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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hashes are not suitable to verify fixity of the public archived web

Web archives, such as the Internet Archive, preserve the web and allow access to prior states of web pages. We implicitly trust their versions of archived pages, but as their role moves from preserving curios of the past to facilitating present day adjudication, we are concerned with verifying the fixity of archived web pages, or mementos, to ensure they have always remained unaltered. A widely used technique in digital preservation to verify the fixity of an archived resource is to periodically compute a cryptographic hash value on a resource and then compare it with a previous hash value. If the hash values generated on the same resource are identical, then the fixity of the resource is verified. We tested this process by conducting a study on 16,627 mementos from 17 public web archives. We replayed and downloaded the mementos 39 times using a headless browser over a period of 442 days and generated a hash for each memento after each download, resulting in 39 hashes per memento. The hash is calculated by including not only the content of the base HTML of a memento but also all embedded resources, such as images and style sheets. We expected to always observe the same hash for a memento regardless of the number of downloads. However, our results indicate that 88.45% of mementos produce more than one unique hash value, and about 16% (or one in six) of those mementos always produce different hash values. We identify and quantify the types of changes that cause the same memento to produce different hashes. These results point to the need for defining an archive-aware hashing function, as conventional hashing functions are not suitable for replayed archived web pages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

TEAMER: Water Tunnel Data from Testing the Pterofin Skimmer Concept

Pterofin's Skimmer concept relies on a flapping and pitching hydrofoil to extract hydrokinetic energy from water flows. The concept aims to utilize unsteady fluid dynamics phenomena (added mass, shed vorticity, and unsteady boundary layer development) to achieve higher lift coefficients, enabling increased power density of the hydrokinetic device and a fundamental shift in the rpm/torque scaling of the power take off compared with turbines. The Applied Research Laboratory at Penn State, in collaboration with Pterofin, designed and built a proof-of-concept flapping/pitching mechanism which was subsequently tested in ARL's 12-inch water tunnel facility. The mechanical power supplied to or extracted from the mechanism was measured for a range of hydrofoils provided by Pterofin over operating conditions including reduced frequency, Reynolds number, and the ratio between pitching and flapping amplitudes. The power lost to friction in the mechanism was removed from the net power measurement by means of a bare hub tare, with the resultant hydrodynamic power being used to calculate a mechanism-independent and non-dimensional power coefficient. The product of this effort is a dataset describing the power coefficient of a hydrofoil having simultaneous pitching and flapping motions, both of which are approximately sinusoidal. Power coefficients were collected for a range of primary design variables including: - Reduced frequency: 0.01 to 0.95 - Pitching/flapping peak angle ratio: 1.5 to 3.0 - Chord-based Reynolds number: 60,000 to 560,000 Secondary design variables relating to the hydrofoil geometry were explored including: - Aspect ratio - Planform shape - Section thickness distribution - Hydrofoil position relative to the pitching axis - Hydrofoil sweep angle relative to the pitching axis Measured data are provided in mean and time series formats. MATLAB scripts are provided which can be used to generate figures of time-averaged and phase-averaged hydrodynamic power coefficients calculated from the measured data. A complete description of the experiment and data reduction can be found in the Post Access Report for the Pterofin Skimmer test effort which will be available on the TEAMER website. This work was supported by the Pacific Energy Ocean Trust via a TEAMER award.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Dataset 1: A National and City Dataset on Human Factors in Pooled Rideshare, 2021

Dataset 1: A National and City Dataset on Human Factors in Pooled Rideshare, 2021. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 1 (2021, N = 5,385). This dataset captures responses from a nationally representative sample of 5,385 adults across the United States to understand public acceptance, preferences, and behavioral intentions related to pooled rideshare (PR) services. The primary objective of this research is to provide actionable insights to inform the design, deployment, and policy development of sustainable shared mobility systems. Data was collected via an online survey administered through a national panel provider. Participants ranged in age from 18 to 95 years, and representation from all U.S. regions. The survey instrument was designed to explore numerous dimensions related to PR adoption including demographic traits, current travel habits, rideshare familiarity, trust, safety, environmental attitudes, and user experience preferences. Both rideshare users and non-users were included, offering a diverse range of perspectives. - Phase_1_Final - The dataset includes survey items developed from literature reviews, and prior field studies. Each row represents an individual respondent, and each column corresponds to a variable such as willingness to use pooled rideshare, attitudes toward specific service features, and sociodemographic data. The data is available in both .CSV and .SAV formats. - Phase_1_Final_MapFile - The accompanying data dictionary explains all variable labels, response scales, and codes. An .XLSX format of the full survey instrument is also included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Exploring Data Set Bias and Decision Support with Predictive Uncertainty Through Bayesian Approximations and Convolutional Neural Networks

