This paper presents a preliminary investigation on controlling the existing high voltage dc (HVDC) links connecting the North American western interconnection (WI) to the other interconnections, to provide damping to inter-area oscillations. The control scheme is meant to damp inter-area modes of oscillation in the WI by using wide area synchrophasor feedback. A custom model is developed in General Electric's PSLF software for the wide area damping control scheme, and simulations are analyzed on a validated full 22,000 bus WI model. Results indicate that implementing the proposed control technique to the existing HVDC links in the WI can significantly improve the damping of the inter-area modes of the system.
The microstructures of 316 L stainless steel created by rapid solidification are investigated by comparing the similar microstructures of individual hatches of directed energy deposition additive manufacturing (DED-AM) and those of single, laser surface-melted tracks formed on a solid plate. High recoil pressure, which is exponentially dependent on the laser beam power density, induces convection of the melt pool, which causes formation of microstructural bands in the as-solidified microstructure. The microstructural bands are associated with changes in the chromium concentration and are a significant component of the inhomogeneous microstructure of DED-AM.
Liquid thermometry during primary and secondary breakup of liquid sprays is challenging due to the presence of highly dynamic, optically complex flow features. This work evaluates the use of x-ray scattering from a focused, monochromatic beam of the Advanced Photon Source at Argonne National Laboratory for the measurement of liquid temperatures within the mixing zone of an impinging jet spray. The measured scattering profiles are converted to temperature through a previously developed two-component partial least squares (PLS) regression model. Transmitive mixing during jet merging is inferred through spatial mapping of temperatures within the impingement region. The technique exhibits uncertainties of ±2 K in temperature and 2% in capturing the correct scattering profile, showing its potential utility for probing liquid temperature distributions in multiphase flows.
In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.
It is well known that ultraviolet photoexcitation of iron pentacarbonyl results in rapid loss of carbonyl ligands leading to the formation of coordinatively unsaturated iron carbonyl compounds. We employ ultrafast mid-infrared transient absorption spectroscopy to probe the photodissociation dynamics of gas-phase iron pentacarbonyl following ultraviolet excitation at 265 and 199 nm. After photoexcitation at 265 nm, our results show evidence for sequential dissociation of iron pentacarbonyl to form iron tricarbonyl via a short-lived iron tetracarbonyl intermediate. Photodissociation at 199 nm results in the prompt production of Fe(CO)3 within 0.25 ps via several energetically accessible pathways. An additional 15 ps time constant extracted from the data is tentatively assigned to intersystem crossing to the triplet manifold of iron tricarbonyl or iron dicarbonyl. Mechanisms for formation of iron tetracarbonyl, iron tricarbonyl, and iron dicarbonyl are proposed and theoretically validated with one-dimensional cuts through the potential energy surface as well as bond dissociation energies. Ground state calculations are computed at the CCSD(T) level of theory and excited states are computed with EOM-EE-CCSD(dT).
On April 6-8, 2021, Sandia National Laboratories hosted a virtual workshop to explore the potential for developing AI-Enhanced Co-Design for Next-Generation Microelectronics (AICoM). The workshop brought together two themes. The first theme was articulated in the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report, which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme was the recognition of the continuing breakthroughs in artificial intelligence (AI) that are currently enhancing and accelerating the solution of traditional design problems in materials science, circuit design, and electronic design automation (EDA).
One of the greatest barriers to geothermal energy expansion is the high cost of drilling during exploration, assessment, and monitoring. Microhole drilling technology—small-diameter 2–4 in. (~5.1–10.2 cm) boreholes—is one potential low-cost alternative for monitoring and evaluating bores. However, delivering high weight-on-bit (WOB), high torque rotational horsepower to a conventional drill bit does not scale down to the hole sizes needed to realize the cost savings. Coiled tube drilling technology is one solution, but these systems are limited by the torque resistance of the coil system, helical buckling in compression, and most of all, WOB management. The evaluation presented herein will: (i) evaluate the technical and economic feasibility of low WOB technologies (specifically, a percussive hammer and a laser-mechanical system), (ii) develop downhole rotational solutions for low WOB drilling, (iii) provide specifications for a low WOB microhole drilling system, (iv) implement WOB control for low WOB drilling, and (v) evaluate and test low WOB drilling technologies.
