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Panel presentation providing an overview of the Energy Systems Integration Facility (ESIF). Provided examples of thermal and power testing of computer components. Showcased the ESIF data center and testing capabilities for computer equipment at scale.
This report documents the experimental conditions and results for the testing of the Mirion Technologies CAAS-3S Criticality Accident Alarm System (CAAS) for the Y-12 National Security Complex (Y-12) and Uranium Processing Facility (UPF). The testing was conducted at the Godiva IV Burst Reactor at the National Criticality Experiments Research Center (NCERC) at the Nevada National Security Site (NNSS) during the week of January 11, 2021. The testing was conducted in accordance with 25774-QL-POA-JR00-00002-VDE-27.0, CAAS-3S Radiation Testing of Cabinet Components Test Plan. The tests subjected the CAAS-3S system to intense and short duration mixed neutron and gamma radiation fields and to high integrated radiation doses. The purpose of the test was to environmentally qualify various CAAS-3S components in a radiation environment. Successful completion of the tests was predicated on the collaborative efforts of several organizations. Sandia National Laboratory (SNL) and Lawrence Livermore National Laboratory (LLNL) provided dosimetry to estimate photon and neutron dose. Los Alamos National Laboratory (LANL) manages the NCERC facility, operated the Godiva IV reactor, and provided field support for CAAS-3S placement and communications interface for data acquisition systems supplied by Mirion Technologies. Throughout the test campaign, Y-12 and UPF personnel provided technical support in determining changes to the test schedule, as needed, interpretation of the results, and comparison between the measured dosimetry data and predicted results. Throughout the test campaign, Mirion personnel provided technical support in the operation of the CAAS-3S system itself.
Electrolysis has been a commercially available product for a while and electrolyzers have been a proven capability to provide additional benefits (e.g. controllable load for grid services) in addition to production of hydrogen. The hydrogen output is typically compressed for storage and dispensing. Compression adds cost and decreases system reliability. Honda’s electrolyzer systems have been developed to include electrochemical compression to leverage the production system itself for at least partial compression. In this project, the team will evaluate Honda’s PEM based electrochemical compression system. The system is capable of compressing hydrogen up to 70 MPa electrochemically. Validation testing is the next step to accelerate this technology into the marketplace, as the validation will provide needed data under a variety of operation conditions and controls. These operating conditions and controls are based on over a decade of NLR research and development with low-temperature electrolysis. The validation testing will include preparing NLR’s site for third party evaluation, benchmark testing of Honda’s stack and system, and simulating operation connected to renewables or in a grid service profile. NLR’s Energy System Integration Lab will be the location for the electrolyzer validation research and integrated into the Hydrogen Infrastructure Test & Research Facility (HITRF). This will build into the existing retail style hydrogen fueling station for a fully integrated experimental setup.
Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.
Time-integrated radiography using MeV Bremsstrahlung X-ray sources is the norm for imaging during system-level testing of components and structures under dynamic condition. One source of error in the analysis of the time-integrated radiography data sets stems from motion blur which smears out sharp interfaces to a greater degree with longer exposure times, which become necessary to provide sufficient signal-to-noise with low X-ray penetration of objects of interest. To quantify motion blur, a 1D shock wave through PMMA was investigated experimentally at The Dynamic Compression Sector at The Advanced Photon Source (DCS@APS) with tapered broadband and 25.46 ± 1.06 keV narrowband X-rays. Four cameras with different exposure times were used for each experiment to compare the effect that exposure time has on motion blur. In addition, our methodology to accurately simulate motion blur in terms of transmission and shape is presented and compared to our experimental results and quantified. There is a high level of agreement between the experimental and simulation results across the range of data sets investigated in this study with a percent difference range of 0.29–1.31% for the four shots. The methodology of this work serves as a steppingstone towards a physically validated model that could be used in conjunction with experimental results to deconvolve physical parameters, densities, and interfaces of interest in a way that would not be possible with experimental results alone.
