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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 NREL Hydrogen sensor laboratory aims to ensure that hydrogen sensor technology is available to meet end-user needs and to foster the proper use of sensors by advancing next generation sensing and analysis techniques, supporting codes and standards development, and improving component reliability systems.
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This final technical report documents the work performed under DOE Award DE‑EE0008799 by GTI Energy over the course of the project period. The project focused on advanced fueling methods for compressed natural gas (CNG) vehicles which have historically suffered from underfilling.
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Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.
High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.
The Stability Graph is a widely used tool for the design of open stopes in underground mining. Many users of the Stability Graph still apply this design method manually. Although the manual approach has benefits, using multiple graphs and stability number computation charts for each stope surface is time-consuming, even for the experienced mining engineer. Current practice in the use of the method also limits data sharing. This paper presents a StopeSoft web-based tool for open stope stability prediction that is developed on the basis of the Stability Graph method and is available at openstope.com. StopeSoft incorporates flexibility in terms of Stability Graph options and incorporates additional critical factors often overlooked. As a web-based tool, StopeSoft encourages and makes data sharing possible globally, focused on expanding the database and improving the current limitations of the Stability Graph to provide practical, reliable solutions for mining engineers, consultants, and academics. The StopeSoft automated process facilitates the process of open stope stability prediction, saving time and minimizing potential human errors. Statistical treatment of the data accounts for the variability of input parameters to emphasize the probabilistic nature of the Stability Graph method. The probabilistic interpretation of the stability states of stope surfaces eliminates the false feeling of absolute stope performance based on its location on the Stability Graph , as implied by the deterministic approach.
This presentation provides an overview of artificial intelligence (AI) applications in hydropower.
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An example method involves receiving, at a first memory node, data to be written at a memory location in the first memory node. The data is received from a device. At the first memory node, old data is read from the memory location, without sending the old data to the device. The data is written to the memory location. The data and the old data are sent from the first memory node to a second memory node to store parity information in the second memory node without the device determining the parity information. The parity information is based on the data stored in the first memory node.
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According to one embodiment, a method includes receiving light on a photoconductive layer of an electrophoretic deposition (EPD) device, the EPD device having a chamber defined by a first sheet, a second sheet and a spacer between the first and second sheets, where the first sheet is nonopaque and includes the photoconductive layer, where the second sheet is nonopaque and spaced from the first sheet, where a fluidic solution having a plurality of particles is in the chamber. The particles in the solution are attracted from suspension to illuminated portions of the photoconductive layer in the absence of an external voltage applied to the first and second sheets. The particles become deposited on the illuminated portions of the photoconductive layer.
Various examples are provided for gas leakage detection from geologic storage sites. In one example, a system for detection of gas leakage from a geologic storage site includes permanent down-hole gauges (PDGs) disposed at different depths within a well and an evaluation system that can determine a three-dimensional (3D) location of the gas leakage from the geologic storage site and a leakage indicator. The 3D location and leakage indicator can be determined using pressure data provided by the PDGs. The geologic storage site can store, e.g., CO2, natural gas or other type of gas. In another example, a method for detecting gas leakage from a geologic storage site includes receiving pressure data provided by PDGs disposed within one or more wells associated with the geologic storage site, determining key performance indicators using the pressure data and determining a 3D location and a leakage indicator using the key performance indicators.
The progressive integration of renewable energy resources in the modern power grid can result in unsatisfactory frequency responses. To address this problem, wind turbine generators (WTGs), in particular, can be employed to support the power grid. Most works, however, only consider a simple step disturbance. In this paper, a new class of worst-case disturbances is introduced and their nefarious impact on renewable-penetrated power systems is investigated. The worst-case disturbances are derived using optimal control theory. More importantly, the proposed mechanism allows to determine the reaction time to trigger supportive control actions of WTGs to ensure satisfactory frequency response. Numerical results are provided and the effects of the worst-case disturbance on the reaction time are presented.
This paper is concerned about phasor-based linear state estimation of three-phase power grids which contain non transposed lines and unsymmetrical branches. It builds upon our previous work where the linear measurement equations were decoupled into three independent sets via the symmetrical component transformation. Such a transformation may however not fully decouple the measurement equations if there are nontransposed transmission lines and unsymmetrical branches in the system. This paper addresses this limitation by deriving additive terms that compensate for the remaining coupling between sequence measurements after the application of the conventional transformation. It will be shown that general unbalanced threephase power grids can still be transformed into three decoupled subsystems and their state estimation solution can be obtained in parallel by the proposed approach.
The building envelope has traditionally been seen as a static component. Much of the past thermal advancements in building envelopes has consisted of developing higher R/inch insulation. While a suitable approach for static situations, it does not consider the dynamic nature of the ambient environment or the varying needs of the electrical grid. This paper will examine three possible ways that building envelopes can be actively managed: 1. thermal switches, 2. thermal storage, and 3. the coupling of the two. Spurring innovation to make building envelopes smarter will help reduce building energy consumption and peak energy usage and contribute to flexibility in energy demand in the future.