal of improving the shot rate at NIF, identified the experiment editor as a clear barrier to a more productive and efficient experiment lifecycle, exposing unnecessary levels of detail, lacking adequate automation, and lacking adequate integration with related activities and data sources in what should be a shared data environment.

42 ENGINEERING↗
DOE OSTI2016

Heart of the Hearth: Making the Popular Clean, Not the Clean Popular - Technology Research, Development, and Tools for Clean Biomass Cookstoves

This technical report summarizes the work completed by BioLite in fulfilment of the US DOE EERE award. The work plan focused on three key objectives: developing an optimized combustion system that demonstrates high combustion efficiency and low PM 2.5 and CO emissions, integrate the system into popular stove phenotypes – side-fed rocket stove architecture like the BioLite HomeStove, and the Patsari chimney stove in Mexico such that they maintain their important phenotypical characteristics, independently evaluate quantitative fuel and emissions performance of the integrated ‘Turbo-Patsari’ in Mexican households. The project activities were organized into six major tasks: A. Develop, fabricate, and test proof-of-concept prototypes B. Develop field prototypes, assess user feedback and field performance C. Define revised stove design for pre-production model, Identify manufacturing requirements and estimated cost to build, Conduct reliability, emissions, and performance testing of pre-production Turbo-Patsari D. Build pre-production Turbo-Patsari stove combustion cores E. Conduct pre-production field trials F. Summarize field trial results and evaluate Turbo-Patsari for potential volume production. A two-pronged approach was adopted for the above tasks. The first involved building a modular test platform that allowed parametric variation of multiple stove design parameters that directly affect its performance – heat output, thermal efficiency, and emissions. The second part of the approach comprised of building a surrogate Patsari based on GIRA’s specifications that could then be modified or retrofitted for optimum performance based on the learnings from the modular test platform. The following sections of the report will describe the findings of tests on these platform, the subsequent development, design, and installation of the Turbo-Patsari, and finally the in-home field trial.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Visible Quantum Nanophotonics.

The goal of this LDRD is to develop a quantum nanophotonics capability that will allow practical control over electron (hole) and photon confinement in more than one dimension. We plan to use quantum dots (QDs) to control electrons, and photonic crystals to control photons. InGaN QDs will be fabricated using quantum size control processes, and methods will be developed to add epitaxial layers for hole injection and surface passivation. We will also explore photonic crystal nanofabrication techniques using both additive and subtractive fabrication processes, which can tailor photonic crystal properties. These two efforts will be combined by incorporating the QDs into photonic crystal surface emitting lasers (PCSELs). Modeling will be performed using finite-different time-domain and gain analysis to optimize QD-PCSEL designs that balance laser performance with the ability to nano-fabricate structures. Finally, we will develop design rules for QD-PCSEL architectures, to understand their performance possibilities and limits.

77 NANOSCIENCE AND NANOTECHNOLOGY↗