Lockheed Martin Overview of the AFRL EXPEDITE Program: Power and Thermal Management System
Bibliographic record
Abstract
The Skunk Works has a long history of aircraft design with a very broad portfolio of products and technologies. As aerospace development programs have become more complex and demanding, the need for multi-disciplinary integration has become and essential facet of aerospace engineering. The Skunk Works began our major investment in MDO with our Rapid Conceptual Design (RCD) effort in 1997. While Lockheed Martin had been working with the Air Force Research Laboratory’s (AFRL) Multidisciplinary Science & Technology Center (MSTC), which is part of the Aerospace Systems Directorate, on other efforts for many years our latest MDO studies ramped up in 2011 with the award of the ESAVE program which expanded MDO for fighter design. In April of 2017 AFRL’s MSTC issued a Broad Area Announcement (BAA) for the Expanded Multidisciplinary Design Optimization (MDO) for Effectiveness Based Design Technologies (EXPEDITE) program. The EXPEDITE program seeks to advance MDO technologies in: state- based modeling, effectiveness-based design, path dependency, transient operation of systems and subsystems, uncertainty quantification, utilizing high performance computing, and cost and reliability. In July 2017 Lockheed Martin Aeronautics Advanced Development Programs (ADP) was pleased to be selected as the winner of the competition for AFRL’s latest MDO program [1]. This paper will provide an overview of the Lockheed Martin (LM) PTMS design process and touch upon some early PTMS/FTMS sensitivity studies. The initial results represent a proof-of-concept, that with proper modifications/specificity, could be applied to solve real-world problems. The high cooling demands from this PTMS architecture demonstrate the vehicle level impacts and importance of a MADO process at the conceptual level, which is ultimately the goal of EXPEDITE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".