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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks

Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.

electric load forecasting↗
DOE OSTI2021

Inertia Emulation Control using Demand Response via 5G Communications

Building energy equipment is moving rapidly towards Internet of Things (IoT)-driven devices to provide consumer connectivity and device management. These device-level interfaces along with 5G communications will be leveraged to develop control architectures to engage a large number of monitoring and control devices and provide real-time and reliable energy services. Emerging 5G networks have high potential to provide the communication technology for demand response, with fast transfer speed, high reliability, and high number of connections. Guaranteed inertial response to limit frequency fluctuations is one of the main challenges in modern power systems due to the increased penetration of renewable generation, and it is largely affected by communication delays and packet losses. This paper analyzes inertial response and rate of change of frequency in a power system model with inverter-interfaced air conditioners. The control loop considers time delays and packet losses to show the need to switch to 5G networks in future smart grids.

Morovati, Samaneh↗
DOE OSTI2021

Measurement of Local Differential Privacy Techniques for IoT-based Streaming Data

Various Internet of Things (IoT) devices generate complex, dynamically changed, and infinite data streams. Adversaries can cause harm if they can access the user’s sensitive raw streaming data. For this reason, protecting the privacy of the data streams is crucial. In this paper, we explore local differential privacy techniques for streaming data. We compare the techniques and report the advantages and limitations. We also present the effect on component (e.g., smoother, perturber) variations of distribution-based local differential privacy. We find that combining distribution-based noise during perturbation provides more flexibility to the interested entity.

Afrose, Sharmin↗
DOE OSTI2022

Analyzing Data Privacy for Edge Systems

Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

Kotevska, Olivera↗
DOE OSTI2022

Deep Learning for Spectral Filling in Radio Frequency Applications

Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. This poses complex challenges in modern digital communications, and calls for the development of technological innovations that (i) optimize capacity (bitrate) in limited bandwidth environments, (ii) integrate cooperatively with already-deployed RF protocols, and (iii) are adaptive to the ever-changing demands in modern digital communications. In this paper we present methods for applying deep neural networks for spectral filling. Given an RF channel transmitting digital messages with a pre-established modulation scheme, we automatically learn novel modulation schemes for sending extra information, in the form of additional messages, “around” the fixed-modulation signals (i.e., without interfering with them). In so doing, we effectively increase channel capacity without increasing bandwidth. We further demonstrate the ability to generate signals that closely resemble the original modulations, such that the presence of extra messages is undetectable to third-party listeners. We present three computational experiments demonstrating the efficacy of our methods, and conclude by discussing the implications of our results for modern RF applications.

Setzler, Matthew D.↗
DOE OSTI2024

Trust Model Utilization for Energy Grid Communication

The internet information that is used by the Energy Grid of Things requires both preventative security measures as well as surveillance measures. The preventative security measures include certificates, encryption, and all of the basic security protocols as defined by published standards. The surveillance measures include monitoring information flow activities and evaluating these messages for indications of potential security attacks. We describe in this paper the utilization of a Distributed Trust Model that was developed specifically for monitoring communication within an Energy Grid of Things. The goal for the Distributed Trust Models is to provide a level of aggregate trust that a Distributed Energy Resource Management System can meet its grid service obligations, as opposed to a detailed individual Distributed Energy Resources assessment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

A Proxy Signature-Based Drone Authentication in 5G D2D Networks

5G is the beginning of a new era in cellular communication, bringing up a highly connected network with the incorporation of the Internet of Things (IoT). To flexibly operate all the IoT devices over a cellular network, Device-toDevice (D2D) communication standard was developed. However, IoT devices such as drones utilizing 5G D2D services could be a perfect target for malicious attacks as they pose several safety threats if they are compromised. Furthermore, there will be heavy traffic with an increased number of IoT devices connected to the 5G core. Therefore, we propose a lightweight, fast, and reliable authentication mechanism compatible with the 5G D2D ProSe standard mechanisms. Specifically, we propose a distributed authentication with a delegation-based scheme instead of the repeated access to the 5G core network key management functions. Hence, a legitimate drone is authorized by the core network via offering a proxy signature to authenticate itself to other drones. We implemented the proposed protocol in ns-3 that supports 5G D2D-based communication. We also conducted computational calculations on the RaspberryPi3 IoT device to mimic the drone calculation process and delays. The results demonstrate that the proposed protocol is lightweight and reliable