This project is part of a multi-lab consortium that leverages U.S. research expertise and facilities at national labs and universities to significantly advance electric drive power density and reliability, while simultaneously reducing cost. The final objective of the consortium is to develop a 100 kW traction drive system that achieves 33 kW/L, has an operational life of 300,000 miles, and a cost of less than 6 dollars/kW. One element of the system is a 100 kW inverter with a power density of 100 kW/L and a cost of 2.7 dollars/kW. New materials such as wide-bandgap semiconductors, soft magnetic materials, and ceramic dielectrics, integrated using multi-objective co-optimization design techniques, will be utilized to achieve these program goals. This project focuses on a subset of the power electronics work within the consortium, specifically the design, fabrication, and evaluation of vertical GaN power devices suitable for automotive applications.
The electric grid is rapidly being modernized with novel technologies, adaptive and automated grid-support functions, and added connectivity with internet-based communications and remote interfaces. These advancements render the grid increasingly 'smart' and cyber-physical, but also broaden the vulnerability landscape and potential for malicious, cascading disturbances. The grid must be properly defended with security mechanisms such as intrusion detection systems (IDSs), but these tools must account for power system behavior as well as network traffic to be effective. In this paper, we present a cyber-physical IDS, the proactive intrusion detection and mitigation system (PIDMS), that analyzes both cyber and physical data streams in parallel, detects intrusion, and deploys proactive response. We demonstrate the PIDMS with an exemplar case study exploring a packet replay attack scenario focused on photovoltaic inverter communications; the scenario is tested with an emulated, cyber-physical grid environment with hardware-in-the-loop inverters.
The use of grid-edge sensing in distribution model calibration is a significant aid in reducing the time and cost associated with finding and correcting errors in the models. This work proposes a novel method for the phase identification task employing correlation coefficients on residential advanced metering infrastructure (AMI) combined with additional sensors on the medium-voltage distribution system to enable utilities to effectively calibrate the phase classification in distribution system models algorithmically. The proposed method was tested on a real utility feeder of ∼800 customers that includes 15-min voltage measurements on each phase from IntelliRupters® and 15-min AMI voltage measurements from all customers. The proposed method is compared with a standard phase identification method using voltage correlations with the substation and shows significantly improved results. The final phase predictions were verified to be correct in the field by the utility company.
Distribution system model accuracy is increasingly important and using advanced metering infrastructure (AMI) data to algorithmically identify and correct errors can dramatically reduce the time required to correct errors in the models. This work proposes a data-driven, physics-based approach for grouping residential meters downstream of the same service transformer. The proposed method involves a two-stage approach that first uses correlation coefficient analysis to identify transformers with errors in their customer grouping then applies a second stage, using a linear regression formulation, to correct the errors. This method achieved >99% accuracy in transformer groupings, demonstrated using EPRI's Ckt 5 model containing 1379 customers and 591 transformers.
Traditional protective relay voting schemes utilize simple logic to achieve confidence in relay trip actions. However, the smart grid is rapidly evolving and there are new needs for a next-generation relay voting scheme. In such new schemes, aspects such as inter-relay relationships and out-of-band data can be included. In this work, we explore the use of consensus algorithms and how they can be utilized for groups of relays to vote on system protection actions and also reach consensus on the values of variables in the system. A proposed design is explored with a simple case study with two different scenarios, including simulation in PowerWorld Simulator, to demonstrate the consensus algorithm benefits and future directions are discussed.
Galindo-Tellez, A.; Keefe, Kevin P.; Adamek, E.; Brubaker, Erik M.; Crow, B.; Dorrill, R.; Druetzler, A.; Felix, C.J.; Kaneshige, N.; Learned, J.G.; Manfredi, J.J.; Nishimura, K.; Souza, B.P.; Schoen, D.; Sweany, Melinda D.
