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This slide presentation shows several case studies for fault protection. The cases involve a discovery-class mission to excavate material from a comet, rendezvous with two asteroids and develop a prototype system for a next-generation Deep Space Network consisting of ndca large array of small antennas.
We develop the foundations for a theory of patterns of safety case argument structures, clarifying the concepts involved in pattern specification, including choices, labeling, and well-founded recursion. We specify six new patterns in addition to those existing in the literature. We give a generic way to specify the data required to instantiate patterns and a generic algorithm for their instantiation. This generalizes earlier work on generating argument fragments from requirements tables. We describe an implementation of these concepts in AdvoCATE, the Assurance Case Automation Toolset, showing how patterns are defined and can be instantiated. In particular, we describe how our extended notion of patterns can be specified, how they can be instantiated in an interactive manner, and, finally, how they can be automatically instantiated using our algorithm.
Safety assurance using argument-based safety cases is an accepted best-practice in many safety-critical sectors. Goal Structuring Notation (GSN), which is widely used for presenting safety arguments graphically, provides a notion of modular arguments to support the goal of incremental certification. Despite the efforts at standardization, GSN remains an informal notation whereas the GSN standard contains appreciable ambiguity especially concerning modular extensions. This, in turn, presents challenges when developing tools and methods to intelligently manipulate modular GSN arguments. This paper develops the elements of a theory of modular safety cases, leveraging our previous work on formalizing GSN arguments. Using example argument structures we highlight some ambiguities arising through the existing guidance, present the intuition underlying the theory, clarify syntax, and address modular arguments, contracts, well-formedness and well-scopedness of modules. Based on this theory, we have a preliminary implementation of modular arguments in our toolset, AdvoCATE.
A systematic approach is presented to constructing a radiation assurance case using Goal Structured Notation (GSN) for spacecraft containing COTS parts. The GSN paradigm is applied to an SRAM single-event upset experiment board designed to fly on a CubeSat November 2016. Construction of a radiation assurance case without use of hardened parts or extensive radiation testing is discussed.
Aurorasaurus is a citizen science project that offers a new, global data source consisting of ground-based reports of the aurora. For this case study, aurora data collected during the 17-18 March 2015 geomagnetic storm are examined to identify their conjunctions with Defense Meteorological Satellite Program (DMSP) satellite passes over the high latitude auroral regions. This unique set of aurora data can provide ground-truth validation of existing auroral precipitation models. Particularly, the solar wind driven, Oval Variation, Assessment, Tracking, Intensity, and Online Nowcasting (OVATION) Prime 2013 (OP-13) model and a Kp-dependent model of Zhang-Paxton (Z-P) are utilized for our boundary validation efforts. These two similar models are compared for the first time. Global equatorward auroral boundaries are derived from the OP 13 model and the DMSP Special Sensor Ultraviolet Spectrographic Imager (SSUSI) far ultraviolet (FUV) data using the Z-P model at a fixed flux level of 0.2 erg cm(exp -2)s(exp -1). These boundaries are then compared with citizen science reports as well as with each other. Even though there are some large differences between the global boundaries for a few cases, the average difference is about 1.5 deg in geomagnetic latitude, with OP-13 being equatorward of Z-P model. When these boundaries are compared with each other as a function of local time, no clear overall trend as a function of local time was observed. It is also found that the ground based reports are more consistent with the predictions of the OP-13 model.
Internship opportunities can be immensely beneficial to both the students who pursue them and the labs that employ them. However, optimal results for all stakeholders can be uncertain. Several factors compound this issue. In this case study, the highly multidisciplinary nature of the BioEngineering Instrumentation Group at NASA Ames Research Center has made productively integrating short-term summer interns challenging. In general, taking on more undergraduate interns during a given time period can lead to diminishing returns, particularly if those interns are primarily short-time interns with few long-term senior students present.Introducing a level of industry-style project management processes can help. On the one hand, extreme formality can reduce the enthusiasm that students can bring; also, time constraints on internships make extensive training in formal process an investment with comparatively little return. On the other hand, lack of process can result in many forms of wastage. A moderate, lightweight level of process i.e., simple processes with high payoff to time invested ratios, including processes to effectively handle multidisciplinary issues can yield both a far more efficient lab, and a richer educational experience for the interns. In this case study, we implemented simple processes such as brief weekly stand-up meetings, a standard action item bug tracking process, and deadline-based scheduling with prudent reserves and critical path tracking.To evaluate these steps, several parameters were considered: the number of projects the lab could successfully pursue, the number of interns that could be successfully integrated onto each project, diversity of student intern educational backgrounds, student satisfaction, student understanding of expectations, and student preparation for industry-standard work. Generally, outcomes were enhanced across the board, even with imperfectly implemented processes. Lightweight process implementation is shown to be tremendously effective in a multidisciplinary internship context.
