Results for “Biomass pre-processing system”

NASA NTRS2000

A Simulation Study Comparing Incineration and Composting in a Mars-Based Advanced Life Support System

The objective of this study is to compare incineration and composting in a Mars-based advanced life support (ALS) system. The variables explored include waste pre-processing requirements, reactor sizing and buffer capacities. The study incorporates detailed mathematical models of biomass production and waste processing into an existing dynamic ALS system model. The ALS system and incineration models (written in MATLAB/SIMULINK(c)) were developed at the NASA Ames Research Center. The composting process is modeled using first order kinetics, with different degradation rates for individual waste components (carbohydrates, proteins, fats, cellulose and lignin). The biomass waste streams are generated using modified "Eneray Cascade" crop models, which use light- and dark-cycle temperatures, irradiance, photoperiod, [CO2], planting density, and relative humidity as model inputs. The study also includes an evaluation of equivalent system mass (ESM).

Hogan, John↗

NASA NTRSDate not supplied

Fostering Open Science Inclusiveness for Interdisciplinary Users of Earth Observations

The term Open Science is subject to a variety of interpretations because of a key (and useful) ambiguity in the meaning of “Open”. Open in the sense of Transparency enables more trust in science research by making the details of the scientific process visible and accessible to anyone. “Open” in the sense of Inclusiveness enables more scientists from other disciplines to participate in research in a given discipline, thus producing more interdisciplinary research. Data Systems can play a major role in enabling Open (Inclusive) Science by making it easier for users from other disciplines to work with data within a given discipline. This is challenging for Earth Observation datasets, most of which are the product of advanced instrumentation and sophisticated, specialized variable retrieval algorithms and code. Serving the “extra-disciplinary”communities begins with simple things, like accessible, readable data documentation with adequate scaffolding. But just as important is provisioning Analysis-Ready data that does not require expert pre-processing. Disciplines also often have dominant toolsets, such as R in the biomass community or GIS in many applications communities. Ensuring that EO data are easy to use in the tools favored in other communities will enable more interdisciplinary research. Ideally, interdisciplinary research also benefits from scientists with different domain expertise. Platforms and frameworks that facilitate frictionless collaboration with discipline experts, together with capacity building efforts in those external disciplines also improve the inclusiveness aspect of Open Science. In short, Open Science is at root a way of thinking about how users from diverse discipline can best access and use data and services from a particular discipline.

Christopher Lynnes↗