Individual seismic catalogs can contain multiscale observations from fault level to global scales and associated waveforms from discrete events reflect crustal structure across many different scales and locations. Seismic network aperture, geographic location, and observation distance may not provide informative guidance or intuition on how different catalogs will behave across models trained under different conditions. We rely on uncertainty to provide guardrails for when to trust model decisions, but understanding when our uncertainty is trustworthy is an open challenge. Here, in this work, we explore Bayesian approximation methods for assigning predictive uncertainty in seismic event classification problems. We find that computationally expensive Bayesian approximations do not outperform simple ensemble methods. We also find that when exploiting multiple seismic event catalogs, joint training with data from all the catalogs combined with Bayesian approximations and supervised training for classification can obscure bias and result in less robust uncertainty while also not providing substantial performance benefits compared to training individual models for each catalog.

58 GEOSCIENCES↗
DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗
DOE OSTI2022

Strengthening the Security of Operational Technology: Understanding Contemporary Bill of Materials

The evolution of cyber-physical infrastructure has made its security more challenging. The last few years have witnessed a convergence of hardware and software segments in various domains, including operational technology (OT) which is responsible for carrying out critical tasks such as monitoring and controlling power grids, nuclear plants, transportation, and emergency services. Both hardware and software encapsulate numerous open source and proprietary subcomponents, making it crucial for end-users to understand the composition of the products they are using. For example, wind turbines incorporate thousands of lines of code (software) used for the turbine's design, planning, operation, and analytics in addition to the numerous hardware subcomponents that construct it. Due to the highly complex nature of software and hardware, knowledge of the components and subcomponents is required to mitigate cyber vulnerabilities and defend against cyberattacks. There has also been a transformation from a traditional linear supply chain into a global, dynamic, diverse, and interconnected system. The digitization of the supply chain makes it easier to find and exploit vulnerabilities. Critical infrastructures (e.g., power grids, oil, natural gas, water, and wastewater) rely on OT to function, and if the OT is compromised, equipment damage and potential interruption of services could result. A significant security measure to protect OT systems from disruption is to develop a supply chain bill of materials (BoM) corresponding to the software and hardware used in OT, along with attestations amongst vendors and asset owners. A supply chain BoM is a proactive way to understand the inherent vulnerabilities in the system and mitigate them in advance of being exploited. BoMs bolster the trust placed in the digital infrastructure and enhance software supply chain security by sustaining the management of component obsolescence and compliance, along with the seclusion of unsafe segments of a specific product. Adopting BoM tools is becoming increasingly important across various government sectors, as evidenced by the recent U.S. executive order on cybersecurity (NIST 2021). This paper aims to classify BoMs based on structure, functionality, component type, and architecture. The work also discusses case studies to further highlight the benefits of BoMs. In addition, it identifies missing pieces in existing BoM implementations so that future research may identify bounds on where it could expect to make improvements and directly enable researchers to identify promising areas for exploration. Further, the authors provide valuable recommendations to tool developers, researchers, and standardizing organizations (policymakers), additionally benefitting critical infrastructure owners and government executives. This aids in paving a path for future work, thereby, providing suggestions to determine a tool for consumers that best suit their needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists’ model development workflows.

Arendt, Dustin L.↗
DOE OSTI2020

Bilevel parameter optimization for learning nonlocal image denoising models

We propose a bilevel optimization approach for the estimation of parameters in nonlocal image denoising models. The parameters we consider are both the space-dependent fidelity weight and weights within the kernel of the nonlocal operator. In both cases we investigate the differentiability of the solution operator in function spaces and derive a first order optimality system that characterizes local minima. For the numerical solution of the problems, we propose a second-order trust-region algorithm in combination with a finite element discretization of the nonlocal denoising models and we introduce a computational strategy for the solution of the resulting dense linear systems. Several experiments illustrate the applicability and effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

CCE Phase 4: Mitigations and Protections

During the first three phases, the CCE Team identified any instances of unverified trust in the organization’s technologies, processes, and procedures, any or all of which could be used to adversely impact the system. In Phase 4, the primary goal is to remove the possibility of the end effect—that is, to develop means or mechanisms that will ensure an adversary cannot achieve their Objective (identified in Phase 1) via cyber means. Such measures are known as “protections.” In some cases, this may not be possible, or the implementation of protections may not be desirable due to other considerations. In such cases, means and mechanisms should be developed that focus on putting an organization in a better position to identify adversary activities directed against it, increasing the cost of cyber-enabled sabotage for the adversary (including making things more difficult for the adversary and attempting to lower the chances an adversary may succeed), or decreasing the recovery cost of a victim organization. These measures are known as “mitigations.”

