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David Mauro

Publications and source records attributed to David Mauro.

7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

2020 I.F._Wickizer_New CE Tool for MDC_Final Report

Our team surveyed available products and found no readily-available/U.S. commercial or Agency products which supported our envisioned workflow for the Mission Design Center and the concurrent engineering (CE) process we use. Therefore, we sought to create our own CE tool. Changes in agency policy during the performance period allowed us to shift direction and instead adapt our previous legacy tool (Atlas) to include the collaboration features we sorely needed. Atlas relied upon databases for its back-end data management. Poseidon, the work proposed under this effort, was also intended to leverage from that previous back end (in part). However, in May 2021, EUSO announced that “SBU/CUI Information Can Now Be Shared/Stored Within O365 Without Encryption.” Consequently, we made the decision to use O365 in place of our back end. This change provides for version history, collaborative simultaneous editing, and other collaboration tools agency-wide that were previously unavailable with the old database architecture. The new Atlas O365 has more flexibility and capability for users. It’s now easier for users to switch between using the tool for concurrent engineering and individual subsystem engineering. A version was delivered for concurrent engineering of small satellite missions; so far, it has been used successfully on the Aeolus MDC study. Atlas O365 will facilitate the design and assessment of Small Satellite Missions at low Concept Maturity Level at Ames. Feasibility assessments on mission concepts still at a low CML permit strategic planning and decision-making efforts at the center level about which concepts should be pursued and proposed.

concurrent engineering↗
NASA NTRSDate not supplied

A New Concurrent Engineering Tool for the New Mission Design Center at NASA Ames Research Center

The NASA Ames Mission Design Center (MDC) has undergone a significant transformation process in recent years, culminating in becoming a true Concurrent Engineering Center. The underlying goal of this transformation is to better serve the Ames PI community in a cost-effective and rapidly-responsive way, providing quality products pivotal to the decision-making proposal strategy of our center. The four pillars of the change focus on four areas: personnel, physical places, tools, and training. Personnel: the MDC switched from using a dedicated pool of engineers—mostly entry-level career—for a matrixed approach with more experienced Subject Matter Experts, usually involved in spaceflight projects. These SMEs get detailed to the MDC for the duration of the study on a part-time or full-time basis. Physical places: The physical aspect pertains mostly to the layout of the new Engineering building and the dedicated concurrent engineering layout to foster collaboration. The current plan includes a dedicated space for concurrent engineering sessions as well as dedicated project rooms for concept studies. Tool: the MDC team is developing a new in-house concurrent engineering tool to facilitate concurrent engineering sessions. The team leveraged the many lessons learned and insight gained from using the current concurrent engineering tool, in use since 2007. SMEs have been involved throughout its design and development, ensuring accuracy and providing validity to the implementation approach. This new tool is database-driven and allows a team to work concurrently on the same model of a mission concept. “Workspaces” are provided for each of the traditional spacecraft mission design disciplines, including cost accounting and systems engineering. There are multiple tiers of fidelity available within each workspace, which can be completed parametrically or independently from the other disciplines as needed or synchronously with the other disciplines. A “commit” step synchronizes a workspace with the mission database and identifies any potential conflicts, along with the user who entered the conflicting data. A Mission Summary workspace enables a Study Lead to run an effective CE session, displaying key graphics, workspace status information, and technical and monetary budget information. This new CE tool will be undergoing Beta Testing with its primary user base and a limited Parts database in the summer of 2020. The team plans to release the first fully operational version in December 2020. Training: working in early concept development and maturation for a space mission requires a unique mindset and being comfortable with uncertainties in an environment where requirements are not yet fully developed, and where changes and trade-offs happen at a high tempo. The MDC started a weekly training program to ensure a common knowledge base on how do develop and mature early concept studies, as well as how to operate in a concurrent engineering environment. In conclusion, the MDC transformation will provide the overall PI community focused on cost-effective small satellite scientific missions with a powerful tool to address and mature early concepts more effectively and efficiently.

David Mauro↗
NASA NTRSDate not supplied

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