5G security↗
DOE OSTI2019

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Digitally enhanced services for wind power could reduce Operations and Maintenance costs as well as the Levelised Cost Of Energy. Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things and Big Data Analytics. The heart of wind turbines is an epicyclic gearbox and rolling element bearings are often responsible for machinery stops. The vibration signatures of bearings are rather weak compared to other components. As a result a number of signal processing techniques have been proposed, focusing towards accurate and early fault detection with limited false alarms and missed detections. In Envelope Analysis an envelope of the vibration signal is estimated usually after filtering around a frequency band. Different tools, such as Kurtogram, have been proposed to select the optimum filter parameters. Monitoring techniques have reached a mature level for steady speed and load operating conditions. On the other hand, under chaniging operating conditions, it is still unclear whether the change of the monitoring indicators is due to the change of the machinery's health or due to the change of operating parameters. Recently, the authors have proposed a new tool called IESFOgram, based on Cyclic Spectral Coherence for the automatic selection of the filtering band. In this paper, the performance of the tool is evaluated on the National Renewable Energy Laboratory wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.

17 WIND ENERGY↗
DOE OSTI2019

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.

17 WIND ENERGY↗
DOE OSTI2021

IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools

Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

Thien, Austen↗
DOE OSTI2025

An Open-Source Python Package for CFD Solution Verification

Informed decision-making using computational fluid dynamics (CFD) results requires quantifying the errors and uncertainties of a simulation. Verification, validation, and uncertainty quantification (VVUQ) methods were developed to address this need and have matured. However, these VVUQ analyses are often non-trivial and require CFD analysts and practitioners to have specific skill sets. This has led to the uneven adoption of VVUQ analyses, in part, based on the availability of software tools to aid CFD analysts and practitioners. Solution verification, a procedure to evaluate the accuracy of a simulation by estimating potential errors arising from the computational model and computing the uncertainties without comparing to results from a physical system, is one of the lagging VVUQ analyses as the absence of software has forced CFD analysts and practitioners to develop their own codes or piece together incomplete software from across the internet. This work presents an opensource Python package, CFDverify, to lower the barrier of entry and fill in the technological gap in solution verification. CFDverify also provides a streamlined framework to remove some potential errors in post-processing CFD results. The hope is that CFDverify can improve the quality and quantity of CFD solution verification in scientific and research studies and attract interest in developing a communal tool. This paper describes the design, features, and an example use of CFDverify.

Weinmeister, Justin [ORNL] (ORCID:0000000160090237↗
DOE OSTI2020

Cybersecurity impact of the growth of data in sports

As the sporting industry continues to increase dependence on new and often massive data sets, they also risk exposing themselves, as well as the leagues, teams, venues, players, and fans, to risks posed by not protecting the confidentiality and integrity of this data. The industry, especially true for professional sports, demands an ever increasing supply of new forms of data to achieve their goals of enticing new fans; enhancing experience; expanding the fan experience from venues and television to the internet and mobile devices; measuring and evaluating the performance and safety of players; and increasing revenue. New methods have emerged to collect data about a variety of aspects of sports ranging from players to fans to teams to venues. Data collected during sporting events is combined with historical data to also help teams analyze and predict performance. To synthesize these predictive analytical and historical memories, sports teams and organizations mix a variety of computer technologies. System integration tends to focus on ensuring the availability of the data and reliability of the technologies but might introduce risks to the confidentiality (privacy) and integrity of the data. Recognizing the potential risks requires review and evaluation. Much of the data is collected for publicly providing information to the fans, so threats to confidentiality pose fewer risks. The greatest risk is violating the privacy of players, such a private performance test, or using technology to gain a competitive advantage.

