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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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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Reconfigurable acquisition system with integrated optics for a portable flow cytometer

Portable and inexpensive scientific instruments that are capable of performing point of care diagnostics are needed for applications such as disease detection and diagnosis in resource-poor settings, for water quality and food supply monitoring, and for biosurveillance activities in autonomous vehicles. In this paper, we describe the development of a compact flow cytometer built from three separate, customizable, and interchangeable modules. The instrument as configured in this work is being developed specifically for the detection of selected Centers for Disease Control (CDC) category B biothreat agents through a bead-based assay: E. coli O157:H7, Salmonella, Listeria, and Shigella. It has two-color excitation, three-color fluorescence and light scattering detection, embedded electronics, and capillary based flow. However, these attributes can be easily modified for other applications such as cluster of differentiation 4 (CD4) counting. Proof of concept is demonstrated through a 6-plex bead assay with the results compared to a commercially available benchtop-sized instrument.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2017

Integrated Earth System Model (iESM)

The iESM is a simulation code that represents the physical and biological aspects of Earth's climate system, and also includes the macro-economic and demographic properties of human societies. The human aspect of the simulation code is focused in particular on the effects of human activities on land use and land cover change, but also includes aspects such as energy economies. The time frame for predictions with iESM is approximately 1970 through 2100.

Thornton, PeterEdmond↗
DOE OSTI2013

Sustainable Transportation Program FY 2012 Annual Report

Oak Ridge National Laboratory’s (ORNL) Sustainable Transportation Program performs research and development (R&D) leading to new vehicle and transportation system technologies. Its R&D portfolio includes alternative fuels, advanced combustion engines, efficient vehicle components, advanced materials, transportation electrification, vehicle systems integration, and intelligent transportation systems and operations. ORNL also provides data and analysis supporting policy- and decision-making by the Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) and the Department of Transportation (DOT).

Vaughan, Kathi H.↗
DOE OSTI2013

Trip report on IAEA Training Workshop on Implementation of Integrated Management Systems for Research Reactors (T3-TR-45496).

From 17-21 June 2013, Sandia National Laboratories, Technical Area-V (SNL TA-V) represented the United States Department of Energy/National Nuclear Security Administration (DOE/NNSA) at the International Atomic Energy Agency (IAEA) Training Workshop (T3-TR-45486). This report gives a breakdown of the IAEA regulatory structure for those unfamiliar, and the lessons learned and observations that apply to SNL TA-V that were obtained from the workshop. The Safety Report Series, IAEA workshop final report, and SNL TA-V presentation are included as attachments.

Pratt, Richard J.↗
DOE OSTI2021

Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches

Focal Area(s): 1. Data acquisition enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, and hardware-related efforts involving AI. 2. Insight gleaned from complex measurements using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI Science Challenge and Rationale: Atmospheric processes are stochastic, occur at scales from the micrometer to many kilometers, and are constantly changing over time. Characterizing these interactions and associated environmental conditions using traditional measurement techniques is difficult and can take years to build statistics on atmospheric phenomena that occurs episodically. Developing new innovative approaches to modify sampling strategies in real-time would enable the routine collection of targeted measurements focused on a specific set of science questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes

The research proposed here focuses on improving the predictive power of the land component of earth system models (ESMs) using (1) model-data fusion enabled by machine learning (ML) and artificial intelligence (AI), (2) predictive modeling through the combination of ML, AI, and big-data (comprising both model output and observations), and (3) insight of ESM structure and process mechanisms gleaned from complex data using ML and AI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Research and Development of a Ventilation-Integrated Comfort System

From an indoor air quality perspective, the best residential ventilation strategies include filtering outdoor air and distributing that air to all occupied parts of a home. From an energy standpoint, it's desirable that energy be transferred from the exhaust air to the incoming outdoor air to limit heating and cooling impacts. Heat or energy recovery ventilation systems (HRVs or ERVs) can provide these functions, but researchers have seen many poor installations related to design, installation, and operation and maintenance. More robust ventilation systems may involve an ERV with a dedicated duct distribution system and controls. Such a duct system can be costly to install, and many builders reduce these costs by connecting an ERV to a central heating and cooling duct system. While this can sometimes be done effectively, researchers have seen consistent challenges with low, inconsistent, or imbalanced flow rates; high electricity consumption; and - of greatest concern - outdoor air short-circuiting or not being delivered to occupied spaces at all. Most ERVs are designed to operate with their own duct system; they are not designed as an add-on to much larger HVAC systems. With support from the US DOE Building Technologies Office, Steven Winter Associates, Inc. (SWA) partnered with Mitsubishi Electric Trane US, CORE Energy Recovery Solutions, and Therma-Stor LLC to design and test VICS prototypes. A conceptual diagram is shown in Figure 1, and the latest prototype (shown in Figure 2) was manufactured by Therma-Stor and tested in SWA's facility in Norwalk, CT. The ERV heat exchanger was provided by CORE, and the VICS was installed in conjunction with a 1-ton, inverter heat pump provided by Mitsubishi.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