Description
NSWC Crane is interested in receiving research proposals in the following areas: a. 3-Dimensional Modeling, Simulation, and Visualization for Identifying Reliability Drivers and Aiding in Corrective Actions Naval Surface Warfare Center, Crane Division is interested in receiving proposals for the development of 3-D models and simulation approaches, which incorporate finite element analysis, fatigue analysis, and/or deep learning methods, to identify component, and ultimately system, lifetimes. These lifetime predictions should be based on the combination of mechanical and chemical aging of materials in operational environments. They should also encompass interactive materials effects. Lastly, the predicted failures sites within a component or system should be presented via a virtual and/or augmented reality visualization techniques. These visualization mechanisms are intended to allow field operators to analyze the results in an intuitive way and implement maintenance schedules and/or corrective actions. b. Investigating Radiation Effects in Quantum Information Technologies Naval Surface Warfare Center Crane Division is interested in receiving proposals for the investigation of radiation effects in critical components used in quantum information systems. For quantum information technologies to be effectively implemented in real world conditions beyond the lab, susceptibility to various forms of radiation must be characterized. To date, little is known regarding the impacts of various forms of radiation on these systems and whether these concepts are even capable of enabling improved operations in a high radiation environment. Robust efforts, therefore, are required to develop the scientific understanding of radiation effects in qubit architectures, as well as the integrated photonic structures that are used to generate and manipulate entangled photons. Qubit architectures that will be implemented in future quantum computers are still under intense investigation. There are many physical implementations of qubits. Several implementations, however, are gaining popularity for use in large-scale quantum computation. Those include, but not limited to, superconducting Josephson junctions (JJs), supercooled trapped ions, semiconductor-based quantum dots, nitrogen vacancy centers in diamond and silicon carbide divacancies. Although research into qubit architectures is still ongoing, now is the time to understand potential vulnerabilities caused by radiation exposure. Thus, providing timely technical leadership that will enable the R&D community to undertake efforts to improve qubit resiliency in such environments. In addition to the development of qubit architectures utilizing low-temperature physical phenomenon, researchers have begun realizing chip-scale approaches for generating photon entanglement utilizing integrate silicon photonics. Despite major advancements, little is known about the susceptibility of these chip-based photonic quantum systems to radiation such as gamma rays and high-energy electrons. As is the case with optical fibers, ionizing radiation can damage the semiconductor’s crystalline lattice, as well as change the index of refraction by modifying the doping concentration via generation of free carriers. Further basic research is required to understand susceptibility of chip-based photon entanglement and quantum correlation measurements to radiation induced single event upsets, as well as long-term exposure (total ionizing dose). NSWC Crane is interested in basic research efforts that advance scientific understanding of radiation susceptibility in various qubit architectures, as well as integrated photonic platforms. c. High optically transparent coating for electromagnetic interference (EMI) protection Naval Surface Warfare Center, Crane Division is interested in receiving proposals for the development of a transparent optical coating capable of providing electromagnetic interference (EMI) protection. To achieve this EMI level of protection the coating needs to have a sheet resistance of 97% from 400 nm to 1600 nm and a reflectance <1.5% on the same wavelength range. The solution needs to adhere and be compatible with either fused silica or n-BK7 glass. Solution may be either passive or active (i.e. needs power); however, active solutions will be evaluated based on power usage comparable to the platform’s aperture and existing power draw. d. Spectrum Machine Learning The Navy deploys multiple spectrum sensing systems continuously on air, sea, and submarine assets. There is a desire to use machine learning techniques to allow these sensors to more quickly and efficiently understand the signals they receive in order for sailors to maintain a better awareness of their environment. Machine Learning has proven itself in other domains such as image processing, audio processing, and text processing as a powerful tool for pattern recognition, anomaly detection and other applications. Specific research proposals in the following areas would begin to develop the underlying technology needed for the Navy to address the complicated spectrum environment expected in the future: -Signal identification: modulation, type, fine details in physical transmitter or channel -Signal separation: ability to separate signals that are coincident with each other in time and frequency to achieve reception of both -Spectrum information compression: compression of information in the spectrum to retain salient properties using significantly less storage than required for a Nyquist sampled record -Anomaly detection: finding signals new to an environment or similar to existing signals in the environment but slightly modified -The use of multiple sensors to accomplish any of the above: the amount of information needing to be shared must be considered