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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hybrid Power Purchase Agreements for Flexible 24/7 Energy Delivery – A Comprehensive Review of Current Practices and Research Pathways

Power Purchase Agreements (PPAs) are becoming increasingly preferred among large energy consumers, such as data centers, to secure cost-effective energy and meet accelerating demand growth. Traditionally, variable renewable energy (VRE)-based PPAs operate on a pay-as-produced basis, balancing supply and demand for a relatively longer duration (e.g., annually). However, the focus is shifting toward matching supply and demand on an hourly basis to fully meet energy needs. This shift requires the integration of flexible energy resources, such as hydropower, thermal generation, and energy storage, to complement VRE sources like wind and solar, forming the foundation for 24/7 PPA. This work contributes by: (i) reviewing emerging market trends and current practices in PPA procurement, supported by data on PPA prices and technology portfolios; (ii) synthesizing the existing literature on modeling approaches for contract pricing, quantities, hybrid resource procurement, and risk management in 24/7 PPA design, while identifying key research gaps; and (iii) proposing an integrated 24/7 PPA design framework along with two contracting mechanisms from the perspectives of both PPA providers and consumers. The proposed framework highlights critical modeling challenges, risk-allocation issues, and future research opportunities for 24/7 PPA design.

24/7↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗
DOE OSTI2017

Cyber Security Primer for DER Vendors Aggregators and Grid Operators

This report provides an introduction to cyber security for distributed energy resources (DER) - such as photovoltaic (PV) inverters and energy storage systems (ESS). This material is motivated by the need to assist DER vendors, aggregators, grid operators, and broader PV industry with cyber security resilience and describe the state-of-the-art for securing DER communications. The report outlines basic principles of cyber security, encryption, communication protocols, DER cyber security recommendations and requirements, and device-, aggregator-, and utility-level security best practices to ensure data confidentiality, integrity, and availability. Example cyber security attacks, including eavesdropping, masquerading, man-in-the-middle, replay attacks, and denial-of-service are also described. A survey of communication protocols and cyber security recommendations used by the DER and power system industry are included to elucidate the cyber security standards landscape. Lastly, a roadmap is presented to harden end-to-end communications for DER with research and industry engagement.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Toward a North American Standard for Mobile Data Services

The rapid introduction of digital mobile communications systems is an important part of the emerging digital communications scene. These developments pose both a potential problem and a challenge. On one hand, these separate market driven developments can result in an uncontrolled mixture of analog and digital links which inhibit data modem services across the mobile/Public Switched network (PSTN). On the other hand, the near coincidence of schedules for development of some of these systems, i.e., Digital Cellular, Mobile Satellite, Land Mobile Radio, and ISDN, provides an opportunity to address interoperability problems by defining interfaces, control, and service standards that are compatible among these new services. In this paper we address the problem of providing data services interoperation between mobile terminals and data devices on the PSTN. The expected data services include G3 Fax, asynchronous data, and the government's STU-3 secure voice system, and future data services such as ISDN. We address a common architecture and a limited set of issues that are key to interoperable mobile data services. We believe that common mobile data standards will both improve the quality of data service and simplify the systems for manufacturers, data users, and service providers.

Dean, Richard A.↗
NASA NTRS2003

Countermeasure Evaluation and Validation Project (CEVP) Database Requirement Documentation

The initial focus of the project by the JSC laboratories will be to develop, test and implement a standardized complement of integrated physiological test (Integrated Testing Regimen, ITR) that will examine both system and intersystem function, and will be used to validate and certify candidate countermeasures. The ITR will consist of medical requirements (MRs) and non-MR core ITR tests, and countermeasure-specific testing. Non-MR and countermeasure-specific test data will be archived in a database specific to the CEVP. Development of a CEVP Database will be critical to documenting the progress of candidate countermeasures. The goal of this work is a fully functional software system that will integrate computer-based data collection and storage with secure, efficient, and practical distribution of that data over the Internet. This system will provide the foundation of a new level of interagency and international cooperation for scientific experimentation and research, providing intramural, international, and extramural collaboration through management and distribution of the CEVP data. The research performed this summer includes the first phase of the project. The first phase of the project is a requirements analysis. This analysis will identify the expected behavior of the system under normal conditions and abnormal conditions; that could affect the system's ability to produce this behavior; and the internal features in the system needed to reduce the risk of unexpected or unwanted behaviors. The second phase of this project have also performed in this summer. The second phase of project is the design of data entry screen and data retrieval screen for a working model of the Ground Data Database. The final report provided the requirements for the CEVP system in a variety of ways, so that both the development team and JSC technical management have a thorough understanding of how the system is expected to behave.