Jenkins, Stephanie↗
DOE OSTI2021

Illinois Express Quantum Network (IEQNET): metropolitan-scale experimental quantum networking over deployed optical fiber

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Qnodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation.

Chung, Joaquin↗
DOE OSTI2018

Compliant and stretchable thermoelectric coils for energy harvesting in miniature flexible devices

With accelerating trends in miniaturization of semiconductor devices, techniques for energy harvesting become increasingly important, especially in wearable technologies and sensors for the internet of things. Although thermoelectric systems have many attractive attributes in this context, maintaining large temperature differences across the device terminals and achieving low–thermal impedance interfaces to the surrounding environment become increasingly difficult to achieve as the characteristic dimensions decrease. Here, we propose and demonstrate an architectural solution to this problem, where thin-film active materials integrate into compliant, open three-dimensional (3D) forms. This approach not only enables efficient thermal impedance matching but also multiplies the heat flow through the harvester, thereby increasing the efficiencies for power conversion. Interconnected arrays of 3D thermoelectric coils built using microscale ribbons of monocrystalline silicon as the active material demonstrate these concepts. Quantitative measurements and simulations establish the basic operating principles and the key design features. The results suggest a scalable strategy for deploying hard thermoelectric thin-film materials in harvesters that can integrate effectively with soft materials systems, including those of the human body.

42 ENGINEERING↗
DOE OSTI2021

Metagames and Hypergames for Deception-Robust Control

Cyber-physical systems (CPSs) consist of computing and communication devices integrated with physical components such as sensors and actuators. Increasing connectivity to the Internet for remote monitoring and control has made CPSs more vulnerable to deliberate attacks, which are distinctly different from random perturbations in the system. This provides a way for purely cyber attacks to have physical consequences. Stuxnet is a prominent example of such an attack, one in which the malware acted over an extended period of time while deliberately remaining undetected. Such attacks can be described as Advanced Persistent Threats (APTs) -- long-term, stealthy attacks. Here, we extend our previous work on hypergames to develop defender strategies that are robust to deception and do not rely on attack detection. We prove that the defender can bound the attacker payoff with these strategies even when the attacker can choose between different attack modes, and we numerically demonstrate our approach on a realistic building control system. Finally, we discuss next steps in extending this approach towards an operational capability.

hypergames, cyber-physical systems, robust control↗
DOE OSTI2022

Neuromorphic Computing is Turing-Complete

Neuromorphic computing is a non-von Neumann computing paradigm that performs computation by emulating the human brain. Neuromorphic systems are extremely energy-efficient and known to consume thousands of times less power than CPUs and GPUs. They have the potential to drive critical use cases such as autonomous vehicles, edge computing and internet of things in the future. For this reason, they are sought to be an indispensable part of the future computing landscape. Neuromorphic systems are mainly used for spike-based machine learning applications, although there are some non-machine learning applications in graph theory, differential equations, and spike-based simulations. These applications suggest that neuromorphic computing might be capable of general-purpose computing. However, general-purpose computability of neuromorphic computing has not been established yet. In this work, we prove that neuromorphic computing is Turing-complete and therefore capable of general-purpose computing. Specifically, we present a model of neuromorphic computing, with just two neuron parameters (threshold and leak), and two synaptic parameters (weight and delay). We devise neuromorphic circuits for computing all the μ-recursive functions (i.e., constant, successor and projection functions) and all the μ-recursive operators (i.e., composition, primitive recursion and minimization operators). Given that the μ-recursive functions and operators are precisely the ones that can be computed using a Turing machine, this work establishes the Turing-completeness of neuromorphic computing.

Date, Prasanna↗