Impact of Crewmember Schedule on System Performance

Ang, Chit Hui; Chiam, Tze Chao; Yih, Yuehwern

doi:10.4271/2005-01-2918

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…The crewmembers assumed in this study have the characteristics and schedule of those presented in [6] and [1,2,3]. A one-shift crewmember schedule is used in this research because of drastic differences in human water consumption.…”

Section: Water Model Overviewmentioning

confidence: 99%

Control Policies for a Water-Treatment System Using the Markov Decision Process. Part 2: Simulation and Analysis

Chiam¹,

Yih²,

Mitchell³

2009

habitation (elmsford)

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This article is the second part of a two-part research study of a theoretical water-treatment system based on the NASA Baseline Values and Assumptions Document (BVAD) [7]. It focuses on the decisionmaking model created to choose the "best" policy to be applied to the water-treatment system based on hourly system conditions. Due to the resemblance between the behavior of this system and the Markovian process, this system is constructed based on the Markovian model. The water system consists of two subsystems: a hygiene-water subsystem that supplies water for laundry, urinal fl ush, dish wash, oral hygiene and shower; and a potable-water subsystem that supplies water for drinking and food rehydration. In order to assess the conditions of the water system, various aspects of the system, such as hourly and accumulated water defi ciency, and amount of clean water available for use, are captured on an hourly basis. A baseline policy and policies derived from it are tested to fi nd the best policy for the system to operate under the most economical conditions while providing enough clean water for crew consumption. The best policy is obtained through various mathematical modeling techniques. Outcomes are compared against a system that uses the baseline policy. Results show that an intuitively "good" policy may not always be the best policy for the system. The system performance is measured in terms of a reward value, which is assigned based on the system conditions.

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Section: Water Model Overviewmentioning

confidence: 99%

Control Policies for a Water-Treatment System Using the Markov Decision Process. Part 2: Simulation and Analysis

Chiam¹,

Yih²,

Mitchell³

2009

habitation (elmsford)

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“…The model used in this research is based on the system described in [4] and [1,2,3]. Several modifi cations and assumptions were made to refl ect an actual system more closely.…”

Section: Water Systemmentioning

confidence: 99%

Control Policies for a Water-Treatment System Using the Markov Decision Process. Part 1: Mathematical Formulation

Chiam¹,

Yih²,

Mitchell³

2009

habitation (elmsford)

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A model is developed to assist decision-making for choosing control policies for a closed-loop space life-support system. The water-treatment system, which consists of hygiene and potable-water subsystems, is chosen as the focus of this model due to its direct impact to the crewmembers' water requirements. This paper is the fi rst part of the study where mathematical formulations of processes involved are presented. Input values and some constants are derived from the NASA Baseline and Assumptions Document (BVAD) [5] and previous research. In order to bridge the gap between descriptions in these documents and a realistic system, a simulation is built. Random processes are also introduced in the model to refl ect the stochastic nature of some physical processes. Relationships among these physical processes are described by the mathematical formulations. These formulations are set up in such a way that the conditions of the system can be assessed at the beginning of every hour. Output values are obtained from the simulation and are described in Part Two of the study (companion paper). Some benefi ts from this study include enhancing understanding of controlling physical systems as well as providing formulations that could serve as a base for further developments of other resource supply-and-demand models for closed-loop systems.

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Development of theoretical approaches to the control of a water-treatment system

Chiam

Yih

Mitchell

2010

Advances in Space Research

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Impact of Crewmember Schedule on System Performance

Cited by 5 publications

References 9 publications

Control Policies for a Water-Treatment System Using the Markov Decision Process. Part 2: Simulation and Analysis

Control Policies for a Water-Treatment System Using the Markov Decision Process. Part 2: Simulation and Analysis

Control Policies for a Water-Treatment System Using the Markov Decision Process. Part 1: Mathematical Formulation

Development of theoretical approaches to the control of a water-treatment system

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