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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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572 records · Page 32

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗

NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗

NASA NTRS2024

Effective structural impact detection and localization using convolutional neural network and Bayesian information fusion with limited sensors

Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on bridges) go unnoticed or get reported many hours later. However, they can induce structural failures or hidden damage that accelerates the structure’s long-term degradation. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid maintenance of structures. Most existing impact detection strategies are developed for aircraft composite panels utilizing high-rate synchronized measurement from densely deployed sensors. Limited efforts have been made for infrastructure or human habitats which generally require large-scale but low-rate measurement. In particular, due to harsh environments (e.g., deep space habitats under meteoroids), structural impact localization must be robust to limited sensors (e.g., sensor damage during impacts) and multi-source errors (e.g., measurement errors). In this study, an effective impact detection and localization strategy is proposed using a limited number of vibration measurements, especially in harsh environments (e.g. in deep space). Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to evaluate the effect of both measurement error and modeling error in the analysis. The proposed strategy is illustrated using 1D structure, and further validated in 3D geodesic dome structure numerically. The results demonstrate that it can detect and localize impact events accurately and robustly on structures.

Yuguang Fu↗

DOE OSTI2020

Ghostbusters for Batteries

End-of-life lithium-ion batteries are Class 9 hazardous materials due to flammability of electrolyte and lithium. It is why the transportation cost ranges from $2.49/kg - $7.20/kg for civilian and defense disposition, that is over 50% of the cost of recycling. Damaged batteries have many times greater transportation cost. Dead batteries require costly bunker-storage that occupies real estate and personnel that could be used for productive purposes. Insurance is expensive and accidents are almost assured even with relatively low volumes of waste. Tipping fees on top of this adds an unpredictable, inflated cost for the EV industry, which is very cost sensitive for survival. Transforming lithium-ion into a class of non-hazardous material reduces transportation cost to $0.10/kg, providing a value opportunity of ~$2/kg to ~$6/kg for civilian and defense disposition respectively. OnTo can eliminate the risk of thermal runaway with a brief, non-toxic treatment to eliminate flammability and toxic risks in most any battery, except Pb, which is inherently toxic. With the service, OnTo also can provide rigorous certainty that cells are rendered inert. OnTo’s patented and patent pending deactivation technology improves the cost and safety for disposition of end-of-life lithium-ion battery packs and cells. Deactivation uses non-toxic chemical processing to remove inherent risks in lithium-ion and other primary and secondary chemistries. The technology has been demonstrated and developed through DOE contract with OnTo Technology LLC (EE0008475).

25 ENERGY STORAGE↗

DOE OSTI2021

Voices of Experience: Microgrids for Resiliency

Modern attitudes around microgrids have evolved, from the idea that microgrids represent "grid defection" to perceiving them as a tool that utilities can use to solve challenges. One such challenge is improving resiliency. Faced with unpredictable, yet intensifying and dynamic weather and cyber risks, the value of resiliency is only growing for utility customers, regulators, and policymakers. Moreover, policymakers and regulators are increasingly asking utilities to accommodate the expansion and enhancement of such resiliency. The purpose of Voices of Experience: Microgrids for Resiliency is to guide discussions around this topic - everything from defining the many types of microgrids, to siting, ownership, control, and value streams. Utilities who participated in this project were generous with their time, insights, and examples of how they are using microgrids to solve one of their most urgent challenges: resiliency.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Performance Evaluation of High-Temperature Ultrasonic Communication System

Ultrasonic communication is a technique that allows transmitting information through a solid material such as tubes or bars using high-frequency elastic waves. The communication channels often extend into a high-temperature range in the field of power, oil, or nuclear industry. When the temperature is taken into consideration, the ultrasonic wave propagation through the solid channel is complex and unpredictable. It is necessary to study the influence of temperature on the ultrasonic guided wave to improve ultrasonic communication. In this study, a temperature control system is assembled to adjust the temperature of a steel pipe channel ranging from room temperature to 150 degrees C. The temperature performance evaluation is conducted to estimate the signal-to-noise ratio (SNR) and bit error rate (BER) of transferring the ultrasonic signal. The work aims to develop a reliable ultrasonic communication system in a high-temperature environment. The system temperature can be maintained at 150 degrees C and the LiNbO3 transducers are used as transmitters and receivers. No power amplifier is used in the communication system. The high-temperature ultrasonic communication system (HT-UCS) is feasible at 150 degrees C and can achieve a bit rate of 10 kbps without any error.