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

The 501vc®Fund - A New Model for Investing in Energy Innovation. Final report

Clean Energy Trust (CET) set out to identify approaches to attract new investors to early-stage cleantech businesses. Over the course of the project, significant research and validation was conducted on the feasibility of different pathways by which to secure additional financial support for early-stage clean energy startups. CET explored both a hybrid fund approach and a stand-alone, affiliated fund approach. Over the course of this work, CET developed quantitative models for optimum fund size and specifications, conducted extensive market research, evaluated fund implementation best practices, developed a strong understanding of the legal requirements for fund design, and identified likely candidates who could replicate this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

PNT Resilience RFI Response

The use of the Global Positioning System (GPS) is a fundamental requirement for most navigation systems today, and this heavy reliance means that denial of GPS service (or extended threats) can pose a significant risk to modern navigation. There is an urgent need for enabling, high-accuracy navigation technologies that can operate without the need for GPS. Ideally, these solutions must be able to initialize in a completely GPS-free environment and continue to navigate even through challenging scenarios. The increasing risk posed to GPS means that trust in this platform is waning—and solutions are required. A future navigator should leverage GPS whenever possible and be capable of identifying and responding to risks while maintaining mission accuracy needs. In the absence of GPS, fully alternative navigation (altnav) technologies are required. This report describes an introductory view of altnav for GPS-impaired and contested environments. Various technologies are collected, presented, and evaluated as potential solutions. A wide snapshot of currently available technologies with a first-order summary of their potential is presented. While this report attempts to be as broad and complete as possible, this is a quickly evolving field.

42 ENGINEERING↗
DOE OSTI2021

Global Overview of Energy Storage Performance Test Protocols

As part of the World Bank Energy Storage Partnership, this document seeks to provide support and knowledge to a set of stakeholders across the developing world as we all seek to analyze the emerging opportunities and technologies for energy storage in the electric sector. As global prices for renewable energy have dropped dramatically over the last decade and continue to decline and the value of energy storage has increased in many systems, the World Bank technical teams and others have been hearing of a variety of problems. Related, developing countries have been asking a series of questions in this new area. This working group seeks to address the issues raised in part by creating this document and working to gather a variety of experts in this area from across the globe in support of the World Bank efforts. Performance testing, in combination with test beds (Working Group 2), is critical to fulfill the promise offered by these breakthrough technologies and critical to increasing trust in these systems and reducing risk. This document seeks to provide information to stakeholders in developing countries on the current global performance testing landscape of the battery (and broader) performance testing landscape. This document does that by summarizing testing protocols published by key global entities. From this summary, it can be concluded that there are several organizations within each region that set protocols for the testing and specifications of stationary energy storage systems. Across most of these entities, there are extensive protocols for testing batteries for electrical vehicles and mobile devices, but less for large scale energy storage system and their usage cases. The working group and the Partnership more generally agree that the nascent markets for certain technologies and rapid growth make testing more important than ever as these markets continue to mature. This document also seeks to provide a set of "guideposts" to new entrants by pointing out some of the key organizations globally that are currently engaged in performance testing of energy storage systems (often batteries but the larger organizations are likely to engage in tailored tests for emerging thermal and other storage technologies).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

GANpiler

Focal Area(s): Rather than augment or replace physical models with machine learning, we instead preserve the existing models and augment the underlying code with faster surrogate models created by generative adversarial networks (GAN). To leverage the performance optimization, we also propose a runtime system and user interfaces that allow prediction and tracking of accumulated error as well as dynamic, per-process decision making as to which model (if any) to use for each iteration. Science Challenge: This proposal sits at the nexus of two hard problems. First, climate models based on machine learning will be, by their nature, difficult to trust once their predictions begin diverging from the consensus. Second, compilers and hardware have been making only incremental performance gains for decades. GPGPUs have provided a welcome performance boost for codes that can take advantage of them, but there is no similar technology on the horizon to provide the next performance leap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deployment of Community Scale Solar Photovoltaic Energy Systems in Forest County Potawatomi Community