Traditionally, a single-loop fixed-gain controller is applied to supply fan (SF) and cooling coil (CC) valve controls while a fixed-damper position control is applied to outdoor air (OA) damper control at air handling units (AHUs) in commercial buildings. With the increasing application of occupancy sensors, the information generated by occupancy sensors is applied to not only reduce the electricity loads from lighting and controllable plug loads, but also reset OA intake and minimum supply airflow setpoints. Meanwhile, these intermittent operation actions greatly elevate the dynamics of AHU systems, which may introduce unstable SF and CC valve operations and inaccurate OA flow control at AHUs and consequently degrade maximum energy efficiency gains. With virtual fan and valve flow meter technologies, two advanced controls, including cascade control and gain scheduling control, can be implemented on both the SF and CC valve, and an advanced control using a virtual OA flow meter can be implemented on OA damper integrated with occupancy sensors. The goal of this project is to demonstrate the savings, cost, and performance of an integrated solution that integrates the three advanced HVAC controls with occupancy sensors to allow accurate and stable AHU operations in real buildings. The project objectives are to: 1) develop and validate an advance SF control algorithm; 2) develop and validate an advanced CC valve control algorithm; 3) validate an algorithm to implement a virtual OA flow meter; and 4) demonstrate the savings, cost, and performance of the integrated solution in real buildings. The technical approaches are to: 1) select a test system at the University of Oklahoma; 2) develop and implement the algorithms of advanced SF and CC valve controls and validate the performance; 3) develop and implement the advanced OA control using a virtual OA flow meter and validate the performance; 4) demonstrate the savings, cost, and performance of the proposed integrated solution with and without three advanced HVAC controls; and 5) disseminate the project results through publications and presentations. For the SF control, both the gain scheduling and cascade controls can improve the fan energy performance by reducing the fan power during the transient period and the fan control performance at lower speeds by reducing fan speed variation. Moreover, the gain scheduling control provides a simple and low-cost solution and is recommended. The fan power savings can reach 30% in a transient period. For the CC valve control, the gain scheduling control can considerably reduce the supply air temperature oscillation range and frequency under both higher and lower load conditions and the control valve response is much more stable. As a result, the gain scheduling control is recommended. The projected pump energy consumption can be reduced by 68.5%. With the developed virtual OA flow meter, the OA can be accurately controlled at its setpoint, which is determined based on the actual number of occupants in the building provided by occupancy sensors. The RMSE of the proposed OA control is 15.9 L/s. The energy data shows that the fan power and CC cooling energy were significantly reduced. On the other hand, the energy savings majorly results from the occupancy sensors and the energy savings by the advanced HVAC controls is minimal because that the controllers in the test AHU were tuned with very slow response. An annual technical savings potential is estimated as 0.5 quads in the commercial sector.
This final technical report describes work conducted by Membrane Technology and Research, Inc. (MTR) for the U.S. Department of Energy (DOE), National Energy Technology Lab (NETL) on the development of membranes with transformational performance for carbon capture under award number DE-FE0031596. The work was performed from June 1, 2018 through May 31, 2024. For more than a decade, MTR has worked in partnership with DOE to develop an innovative membrane-based CO 2 capture process. This effort has included the first test of membrane modules with coal-fired flue gas at the Arizona Public Services (APS) Cholla plant in 2010; the accumulation of >11,000 hours of flue gas operation for Polaris modules on a bench-scale 1 tonne/day (TPD) system at the National Carbon Capture Center (NCCC); scale-up of first-generation (Gen-1) Polaris to a 20 TPD small pilot system, and successful operation of this system on a flue gas slipstream at NCCC and in integrated boiler testing at Babcock & Wilcox (B&W). Through continued development efforts, a second-generation (Gen-2) version of the Polaris membrane has been scaled-up to pilot production. This membrane offers 70% higher CO 2 permeance with similar selectivity to the base case Polaris. MTR also developed planar modules designed specifically for the low-pressure, high-volumetric flow rate process conditions of flue gas operation. These new modules have significantly lower pressure-drop values compared to the type originally used (spiral-wound modules), which results