Naval Service Warfare Center (NSWC) Crane Office-Wide | CRANBAA20 0001

FAQs: NSWC Crane Office-Wide BAA (CRANBAA20 0001)

What is this funding opportunity?

This is the Naval Surface Warfare Center (NSWC) Crane Office-Wide research solicitation (Funding Opportunity Number CRANBAA20 0001). It is a Department of Defense discretionary research grant opportunity focused on advancing science and technology that directly supports Navy needs.

What is the CFDA number for this opportunity?

The opportunity is listed under CFDA 12.300.

Who is eligible to apply?

Eligibility is limited to U.S. public and state-controlled institutions of higher education and private institutions of higher education.

How many awards does NSWC Crane anticipate making?

The posting indicates an anticipated total of about four awards.

What is the maximum award amount?

The maximum award size (ceiling) is up to $2,000,000.

When was this opportunity created and when did it originally close?

The opportunity was created on February 3, 2020, and the original closing date was March 4, 2020.

What research areas are included in this solicitation?

The solicitation covers four distinct topic areas: (1) 3D modeling, simulation, and visualization for reliability analysis and corrective action planning; (2) radiation effects in quantum information technologies (including qubits and quantum photonics); (3) optically transparent coatings that provide electromagnetic interference (EMI) protection; and (4) spectrum machine learning for sensing and situational awareness in complex electromagnetic environments.

Topic 1: What is the focus of the 3D modeling, simulation, and visualization effort?

This topic seeks methods to predict component life and ultimately system life in realistic operational environments where mechanical loading and chemical/material aging occur together and may interact. It emphasizes approaches that combine physics-based and data-driven methods.

Topic 1: What kinds of methods are explicitly highlighted?

The solicitation specifically highlights finite element analysis, fatigue analysis, and deep learning as examples of methods of interest, particularly in combined physics-based and data-driven approaches.

Topic 1: What is expected in terms of communicating results to maintainers and operators?

A key deliverable aspect is field-usable communication of results: predicted failure sites or degradation hotspots should be presented using virtual reality and/or augmented reality visualization so maintainers and field operators can interpret outputs quickly and plan maintenance or corrective actions with more confidence.

Topic 1: What operational problem is this topic trying to solve?

The topic is aimed at improving reliability analysis and corrective action planning by producing more realistic life predictions that account for interacting stressors and by presenting actionable insights in a form usable by maintainers and field operators.

Topic 2: What is meant by radiation effects in quantum information technologies?

This topic calls for basic research to characterize and explain how various radiation types affect critical quantum components, including qubit architectures and integrated photonic structures used for generating and manipulating entangled photons.

Topic 2: Which qubit technologies are mentioned?

The description notes that multiple qubit implementations are under consideration, with specific mention of superconducting Josephson junction-based qubits and trapped-ion approaches.

Topic 2: What quantum photonic technologies are mentioned?

The solicitation highlights integrated photonic structures and chip-scale silicon photonics for quantum applications, especially as they relate to generating/manipulating entangled photons and measuring quantum correlations.

Topic 2: What radiation-related knowledge gaps is the Navy trying to address?

The opportunity notes that little is currently known about how radiation impacts performance, stability, error rates, or failure mechanisms in quantum devices, and seeks research that establishes underlying science and identifies vulnerabilities.

Topic 2: What types of radiation effects are of interest?

Research is encouraged on both short-term disruptive events (single event upsets) and cumulative long-term degradation (total ionizing dose), particularly as these effects influence entanglement generation and quantum correlation measurements.

Topic 2: What damage mechanisms are suggested for photonic components?

The description suggests potential mechanisms similar to those seen in optical fibers and semiconductors, including lattice damage and refractive index changes due to carrier generation and doping modification.

Topic 3: What is the goal of the transparent EMI-protection coating topic?

This topic seeks a highly optically transparent coating that also provides electromagnetic interference (EMI) protection, with performance targets that exceed common indium tin oxide (ITO) approaches.

Topic 3: What electrical performance is required?

The desired coating must achieve a sheet resistance under 20 ohms per square.

Topic 3: What optical transmission and reflectance targets are specified?

The coating must provide greater than 97% optical transmission and less than 1.5% reflectance across a wide spectral band from 400 nm to 1600 nm (visible through short-wave infrared).

Topic 3: What substrates must the coating be compatible with?

The coating must adhere to and be compatible with fused silica or n-BK7 glass.

Topic 3: Are both passive and active coating concepts allowed?

Yes. Both passive and active concepts are allowed.

Topic 3: If an approach requires power, how will it be considered?

If the approach requires power, it will be evaluated based on power usage and whether it fits within realistic platform constraints tied to aperture size and existing power draw.

Topic 3: How does this differ from standard ITO solutions?

The solicitation explicitly states the targets go beyond common ITO solutions, seeking alternatives that maintain conductivity for EMI shielding without sacrificing broadband optical throughput and low reflectance.

Topic 4: What is meant by spectrum machine learning in this solicitation?

This topic seeks machine learning methods that help spectrum sensing systems interpret complex, crowded electromagnetic environments faster and more effectively, improving situational awareness for operators across platforms such as aircraft, surface ships, and submarines.

Topic 4: What specific problem areas are called out?

The solicitation lists signal identification, signal separation, spectrum information compression, and anomaly detection as key problem areas of interest.

Topic 4: What does signal identification include?

Signal identification includes recognizing modulation, signal type, and subtle transmitter or channel signatures.

Topic 4: What does signal separation refer to?

Signal separation refers to disentangling overlapping signals that share time and frequency resources.

Topic 4: What is meant by spectrum information compression?

It refers to reducing storage and bandwidth needs while preserving the most important information, potentially far below Nyquist-rate raw recording requirements.

Topic 4: What does anomaly detection mean in this context?

Anomaly detection means finding previously unseen signals or detecting meaningful deviations of known signals.

Topic 4: Does the solicitation encourage multi-sensor approaches?

Yes. It explicitly invites approaches that fuse or coordinate multiple sensors, while also noting constraints on data sharing and communication overhead.

Topic 4: What practical constraints are emphasized for multi-sensor or distributed methods?

The solicitation notes that data sharing and communication overhead must be considered, implying interest in distributed, bandwidth-aware, or edge-processing approaches rather than assuming unlimited data movement.

Is this solicitation focused on basic research, applied research, or both?

Based on the topic descriptions, the solicitation includes basic research elements (for example, establishing underlying science for radiation effects in quantum systems) and also emphasizes operational relevance and field usability (for example, VR/AR visualization for maintenance decisions and practical constraints for deployed ML systems).

What is the common thread across all four topics?

The common theme is enabling technologies that can transition from research into tools and components that work under operational constraints, with attention to field usability, environmental stressors, and practical deployment considerations.