FCPC is a federally recognized tribe with a membership of more than 1400. The Tribe has a reservation in Forest County, Wisconsin, and tribal trust lands in Milwaukee County, Wisconsin, with significant governmental and business operations in both areas. Until relatively recently, FCPC was one of the poorest tribes in the Midwest. Due to its successful gaming operations, it now has improved its financial situation and is using its resources to benefit both its members and the general public through environmental and other community efforts, many of which are long-delayed because of the extended period of abject poverty. The Tribe has developed the long-term goal of becoming energy independent using renewable resources. In order to meet this goal, the Tribe has taken several important steps. As an interim step, FCPC is purchasing nearly 55 million kilowatt hours of renewable energy certificates (“RECs”) annually from certified renewable energy facilities in the United States. These RECs offset more than 100% of the present electricity use at all of the Tribe’s facilities. The Tribe is currently ranked number 11 on the Environmental Protection Agency’s (EPA) Top 20 Local Government list of green power purchasers and is a member of the EPA’s Green Power Partnership. The original project suggested small scale installations on tribal homes and certain government buildings. The Tribe suggested that the project would result in 730 kw installed. By revising the project, the Tribe was able to install 938 kw and owns the largest installation in Northern Wisconsin with the 800 kW installation at the Potawatomi Carter Casino Hotel (“PCCH”). The PCCH installation, in summer months, can provide near 90% of the use during daylight hours. Although tracking was not immediately available FCPC has since confirmed significant production with certain quarterly savings nearing $210,000. These installations provide a reduction in facility costs providing more revenue for Tribal services including cultural, health, sustainable food sources, environmental preservation and other Tribal initiatives. Further, the installations have resulted in positive steps towards the Tribe’s goals of energy sovereignty and the use of 100% renewable clean energy. Finally, the PCCH project, specifically, has had the additional benefit of providing a likely area for a microgrid. The size of the installation and offset achieved have pushed the Tribe toward planning for storage and a microgrid system that would both further the Tribe’s energy goals and provide a safe haven for the Community during times of need.

14 SOLAR ENERGY↗
DOE OSTI2021

U.S. and Allied Cyber Security Cooperation in the Indo-Pacific (Workshop Summary)

On March 30, 31, and April 1, 2021, the Center for Global Security Research (CGSR) at Lawrence Livermore National Laboratory (LLNL) hosted a workshop titled "U.S. and allied Cyber Security Cooperation in the Indo-Pacific". This session brought together participants drawn across the policy, military, and private sector in the United States and among allied countries in Europe and the Indo-Pacific. The workshop evaluated threat perceptions, assessed cyber power in a regional context, and explored opportunities for building capacity and trust with allies, partners, and the private sector. The region's cyber threat landscape is marked by increasing malicious activity, with threats persistent and ever-growing. Grounded by this strategic reality, the workshop advocated for 1) a concerted push into the arena of norm setting from the bottom up, 2) an agreed metric by which to assess progress, and 3) the creation of future lines of collaborative effort for the United States and its allies to ensure an open, interoperable, and secure internet.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Manufacturing Management System Overview [Slides]

Vision: Institute a trusted manufacturing capability within the ALDWP Production Agency enabling digital product acceptance and access to digital manufacturing post-production activities. Mission: Establish a modern manufacturing infrastructure that ensures operator credentials, minimized human transcription errors, capable of managing all aspects of manufacturing including digital data acquisition, NCR resolution, and product acceptance, while providing an effective and intuitive user experience. Strategy: Constitute the modern manufacturing infrastructure to minimize risk and plant disruption while incrementally involving the capability.

42 ENGINEERING↗
DOE OSTI2021

Optimization-based algorithms for nonlinear mechanics and frictional contact

An optimization-based strategy for solving nonlinear mechanics problems is proposed. In contrast to typical nonlinear equation solver algorithms that aim to find zeros in the residual force function, we minimize an energy (or energy-like) function to encourage solutions which are locally stable equilibria. These smooth and potentially non-convex objective functions are minimized using a preconditioned conjugate-gradient trust-region algorithm. Contact is formulated as an inequality constrained minimization problem, and is solved with an augmented Lagrangian algorithm. Friction is included in the approach via a regularized quasi-potential energy, and other dissipative behavior is included through the use of variational constitutive updates. Finally, to accelerate convergence rates for the Lagrange multipliers, we propose a novel multiplier update algorithm utilizing the Fischer-Burmeister function, and demonstrate super-linear solver convergence for some applications.

42 ENGINEERING↗