Power consumption during all phases of spacecraft flight is of great interest to the aerospace community. As a result, significant analysis effort is exerted by both system and electrical-domain engineers to understand the rates of electrical energy generation and consumption under many operational scenarios of the system. Previously, no standard tool existed for creating and maintaining a Power Equipment List (PEL) of spacecraft components that consume power, and no standard tool existed for generating power load profiles based on this PEL information during mission design phases. Projects have traditionally either developed ad-hoc spreadsheet-based tools, or adapted complex simulation tools to compute such resource predictions; both of these approaches have significant limitations.
The Scenario Power Load Analysis Tool (SPLAT) is a model-based systems engineering tool (a plug-in for the MagicDraw modeling tool) that aids in creating and maintaining a PEL, and generates a constraint set in Maple language syntax that can be solved in Maple to show electric power load profiles (i.e. power consumption from loads over time). SPLAT creates these load profiles from three modeled inputs: 1) a list of system components and their respective power modes, 2) a decomposition hierarchy of the system into these components, and 3) the specification of at least one scenario, which consists of temporal constraints on component power modes (indicating how the power states of the individual components change over time). Once these modeled inputs have been read into the SPLAT tool, it combines them to produce a Timeline representation of the power load constraints, which is stored as OWL2 ontology individuals in an RDFXML file and then transformed into Maple language syntax.
The constraints are solved within Maple, which is a symbolic solver, and load profiles can be generated for any level of aggregation specified in the decomposition hierarchy. SPLAT supports parameterizing both power load values as well as scenario time durations, which allows users to quickly assess impacts of different parameter values in Maple without the need to re-execute SPLAT. Due to the flexibility of modeling components in SPLAT and its ability to incorporate parameterized variables, the tool is useful for both low-fidelity models (e.g. as used in the formulation phase) as well as high-fidelity models (e.g. as used in mission operations). Additionally, while SPLAT is specialized for the power domain, the transformation from scenario constraints to Maple code is generic enough to be used for any time-dependent domain, such as data generation rates.
SPLAT improves upon the existing spreadsheet-based power loads analysis approach by maintaining a single source of truth (the system model) that can be easily maintained. This approach has several additional benefits, such as reducing human-induced errors (e.g. copy-paste or math errors) because transformations are scripted, improving end-product quality as scripts perform error checking, and increasing productivity because automated scripts allow analysis results to be generated/updated in the background without much human intervention (less time to obtain a higher-quality product).