in significant energy savings. The goal of the work described in this report was to improve on the Polaris Gen-2 membrane with the ultimate aim to reduce the cost of carbon capture. The majority of the effort was to develop improved support membranes for the multi-layer composite structure of MTR’s Polaris membrane. Earlier work at MTR had identified the support structure as limiting membrane permeances, not because the support itself represents a permeation resistance, but because the distribution of pores at the surface of the support imposes a geometric restriction to diffusion in the layers above it. Support membranes were prepared from a range of polymers, including commercially available block copolymers and a custom synthesized block copolymer alternative. The best support membranes developed in this project reduced the geometric restriction by a factor of two to three. These supports then were used to produce Polaris composite membranes with improved permeances. The second topic was to create a high-selectivity version of the Polaris membrane. The high-selectivity version uses a novel selective polymeric material and high selectivities were confirmed in experiments at MTR. The material is not easily made into very thin films. Consequently, the permeances are significantly lower than the Polaris Gen-2 membrane. The utility of this membrane is therefore limited to the carbon dioxide purification step that produces liquid CO 2 . A Technical and Economic Analysis (TEA) was performed for a carbon capture system that uses both advanced membrane types. The TEA shows the novel advanced membranes reduce the cost of capture by 10%, from $63.32/tonne CO 2 to $56.90/tonne CO 2 (2022 USD). Most of the development work was carried out with laboratory-scale casting and coating equipment. A number, but not all, of the improvements identified have been implemented on commercial-scale manufacturing equipment. The focus of future work at MTR is to incorporate the advancements made into the Polaris membrane manufacturing process.
The design of the ITER low-field side reflectometer (LFSR) has matured to a complete end-to-end preliminary design. LFSR will supply three key plasma measurements: (1) electron density profile, (2) electron density fluctuations, and (3) poloidal rotation. Simultaneous measurements of the three quantities are enabled by an array of six monostatic antennas which inject from an equatorial port on the outboard side of the ITER vessel. Low-loss transmission lines, consisting of corrugated, overmoded waveguide and miter bends, transmit the 30–165 GHz, O- and X-mode signals to and from the ITER plasma. Integrated transmission-line components serve a range of purposes, such as protection from high-power stray radiofrequency radiation, accommodation of transmission-line displacement, and simultaneous measurement of reference and plasma phases during the discharge. Broadband transmission signals are realized by full-band microwave transceivers combined with quasi-optical multiplexing. A field-programmable gate array (FPGA) processor demodulates the profile reflectometer signals, enabling real-time density profile measurements for plasma control system feedback. A full-scale transmission line test facility provides an integrated environment to assess the performance of critical LFSR components. Theoretical modeling together with insertion loss measurements provide the basis for a comprehensive power budget, which accounts for transmitted output power, transmission-line losses, antenna coupling, and plasma effects. Results indicate that high signal-to-noise ratios are achievable with the current design. A synthetic reflectometer model, using real design parameters and baseline ITER profiles, has been developed to estimate the return signal. With evolving microwave and data acquisition technologies, full-band, ultrafast sweeps (<1 μs) will be realizable for ITER.
A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m 2 , would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.
As Machine Learning (ML) continues to advance, it is being integrated into more systems. Often, the ML component represents a significant portion of the system that reduces the burden on the end user or significantly improves task performance. However, the ML component represents an unknown complex phenomenon that is learned from collected data without the need to be explicitly programmed. Despite the improvement in task performance, the models are often black boxes. Evaluating the credibility and the vulnerabilities of ML models poses a gap in current test and evaluation practice. For high consequence applications, the lack of testing and evaluation procedures represents a significant source of uncertainty and risk. To help reduce that risk, here we present considerations to evaluate systems embedded with an ML component within a red-teaming inspired methodology. We focus on (1) cyber vulnerabilities to an ML model, (2) evaluating performance gaps, and (3) adversarial ML vulnerabilities.