HT-UCS↗

DOE OSTI2021

DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers

Modern scientific workflows are increasing in complexity with growth in computation power, incorporation of non-traditional computation methods, and advances in technologies enabling data streaming to support on-the-fly computation. These workflows have unpredictable runtime behaviors, and a fixed, predetermined resource assignment on supercomputers can be inefficient for overall performance and throughput. Inability to change resource assignments further limits the scientists to avail of science-driven opportunities or respond to failures.We introduce DYFLOW, a flexible framework that orchestrates scientific workflows on supercomputers based on user-designed policies. DYFLOW compartmentalizes orchestration stages into simplified constructs, and end-users can program and reuse them according to their workflow requirements through an easy-to-use interface. These constructs hide the intricacies involved in runtime management from end-users, for instance, procurement of information to understand the workflow state, assessment, and supervision of the runtime changes. DYFLOW is designed to work alongside existing workflow management systems and reuse the available (static) support for workflow management. We have integrated DYFLOW with an existing workflow management tool as a demonstration. With experiments performed on use cases from three types of scientific workflows and two different parallel architectures, we show that DYFLOW achieves the desired orchestration incurring a small cost to carry out the runtime changes.

Singhal, Swati↗

DOE OSTI2022

Assessment of Economic Impact of Permitting Timelines on Produced Geothermal Power in Imperial County, California

Despite having a large geothermal power potential in the United States, only a small fraction has been developed for power generation. Various barriers, including technical, financial, and regulatory permit delays, are attributed to lower contribution of geothermal energy in the national grid. Unpredictable environmental reviews and permitting timelines are some of the non-technical barriers that can cause delays in geothermal exploration and utilization plans. Our study shows that the geothermal permitting timelines can vary from six months to several years, depending on the presence or absence of biological resources, cultural resources, and sensitive environmental issues at the project site. The potential impacts of these permit barriers can range from investors abandoning geothermal development to making the product (i.e., electricity) more expensive and uncompetitive. In this study, we conducted economic analysis to assess the impact of permitting timelines on cost of produced electricity from geothermal resources using data from existing geothermal plants as well as prospective sites. In this paper, we present collected timelines data, approach, and results of economic impact of permitting timelines on geothermal power. We evaluated the various environmental management and permit review processes by considering a hypothetical geothermal project in the Salton Sea Known Geothermal Resource Area. Because of the variety of the biological and environmental issues and the involvement of local, state, and federal agencies with overlapping jurisdictions, this project could go through one of the many California Environmental Quality Act (CEQA) and National Environmental Policy Act (NEPA) review scenarios that range from the least to the most complex in its circumstances. The fastest CEQA/NEPA review timelines would have the project completed in six years. In contrast, the project would substantially need longer time to complete if it were located in an area with significant environmental resources or cultural issues that required permitting from various agencies. With increasing project completion timelines, the simplified levelized cost of electricity (sLCOE) can be 4 to 11% higher with longer CEQA/NEPA review timelines than the sLCOE value with the fastest CEQA/NEPA review timeline. Lengthier CEQA/NEPA review timelines could also result in loss of $64 million to $227 million in potential revenue. Such significant economic impacts could determine the success of a geothermal project.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Robustifying Links To Combat Reference Rot

Links to web resources frequently break, and linked content can change at unpredictable rates. These dynamics of the Web are detrimental when references to web resources provide evidence or supporting information. In this paper, we highlight the significance of reference rot, provide an overview of existing techniques and their characteristics to address it, and introduce our Robust Links approach, including its web service and underlying API. Robustifying links offers a proactive, uniform, and machine-actionable way to combat reference rot. In addition, we discuss our reasoning and approach aimed at keeping the approach functional for the long term. To showcase our approach, we have robustified all links in this article.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

"Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test"

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Jang, Kathy↗

DOE OSTI2024

Hot Springs and Geysers: Exploring Historical and Modern Impacts of Geothermal Energy Production on Associated Natural Surface Systems and Standardizing Management Practices

