Lunar Bioregenerative Life Support Modeling

Drysdale, Alan

doi:10.4271/941456

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Several attempts have been made to model and simulate advanced life support systems that could exist on potential Lunar (Drysdale, 1994) and Mars Surface Habitats (Fleisher et al, 1999;Rodriguez et al, 1999). For Mars Surface Habitats, in particular, one issue that is not usually considered is that of habitat evolution between missions.…”

Section: Introductionmentioning

confidence: 99%

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Application of Supply Chain Optimization and Protocol Environment Architecture to ALS Modeling and Visualization of a Mars Surface Habitat

Lasinski

Russell

Orçun

et al. 2006

SAE Technical Paper Series

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A significant amount of software has been developed to model the advanced life support aspects of a Mars surface habitat. Models, such as the BIO-Plex Baseline Simulation Model (Finn, 1999), have been useful in studying advanced life support systems. These models have been used to conduct trade study comparisons to determine which Advanced Life Support (ALS) technologies should currently be used in a habitat design. However, the present models and approaches require significant overhead to exchange one technology for another mostly because the models are mission centric and assume either that the habitat will be stationary or that the life of the habitat will be same as the mission duration. In other words, these models lack the desired level of modularity necessary to quickly complete multiple trade studies of different missions as the habitat evolves from mission to mission. The XML-based (Extensible Markup Language) Supply Chain Optimization and Protocol Environment (SCOPE) architecture provides a mechanism to achieve the required level of plug-and-play capability. SCOPE has previously been used to study policy interactions within several different supply chain networks (Orcun et. al). In this work, the SCOPE methodology is applied to a Mars surface habitat simulation without optimization. This results in a network of components such as crew members, storage, and ALS technologies. Each component is represented as a self-contained node in the network. For each node, there is an XML description of the required material inputs and outputs. The simulation engine interprets each description to form the entire network and to properly handle the interactions between each node in the network. In this work we will present a preliminary implementation of this architecture for habitat analysis and three case studies that demonstrate the challenges of habitat evolution. We also describe a virtual environment that visually displays the results from the simulation architecture.

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Section: Introductionmentioning

confidence: 99%

“…Facility and Storage Assumptions Assumption ExplanationStorage Module Volume 300.24 m3 . This is based off of the shuttle payload volume.…”

mentioning

confidence: 99%

Application of Supply Chain Optimization and Protocol Environment Architecture to ALS Modeling and Visualization of a Mars Surface Habitat

Lasinski

Russell

Orçun

et al. 2006

SAE Technical Paper Series

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“…The works presented in the literature can be grouped into three major categories. First group of studies concentrate on mass balance studies (Volk and Rummel, 1987;Drysdale, 1992;Drysdale et al, 1994a;Drysdale et al, 1994b;Drysdale, 1997a;Drysdale, 1997b;Finn, 1998;McGlothlin et al, 1999; Baro et al, 1999). Usually early studies fall into this category.…”

Section: Introductionmentioning

confidence: 99%

Aggregate System Level Material Analysis for Advanced Life Support Systems

Aydogan¹,

Orçun²,

Blau³

et al. 2003

SAE Technical Paper Series

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In this paper, an aggregate system level modeling and analysis framework is proposed to facilitate the integration and design of advanced life support systems (ALSS). As in process design, the goal is to choose values for the degrees of freedom that achieve the best overall ALSS behavior without violating any system constraints. At the most fundamental level, this effort will identify the constraints and degrees of freedom associated with each subsystem and provide estimates of the system behavior and interactions involved in ALSS. This work is intended to be a starting point for developing insights into ALSS from a systems engineering point of view. At this level, simple aggregate static input/output mapping subsystem models from existing data and the NASA ALS BVAD document are used to debug the model and demonstrate feasibility. Table 1. Entity and component list Entity Components Carbon dioxide Carbon Dioxide Edible Food Dry edible mass (DEM), Water Human Waste Dry solid waste (DSW), Water, Brine Non-edible Stuff Dry non-edible mass (DNEM), Water Oxygen Oxygen Raw Food DEM, DNEM, Water Waste DSW, Water, Brine Water Water Meal DEM, Water completely based on a mission scenario without leaving out any degrees of freedom. This also requires technologies to be specified in order to be able to complete the study.

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