This LWRS research seeks to assist in breaking the impasse which has precluded digital safety system upgrades by generating and demonstrating a process and related business case tool to enable a Business Case Analysis (BCA). The purpose of a BCA is to show such upgrades can be economically justified. This BCA methodology first systematically establishes a forecast of expected lifecycle costs for I&C identified for upgrade by: • Definitively bounding the scope of current I&C systems envisioned for upgrade. • Collecting historical labor and material usage data that bound cost contributors related to the systems to be upgraded. • Synthesizing and analyzing the data to establish lifecycle cost forecasts for the current systems. In collaboration with engineers familiar with the attributes of the digital equipment to be used in the upgrade and how it is envisioned to be applied, cost savings categories and expected savings in those categories are then identified and applied using the analysis tools developed for this purpose. The result is an estimate NPV of savings enabled by the upgrade. This includes both direct cost savings (e.g., surveillance labor costs) as well as cost avoidance items (e.g., inventory carrying costs). Finally, when utility-provided digital upgrade costs estimates are included, the resultant BCA provides an NPV for the upgrade project. Development of a useful BCA methodology requires a real-world basis. With the cooperation of Exelon Generation (“Owner”), an Exelon-owned 2-Unit BWR Station (“Station”) was used as the foundation for this research. Exelon is pursuing a digital upgrade of current, first-echelon, safety-related I&C systems at these units, including the following: • Reactor Protection System (RPS) • Nuclear Steam Supply Shutoff System (N4S) • Emergency Core Cooling Systems (ECCS) • Anticipated Transient Without Scram (ATWS) Mitigation System
This report provides the status of tensile, creep, fatigue, and creep-fatigue testing to date conducted at Argonne National Laboratory, Idaho National Laboratory, and Oak Ridge National Laboratory to generate the data package required to qualify Alloy 709 in American Society of Mechanical Engineers, Boiler and Pressure Vessel Code, Section III, Division 5. The Division 5 Class A Alloy 709 Code Case requires data generated from a minimum of three commercial heats. Extensive mechanical properties data have been generated on two commercial heats, and some initial test data have been generated from the third commercial heat. The room temperature tensile test results for the three commercial heats met the specification minimum of the American Society of Mechanical Engineers, Boiler and Pressure Vessel Code, Section II, Part A, SA-213/SA-213M for Grade TP310MoCbN (UNS S31025) seamless tubing. These three commercial heats of Alloy 709 are suitable for generating the code case data.
This report presents the thermophysical properties measurements conducted in the Fiscal Year 2024 to extend the database up to 1000?. The new values are compared against properties of high Molybdenum steels in the ASME Section II Part D tables. This report also shows the comparison between new measurements against the previously measured values in literature and provides recommended thermophysical properties for A709 code case. These values will support the thermophysical property tables in near term code case of A709.
Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
ABSTRACT Cryptogenic species are those that are not yet “demonstrably native or introduced” in a given area, such as crayfish in western Montana, USA. Delving into evidence from diverse fields can help clarify the status of cryptogenic species. Primary historical sources and indigenous knowledge have informed various ecological questions but are seldom used to clarify the status of cryptogenic species, especially aquatic taxa. We summarize types of evidence used to illuminate species status and offer a case study applying historical sources to cryptogenic crayfish in Montana. We searched for crayfish mentions in dictionaries of indigenous languages and primary historical documents (e.g., travel journals, newspaper accounts, early biological surveys). Early explorer accounts we examined did not mention crayfish in Montana, though they noted them in the nearby Snake River drainage of Idaho and Wyoming. The first mention of crayfish in Montana that we found was from an 1868 newspaper account, probably referencing the upper Missouri River. The species was not noted, but an 1887 article suggested signal crayfish Pacifastacus leniusculus introductions near Bozeman, where non‐native signal crayfish still persist. The earliest crayfish mention we found west of the Great Divide in Montana was from 1934 in Ninepipes and probably referred to non‐native virile crayfish ( Faxonius virilis ). Signal crayfish were not mentioned west of the Divide until a 1944 newspaper account noted their 1937 introduction to the Bitterroot River drainage. The case study demonstrates the value of historical sources in identifying the absence of species and documenting their presence or introduction during periods predating scientific data.
Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.
Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.
Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.
Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of tracking a pre-calculated optimal setpoint improves controller performance. This paper presents a novel multistage economic nonlinear model predictive control (E-NMPC) framework for dynamic systems operating under uncertainty, with specific application to natural gas transmission networks. A key innovation lies in the integration of cyclic steady-state (CSS) constraints within the multistage MPC formulation, enabling the controller to manage periodic operating conditions commonly observed in energy systems. A Lyapunov-based descent condition is enforced to ensure robust stability of the controller. The multistage economic MPC framework is validated on two gas pipeline case studies, where it successfully minimizes net energy consumption, respects operational constraints under uncertain demand profiles, and guides the network to optimal cyclic operation. The Lyapunov function remains bounded in both case studies, validating the robust stability of multistage E-NMPC.
Soiling, particulate accumulation on photovoltaic (PV) module surfaces, reduces the available solar resource and the resulting generated device power. This case-study summarizes initial results of 5-year research on the contrasting soiling conditions in the tropical, subtropical, and semi-arid climates in Brazil. A major objective is to present a case study of the effects of soiling on PV module performance in different Brazil climate zones that represent the primary areas for the current and expanding Brazil solar installations. For this, the paper presents methodologies to quantify the soiling ratio (SRatio) and soiling rate (SRate) for two representative commercial technologies, polycrystalline or multicrystalline silicon (mc-Si) and thin-film cadmium telluride (CdTe) modules, through soiling monitoring stations deployed in the selected climate regions. An aim is to add to the growing soiling-research knowledge base through addressing these key factors and their relationships to critical electrical, solar resource, thermal, and local meteorological and environmental parameters. This paper presents, evaluates, and compares soiling rates and losses in Belo Horizonte, Minas Gerais (Equatorial Tropical: 19.92° S, 43.99° W), Porto Alegre, Rio Grande do Sul (Humid-Subtropical: 30.05° S, 51.17° W), and Brotas de Macaúbas, Bahia (Semi-Arid: 12.00° S, 42.63° W). The results show that soiling is moderate in all 3-regions, for example with 0.1%/day < SRate < 0.2%/day for Belo Horizonte. Precipitation dominates the cleaning of the modules in the summertime in this climate zone, while it is the major factor year-round in Rio Grande do Sul. Wind is the major issue mitigating the soiling accumulation for the Bahia installation. The methodology incorporates several key refinements, including the normalization and adjustment for the meteorological parameters (temperature, irradiance, wind, precipitation). The evaluations include the region-specific differing effects of non-uniform soiling, natural cleaning, and ambient temperatures.
Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.
Heating, ventilation, and air-conditioning (HVAC) systems in commercial buildings have proven to be a great asset for providing secondary frequency regulation (FR) service to the power grid. However, how the provision of FR service affects the HVAC system Quality of Service (QOS) is still largely unknown. In particular, there exists a controversy regarding the energy efficiency impact. This paper investigates how a HVAC system performance and its associated controls are impacted during and after the FR service. For the assessment, we developed a dynamic Modelica-based medium office building model as a virtual testbed with the Air Handling Unit (AHU) fans providing the FR service. Different regulation capacities (demand levels) and standard test signals under various load profiles in the U.S. are considered. The results show that FR has little or no impact on the HVAC operation and occupant thermal comfort while the building is providing the service if an appropriate regulation capacity is pre-determined. However, if the regulation capacity is overestimated, the quality of the FR service provided by the HVAC system and the HVAC energy efficiency will be affected due to the equipment operating constraints. In addition, the AHU supply air temperature fluctuates, and the controller cannot maintain the desired setpoint as the FR magnitude is increased. After the FR service time, the system takes a certain period to return to normal operation. The recovery period extends with the increase in the FR magnitude. For this case study, there exists a control conflict between the FR control and the existing HVAC control (from ASHRAE Guideline 36-2018) during high load conditions, causing the fan speed to saturate. The fan power consumption could increase significantly; up to 77% increase compared to the baseline was observed. This case study demonstrates that the HVAC system controls need to be enhanced and adapted to minimize the interaction between the FR controller and the indoor environmental controls in order to preserve the HVAC system QOS while (during and after) providing satisfactory FR service.