A Computer Architecture for the Automatic Design of Modular Systems With Application to Photovoltaic Reverse Osmosis
Bibliographic record
Abstract
Systems such as electronics, cars, computers, and robots are assembled from modular components for specific applications. Photovoltaic reverse osmosis (PVRO) systems, which can be custom-tailored for the water demands and solar properties of particular communities, are an important potential application of modular systems. Clearly, to be financially viable, such systems must be assembled from commercially available components and subsystems (modules). Designing a system from modular components for a specific application is not simple. Even for a relatively small inventory of modular components, the number of possible system configurations that exist is extremely large. For a small community, determining the best system configuration is an overwhelming task due to lack of expertise. This paper presents a modular design architecture that can be implemented on a laptop so nonexperts can configure systems from modular components. The method uses a hierarchy of filters, which can be provided from an expert system, to limit the large design space. Optimization methods and detailed models are then used to configure the location-specific system from the reduced design space. The method is applied here to community-scale PVRO systems and example cases demonstrate the effectiveness of the approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".