Additive Manufacturing and Testing of High Metal Content High Performance Ramjet Grains

NPS NRP Project PosterFuels with high efficiency and energy densities are needed to maximize the range and speed of future air-breathing systems such as solid fuel ramjets (SFRJ). The performance of the fuel mixtures that include large amount of metal additives suffer due to the poor combustion efficiency of the metal powders as they often do not burn completely during the short residence time in the combustor. Recent research has improved the reactivity of these fuels, but introducing them into a binder at high loading densities is a challenge due to the poor rheology. In order to develop and maximize the energy density and performance of SFJR fuel grains, advancements in additive manufacturing (AM) systems will be leveraged. This study will utilize vibration-assisted printing (VAP) and liquid metal printing (LMP) with the Xerox ElemX system to print fuel grains with metal powders and aluminum alloys, and use spray dried nanocomposite mesoparticles as additives. The research will test the physical limits of these approaches and determine optimal printing parameters for producing high quality printed fuels. The fuels will be evaluated mechanically and optimized using fly out calculations and they will be characterized with small scale combustion studies.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Additive Manufacturing and Testing of High Metal Content High Performance Ramjet Grains

NPS NRP Executive SummaryFuels with high efficiency and energy densities are needed to maximize the range and speed of future air-breathing systems such as solid fuel ramjets (SFRJ). The performance of the fuel mixtures that include large amount of metal additives suffer due to the poor combustion efficiency of the metal powders as they often do not burn completely during the short residence time in the combustor. Recent research has improved the reactivity of these fuels, but introducing them into a binder at high loading densities is a challenge due to the poor rheology. In order to develop and maximize the energy density and performance of SFJR fuel grains, advancements in additive manufacturing (AM) systems will be leveraged. This study will utilize vibration-assisted printing (VAP) and liquid metal printing (LMP) with the Xerox ElemX system to print fuel grains with metal powders and aluminum alloys, and use spray dried nanocomposite mesoparticles as additives. The research will test the physical limits of these approaches and determine optimal printing parameters for producing high quality printed fuels. The fuels will be evaluated mechanically and optimized using fly out calculations and they will be characterized with small scale combustion studies.Naval Air Warfare Center Weapons Division (NAWCWD)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Additive Manufacturing of High Solids Loading Hybrid Rocket Fuel Grains

Hybrid rocket motors offer many of the benefits of both liquid and solid rocket systems. Like liquid engines, hybrid rocket motors are able to be throttled, can be stopped and restarted, and are safer than solid rocket motors since the fuel and oxidizer are in different physical states. Hybrid rocket motors are similar to solid motors in that they are relatively simple and have a high density-specific impulse. One of the major drawbacks of hybrid rocket motors is a slower burning rate than solid rocket motors. Complex port geometries provide greater burning surface area to compensate for lower burning rates but are difficult and expensive to manufacture. Additive manufacturing can reduce manufacturing costs of these complex port geometry fuel grains. It has also been shown that the addition of energetic materials, such as aluminum, can increase the burning rate and density-specific impulse of the rocket motor. Previously, additive manufacturing was restricted to plastics or fast-setting paraffin wax, both with low solids concentrations. This paper investigates the process of printing hybrid rocket fuel grains and the differences in physical characteristics between printed and conventionally cast samples. Using a proprietary printing system, we have successfully printed 85% solids loading aluminum and HTPB fuel samples. Material creep was significant and resulted in samples bulging and sagging as well as gaps between print lines being filled in more completely. The finish and cross sections of printed samples were of comparable quality to cast samples. This indicates that the manufacturing process has not significantly affected the physical characteristics of the fuel samples

Artificial intelligence assisted patient blood and urine droplet pattern analysis for non‑invasive and accurate diagnosis of bladder cancer

Bladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects.The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity.Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer

Multi-messenger searches via IceCube’s high-energy neutrinos and gravitational-wave detections of LIGO/Virgo

We summarize initial results for high-energy neutrino counterpart searches coinciding with gravitational-wave events in LIGO/Virgo\u27s GWTC-2 catalog using IceCube\u27s neutrino triggers. We did not find any statistically significant high-energy neutrino counterpart and derived upper limits on the time-integrated neutrino emission on Earth as well as the isotropic equivalent energy emitted in high-energy neutrinos for each event