The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image neutron sources for non-proliferation applications using the kinematic reconstruction of elastic double-scatter events. Our prototype system consists of 64 EJ-204 organic plastic scintillator bars, each measuring 5 mm × 5 mm × 200 mm and individually wrapped in Teflon tape. The scintillator array is optically coupled to two silicon photomultiplier ArrayJ-60035 64P-PCB arrays, each comprised of 64 individual 6 mm × 6 mm J-Series sensors arranged in an 8 × 8 array. We report on the design details, including component selections, mechanical design and assembly, and the electronics system. The described design leveraged existing off-the-shelf solutions to support the rapid development of a phase 1 prototype. Several valuable lessons were learned from component and system testing, including those related to the detector’s mechanical structure and electrical crosstalk that we conclude originates in the commercial photodetector arrays and the associated custom breakout cards. We detail our calibration efforts, beginning with calibrations for the electronics, based on the IRS3D application-specific integrated circuits, and their associated timing resolutions, ranging from 30 ps to 90 ps. With electronics calibrations applied, energy and position calibrations were performed for a set of edge bars using 22Na and 90Sr, respectively, reporting an average resolution of (12.07 ± 0.03) mm for energy depositions between 900 keVee and 1000 keVee. We further demonstrate a position calibration method for the internal bars of the matrix using cosmic-ray muons as an alternative to emission sources that cannot easily access these bars, with an average measured resolution of (14.86 ± 0.29) mm for depositions between 900 keVee and 1000 keVee. The coincident time resolution reported between pairs of bars measured up to 400 ps from muon acquisitions. Energy and position calibration values measured with muons are consistent with those obtained using particle emission sources.
We introduce an immersed meshfree formulation for modeling heterogeneous materials with flexible non-body-fitted discretizations, approximations, and quadrature rules. The interfacial compatibility condition is imposed by a volumetric constraint, which avoids a tedious contour integral for complex material geometry. The proposed immersed approach is formulated under a variational multiscale based formulation, termed the variational multiscale immersed method (VMIM). Under this framework, the solution approximation on either the foreground or the background can be decoupled into coarse-scale and fine-scale in the variational equations, where the fine-scale approximation represents a correction to the residual of the coarse-scale equations. The resulting fine-scale solution leads to a residual-based stabilization in the VMIM discrete equations. The employment of reproducing kernel (RK) approximation for the coarse- and fine-scale variables allows arbitrary order of continuity in the approximation, which is particularly advantageous for modeling heterogeneous materials. The effectiveness of VMIM is demonstrated with several numerical examples, showing accuracy, stability, and discretization efficiency of the proposed method.
Pore-scale finite-volume continuum models of electrokinetic processes are used to predict the Debye lengths, velocity, and potential profiles for two-dimensional arrays of circles, ellipses and squares with different orientations. The pore-scale continuum model solves the coupled Navier–Stokes, Poisson, and Nernst–Planck equations to characterize the electro-osmotic pressure and streaming potentials developed on the application of an external voltage and pressure difference, respectively. This model is used to predict the macroscale permeabilities of geomaterials via the widely used Carmen–Kozeny equation and through the electrokinetic coupling coefficients. The permeability results for a two-dimensional X-ray tomography-derived sand microstructure are within the same order of magnitude as the experimentally calculated values. The effect of the particle aspect ratio and orientation on the electrokinetic coupling coefficients and subsequently the electrical and hydraulic tortuosity of the porous media has been determined. These calculations suggest a highly tortuous geomaterial can be efficient for applications like decontamination and desalination.
The threat of a large-scale electromagnetic event having a negative impact on the electric grid is real. Whether human-caused, via the detonation of a nuclear device, or natural, via a high-intensity burst of solar radiation, our historic experience with these phenomena indicates that as a global community, we should be prepared for such events and know how to mitigate their impacts. Current studies and related discussions provide a wide range of damage assessments for these events. We recommend continuing current technical investigations and research as well as strengthening collaboration between stakeholders and experts. This would ensure future threats are addressed in a timely and effective manner.
