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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Comparative Evaluation and Selection of High-Temperature Heat Exchangers for Application to Integrated Energy Systems

The following report aims to create a refined and well-structured method for comparatively evaluating heat exchanger technologies for integrated energy systems that caters customers’ specific needs while meeting engineering requirements. For the evaluation, this study elevates previous evaluation metrics, enhances the knowledge base via literature and market surveys, and identifies the figures of merit with robust rationales to enhance the quality of decisions made throughout the proposed heat exchanger evaluation process. The heat exchanger designs evaluated as part of the case study are shell and tube heat exchangers, printed circuit heat exchangers, plate heat exchangers, spiral heat exchangers, and heat pipe heat exchangers. The information presented in this report is meant for industries interested in making a preliminary screening process to identify the most suitable heat exchanger design for their application of interest.

42 - ENGINEERING↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

58 GEOSCIENCES↗
NASA NTRS2003

A new method to evaluate human-robot system performance

One of the key issues in space exploration is that of deciding what space tasks are best done with humans, with robots, or a suitable combination of each. In general, human and robot skills are complementary. Humans provide as yet unmatched capabilities to perceive, think, and act when faced with anomalies and unforeseen events, but there can be huge potential risks to human safety in getting these benefits. Robots provide complementary skills in being able to work in extremely risky environments, but their ability to perceive, think, and act by themselves is currently not error-free, although these capabilities are continually improving with the emergence of new technologies. Substantial past experience validates these generally qualitative notions. However, there is a need for more rigorously systematic evaluation of human and robot roles, in order to optimize the design and performance of human-robot system architectures using well-defined performance evaluation metrics. This article summarizes a new analytical method to conduct such quantitative evaluations. While the article focuses on evaluating human-robot systems, the method is generally applicable to a much broader class of systems whose performance needs to be evaluated.

Space Flight/instrumentation↗
NASA NTRS1992

Utilization of potatoes for life support in space. V. Evaluation of cultivars in response to continuous light and high temperature

Twenty-four potato (Solanum tuberosum L.) cultivars from different regions of the world were evaluated in terms of their responses to continuous light (24 h photoperiod) and to high temperature (30 C) in two separate experiments under controlled environments. In each experiment, a first evaluation of the cultivars was made at day 35 after transplanting, at which time 12 cultivars exhibiting best growth and tuber initiation were selected. A final evaluation of the 12 cultivars was made after an additional 21 days of growth, at which time plant height, total dry weight, tuber dry weight, and tuber number were determined. In the continuous light evaluation, the 12 selected cultivars were Alaska 114, Atlantic, Bintje, Denali, Desiree, Haig, New York 81, Ottar, Rutt, Snogg, Snowchip, and Troll. In the high temperature evaluation, the 12 selected cultivars were Alpha, Atlantic, Bake King, Denali, Desiree, Haig, Kennebec, Norland, Russet Burbank, Rutt, Superior, and Troll. Among the cultivars selected under continuous irradiation, Desiree, Ottar, Haig, Rutt, Denali and Alaska showed the best potential for high productivity whereas New York 81 and Bintje showed the least production capability. Among the cultivars selected under high temperature, Rutt, Haig, Troll and Bake King had best performance whereas Atlantic, Alpha, Kennebec and Russet Burbank exhibited the least production potential. Thus, Haig and Rutt were the two cultivars that performed well under continuous irradiation and high temperature conditions, and could have maximum potential for adaptation to varying stress environments. These two cultivars may have the best potential for use in future space farming in which continuous light and/or high temperature conditions may exist. However, cultivar responses under combined conditions of continuous light and high temperature remains for further validation.