INTRODUCTION: This Report summarizes the research and development project performed by Sunvapor and the subrecipient National Renewable Energy Laboratory (NREL) during the period of 10/01/2018-9/30/2021. The work was aimed at the economic integration of solar steam with an industrial process, and in particular, the advantages that a novel type of thermal energy storage and collector design could bring to the system. The completed work includes experimental research on the storage material, design of the collector, engineering of a complete solar steam plant, and economic analysis. The plant was, in the end, not constructed, due to COVID-19 impacts to the project host. PURPOSE: The purpose of the project was to test the hypothesis that a levelized cost of heat (LCOH) of 2¢/kWh delivered to a steam-consuming process, including energy discharged from storage, could be feasibly achieved. PROJECT OBJECTIVES: For Budget Period 1 our objectives were to engineer a prototype industrial solar steam generation system, and design and test a Latent Heat Energy Storage (LHES) lab-scale system. For Budget Period 2 our objectives were to construct and test the solar steam prototype plant, and to build and factory-test a full LHES module. For Budget Period 3 our objective was to integrate the storage module with the solar steam generator, show successful long duration energy tests of the combined system, and use the results to project the LCOH of delivered steam to a second-generation plant. PROJECT OUTCOMES: Various phase change materials (PCMs) for the LHES were tested for compatibility with industrial steam uses as measured by phase change temperature, stability of latent heat of fusion, and their corrosive effects on steel (the heat exchanger material). Two of these materials (sodium formate and sodium/ potassium) showed potential to meet the technical criteria. The estimated cost of a complete LHES module with these materials did not meet economic targets. The developmental collector design builds from previous work to exploit the potential cost advantages of a spaceframe of lumber construction. Design improvements were achieved in assembly efficiency and structural performance. A complete engineering package was completed for the solar steam system without storage that included the developmental as well as commercial arrays. The plant was permitted for construction. The storage CAPEX for a reference plant was estimate to be greater than $\$ 57$/kWh. The LCOH of the system without storage and with developmental collectors of lumber construction was projected to be 2¢/kWh, assuming a FCR of 8.2% over thirty years. PROJECT MILESTONES: The Milestones fully achieved in the first Budget Period includes obtaining a Letter of Intent from an industrial steam-consuming project host, issuance of a construction permit, and corrosion resistance. The Milestone that was partly achieved was the degradation in the latent heat of fusion. Milestones associated with Budget Periods 2 and 3, related to the construction and operation of the plant were not achieved as a result of the host shutting down the plant due to COVID-19 impacts. CONCLUSIONS: Sunvapor was able to secure an agreement with a host to build a fully engineered and permitted solar steam plant that met the host’s economic goals. As a basis for comparison, the solar field design was comprised of eight commercial collector arrays and one developmental Green Parabolic Trough Collector (GPTC) array. The projected LCOH with a solar field entirely populated with a second generation GPTC met the goal of 2¢/kWh. The experimental program concerning the PCM indicated the feasibility of meeting their technical requirements for industrial steam uses. The cost of the complete LHES exceeded its $\$ 14$/kWh target, and therefore the inclusion of LHES could not be economically justified. Due to COVID-19 impacts on the host, the solar steam plant was not constructed.
A wave energy converter (WEC) and the power take-off (PTO) generator system can be represented by using a mass-spring-dampener model. By incorporating a negative stiffness spring within the PTO, the overall stiffness of the PTO can be lowered allowing the impedance of the PTO to be more closely matched with the WEC. By designing for impedance matching the WEC can greatly enhance its power generation capability. This project has involved the design, fabrication and testing of a new type of linear-stroke length and rotary stroke length adjustable negative stiffness magnetic springs for use within a wave energy converter (WEC). The magnetic springs were studied by using 3-D finite element analysis with the objective of creating high energy density and a long linear stroke length. After the construction and testing of both a proof-of-principle adjustable linear and rotary magnetic spring prototype the rotary (torsional) magnetic spring was selected for scaling up analysis. The selected scaled-up proof-of-principle magnetic spring had a peak torque of 850 Nm with a ±45 degree stroke length. By translating the inner rotor relative to the outer rotor, the stiffness could be adjusted to be either negative or positive stiffness. During this project a magnetic lead screw was also studied, and it was shown that by combining the magnetic lead screw with a linearly translating magnetic spring a very long rotary stroke length could be attained. However, as the magnetic lead screw increased complexity and reduced overall energy density relative to a rotary (torsional) spring, this design approach was not further pursed. Dynamometer testing of the variable