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Environmental control and life support systems are usually associated with high demands for performance robustness and cost efficiency. However, considering the complexity of such systems, determining the balance between those two design factors is nontrivial for even the simplest space missions. Redundant design is considered as a design optimization dilemma since it usually means higher system reliability as well as system cost. Two coupled fundamental questions need to be answered. First, to achieve certain level of system reliability, what is the corresponding system cost? Secondly, given a budget to improve system reliability, what is the most efficient design for component or subsystem redundancy? The proposed analysis will continue from previous work performed on series systems by expanding the scope of the analysis and testing parallel systems. Namely, the online and offline redundancy designs for a Lunar Outpost Mission are under consideration.

A reconfigurable control system is an intelligent control system that detects faults within the system and adjusts its performance automatically to avoid mission failure, save lives, and reduce system maintenance costs. The concept was first successfully demonstrated by NASA between December 1989 and March 1990 on the F-15 flight control system (SRFCS), where software was integrated into the aircraft's digital flight control system to compensate for component loss by reconfiguring the remaining control loop. This was later adopted in the Boeing X-33. Other applications include modular robotics, reconfigurable computing structure, and reconfigurable helicopters. The motivation of this work is to test such control system designs for future long term space missions, more explicitly, the automation of life support systems.

High costs and extreme risks prevent the life testing of NASA hardware. These unavoidable limitations prevent the determination of sound reliability bounds for NASA hardware; thus the true risk assumed in future missions is unclear. A simulation infrastructure for determining these risks is developed in a configurable format here. Positive preliminary results in preparation for validation testing are reported. A stochastic filter simulates non-deterministic output from the various unit processes. A maintenance and repair module has been implemented with several levels of complexity. Two life testing approaches have been proposed for use in future model validation.

BioSim is a simulation tool which captures many basic life support functions in an integrated simulation. Conventional analyses can not efficiently consider all possible life support system configurations. Heuristic approaches are a possible alternative. In an effort to demonstrate efficacy, a validating experiment was designed to compare the configurational optima discovered by heuristic approaches and an analytical approach. Thus far, it is clear that a genetic algorithm finds reasonable optima, although an improved fitness function is required. Further, despite a tight analytical fit to data, optimization produces disparate results which will require further validation.

An ongoing effort is underway at NASA Ames Research Center (ARC) to develop an On-line Project Information System (OPIS) for the Advanced Life Support (ALS) Program. The objective of this three-year project is to develop, test, revise and deploy OPIS to enhance the quality of decision-making metrics and attainment of Program goals through improved knowledge sharing. OPIS will centrally locate detailed project information solicited from investigators on an annual basis and make it readily accessible by the ALS Community via a Web-accessible interface. The data will be stored in an object-oriented relational database (created in MySQL®) located on a secure server at NASA ARC. OPIS will simultaneously serve several functions, including being an research and technology development (R&TD) status information hub that can potentially serve as the primary annual reporting mechanism for ALS-funded projects.

The ALS Metric is the predominant tool for predicting the cost of ALS systems. Metric goals for the ALS Program are daunting, requiring a threefold increase in the ALS Metric by 2010. Compounding the problem is the slow rate new ALS technologies reach the maturity required for consideration in the ALS Metric and the slow rate at which new configurations are developed. This limits the search space and potentially gives the impression of a stalled research and development program. Without significant increases in the state of the art of ALS technology, the ALS goals involving the Metric may remain elusive. A paper previously presented at his meeting entitled, “Managing to the metric: An approach to optimizing life support costs.” A conclusion of that paper was that the largest contributors to the ALS Metric should be targeted by ALS researchers and management for maximum metric reductions.

The handling of uncertain data is demonstrated on an empirical grading function used for the assessment of tomato quality. The grading function studied here is designed to measure the departure of the properties of a tomato or a population of tomatoes from an assumed optimal tomato. Uncertain data are considered using the Taylor Series expansion of the grading function, a function of random variables, which provides the ability to determine the variability of the outcome of the function. Once this variability is quantified, confidence intervals are determined and considered. The degree of confidence in a result has a wide array of ramifications, ranging from providing valuable decision support to assisting in guidance of research activity.

BIO-Plex is a ground-based test bed currently under development by NASA for testing technologies and practices that may be utilized in future long-term life support missions. All aspects of such an Advanced Life Support (ALS) System must be considered to confidently construct a reliable system, which will not only allow the crew to survive in harsh environments, but allow the crew time to perform meaningful research. Effective handling of solid wastes is a critical aspect of the system, especially when recovery of resources contained in the waste is required. This is particularly important for ALS Systems configurations that include a Biomass Production Chamber. In these cases, significant amounts of inedible biomass waste may be produced, which can ultimately serve as a repository of necessary resources for sustaining life, notably carbon, water, and plant nutrients. Numerous biological and physicochemical solid waste processing options have been considered.

Metric analysis of research activity and technology development has become one of the deciding factors in whether or not the research of potential technologies receives the needed funding or a technology is incorporated into a system. It is difficult to accurately predict the configuration of an ALS system that will transport humans to the surface of Mars and support surface exploration. Determining which ALS research activities will support this effort is a very discretionary process, and there simply is not enough information to accurately make these types of decisions. Requirements change as research develops, and it is very difficult to create a metric that can accurately assess a potential or ongoing research project. The SSM team of the NJ-NSCORT has developed an internet platform to perform the assessment of potential technologies for the purpose of the development of an ALS system. The platform is called IFA and it has completed validation with current NJ-NSCORT projects.

Accurate, flexible, and dynamic mathematical computer modeling tools are required for the development and parametric evaluation of the various possible Advanced Life Support Systems (ALSS) configurations. Such models are expected to take a top-level approach to maximize modularity, flexibility in development, and user/developer friendliness. Two approaches are taken for modeling the WPRR component of an ALSS: an object oriented approach coded in Java and a SIMULINK model. Each model exhibits distinct properties for the interchange of technologies, startup requirements, and hardware/software requirements, but are anticipated to provide successful ALSS models.

As ALS goals branch out to extended missions to the moon and Mars, concurrent science and engineering projects take center stage in the development of new ALS technology. It is necessary to optimize the interdisciplinary research activities in order to ensure ALS research goals are met in a timely manner, and to guarantee the reliability of future long term missions. The SSM team of the NJ-NSCORT has developed an internet software platform capable of performing a systems level analysis of the ALS research activity. The information produced by the analysis can assist ALS researchers in the streamlining of research activity.