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JPL RAPSO long range forecasting
The JPL RAPSO is chartered to divide the limited amount of tracking hours amongst the various missions in as equitable allotment as can be achieved. This paper introduces the concept of the long-range forecasting function, which is performed by the group.
Technology Readiness Level as the Foundation of Human Readiness Level
Communication of the maturity of technology through the program/product life cycles helps enhance risk management from the beginning and support decision-making strategies for research, development, and allocation of resources. Currently, many organizations use the technology readiness level (TRL) as a simple metric to indicate the maturity of the technology. This article will discuss the TRL history, define the TRL levels, show how the TRL relates to the technology life cycle, and how the TRL framework contributes to the human readiness level (HRL) structure. Through the TRL advantages and disadvantages, this article will show how the TRL falls short in numerous areas of engineering, including the integration readiness of system/subsystem components and assessment of the readiness of the technology to operate within the human capabilities and limitations. Yet the article also shows how the TRL serves as the foundation for HRL.
Resilience Engineering's Potential for Advanced Air Mobility (AAM)
The national airspace (NAS) will rapidly evolve in the next ten to twenty years. Plans for Advanced Air Mobility (AAM) during that period envision highly automated airspace management systems and electrically powered vehicles. AAM concepts also anticipate limited human roles. The goal of limiting the human role is to minimize the potential for misadventures, yet how the human role is limited needs to be carefully considered in order to also preserve the potential for human successes. The field of resilience engineering (RE) focuses on how systems can change in order to seize an opportunity or withstand an unforeseen challenge. RE methods rely on the use of empirical data to optimize the ability of any system to adapt. RE studies have shown how individual and team initiatives ensure resilient system performance by creating safety through flexibility. Benefits of the RE approach include improved awareness of operational circumstances and how system elements depend on each other, and the ability to allocate limited resources and prepare for surprise. RE offers the ability to account for and incorporate the human role as an essential element in order to ensure NAS systems’ resilient performance. Data on the human contribution to safe and resilient system performance, which is termed “work as done,” are available but are not being considered as the NAS evolves. We present an approach that describes how use of RE can enable the evolving NAS to adapt, and perform, in a resilient manner.
Notional Habitat Science Outfitting for a Lunar Surface Habitat or Mars Transit Habitat
Science outfitting is often given insufficient treatment in the development of early conceptual studies for lunar surface habitats. In some cases, only control masses and volumes are used as placeholders. In others, one science discipline may be given detailed treatment while others are largely ignored. It is not unheard of for science outfitting to be entirely forgotten in initial concept layouts. This is exacerbated by the reality that spacecraft design studies are often initiated long before science objectives have been established for any given human spaceflight destination. When those objectives later emerge, they can be difficult to accommodate if sizing studies did not allocate sufficient resources for them. A notional science outfitting can be created for virtually any human exploration habitat, however, utilizing data created by the International Space Station and Constellation programs. Biology, human research, physics, and geology science laboratories and a general-purpose work area for a lunar surface habitat will be derived using equipment identified by the Constellation-era Optimizing Science and Exploration Working Group (OSEWG) or flown on the International Space Station. Early science outfitting definition and inclusion in surface habitat sizing is critical for developing an exploration architecture that meets science needs. The mass, volume, power, and use cases associated with these notional laboratories impacts all of the habitat subsystems, habitat dimensions, hatch dimensions, logistics operations, launch vehicles and landers.
Global Assessment of Drought Characteristics in the Anthropocene
Contemporary understanding of the impacts of climate change on global drought characteristics (e.g., intensities, extents) is still limited and not well understood. This knowledge is critical because projected changes in climate are expected to impact on future water availability as well as influence decisions on how water resources are allocated. The main aim of this study is to improve understanding of drought characteristics (extents and duration) in the Anthropocene where rapid changes in the environment are caused by the composite influence of human activities and climate change. Multi-scale earth observation data (1980 - 2020) and the Coupled Model Intercomparison Project Phase 6 climate models, which incorporate the Shared Socioeconomic Pathways (2040 - 2070 and 2070 - 2100) are used to assess these characteristics as well as identify climatic hotspots where changes in drought characteristics could drive groundwater hydrology. Results show that towards the end of the 21st century, global land areas under drought will significantly decrease but their durations will not. Generally, there is evidence of significant decline in the proportion of areas that will experience various drought intensities (moderate, severe and extreme drought) in the future and for each category, drought affected areas will not reach 30% on average. Moreover, some regions are potential hotspots of climate–groundwater interactions where drought events could directly impact on groundwater. This is because of the varying degree of strong correlations (positive and negative) between climate and groundwater data in some areas (e.g., Australia, Europe, Southern Africa, Asia). The relatively strong negative correlations in some of these hotspots are indicative of the presence of considerable lags, that could be caused by aridity as well as human groundwater footprints.