NASA Discipline Life Support Systems↗
NASA NTRS2017

Subject Matter Expert Evaluation of Multi-Flight Common Route Advisories

Traffic flow management seeks to balance the demand for National Airspace System (NAS) flight resources, such as airspace and airports, with the available supply. When forecasted weather blocks nominal air traffic routes, traffic managers must re-route affected flights for weather avoidance. Depending on the nature and scope of the weather, traffic managers may use pre-coordinated re-routes such as Playbook Routes or Coded Departure Routes, or may design ad hoc local re-routes. The routes of affected flights are modified accordingly. These weather avoidance routes will, of course, be less efficient than the nominal routes due to increased flight time and fuel burn. In current traffic management operations, the transition into a weather avoidance re-routing initiative is typically implemented more aggressively than the transition out of that initiative after the weather has dissipated or moved away. For example, strategic large-scale Playbook re-routes are sometimes left in place (as initially implemented) for many hours before being lifted entirely when the weather dissipates. There is an opportunity to periodically modify the re-routing plan as weather evolves, thereby attenuating its adverse impact on flight time and fuel consumption; this is called delay recovery. Multi-Flight Common Routes (MFCR) is a NASA-developed operational concept and associated decision support tool for delay recovery, designed to assist traffic managers to efficiently update weather avoidance traffic routes after the original re-routes have become stale due to subsequent evolution of the convective weather system. MFCR groups multiple flights to reduce the number of advisories that the traffic manager needs to evaluate, and also merges these flights on a common route segment to provide an orderly flow of re-routed traffic. The advisory is presented to the appropriate traffic manager who evaluates it and has the option to modify it using MFCRs graphical user interface. If the traffic manager finds the advisory to be operationally appropriate, he or she would coordinate with the Area Supervisor(s) of the sectors that currently control the flights in the advisory. When the traffic manager accepts the MFCR advisory via the user interface, the corresponding flight plan amendments would be sent to the displays of the appropriate sector controllers, using the Airborne Re-Routing (ABRR) capability which is scheduled for nationwide operation in 2017. The sector controllers would then offer this time-saving route modification to the pilots of the affected flights via datalink (or voice), and implement the corresponding flight plan amendment if the pilots accept it. MFCR is implemented as an application in the software environment of the Future Air traffic management Concepts Evaluation Tool (FACET). This paper focuses on an initial subject matter expert (SME) evaluation of MFCR. The evaluation covers MFCRs operational concept, algorithm, and user interface.

Human-in-the-loop Evaluation↗
NASA NTRS2017

Subject Matter Expert Evaluation of Multi-Flight Common Route Advisories

Traffic flow management seeks to balance the demand for National Airspace System (NAS) flight resources, such as airspace and airports, with the available supply. When forecasted weather blocks nominal air traffic routes, traffic managers must re-route affected flights for weather avoidance. Depending on the nature and scope of the weather, traffic managers may use pre-coordinated re-routes such as Playbook Routes or Coded Departure Routes, or may design ad hoc local re-routes. The routes of affected flights are modified accordingly. These weather avoidance routes will, of course, be less efficient than the nominal routes due to increased flight time and fuel burn. In current traffic management operations, the transition into a weather avoidance re-routing initiative is typically implemented more aggressively than the transition out of that initiative after the weather has dissipated or moved away. For example, strategic large-scale Playbook re-routes are sometimes left in place (as initially implemented) for many hours before being lifted entirely when the weather dissipates. There is an opportunity to periodically modify the re-routing plan as weather evolves, thereby attenuating its adverse impact on flight time and fuel consumption; this is called delay recovery. Multi-Flight Common Routes (MFCR) is a NASA-developed operational concept and associated decision support tool for delay recovery, designed to assist traffic managers to efficiently update weather avoidance traffic routes after the original re-routes have become stale due to subsequent evolution of the convective weather system. MFCR groups multiple flights to reduce the number of advisories that the traffic manager needs to evaluate, and also merges these flights on a common route segment to provide an orderly flow of re-routed traffic. The advisory is presented to the appropriate traffic manager who evaluates it and has the option to modify it using MFCRs graphical user interface. If the traffic manager finds the advisory to be operationally appropriate, he or she would coordinate with the Area Supervisor(s) of the sectors that currently control the flights in the advisory. When the traffic manager accepts the MFCR advisory via the user interface, the corresponding flight plan amendments would be sent to the displays of the appropriate sector controllers, using the Airborne Re-Routing (ABRR) capability which is scheduled for nationwide operation in 2017. The sector controllers would then offer this time-saving route modification to the pilots of the affected flights via datalink (or voice), and implement the corresponding flight plan amendment if the pilots accept it. MFCR is implemented as an application in the software environment of the Future Air traffic management Concepts Evaluation Tool (FACET). This paper focuses on an initial subject matter expert (SME) evaluation of MFCR. The evaluation covers MFCRs operational concept, algorithm, and user interface.

Traffic flow management↗
NASA NTRS2021

Technical Note: AQMEII4 Activity 1: Evaluation of Wet and Dry Deposition Schemes as an Integral Part of Regional-scale Air Quality Models

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

Air Quality Model Evaluation International Initiat↗
DOE OSTI2023

Hydrogen Fuel Cell Electric Bus (FCEB) Evaluations in US Public Transit Service

The National Renewable Energy Laboratory (NREL) is a Department of Energy (DOE) national laboratory focused on renewable energy and energy efficiency. NREL has evaluated alternative fuel and advanced propulsion transit buses for DOE and the U.S. Department of Transportation's Federal Transit Administration (FTA). These evaluations are focused on determining the status of fuel cell systems and the corresponding infrastructure in transit applications to help DOE and FTA assess the progress toward technology readiness. For the last 19 years NREL has evaluated FCEBs in service around the United States and in Canada. The results of these evaluations have been published in numerous reports that compare FCEB performance to conventional technology as well as document the implementation experience and lessons learned by the transit agencies and their demonstration teams. Currently, 70 fuel cell buses are in active service in the US and 66 FCEBs are in development. NREL is evaluating a subset of the active FCEBs that includes three transit agencies demonstrating 25 fuel cell electric buses in California. One bus has exceeded 35,000 hours in service and 12 have exceeded 25,000 hours. Fuel economy for the current generation of FCEBs has improved 35% over the previous generation and is double that of conventional buses. Maintenance cost for FCEBs is equivalent to diesel and BEBs. The most up-to-date performance results will be presented, including fuel economy, availability, reliability, and operational costs.