This report compares the performance of three Circular Error Probable (CEP) estimators: the Grubbs-Patnaik estimator, a new, non-iterative, radial-integration estimator, and a median estimator. It also compares the performance of two Spherical Error Probable (SEP) estimators. The performance of each estimator is assessed in terms of bias, uncertainty, robustness, and computational complexity. Robustness is evaluated with respect to outliers, variations in the underlying statistical distribution characterizing munition impact positions, and impact-position measurement errors. The performance assessments indicate the radial-integration and Grubbs-Patnaik estimators perform nearly identically providing the statistical distribution of impact-position coordinates is jointly normal with zero means. In that case, both estimators outperform the median estimator by about 2% relative to the true CEP in terms of estimator uncertainty. The bias performance of the radial-integration and median estimators is close to zero for jointly normal impacts, however, the Grubbs-Patnaik estimator can be significantly biased for jointly normal impacts with non-zero means. When the statistical distribution characterizing impact positions is known, but not jointly normal, the radial-integration estimator is superior. In this case, the median estimator also outperforms the Grubbs-Patnaik estimator but is not quite as good as the radial-integration estimator. If the statistical distribution characterizing impacts is unknown and not jointly normal, or if distribution parameters are difficult or impractical to estimate, or if test data is corrupted with outliers, then the median estimator dramatically outperforms the other estimators, especially in terms of estimation bias. Unexpectedly, measurement noise did not significantly degrade the performance of any of the estimators, except for cases with signal to noise ratios less than five. Although the Grubbs-Patnaik estimator has remained the gold standard for CEP estimation for over half a century, the performance assessments indicate the new, non-iterative, radial-integration estimator and the median estimator offer significant advantages and, in most practical real-world conditions, are superior estimators. These estimators are also useful for SEP estimation whereas the Grubbs-Patnaik estimator does not extend to three dimensions.
The objective of this work is to create an accurate elastic-plastic J2 plasticity model calibration for the Inconel 718 material at room temperature for use in finite element models. This calibration was made using a power-law hardening model of form σ = σy + $Aε^{n}_{p}$ where A and n are empirically determined constants, and σy is the proportional limit.
This work evaluated the iX Cameras iSpeed 727, a commercial CMOS-based continuous- recording high-speed camera. Various parameters of importance in the scheme of accurate time-resolved measurements and photonic quantification have been measured under controlled conditions on the bench, using state-of-the-art instrumentation. We will detail the procedures and results of the tests laid out to measure sensor sensitivity, linearity, signal-to-noise ratio and image lag. We also looked into the electronic shutter performance and accuracy, as exposure time is of particular interest to high-speed imaging. The results of the tests show that this camera matches or exceeds the performance of competing units in most aspects, but that, as is the case for other high-speed camera systems, corrections are necessary to make full use of the image data from a quantitative perspective.
The Bayesian optimal experimental design (OED) problem seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. The goal of OED is to find an experiment that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge about expected data. Therefore, within the context of seismic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize the ability to locate seismic events from arrival time data of detected seismic phases. In order to do utilize Bayesian OED we must develop four elements:1. A likelihood function that describes the uncertainty of detection and travel times; 2. A Bayesian solver that takes a prior and likelihood to identify the posterior; 3. An algorithm to compute EIG; and, 4. An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we can explore many relevant questions to monitoring such as: how and what multiphenomenology data can be used to optimally reduce uncertainty, how to trade off sensor fidelity and earth model uncertainty, and how sensor types, number, and locations influence uncertainty
The objective of this work is to extend the thermal-mechanical, elastic-plastic calibrations for 304L stainless steel [1] and and 6061-T651 aluminum alloy [2] to the regime between room temperature and -40 °C. The basis to extend the calibration consisted of new uniaxial tension tests conducted at -40 °C using the same plate material stocks, circular cylindrical specimen geometries and testing apparatus as previously, followed by attempts to fit power-law hardening functions to replicate the response observed in the specimens and then extend the yield, hardening constant, hardening exponent and rate constant functions in the calibrations to cover the new temperature regime.