Q-Learning Based Impact Assessment of Propagating Extreme Weather on Distribution Grids: Preprint
Increasing number of power outage events due to extreme weather condition is hampering us socioeconomically. Preparing in advance for the extreme weather event is critical and can help utility operators to reduce grid damages, restore grid service quickly, allocate energy resources and repair crews strategically, and hence dramatically increase grid resilience. In this paper, we propose a method to identify the sequence of worst impact zones in the power grid caused by extreme weather events based on Q-learning (a reinforcement learning algorithm). To quantify weather severity and it’s effect on the grid, we model the impact of extreme weather on the grid as a function of intensity, vulnerability and exposure. A modified IEEE 123-node distribution feeder is presented in a mesh grid and experimented for sequences of zones identification. Finally, simulation results present the identified sequences and their associated impacts on the grid caused by the extreme weather events.
Neuromorphic computing architecture with dynamically accessible contexts
Various technologies pertaining to allocating computing resources of a neuromorphic computing system are described herein. Subgraphs of a neural algorithm graph to be executed by the neuromorphic computing system are identified. The subgraphs are each executed by a group of neuron circuits serially. Output data generated by execution of the subgraphs are provided to the same or a second group of neuron circuits at a same time or with associated timing data indicative of a time at which the output data was generated. The same or second group of neuron circuits performs one or more processing operations based upon the output data.
Towards an HPC Complementary Computing Facility
This Letter considers the design for computing facilities that are complementary to the leadership class High Performance Computing (HPC) facilities. This design envisions a future where funding agencies are allocating greater resources for leadership class facilities and these facilities will provide a significant part of the total compute cycles for HEP Experiments. While a leadership class facility (LCF) may provide cycles and advanced architectures, the facility does not necessarily provide all of the services needed to help HEP users make the best use of the HPC facility, as well as the services needed to provide computing for workflows that are not a good fit for the HPC facilities. This Letter outlines some of the necessary components of a facility designed to provide those services and capabilities.
Mental Models for Managing Grid Cybersecurity Risk - ARC Industry Forum, Cybersecurity and Energy Transition
A proper mental model of the concepts of cyber risk and its components of threat and exploitability and consequence is necessary to allocate limited resources to different security activities, especially when considered alongside reliability and regulatory and economic risks. Understanding the consequence and frequency different risks are realized also helps align the right mitigations to the right risks. Cyber-informed engineering (CIE) is an emerging method to “engineer out” cyber risk across product and system lifecycles, and INL has a portfolio of programs operationalizing CIE’s principles for the energy sector.
Evaluating the impacts of Variable Message Signs on Airport Curbside Performance Using Microsimulation
Curbs play a vital role in facilitating vehicle access and egress for individuals at airports. Inefficiently allocating this resource hinders airport accessibility and productivity, resulting in congestion, longer travel times, and increased pollution. As airport demand fluctuates throughout the day and grows over time, airports face intensified curbside pressure. Yet, curb management research is significantly less robust at airports than in urban areas. Given the unbalanced nature of airport demand—riders tend to arrive simultaneously at specific entrances at certain hours—Variable Message Sign (VMS) arises as a cost-effective technology to divert vehicles from congested to underutilized curbs. Still, VMS implementation faces a significant challenge. Historically, airports have managed VMS heuristically and by intuition rather than an evidence-based approach. This research investigates the impacts of implementing VMS on curb performance at airports. By considering different driver compliance rates (DCR), we aim to determine when the sign should be turned on and off to diverge traffic to avoid undesired externalities while enhancing curb performance. Using a validated agent-based microsimulation model, VISSIM, we analyzed the Seattle-Tacoma (SeaTac) Airport as a case study. We modeled sixteen VMS management scenarios and a baseline where the message sign is not displayed, diverging vehicles between the departures and arrivals access levels at four different moments (early morning, morning, afternoon, and late night). We quantified the effects of VMS using seven metrics, including curb productivity index (CPI), curb accessibility (CA), queue length, queue duration, delay, vehicle counts, and emissions. The results of each scenario were compared against the baseline using absolute and relative changes and Repeated Measures ANOVA. Overall, VMS improved curb performance and traffic conditions at the airport, reducing emissions by 14.8% to 8.9%. Moreover, significant reductions in queue length (1,150 ft to 100 ft) and duration (15 to 144 minutes) were observed in the sending link under all VMS policies. However, impacts on the receiving link varied based on congestion, with significant increases in queue duration (9.8 to 24 min) when congested but no substantial changes in free flow. Notably, diverging vehicles to congested links resulted in non-significant results, and activating late and deactivating late VMS affected curb productivity (-5.8% to -61.4%), curb accessibility (-16.5% to -25.8%), cumulative counts (-33.4% to -59.4%), and vehicle delay (95.98% to 594.3%). Activating VMS before congestion begins in the sending link and deactivating before a queue forms in the receiving link yield the most significant improvements: 8.1% to 10.1% in CPI, 9.4% to9.6% in CA, -29.3% to -77.9% in total delay, -11.6% to -13.9% in total emissions, and 101% to 103% in cumulative counts. As the analysis was made with a wide range of time periods, access levels, driver compliance rates, and scenarios, we believe our findings can provide valuable insights into how airports should manage VMS. Our work introduces a novel approach to the scientific airport literature, as some of our metrics were previously unexplored. Additionally, we propose a methodology that other airports can adopt to maximize their curb performance.
