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Dynamic simulation of the Lunar Outpost habitat life support was undertaken to investigate the impact of Extravehicular Activity (EVA). The preparatory static analysis and some supporting data are reported in another paper. (Jones, 2008-01-2184) Dynamic simulation is useful in understanding systems interactions, buffer needs, control approaches, and responses to failures and changes. A simulation of the Lunar outpost habitat life support was developed in MATLAB/Simulink™. The simulation is modular and reconfigurable, and the components are reusable to model other physicochemical (P/C) based recycling systems. EVA impacts the Lunar Outpost life support system design by requiring a significant increase in the direct supply mass of oxygen and water and by reducing the net mass savings of using dehydrated food. The mass cost of EVA depends on the amount and difficulty of the EVA scheduled.

Dynamic simulation of the lunar outpost habitat life support was undertaken to investigate the impact of life support failures and to investigate possible responses. Some preparatory static analysis for the Lunar Outpost life support model, an earlier version of the model, and an investigation into the impact of Extravehicular Activity (EVA) were reported previously. (Jones, 2008-01-2184, 2008-01-2017) The earlier model was modified to include possible resupply delays, power failures, recycling system failures, and atmosphere and other material storage failures. Most failures impact the lunar outpost water balance and can be mitigated by reducing water usage. Food solids and nitrogen can be obtained only by resupply from Earth. The most time urgent failure is a loss of carbon dioxide removal capability. Life support failures might be survivable if effective operational solutions are provided in the system design.

The purpose of dynamic modeling and simulation of Advanced Life Support (ALS) systems is to help design them. Static steady state systems analysis provides basic information and is necessary to guide dynamic modeling, but static analysis is not sufficient to design and compare systems. ALS systems must respond to external input variations and internal off-nominal behavior. Buffer sizing, resupply scheduling, failure response, and control system design are aspects of dynamic system design. We develop two dynamic mass flow models and use them in simulations to evaluate systems issues, optimize designs, and make system design trades. One model is of nitrogen leakage in the space station, the other is of a waste processor failure in a regenerative life support system. Most systems analyses are concerned with optimizing the cost/benefit of a system at its nominal steady-state operating point. ALS analysis must go beyond the static steady state to include dynamic system design.

System failures, dynamics, and nonlinearities can cause unacceptable performance and damaging instability in Advanced Life Support (ALS) systems. Much current ALS modeling assumes that ALS systems are linear, static, and failure-free. But in reality most ALS hardware is subject to failure, real ALS systems are dynamic, and many ALS processors are nonlinear beyond a limited operating range. Modeling and simulation are needed to study the stability and time behavior of nonlinear dynamic ALS systems with failures and to develop appropriate controls. The nonlinear dynamics of ALS systems has many interesting potential consequences. Different equilibrium points may be reached for different initial conditions. The system stability can depend on the exact system inputs and initial conditions. The system may oscillate or even in rare cases behave chaotically. Temporary internal hardware failures or external perturbations can lead to dynamic instability and total ALS system failure.

New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models were developed to simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allowed only changes in light energy and used a less accurate linear approximation. For constant nominal environmental conditions, the simulation outputs of the new MEC models are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have more realistic exponential canopy growth, and have corrected harvest dates for potato and tomato.

To facilitate analysis, Advanced Life Support (ALS) systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. This paper reviews nonlinear models applicable to ALS. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis.