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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Automated Ride Hailing - Status, Projections, and Critical Safety Review

Automated ride-hailing services, commonly known as 'robo-taxis' are beginning to proliferate and scale. Currently they are available in four major US cities with another 10 cities planning their implementation in the near future. In the US, automated ride-hailing services are dominated by a single provider, with announcements from several competitors of service offerings by end of calendar year 2025. Internationally, only three companies are currently in commercial operations, with many more planned. The scaling of robo-taxi services appears to be the leading application of automated vehicles (AVs), and provides evidence of their anticipated safety in comparison to conventional vehicles. This presentation will provide an overview of the status of deployments of fully operational AVs in ride-hailing services, projections of the number of trips, vehicles, and vehicle miles traveled for the immediate future, and a critical review of the safety data that has been reported, both in the US as well as internationally.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2008

Observing Gamma-ray Bursts with GLAST

The Gamma-ray Large Area Telescope (GLAST) is a satellite-based observatory to study the high-energy gamma-ray sky. The Large Area Telescope, the main instrument, is a pair-conversion telescope which will survey the sky in the energy range 20 MeV to greater than 300 GeV. The LAT's wide field of view (greater than 2 sr), large effective area and low deadtime combine to provide excellent high-energy gamma-ray observations of GRB. To tie these frontier high-energy observations to the better-known properties at lower energies, a second instrument, the GLAST Burst Monitor (GBM) will provide important spectra and timing in the 8 keV to 30 MeV range. Upon detection of a GRB by the LAT or the GBM, the spacecraft can autonomously repoint to keep the GRB location within the LAT field of view, allowing high-energy afterglow observations. We describe how the instruments, spacecraft, and ground system work together to provide observations of gamma-ray bursts from 8 keV to over 300 GeV and provide rapid notification of these observations to the wider gamma-ray burst community. Analysis and simulation tools dedicated to the GRB science have been developed. In this contribution we show the expected LAT sensitivity obtained with such simulations, and illustrate the results we expect from GLAST observations with spectral and temporal analysis of simulated GRB.

McEnery, Julie↗
DOE OSTI2022

Rapid Load Transition for Integrated Solid Oxide Fuel Cell - Gas Turbine (SOFC-GT) Energy Systems: A Demonstration of the Potential for Grid Response

Rapid load transition is an essential requirement for integrated energy systems to maintain grid resilience as more renewable resources are added to the grid. Integrated solid oxide fuel cell - gas turbine (SOFC-GT) systems can provide high efficiency and low carbon emissions over a broad range of turndown. These hybrids also have the potential to enable rapid grid response. The challenge has been to demonstrate effective control strategies to manage load transitions. In the present study, a load transition of ~50% was achieved in 10 s using a novel but simple strategy. Power demand on the SOFC and the GT were ramped down concurrently. During this transition, the SOFC anode fuel was manipulated to maintain SOFC fuel utilization while the cathode inlet air flow and temperature were also manipulated to thermally protect the SOFC. This study was conducted using the Hybrid Performance (Hyper) facility at the National Energy Technology Laboratory (NETL) in a co-simulation environment with the Idaho National Laboratory (INL)'s grid-simulation. The load ramping strategy was tested using a hardware-based cyber-physical simulation methodology. The results demonstrate a high-fidelity representation of SOFC-GT hybrid dynamics and validation of the control strategy. Thermal and electrochemical transients indicated that the SOFC was well protected during rapid load turndown without violating operability constraints. This demonstration revealed the non-linear nature of tightly coupled SOFC-GT system components, especially the non-linear response of SOFC cathode air flow and inlet temperature controls. These results highlight the needs and challenges in developing adaptive automatic controls for autonomous rapid load transitions. This work demonstrates that SOFC-GT hybrids are a viable option to provide the fast-ramping characteristics essential to accommodate high levels of variable renewable power while maintaining grid resilience, reliability, and environmental performance. The results also demonstrate the utility of co-simulation in advancing the tightly-coupled integrated energy systems needed to meet goals for zero-carbon power generation.

DIRECT ENERGY CONVERSION,POWER TRANSMISSION AND DI↗
DOE OSTI2025

Development of a Discrepancy Checker for the Digital Twin in a Supervisory Control System for a Thermal Energy Delivery System

Defined as a virtual representation of a physical object, process, or service, and used to support real-world decision-making, a digital twin (DT) can be utilized to combine classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems, and to enable optimal autonomous operations. However, a DT’s usefulness largely depends on its ability to adequately mirror the state of its physical counterpart, and this adequacy should be reflected by the level of uncertainty in the underlying simulation models when estimating and predicting quantities of interest (QOIs). Moreover, simulation models in a DT may involve multiple fidelities of representations—ranging from physics-based models to data-driven ones—but classical uncertainty quantification (UQ) methods struggle to handle numerous uncertainty sources, nor are they designed for real-time applications. This work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system applied to a thermal energy delivery system (TEDS) at Idaho National Laboratory. The discrepancy checker was developed using metadata from an automated DT development process, and these metadata included different combinations of physical model forms and model parameters, training data and hyperparameters for surrogate models, and design parameters for supervisory control systems. Next, correlations between the uncertainty results and the metadata were established and then applied to the DT operations. The discrepancy checker evaluates the discrepancies between model predictions from virtual and sensor measurements and backtraces them to the corresponding major sources of uncertainty. The discrepancy checker showed reasonable performance in detecting discrepancies and diagnosing sources of uncertainty in testing scenarios.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Automated tracking of prefabricated components for areal-time evaluator to optimize and automateinstallation

Trade associations for prefabricated construction estimate that about 50% of prefabricated wall projects have alignment problems that lead to defects and rework. Additionally, component installation times average between 30 and 60 minutes per component. To address these issues, a real-time evaluator (RTE) system was introduced to decrease cost and automate prefabricated component installation by reducing the installation time, decreasing rework, and enhancing energy performance through higher installation quality. The RTE uses commonly available hardware and software to perform autonomous tracking to measure the real-time location and orientation of components as they are crane-lifted and installed. The hardware, software, and algorithms that allow the autonomous tracking of components are detailed. An algorithm to automate the initial search for a component with three attached retroreflectors is proposed. Algorithms to automate the measurement of component position and orientation are also proposed. Simple lab-scale proof-of-concept experiments were conducted to assess the algorithms for automation of component searching, measurement of real-time movement, and measurement of component orientation. With additional development, the system can be used as a tool to generate the commands for autonomous crane operation or single-task construction robots.

Hayes, Nolan↗
NASA NTRS1972

A new model for dual-spin satellites.

A new mathematical model for the analysis of dissipative dual-spin satellites has been developed. This model makes explicit use of the fact that the high speed rotor is symmetric by modeling the rotor energy dissipation with symmetrically placed dampers. This permits the use of a quasi-holonomic transformation for the reduction of the periodic system of variational equations to an autonomous system in accordance with the Floquet Theory and the Liapunov Reducibility Theorem.-

Guha, A. K.↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Overview of NLR Automated Mobility District Implementation Research - Phases I, II, and III. The Convergence of Automation, Electrification, and On-Demand Services: Enabling Resilient Automated Mobility Districts

A research program by the National Renewable Energy Laboratory has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings, referred to as Automated Mobility Districts (AMDs). An AMD emphasizes the deployment of automated vehicles (AV) passenger transport services within a dense urban setting and other major activity centers with intense passenger origin-destination demand patterns, such as those found in large business districts, airports, and university and medical campuses. Phase I and II surveyed 10 early deployment sites and subsequently collected and evaluated the lessons learned from these early deployment sites, with particular attention to fleet operations, impacts of service reliability, and vehicle technology evolution as the field of companies was being progressively winnowed by the challenges of full automation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Robotics for Energy Infrastructure Construction on Earth and Beyond: Pilot Study Exploring Level 3-4 Semi-Autonomous Installation of a Reduced-Scale Photovoltaic Power Plant Prototype: Preprint

There is a growing need to build energy infrastructure at massive scale on both Earth and in space. On Earth, increasing electricity demand from applications such as AI data centers is driving rapid expansion of power systems, while in space, long-duration missions such as lunar operations require reliable and resilient energy infrastructure that can be deployed quickly. These needs outpace the capabilities of conventional construction methods. Robotic construction offers a path to faster, lower-cost, and more consistent deployment, yet most existing approaches focus on automating individual tasks rather than end-to-end assembly. The Industrialized Construction Innovation (ICI) team at the National Laboratory of the Rockies (NLR), in collaboration with the Colorado School of Mines, has been investigating robotics and automation strategies for rapid end-to-end energy infrastructure construction. This paper presents a pilot study demonstrating level 3-4 semi-autonomous, end-to-end robotic assembly of a reduced-scale photovoltaic power plant prototype under terrestrial conditions. The approach combines hardware redesign of photovoltaic components for robotic compatibility, discrete-event and physics-based robotic simulations in synthetic environments, and sim-to-real deployment to execute end-to-end robotic assembly of[NM1.1][NM1.2] tracking components, structural members, photovoltaic modules, and embedded electrical connections. The pilot identifies key limitations like tolerance management, perception and localization, end effector capability, fastening methods, and multi-physics simulation fidelity, that must be addressed to enable end-to-end robotic assembly of full-scale energy infrastructure. The study highlights how these insights inform scaling strategies for terrestrial energy infrastructure and adaptation to unstructured and extreme environments, including off-world scenarios such as robotic assembly of vertical tall lunar towers.

14 SOLAR ENERGY↗
NASA NTRS2017

Auto-Gopher-II – An Autonomous Wireline Rotary-Hammer Ultrasonic Drill

Developing technologies that would enable future NASA exploration missions to penetrate deeper into the subsurface of planetary bodies for sample collection is of great importance. Performing these tasks while using minimal mass/volume systems and with low energy consumption is another set of requirements imposed on such technologies. A deep drill, called Auto-Gopher II, is currently being developed as a joint effort between JPL’s NDEAA laboratory and Honeybee Robotics Corp. The Auto-Gopher II is a wireline rotary-hammer drill that combines formation breaking by hammering using an ultrasonic actuator and cuttings removal by rotating a fluted auger bit. The hammering mechanism is based on the Ultrasonic/Sonic Drill/Corer (USDC) mechanism that has been developed as an adaptable tool for many drilling and coring applications. The USDC uses an intermediate free-flying mass to transform high frequency vibrations of a piezoelectric transducer horn tip into sonic hammering of the drill bit. The USDC concept was used in a previous task to develop an Ultrasonic/Sonic Ice Gopher and then integrated into a rotary hammer device to develop the Auto-Gopher-I. The lessons learned from these developments are being integrated into the development of the Auto-Gopher-II, an autonomous deep wireline drill with integrated cuttings and sample management and drive electronics. Subsystems of the wireline drill are being developed in parallel at JPL and Honeybee Robotics Ltd. This paper presents the development efforts of the piezoelectric actuator, cuttings removal and retention flutes and drive electronics.

Badescu, Mircea↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Power Takeoff Device for a Small Marine Hydrokinetic Turbine Deployed from an Unmanned Floating Platform

The design, development, and field-testing of a power takeoff (PTO) device equipped with a ball-type continuously-variable transmission (B-CVT) for a small marine hydrokinetic (MHK) turbine deployed from a floating unmanned autonomous mobile catamaran platform is described. The turbine is a partially-submerged multi-blade undershot waterwheel (USWW). The objective is to develop a PTO for the optimal conversion of the MHK energy harnessed by the turbine to electric power, which is stored in battery banks onboard the MHK platform. Modeling, simulation, and bench testing of the USWW and PTO show the feasibility of utilizing the B-CVT's variable gear ratio to decouple the USWW and generator speeds, maintaining the waterwheel within its optimal tip speed ratio (TSR) while varying the generator speed, thereby increasing the efficiency of the PTO. Results of bench and field testing in support of characterizing the power conversion capabilities of the PTO are described. The system being developed is in support of potential self-powered autonomous mobile recharge stations for unmanned aerial vehicles (U A V s) operating in coastal zones. Field tests of the complete MHK platform with 9 blades on the waterwheel and wheel sub mergences of 10 and 12 inches (full blade submergence) were performed. The overall proof-of-concept was successfully demonstrated, with the system satisfactorily capturing and converting water flow energy into electricity. The feasibility of utilizing the B-CVT as a means of increasing the PTO power capture capabilities and efficiency is analyzed.

Pimentel, Hugo↗
DOE OSTI2021

Event-Driven Predictive Approach for Real-Time Volt/VAR control with CVR in solar PV rich Active Distribution Network

The focus of this paper is on analyzing the impact of conservation voltage reduction in the presence of active devices such as solar photovoltaic (PV) and developing controls that leverage these distributed energy resources. An event-driven predictive approach for real-time volt/volt-ampere reactive (VAR) optimization, along with local two-level adaptive volt/VAR droop-based control algorithm for advanced distribution management systems, is introduced. The methodology covers aggregated and autonomous controls under different timescale operations, including the impact and effect of unpredicted events such as cloud transients on PV power production. In addition, the control schemes include the uncertainties in PV power generation and load power demand. The proposed methodology is validated in a real-time framework using the real-time digital simulator platform through co-simulation with models based on Python and OpenDSS (Open Distribution System Simulator). The developed methodology is tested on the modified IEEE 123-feeder test system. The results reveal that the proposed methodology works well in the presence of high penetrations of PV power, produces significant energy savings, and mitigates over-/undervoltage problems.

14 SOLAR ENERGY↗
NASA NTRS2012

Real-Time Projection to Verify Plan Success During Execution

The Mission Data System provides a framework for modeling complex systems in terms of system behaviors and goals that express intent. Complex activity plans can be represented as goal networks that express the coordination of goals on different state variables of the system. Real-time projection extends the ability of this system to verify plan achievability (all goals can be satisfied over the entire plan) into the execution domain so that the system is able to continuously re-verify a plan as it is executed, and as the states of the system change in response to goals and the environment. Previous versions were able to detect and respond to goal violations when they actually occur during execution. This new capability enables the prediction of future goal failures; specifically, goals that were previously found to be achievable but are no longer achievable due to unanticipated faults or environmental conditions. Early detection of such situations enables operators or an autonomous fault response capability to deal with the problem at a point that maximizes the available options. For example, this system has been applied to the problem of managing battery energy on a lunar rover as it is used to explore the Moon. Astronauts drive the rover to waypoints and conduct science observations according to a plan that is scheduled and verified to be achievable with the energy resources available. As the astronauts execute this plan, the system uses this new capability to continuously re-verify the plan as energy is consumed to ensure that the battery will never be depleted below safe levels across the entire plan.

Wagner, David A.↗
DOE OSTI2022

Discrete and Integrated Solutions for Hybrid PV Plants Without Momentary Cessation in Low SCR and High Penetration PE Grids

With the increased penetration of power electronic (PE) based loads and sources, advanced solutions may be required for the enhancement of grid stability in regions with low short circuit ratio (SCR) and high penetration PE grids. The requirement of advanced solutions arises from the gradual paradigm shift of the electric grid from the traditional electric machine dominant system to a high penetration of PE-based system. One of the major challenges with such systems in recent times is the momentary cessation during alternating current (ac) grid transmission faults. During momentary cessation, PE-based resources cease to operate, thus creating probable reliability challenges for the grid. In this paper, potential feasible options to provide continuity of operation during such scenarios are presented. The options are considered through identifying upgrades in existing and upcoming discrete development of photovoltaic (PV) and energy storage systems (ESS) termed as discrete hybrid PV plants. Additionally, an advanced concept of integrated development of PV and ESS connecting to ac transmission grid links called multi-port autonomous reconfigurable solar power plant (MARS) is evaluated. The developed new solutions are evaluated for different grid use cases and scenarios in PSCAD.

Marthi, Phani Ratna Vanamali↗
DOE OSTI2020

An Intelligent Adaptable Monitoring Package. Final Report

The “Intelligent Adaptable Monitoring Package” project was a four-year effort that demonstrated the feasibility of integrated sensing packages at tidal and wave energy sites. Such integration is generally required by the breadth of sensors required to understand environmental effects at marine energy sites and the operational difficulty of deploying, maintaining, and recovering such sensors. Over the course of the project, the Adaptable Monitoring Package (AMP) was deployed in multiple settings, each corresponding to a project budget period: - Budget Period 1: Demonstration of cabled deployment at Pacific Northwest National Laboratory’s Marine Science Laboratory. The deployment highlighted AMP hardware endurance over a 4-month deployment in a tidally-dominated environment and laid the groundwork for machine learning algorithms to detect and classify targets present in active sonar data. - Budget Period 2: Demonstration of an autonomous deployment at PacWave South off the coast of Newport, Oregon. The deployment highlighted the stability of AMP hardware and software, with the autonomous package collecting data on a duty cycle over a 1.5-month deployment. - Budget Period 3: Demonstration of an autonomous deployment powered by a wave energy converter at the U.S. Navy’s Wave Energy Test Site. The deployment highlighted the potential of wave energy to power ocean observatories and led to the development of machine learning algorithms to detect and classify targets in optical camera data. In aggregate, this project’s greatest success was demonstrating the AMP’s flexibility in a range of deployment scenarios. Each budget period represented a “first of a kind” demonstration of integrated instrumentation – cabled AMP, autonomous AMP, wave-energy powered AMP – and each deployment helped to identify and set goals for the next. Further, despite the exploratory nature of these deployments, each one achieved high system up-time and proved that flexible integration of multiple sensors in a single package represents a viable strategy for marine energy environmental monitoring. The key lessons learned from the project are: - Without continuous power, either from a shore cable or in situ source, many of the benefits of integration are lost (If continuous power is not available, the ability to detect rare events is lost, as is the ability to minimize the risk of behavioral changes through adaptive sensing. However, even on a duty cycle, there is still value in being able to acquire synchronous data from multiple sensors.); and - Observations from a moving platform present substantially greater data processing challenges than those from stationary platforms. Finally, these deployments also demonstrate an important truth: successful integration alone does not guarantee that relevant data are collected. To grow the knowledge base about environmental interactions with marine energy converters, integrated systems, like the AMP, need to include the right sensor mix and connect the data pipelines to effective processing algorithms. These deployments establish a strong foundation for future collaborations with the environmental research community: not only to understand the environmental effects of marine energy, but also to improve our general ability to study life in the sea.

16 TIDAL AND WAVE POWER↗