STRATOS: A cyber-energy SAAS platform to manage real-time experiment configuration and deployment [SWR-23-16]
STRATOS is a novel management SAAS platform for achieving multi-environment, multi-tenant real-time cyber-energy experimentation. The platform empowers users to configure a wide variety of experiment environments by automating the initialization process and providing a framework for building system configurations. During run-time the platform ensures resources are allocated appropriately and the environment remains stable until the required cyber-energy events have occurred. This capability to manage this complex process in a single user-focused application significantly reduces difficulty and setup time for performing high-impact cyber-energy research.
Steps Toward Optimal Competitive Scheduling
This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, se@sh preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource. This problem arises in many institutional settings where, e.g., different departments, agencies, or personal, compete for a single resource. We are particularly motivated by the problem of scheduling NASA's Deep Space Satellite Network (DSN) among different users within NASA. Access to DSN is needed for transmitting data from various space missions to Earth. Each mission has different needs for DSN time, depending on satellite and planetary orbits. Typically, the DSN is over-subscribed, in that not all missions will be allocated as much time as they want. This leads to various inefficiencies - missions spend much time and resource lobbying for their time, often exaggerating their needs. NASA, on the other hand, would like to make optimal use of this resource, ensuring that the good for NASA is maximized. This raises the thorny problem of how to measure the utility to NASA of each allocation. In the typical case, it is difficult for the central agency, NASA in our case, to assess the value of each interval to each user - this is really only known to the users who understand their needs. Thus, our problem is more precisely formulated as follows: find an allocation schedule for the resource that maximizes the sum of users preferences, when the preference values are private information of the users. We bypass this problem by making the assumptions that one can assign money to customers. This assumption is reasonable; a committee is usually in charge of deciding the priority of each mission competing for access to the DSN within a time period while scheduling. Instead, we can assume that the committee assigns a budget to each mission.This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, se@sh preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource. This problem arises in many institutional settings where, e.g., different departments, agencies, or personal, compete for a single resource. We are particularly motivated by the problem of scheduling NASA's Deep Space Satellite Network (DSN) among different users within NASA. Access to DSN is needed for transmitting data from various space missions to Earth. Each mission has different needs for DSN time, depending on satellite and planetary orbits. Typically, the DSN is over-subscribed, in that not all missions will be allocated as much time as they want. This leads to various inefficiencies - missions spend much time and resource lobbying for their time, often exaggerating their needs. NASA, on the other hand, would like to make optimal use of this resource, ensuring that the good for NASA is maximized. This raises the thorny problem of how to measure the utility to NASA of each allocation. In the typical case, it is difficult for the central agency, NASA in our case, to assess the value of each interval to each user - this is really only known to the users who understand their needs. Thus, our problem is more precisely formulated as follows: find an allocation schedule for the resource that maximizes the sum ofsers preferences, when the preference values are private information of the users. We bypass this problem by making the assumptions that one can assign money to customers. This assumption is reasonable; a committee is usually in charge of deciding the priority of each mission competing for access to the DSN within a time period while scheduling. Instead, we can assume that the committee assigns a budget to each mission.
A Case For Intra-rack Resource Disaggregation in HPC
The expected halt of traditional technology scaling is motivating increased heterogeneity in high-performance computing (HPC) systems with the emergence of numerous specialized accelerators. As heterogeneity increases, so does the risk of underutilizing expensive hardware resources if we preserve today’s rigid node configuration and reservation strategies. This has sparked interest in resource disaggregation to enable finer-grain allocation of hardware resources to applications. However, there is currently no data-driven study of what range of disaggregation is appropriate in HPC. To that end, we perform a detailed analysis of key metrics sampled in NERSC’s Cori, a production HPC system that executes a diverse open-science HPC workload. In addition, we profile a variety of deep-learning applications to represent an emerging workload. We show that for a rack (cabinet) configuration and applications similar to Cori, a central processing unit with intra-rack disaggregation has a 99.5% probability to find all resources it requires inside its rack. In addition, ideal intra-rack resource disaggregation in Cori could reduce memory and NIC resources by 5.36% to 69.01% and still satisfy the worst-case average rack utilization.
Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)
A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.
Playing Games with Optimal Competitive Scheduling
This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, selfish preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource.
Adaptive Management of Computing and Network Resources for Spacecraft Systems
It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time systems.
D-Side: A Facility and Workforce Planning Group Multi-criteria Decision Support System for Johnson Space Center
"To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers" is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach. Keywords: NASA, Multi-Criteria Decision Making, Decision Support System, AHP, Euclidean Distance, 3-D Modeling, Facility Planning, Workforce Planning.