stiffness magnetic springs was first completed by Portland State University and following this the magnetic spring performance was independently verified by Sandia National Laboratory (Sandia). A WEC analysis when using a variable stiffness magnetic spring was completed by using the WecOptTool. WecOptTool is an open-source WEC optimization software developed by Sandia that supports efficient power take-off (PTO) and control optimization. Wave condition data from the Oregon PacWave test-site was used in this study. The analysis showed that a WEC with a tunable stiffness value could consistently achieve about 80% of its maximum theoretical power production. The use of a tunable stiffness WEC, rather than zero-stiffness or a constant stiffness WEC was also shown to lead to a smaller maximum PTO force requirement. The variable stiffness magnetic spring was integrated into an experimental WEC developed by Sandia, called a Wave-Bot. Sandia successfully completed water-tank testing of the Wave-Bot at the Navy’s Carderock, Maryland, wave-basin test site. The wave-basin testing helped to experimentally demonstrate the operating capabilities and increased power generation capability of a WEC containing an integrated variable stiffness magnetic spring. A WEC capacity factor analysis was also completed. The capacity factor was defined as the ratio of annual average WEC generator power to the maximum (RMS) generated power. This capacity factor provided a means of identifying the ratio of potential revenue to cost. It was calculated that if a tunable variable stiffness magnetic spring has RMS constraints it could operate with a capacity factor above 30%. This is comparable to a wind turbine’s capacity factor.
Increased penetration levels of renewable energy and other types of distributed energy resources (DERs) on the modern electric grid-combined with technological advancements for electric system monitoring and control-introduce new cyberattack vectors and increase the cyberattack surface of energy systems. According to the IEEE Std. 1547-2018, DERs must use Modbus, Distributed Network Protocol 3 (DNP3), or Smart Energy Profile 2.0 (SEP2) as their communication protocol. Previous research identified several vulnerabilities and security breaches in each one of these communication protocols; despite this, existing standards for DERs do not recommend cybersecurity measures. In order to reduce vulnerabilities in power distribution systems, this paper presents a novel open-source hardware security module that improves both information and operational security to better protect data and communications on the distribution grid. The security hardware is called “module for operational technology,” or simply Module-OT, and it has been validated and tested in an emulated distribution system application. Module-OT is integrated within a communication system in the transport layer of the Open Systems Interconnection (OSI) model. It improves system security through encryption, authentication, authorization, certificate management, and user access control. The main advancement of Module-OT is the addition of hardware cryptographic acceleration that improves the overall communication performance in terms of end-to-end latency.
Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.
This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.
A numerical method to design nonlinear double- and multi-bend achromat (DBA and MBA) lattices with approximate invariants of motion is investigated. The search for such nonlinear lattices is motivated by Fermilab’s Integrable Optics Test Accelerator (IOTA), whose design is based on an integrable Hamiltonian system with two invariants of motion. While it may not be possible to design an achromatic lattice for a dedicated synchrotron light source storage ring with one or more exact invariants of motion, it is possible to tune the sextupoles and octupoles in existing DBA and MBA lattices to produce approximate invariants. In our procedure, the lattice is tuned while minimizing the turn-by-turn fluctuations of the Courant-Snyder actions J x and J y at several distinct amplitudes, while simultaneously minimizing diffusion of the on-energy betatron tunes. The resulting lattices share some important features with integrable ones, such as a large dynamic aperture, trajectories confined to invariant tori, robustness to resonances and errors, and a large amplitude-dependent tune-spread. Compared to the nominal NSLS-II lattice, the single- and multi-bunch instability thresholds are increased and the bunch-by-bunch feedback gain can be reduced.
In today’s communication landscape there are multiple technologies and protocols used for communication between end devices. Within security paradigms for these protocols, integrity management is a common goal of system designers. Communication protocols focused on maintaining message integrity can provide assurance that some received data has not been altered or tampered with. While integrity is often coupled with confidentiality in protocol design, this analysis focuses on an evaluation of only integrity protocols. This report outlines various ways message integrity may be preserved with respect to high performance operational technology (OT) systems. It describes a series of experiments and an evaluation framework used to evaluate the performance of the identified integrity approaches regarding common system design goals. Finally, it addresses the testing environment utilized and closes the report with a summary of experimental results.
A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.