Disclosed are systems and methods for workload scheduling in compute clusters using coolant health monitoring to optimize performance. In-situ sensors measure coolant properties in liquid cooling loops of compute nodes. A processing unit analyzes sensor data to determine coolant health levels and reallocates workloads from nodes with degraded coolant to nodes with higher coolant health levels, preempting thermal failures. A machine learning model processes coolant sensor data and performance metrics to generate cooling efficiency scores for each node. A cluster management module dynamically distributes computational tasks based on cooling system assessments, optimizing cluster efficiency and maintaining performance.

A fuel tank system is disclosed. The fuel tank system includes a fuel tank and a detection system associated with the fuel tank. The detection system includes a source device emitting light into the fuel tank and an analysis device. The analysis device includes a collection device capturing scattered light resulting from the emitted light interacting with fuel in the fuel tank and providing an optical signal corresponding to the captured scattered light. The analysis device includes an optical detection device converting the optical signal to an electronic signal representative of properties of the captured scattered light and a computing device. In one or more embodiments, the computing device generates, based on the electronic signal, data including an indication of: a presence or absence of one or more contaminants in the fuel tank, a type of the one or more contaminants, and location information of the fuel and contaminants.