Aerosol Jet Printing (AJP) is one technique of additive manufacturing used in the printing of electronics components. AJP enables the patterning of features at the ∼10 μm-100 μm scale based on hardware and print parameters. Optimization of print conditions enables the printing of high-resolution features with linewidths approaching 10 μm. The aerosol jet printing of electronic parts can be limited by the conductivities which are achievable by Ag nanoparticle inks (typically 15%-25% of bulk Ag). For certain electronics applications, the increased conductivity produces unacceptable loses during operation and methods are needed to increase the conductance of the devices without sacrificing resolution. Here, we report on the AJP of inductor spirals conductor traces of linewidth 50 μm separated by 55 μm gaps. The conductivity of these features is enhanced by electrodeposition of Cu onto the Ag, resulting in a decrease in resistance of 35-45x. Impedance measurements demonstrate that the addition of Cu by electrodeposition to a 27-turn spiral inductor resulted in an inductance of 3.6 μH. Finally, we demonstrate the use of a lift-off process to produce free-standing, flexible, conductive films using AJP.
In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.
Nuclear Power Plants (NPPs) are a complex system of coupled physics controlled by a network of Programmable Logic Controllers (PLCs). These PLCs communicate process data across the network to coordinate control actions with each other and inform the operators of process variables and control decisions. Networking the PLCs allows more effective process control and provides the operator more information which results in more efficient plant operation. This interconnectivity creates new security issues, as operators have more access to the plant controls, so will bad actors. As plant networks become more digitized and encompass more sophisticated controllers, the network surface exposed to cyber interference grows. Understanding the dynamics of these coupled systems of physics, control logic, and network communications is critical to their protection. The research into the cybersecurity of the Operational Technologies of NPPs is developing and requires a platform that can allow high fidelity physics simulations to interact with digital networks of controllers. This will require three main components: a network simulation environment, a physics simulator, and virtual PLCs (vPLC) that represent typical industry hardware. A platform that incorporates these three components to provide the most accurate representation of actual NPP networks and controllers is developed in this paper.
Topology identification in transmission systems has historically been accomplished using SCADA measurements. In distribution systems, however, SCADA measurements are insufficient to determine system topology. An accurate system topology is essential for distribution system monitoring and operation. Recently there has been a proliferation of Advanced Metering Infrastructure (AMI) by the electrical utilities, which improved the visibility into distribution systems. These measurements offer a unique capability for Distribution System Topology Identification (DSTI). A novel approach to DSTI is presented in this paper which utilizes the voltage magnitudes collected by distribution grid sensors to facilitate identification of the topology of the distribution network in real-time using Linear Discriminant Analysis (LDA) and Regularized Diagonal Quadratic Discriminant Analysis (RDQDA). The results show that this method can leverage noisy voltage magnitude readings from load buses to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.
As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.
Synapse Energy Economics has conducted structured interviews to better characterize the current landscape of resilience planning within and across jurisdictions. Synapse interviewed representatives of a diverse group of communities and their electric utilities. The resulting case studies span geographies and utility regulatory structures and represent a range of threats. They also vary in terms of population density and size. This report summarizes our approach and the findings gleaned from these conversations. All the communities and utilities we interviewed see increased interest in and commitment of resources for energy-related resilience. The risks and consequences these communities and utilities faced in the past, face now, and will face in the future drove them to improve engagement, advance processes, further decision-making, and in many cases invest in projects. While no process used by communities and utilities was the same, the different processes used by communities and utilities allowed each one to make progress in its own way. Several approaches are emerging that can provide good models for other communities and utilities with an interest in improving resilience.
Sandia provided technical assistance to RingIR to test and evaluate of the RingIR molecular detector. The detector can identify gas phase species using molecular fingerprinting and has potential application for SARS-CoV-2 detection in near real time. As part of the development process Sandia will utilize the biological aerosol test bed deployed at the Aerosol Complex to evaluate the penetration of MS2 bacteriophage aerosol through the RingIR system. The objective of this project is to provide experimentally derived measurements of the RingIR molecular detector penetration efficiency, including external exhaust filter for mitigation of exhaust aerosol and operation using MS2 bacteriophage as a biological surrogate to the SARS-CoV-2 virus.
