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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The objectives of this paper are to give a brief insight into the launch vehicle implications for a manned Mars expedition and follow-on exploration; and secondly, to show the effects of such a mission requirement on the launch vehicle system. Due to the complexity and intricacy of the overall mission, vehicle system, and their interrelationships, only the very broad and salient points will be discussed.
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This is the Appendix containing a description of the solution for Case 1 in the assessment, “Expansion of Check-Cases for 6DOF Simulation”. For cases of spherical gravity, it is possible to provide a two-body solution without recourse to numerical integration and thus it is accurate to machine precision. Python code for a Keplerian Propagator (propagate.py) which produced a reference trajectory for Case 1 is provided in this appendix. There is also code for generating test cases which was used as an independent verification of the propagator. This is a high-level description of the algorithm employed. The documentation of each function includes implementation details, including equations for each task.
It is argued that sophisticated battery control systems are required to support the high power, high energy spacecraft secondary battery systems of the post 1985 time period. Four categories of battery control system functions are defined and discussed: battery operational control, auxiliary system control, battery system status indication and fault detection fault isolation. A concept for implementation of such a control system is also presented and discussed.
Supercapacitors are widely recognized as a favorable option for energy storage due to their higher power density compared to batteries, despite their lower energy density.
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Nuclear Electric Propulsion (NEP) has been considered for crew and cargo class missions to Mars for more than six decades. The Primary Heat Rejection Subsystem (PHRS) of an NEP vehicle expels the megawatts of waste heat generated by the Power Conversion Subsystem (PCS). In previous studies, integrating the majority of the NEP vehicle within a single launch fairing required the use of complex deployable elements and resulted in reduced PCS efficiency. To achieve the necessary radiating area, past design studies have utilized massive multi-wing folding radiators resulting in conceptual PHRS designs that have been widely acknowledged as non-optimal. The Modular Assembled Radiators for NEP VehicLes (MARVL) project is incorporating in-Space Assembly (iSA) into the design process to realize lightweight, modular, and more capable PHRS radiators. Since the project’s Authority to Proceed (ATP) date of October 2024, the project team has been developing a modular design for the PHRS radiators that will be suitable for both the Mars mission environments and iSA commissioning operations. This document captures the current progress of the incremental technology development for the thermal components of the MARVL System of Interest (SOI).
This work focuses on the development and testing of a low-cost wireless communication system for heliostat fields, enabling significant capital costs reductions for concentrating solar thermal systems. Outputs of this work include a working demonstration of a multi-node communication system, clear reporting of system performance, and technical documentation of system development and architecture for reproducibility. Through this process, an open-source repository was created for manufacturing hardware at ~$30/heliostat. The system includes software for cybersecurity, achieving sub-second communication latencies and derisking of hardware for eventual scale-up to tens of thousands of heliostats. While the system is not currently off-the-shelf ready, there is now a clearly defined pathway for scaling up and completing the commercial development process.
Experimental and computational fluid dynamic activities in rocket propulsion were discussed. The workshop was an open meeting of government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.
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High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.
We propose a universal solid electrolyte design that broadens the selection of ceramic LICs for solid-state lithium metal batteries, without requirements of electronic insulation or (electro)chemical stability.
PAL 2.0 provides an efficient discovery tool for advanced functional materials, ameliorating a major bottleneck to enabling advances in next-generation energy, health, and sustainability technologies.
This document consists of presentation slides that review the current status of data mining to support the work with the Integrated Systems Health Management (ISHM) for the systems associated with Liquid Rocket Propulsion. The aim of this project is to have test stand data from Rocketdyne to design algorithms that will aid in the early detection of impending failures during operation. These methods will be extended and improved for future platforms (i.e., CEV/CLV).
We introduce HANNA, the first hybrid neural network model that strictly complies with all thermodynamic consistency criteria for predicting activity coefficients and outperforms current benchmark methods in terms of accuracy and applicability.