bus↗
DOE OSTI2022

Durability Evaluation of Advanced Fenestration Technologies

In the U.S., more than 40% of primary energy and 70% of electricity is consumed in residential and commercial buildings, resulting in annual energy costs of more than $430 billion. Approximately 35% of this consumption can be attributed to losses through the building envelope, of which windows are currently the weakest link. Multiple technologies are under development to improve this performance. This includes dynamic and photovoltaic glazing as well as emerging highly insulating technologies including vacuum insulating glass (VIG), aerogels and thin-glass based multi-pane configurations. While windows are specified based on expected performance as installed, the energy savings realized by high performance windows are delivered over time. This makes it critical to understand and maximize the durability of high-performance windows to ensure those projected energy savings are delivered. Present methods for evaluating durability are based on existing technologies. These methods may not apply or be adequate for newer emerging technologies which often present novel failure mechanisms that need to be understood and evaluated differently. In this presentation, we will discuss our efforts to define appropriate methods to improve the evaluation of many existing technologies as well as evaluate the durability of emerging window technologies. This work results from the combination of an extensive review of various international standards as well as existing scientific literature. In addition, input was gathered from multiple industry stakeholders regarding present practice as well as feedback on proposed improvements to existing methods. Here we will share these findings and present proposed improvements to developing and existing durability evaluation protocols.

building envelope↗
DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER’s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

The Technical, Economic, Risk, and Adoption Assessment for Evaluating Work Reduction Opportunities in the Nuclear Industry

Automation and cost-saving initiatives, such as process automation with advances in artificial intelligence, are gaining traction in modernization efforts across the nuclear industry. As these innovations are increasingly adopted, it becomes crucial to evaluate their impacts comprehensively. Various technical and economic attributes, along with risk and human readiness factors, must be achieved to ensure that innovative projects enabling automation and modernization are successful. However, no systematic or integrated framework exists that allows plants to evaluate these innovative projects. To address this gap, the Technical, Economic, Risk, and Adoption (TERA) assessment offers a structured method to evaluate innovative technologies, ensuring solutions meet both operational and safety standards. The TERA framework integrates the disparate perspectives to assess modernization opportunities in nuclear operations. It combines qualitative and quantitative models to evaluate the relationship between performance and business impacts, while also enabling continuous re-evaluation during project development. This approach helps plant owners identify high-priority opportunities, optimize cost savings, and minimize risks, ensuring projects remain on track to achieve desired returns. By providing a comprehensive, systematic methodology, TERA enables informed, data-driven decisions that support successful modernization efforts, enhancing efficiency, safety, and cost savings across nuclear operations. This paper explains the TERA framework and its benefits for the nuclear industry.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER?s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use. PowerPoint for conference that was reviewed in PRS and LRS PRS/CON-25-05379 and INL/CON-25-82946

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Efficiently evaluating loop integrals in the EFTofLSS using QFT integrals with massive propagators

We develop a new way to analytically calculate loop integrals in the Effective Field Theory of Large Scale-Structure. Previous available methods show severe limitations beyond the one-loop power spectrum due to analytical challenges and computational and memory costs. Our new method is based on fitting the linear power spectrum with cosmology-independent functions that resemble integer powers of quantum field theory massive propagators with complex masses. A remarkably small number of them is sufficient to reach enough accuracy. Similarly to former approaches, the cosmology dependence is encoded in the coordinate vector of the expansion of the linear power spectrum in our basis. We first produce cosmology-independent tensors where each entry is the loop integral evaluated on a given combination of basis vectors. For each cosmology, the evaluation of a loop integral amounts to contracting this tensor with the coordinate vector of the linear power spectrum. The 3-dimensional loop integrals for our basis functions can be evaluated using techniques familiar to particle physics, such as recursion relations and Feynman parametrization. We apply our formalism to evaluate the one-loop bispectrum of galaxies in redshift space. The final analytical expressions are quite simple and can be evaluated with little computational and memory cost. We show that the same expressions resolve the integration of all one-loop N-point function in the EFTofLSS. This method, which is originally presented here, has already been applied in the first one-loop bispectrum analysis of the BOSS data to constraint ΛCDM parameters and primordial non-Gaussianities [1, 2].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Applying queueing theory to evaluate wait-time-savings of triage algorithms

Abstract In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Thompson, Yee Lam Elim (ORCID:0000000196537707)↗
DOE OSTI2025

Benchmark of numerical modeling approaches on the systematic performance evaluation of wave energy converters

Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.

Fatigue↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