Scheduling lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler

Autonomous power system: Integrated scheduling

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control and scheduling techniques to space power distribution hardware. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis, isolation, and recovery (FDIR), the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space-based power system. Faults can be introduced into the Brassboard and in turn, be diagnosed and corrected by APEX and AIPS. The Autonomous Intelligent Power Scheduler controls the execution of loads attached to the Brassboard. Each load must be executed in a manner that efficiently utilizes available power and satisfies all load, resource, and temporal constraints. In the case of a fault situation on the Brassboard, AIPS dynamically modifies the existing schedule in order to resume efficient operation conditions. A database is kept of the power demand, temporal modifiers, priority of each load, and the power level of each source. AIPS uses a set of heuristic rules to assign start times and resources to each load based on load and resource constraints. A simple improvement engine based upon these heuristics is also available to improve the schedule efficiency. This paper describes the operation of the Autonomous Intelligent Power Scheduler as a single entity, as well as its integration with APEX and the Brassboard. Future plans are discussed for the growth of the Autonomous Intelligent Power Scheduler

Autonomous power expert system

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control technologies to the Space Station Freedom Electrical Power Systems (SSF/EPS). The objectives of the program are to establish artificial intelligence/expert system technology paths, to create knowledge based tools with advanced human-operator interfaces, and to integrate and interface knowledge-based and conventional control schemes. This program is being developed at the NASA-Lewis. The APS Brassboard represents a subset of a 20 KHz Space Station Power Management And Distribution (PMAD) testbed. A distributed control scheme is used to manage multiple levels of computers and switchgear. The brassboard is comprised of a set of intelligent switchgear used to effectively switch power from the sources to the loads. The Autonomous Power Expert System (APEX) portion of the APS program integrates a knowledge based fault diagnostic system, a power resource scheduler, and an interface to the APS Brassboard. The system includes knowledge bases for system diagnostics, fault detection and isolation, and recommended actions. The scheduler autonomously assigns start times to the attached loads based on temporal and power constraints. The scheduler is able to work in a near real time environment for both scheduling and dynamic replanning

Design and implementation of an experiment scheduling system for the ACTS satellite

The Advanced Communication Technology Satellite (ACTS) was launched on the 12th of September 1993 aboard STS-51. All events since that time have proceeded as planned with user operations commencing on December 6th, 1993. ACTS is a geosynchronous satellite designed to extend the state of the art in communication satellite design and is available to experimenters on a 'time/bandwidth available' basis. The ACTS satellite requires the advance scheduling of experimental activities based upon a complex set of resource, state, and activity constraints in order to ensure smooth operations. This paper describes the software system developed to schedule experiments for ACTS

Autonomous power system intelligent diagnosis and control

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System

Projecting global mean sea-level change using CMIP6 models

The effective climate sensitivity (EffCS) of models in the Coupled Model Intercomparison Project 6 (CMIP6) has increased relative to CMIP5. We explore the implications of this for global mean sea‐level (GMSL) change projections in 2100 for three emissions scenarios. CMIP6 projections of global surface air temperature are substantially higher than in CMIP5, but projections of global mean thermal expansion are not. Using these projections as input to construct projections of GMSL change with IPCC AR5 methods, the 95th percentile of GMSL change at 2100 only increases by 3‐7 cm. Projected rates of GMSL rise around 2100 increase more strongly, though, implying more pronounced differences beyond 2100 and greater committed sea‐level rise. Inter‐model differences in GMSL projections indicate that EffCS‐based model selection may substantially alter the ensemble projections. GMSL change in 2100 is accurately predicted by time‐integrated temperature change, and thus requires reducing emissions early to be mitigated

Historical simulations with HadGEM3-GC3.1 for CMIP6

We describe and evaluate historical simulations which use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3-GC3.1) model and which form part of the UK's contribution to the sixth Coupled Model Intercomparison Project, CMIP6. These simulations, run at two resolutions, respond to historically evolving forcings such as greenhouse gases, aerosols, solar irradiance, volcanic aerosols, land use, and ozone concentrations. We assess the response of the simulations to these historical forcings and compare against the observational record. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. We find that the simulated time evolution of global mean surface temperature broadly follows the observed record but with important quantitative differences which we find are most likely attributable to strong effective radiative forcing from anthropogenic aerosols and a weak pattern of sea surface temperature response in the low to middle latitudes to volcanic eruptions. We also find evidence that anthropogenic aerosol forcings play a role in driving the Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation, which are key features of the North Atlantic ocean. Overall, the model historical simulations show many features in common with the observed record over the period 1850–2014 and so provide a basis for future in-depth study of recent climate change

Aerosol-forced AMOC changes in CMIP6 historical simulations

The Atlantic Meridional Overturning Circulation (AMOC) has been, and will continue to be, a key factor in the modulation of climate change both locally and globally. However, there remains considerable uncertainty in recent AMOC evolution. Here, we show that the multi-model mean AMOC strengthened by approximately 10% from 1850-1985 in new simulations from the 6th Coupled Model Inter-comparison Project (CMIP6), a larger change than was seen in CMIP5. Across the models, the strength of the AMOC trend up to 1985 is related to a proxy for the strength of the aerosol forcing. Therefore, the multi-model difference is a result of stronger anthropogenic aerosol forcing on average in CMIP6 than CMIP5, which is primarily due to more models including aerosol-cloud interactions. However, observational constraints - including a historical sea surface temperature fingerprint and shortwave radiative forcing in recent decades - suggest that anthropogenic forcing and/or the AMOC response may be overestimated