All experiments involving the Pecos target chamber in calendar year (CY) 2020 were dedicated to Pre-Heat studies in the context of Magnetized Liner Inertial Fusion (MagLIF). Activities at the target area included actual laser shots but also diagnostic and maintenance, and preparatory work for experiments with cryogenically cooled targets. The latter took up a large part of CY2020’s shot availability, and consequently there were fewer shots performed than in previous years. Since the Z-Beamlet and Z-Petawatt lasers support multiple campaigns, we can only anticipate laser and laser-operator time for one out of three shot windows in a day, and typically not on every day of the week. For that reason, many of our non-shot activities need to be traded against shots as well.
Heeger, Derek; Garigan, Maeve; Plusquellic, Jim; Tsiropoulou, Eirini E.
Internet of Things (IoT) devices rely upon remote firmware updates to fix bugs, update embedded algorithms, and make security enhancements. Remote firmware updates are a significant burden to wireless IoT devices that operate using low-power wide-area network (LPWAN) technologies due to slow data rates. One LPWAN technology, Long Range (LoRa), has the ability to increase the data rate at the expense of range and noise immunity. The optimization of communications for maximum speed is known as adaptive data rate (ADR) techniques, which can be applied to accelerate the firmware update process for any LoRa-enabled IoT device. In this paper, we investigate ADR techniques in an application that provides remote monitoring of cattle using small, battery-powered devices that transmit data on cattle location and health using LoRa. In addition to issues related to firmware update speed, there are significant concerns regarding reliability and security when updating firmware on mobile, energy-constrained devices. A malicious actor could attempt to steal the firmware to gain access to embedded algorithms or enable faulty behavior by injecting their own code into the device. A firmware update could be subverted due to cattle moving out of the LPWAN range or the device battery not being sufficiently charged to complete the update process. To address these concerns, we propose a secure and reliable firmware update process using ADR techniques that is applicable to any mobile or energy-constrained LoRa device. The proposed system is simulated and then implemented to evaluate its performance and security properties.
Sandia National Laboratories is developing a new method for detecting penetration of tamper - indicating enclosures (TIEs). This method incorporates the use of "bleeding" materials (analogous to visually obvious, colorful bruised skin that doesn't heal) into the design of TIEs. As designed, it will allow inspectors to use simple visual observation to detect attempts to penetrate the external surfaces of a TIE, without providing adversaries the ability to repair damage. A material of this type can enhance tamper indication of current TIEs used to support treaty verification regimes. Current TIE inspections are time - consuming and rely on subjective visual assessment by an inspector, equipment such as eddy current or camera devices, or involve approaches that may be limited due to application environment. The complexities and requirements that volumetric sealing methods (or TIEs) must address are: (1) enclosures that are non - standard in size/shape; (2) enclosures that may be inspectorate - or facility - owned; (3) finding tamper attempts that are difficult and time consuming for an inspector to locate; (4) enclosures that are reliable and durable enough to survive the conditions that exist in the operating environment (including facility handling); and (5) methods that prevent adversaries from repairing penetrations. Early project R&D [1] focused on encapsulated transition metals. Due to the challenges associated with the transition metal - based approach, a mitigation approach was investigated resulting in two separate research paths — one that involves fabricating custom TIE molds that meet the specific (size and shape) needs of safeguards equipment a nd one that can be deployed as a sprayed on or painted coating to an existing TIE or surface. The "custom mold" approach is based on creating thin layers of materials that , when penetrated, expose an inner material to O2 which causes an irreversible color change. The "in-situ coating" approach is based on applying a sensor solution containing color changing microcapsules that bleed when the microcapsule is ruptured. The anticipated benefits of this work are passive, flexible, scalable, robust , cost-effective TIEs with visually obvious responses to tamper attempts. This provides more efficient and effective monitoring , as inspectors will require little or no additional equipment and will be able to detect tamper without extensive time - consuming visual examination. Applications include custom TIEs (cabinets , equipment enclosures or seal bodies ), or spray-coating/painting onto facility-owned items, walls or structures, or circuit boards. The paper describes research and testing completed to-date on the method and integration of select system components.
