Communication system for a manned orbital research laboratory.
Communication system with ground support equipment providing data from MORL noting transmitters, receivers, antennas, etc
SPACE COMMUNICATION↗
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Results for “MORL”
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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Report on the development of the Manned Orbital Research Laboratory /MORL/ SYSTEM utilization potential. Task Area I - Analysis of space related objectives, book 2
Data bank summaries of proposed MORL experiments in space sciences, earth-oriented applications, and space travel support
MANNED ORBITAL RESEARCH LABORATORY /MORL/↗
Space station mission simulation mathematical model Summary report
Space station mission simulation computerized mathematical model for analyzing and implementing MORL system or similar programs
MANNED ORBITAL RESEARCH LABORATORY /MORL/↗
System reliability analysis of the manned orbiting research laboratory.
MORL system reliability analysis, discussing Monte Carlo simulation, major parameters, constraints, program contingencies, etc
RELIABILITY↗
Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators
Abstract Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraints on energy requirements of the beam. Using historical accelerator data, we develop a physics-based surrogate model which is differentiable and allows for back-propagation of gradients. The results are evaluated in the form of a Pareto-front with two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.
43 PARTICLE ACCELERATORS↗