Shin, Sung Y.↗
DOE OSTI2022

Flexible Financial Credit Agreements: Blockchain

Flexible Financial Credit Agreements is a broad term used to describe a suite of solar products with innovative features not currently offered in traditional solar financing programs. This brief focuses on blockchain, which is a highly secure, decentralized cryptographic technology that verifies transactional data and stores it on all participating computers in an immutable ledger. Blockchain is best known for its first application in the creation of Bitcoin cryptocurrency, but its principles offer a basis for innovation in a variety of industries where the complexity of transactional data is better suited for secure, decentralized management.

blockchain↗
DOE OSTI2022

Modular Security Apparatus for Managing Distributed Cryptography for Command-and-Control Messages on Operational Technology Networks (Module-OT)

Module-OT is a bump- in- the- wire solution acting as a secure conduit for data between devices or systems across a network. Using the latest in open-source cryptographic libraries, all defined communications undergo authentication, authorization, and encryption. The core system can be easily installed through industry standard processes, and the use of popular open-source packages allows for it to be customizable customized by the developer community or deployed in unique embedded environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Application of Cyber-Informed Engineering for Protecting BESS

This white paper synthesizes an array of crucial grid services provided by BESS technology, assesses its architecture and communications, and presents a case study for analysis against the principles introduced by Cyber-Informed Engineering (CIE). Furthermore, in walking through the analysis, this paper presents a framework to evaluate risks and solutions when considering BESS components. Asset owners and buyers could perform this analysis to assess their BESS product implementations, alternative inverter-based resources (IBR), and energy management systems (EMS). Battery systems fulfill various roles contingent on the unique market demands and the specific challenges presented by regional grid infrastructures. These roles also vary due to the differing utility models for ownership and operation, which are adapted to meet regional and local capabilities and requirements. Concerns have been raised regarding the potential for adversaries to exploit knowledge of battery operational patterns to orchestrate decisive attacks. However, the security of operational data for these systems may not be the primary vulnerability, as much of this information is already well-understood within the community. Applying a modest degree of subject matter expertise can often yield valuable predictions regarding how a battery will respond under certain conditions, such as grid emergencies, high or low-temperature days, Public Safety Power Shutoff (PSPS) events, and outages. The operational characteristics of batteries are well-documented, and their capabilities, including the risks associated with misoperation and the resulting consequences, are published and understood within the industry. CIE practices represent the next step in gaining functional assurance and providing an acceptable level of risk, regardless of whether a battery vendor can support a trusted and validated supply chain. While this issue has exacerbated supply chain challenges, it is not an isolated condition. This foreign supply route is the primary source of BESS for the U.S. market. Significant efforts are underway through the Bipartisan Infrastructure Law (BIL) to change that. Still, strategic short-term operational mitigations are needed to ensure the security of our operational technology (OT) systems, which are enhanced by instilling trust and are separate from vendors implementing CIE principles.

25 ENERGY STORAGE↗
DOE OSTI2022

Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Supermassive black holes and their host galaxies. I. Bulge luminosities from dedicated near-infrared data

In an effort to secure, refine, and supplement the relation between central supermassive black hole masses, M {sub •}, and the bulge luminosities of their host galaxies, L {sub bul}, we obtained deep, high spatial resolution K-band images of 35 nearby galaxies with securely measured M {sub •}, using the wide-field WIRCam imager at the Canada-France-Hawaii-Telescope. A dedicated data reduction and sky subtraction strategy was adopted to estimate the brightness and structure of the sky, a critical step when tracing the light distribution of extended objects in the near-infrared. From the final image product, bulge and total magnitudes were extracted via two-dimensional profile fitting. As a first order approximation, all galaxies were modeled using a simple Sérsic-bulge+exponential-disk decomposition. However, we found that such models did not adequately describe the structure that we observed in a large fraction of our sample galaxies which often include cores, bars, nuclei, inner disks, spiral arms, rings, and envelopes. In such cases, we adopted profile modifications and/or more complex models with additional components. The derived bulge magnitudes are very sensitive to the details and number of components used in the models, although total magnitudes remain almost unaffected. Usually, but not always, the luminosities and sizes of the bulges are overestimated when a simple bulge+disk decomposition is adopted in lieu of a more complex model. Furthermore, we found that some spheroids are not well fit when the ellipticity of the Sérsic model is held fixed. This paper presents the details of the image processing and analysis, while we discuss how model-induced biases and systematics in bulge magnitudes impact the M {sub •}-L {sub bul} relation in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗
DOE OSTI2018