Surface thermal features, most notably hot springs and geysers are increasingly being recognized for their importance to ecosystems, indigenous cultures, and in some cases agriculture, recreation, and tourism. Geothermal project development poses a potential risk to these natural features but current regulatory requirements for assessing and managing these risks during exploration, permitting and monitoring are somewhat inconsistent and unpredictable across different geothermal fields. This has resulted in uncertainty and increases in exploration risk for geothermal energy developers that have led to costly project delays, cancellations, or hesitation to commit. Varying regulatory requirements may also influence public perception, fostering confusion, distrust and ultimately opposition to geothermal projects, further contributing to project delays or cancellations. At a time when there is an increasing urgency for reliable baseload clean energy, geothermal is a net-zero, renewable solution that additionally provides access to more equitable and environmentally just clean power. Continued integration of geothermal energy into the national energy roadmap can be facilitated through consistent and predictable permitting, providing regulators the framework they need, developers a clear path forward, and transparency that the public deserves. This project, currently in its beginning phases, seeks to address this important issue by providing a technical basis from which to build a preliminary protocol for assessing and managing potential impacts from new or existing geothermal energy projects to surface thermal features and their associated ecosystems. Development of this preliminary protocol will be informed by (1) a literature review of well-documented case studies in the western U.S. and New Zealand to understand the range of conditions that exemplify geothermal-surface thermal systems; (2) development of generic illustrative conceptual-numerical models to quantify, understand, and predict the first-order controls (e.g., pressure and permeability) on surface flows; and (3) additional independent and scientifically rigorous evaluations of geothermal-surface thermal system case studies from the Basin and Range Province that incorporate publicly available data as well as data provided by industry through data-sharing agreements. Learning from the successes of the process used to develop the Induced Seismicity Management Protocol (ISMP), we ultimately aim to use these initial efforts as a springboard for establishing a surface thermal feature management working group that will work collaboratively to finalize the protocol as well as co-create recommended best practices for implementation. We envision that the working group will primarily be composed of representatives from regulatory entities, government agencies, Tribes, academia, national laboratories, and industry, and will include early and regular engagement with community organizations and environmental groups. This will help ensure broad acceptance and implementation of the protocol, which will facilitate a more consistent, predictable, and standardized regulatory process, and help to ensure that geothermal energy continues to provide a reliable source of clean energy, and a pathway to achieving greater energy equity in the U.S.

Best Practices↗

DOE OSTI2025

A comparison of plastic and visco-plastic constitutive models for accurate predictions in PM-HIP

Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that can efficiently produce near net shape parts with high material utilization and uniform microstructures. While the PM-HIP process is used frequently to produce small-scale components, its application to large-scale components is still limited due to inadequate understanding of its complex processes that cause unpredictable post-HIP shape distortions. A computational model can be a cost-effective alternative to exhaustive experimentation required to understand the PM-HIP process fully. Therefore, we present a comparison of plastic and visco-plastic models that are frequently used to model the PM-HIP process. We show that the predictions of powder densification behavior and shape distortions obtained from both the models are similar under most conditions and agree well with experimental observations.

Sarkar, Subrato [ORNL]↗

DOE OSTI2026

Policy Reforms to Unleash Domestic Critical Minerals Mining and Processing

The United States faces growing strategic and economic risks due to its limited ability to mine, process, and refine the minerals required for national defense, energy systems, advanced manufacturing, and emerging technologies. Although the country possesses significant geological resources, development has been slowed by long and unpredictable permitting timelines, fragmented regulatory responsibilities, limited midstream processing capacity, and a shrinking technical workforce. These structural barriers have created supply chain vulnerabilities that constrain industrial growth and reduce national resilience. This report presents a comprehensive set of reforms intended to modernize the nation’s approach to critical minerals. The recommendations address federal permitting, environmental review processes, the legal framework governing mining activities, interagency coordination, domestic processing and refining capacity, and the education and workforce systems needed to support long term industry development. The analysis emphasizes practical steps to shorten project timelines, improve regulatory clarity, expand processing infrastructure, enable recovery from both conventional and nontraditional sources, and update outdated requirements that hinder the development of essential materials. Taken together, the recommended reforms would strengthen domestic supply chains, improve investment certainty, and reduce dependence on external minerals and processing infrastructure. By aligning policy, regulatory frameworks, and workforce capabilities with national needs, the United States can build a more resilient and secure critical minerals ecosystem that supports long term economic competitiveness and technological leadership.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

NASA NTRS2009

RFI Study for the SMAP Radar

The Soil Moisture Active/Passive (SMAP) mission has the scientific objective of measuring both soil moisture and freeze/thaw state from space. The mission will make both active radar and passive radiometer measurements at L-Band in order to retrieve soil moisture. Some studies, how-ever, indicated that these measurements are susceptible to radio frequency interference (RFI) in several geographic locations. As SMAP is a global mission and its mission life is 3 years, it is crucial for SMAP to understand the RFI over the whole globe and its temporal behavior. There will be impacts to the instrument system design and ground data processing in order to mitigate RFI. In this paper, strategies and procedures for performing this RFI study will be presented, and some results utilizing the RFI observed in the ALOS PALSAR data are described. The nature of the ob-served RFI is characterized and suggests some bands are relative free of RFI. The SMAP radar system will use a 1 MHz bandwidth, which can be placed within these suggested "clear" frequencies. The ALOS PALSAR data covers 28 MHz within the 80 MHz allocated for active L-Band remote sensing. In addition, an initial analysis with UAVSAR data, which uses the entire 80 MHz allocation, indicates the relative severity of RFI over the whole band. An algorithm to remove RFI is suggested and its performance is shown for some data from ALOS PALSAR and UAVSAR.

ALOS PALSAR↗