Statistical methods for network surveillance

The term network surveillance is defined in general terms and illustrated with many examples. Statistical methodologies that can be used as tools for network surveillance are discussed. Details for 3 illustrative examples that address network security, surveillance for data network failures, and surveillance of email traffic flows are presented. Some open areas of research are identified.

Jeske, Daniel R.↗
DOE OSTI2019

Nature of Moisture-Induced fogging defects in scintillator plastic

Polyvinyl toluene (PVT) scintillator plastic may degrade in the field due to inward water diffusion at elevated temperatures, which exceeds the saturation limit at lower temperatures, and after long periods of time (e.g., many years), leads to the formation of disk-like defects that attenuate scintillation light, leading to detector degradation. In this work, using fractography and high-magnification optical and electron microscopy to characterize the water-induced defects, a model of the fogging process is hypothesized as follows: excess water present at low temperatures diffuses to spheroids to minimize contact with the hydrophobic polymer. Through the hydrophobic effect, the entropy of water increases by forming nanoclusters which minimizes the contact between the water and the PVT. As the water nanoclusters grow, they break and fold the polymer into densely-packed crystalline regions creating more space for water within the spheroids. The polymer outside the spheroid resists the shrinkage which builds up tension within the spheroid. Once the tensile stress exceeds the yield strength of the plastic, the spheroid is torn in half resulting in a defect. Excess water then drains into the cavities along the disk thereby further increasing its entropy. Slower cooling (over 1 day) leads to larger spheroids and hence, larger “permanent” defects. Freezing causes some defects to further grow due to the expansion of water to ice. These findings imply that the remaining lifetime of scintillator plastic in the field could be predicted using temperature and humidity data thereby mitigating security risks of degraded radiation portal monitors.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Graphs, matrices, and the GraphBLAS: Seven good reasons

The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Black hole and galaxy coevolution from continuity equation and abundance matching

We investigate the coevolution of galaxies and hosted supermassive black holes (BHs) throughout the history of the universe by a statistical approach based on the continuity equation and the abundance matching technique. Specifically, we present analytical solutions of the continuity equation without source terms to reconstruct the supermassive BH mass function from the active galactic nucleus (AGN) luminosity functions. Such an approach includes physically motivated AGN light curves tested on independent data sets, which describe the evolution of the Eddington ratio and radiative efficiency from slim- to thin-disk conditions. We nicely reproduce the local estimates of the BH mass function, the AGN duty cycle as a function of mass and redshift, along with the Eddington ratio function and the fraction of galaxies with given stellar mass hosting an AGN with given Eddington ratio. We exploit the same approach to reconstruct the observed stellar mass function at different redshift from the ultraviolet and far-IR luminosity functions associated with star formation in galaxies. These results imply that the build-up of stars and BHs in galaxies occurs via in situ processes, with dry mergers playing a marginal role at least for stellar masses ≲3×10{sup 11} M{sub ⊙} and BH masses ≲10{sup 9} M{sub ⊙}, where the statistical data are more secure and less biased by systematic errors. In addition, we develop an improved abundance matching technique to link the stellar and BH content of galaxies to the gravitationally dominant dark matter (DM) component. The resulting relationships constitute a testbed for galaxy evolution models, highlighting the complementary role of stellar and AGN feedback in the star formation process. In addition, they may be operationally implemented in numerical simulations to populate DM halos or to gauge subgrid physics. Moreover, they may be exploited to investigate the galaxy/AGN clustering as a function of redshift, mass, and/or luminosity. In fact, the clustering properties of BHs and galaxies are found to be in full agreement with current observations, thus further validating our results from the continuity equation. Finally, our analysis highlights that (i) the fraction of AGNs observed in the slim-disk regime, where most of the BH mass is accreted, increases with redshift; and (ii) already at z≳6 a substantial amount of dust must have formed over timescales ≲10{sup 8} yr in strongly star-forming galaxies, making these sources well within the reach of ALMA surveys in (sub)millimeter bands.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.

Peng, Hongwu↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